@article {pmid36963740, year = {2023}, author = {Wu, H and Xie, Q and Pan, J and Liang, Q and Lan, Y and Guo, Y and Han, J and Xie, M and Liu, Y and Jiang, L and Wu, X and Li, Y and Qin, P}, title = {Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120050}, doi = {10.1016/j.neuroimage.2023.120050}, pmid = {36963740}, issn = {1095-9572}, abstract = {Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.}, } @article {pmid36960685, year = {2023}, author = {Chen, XL and Fortes, JM and Hu, YT and van Iersel, J and He, KN and van Heerikhuize, J and Balesar, R and Swaab, D and Bao, AM}, title = {Sexually dimorphic age-related molecular differences in the entorhinal cortex of cognitively intact elderly: Relation to early Alzheimer's changes.}, journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association}, volume = {}, number = {}, pages = {}, doi = {10.1002/alz.13037}, pmid = {36960685}, issn = {1552-5279}, abstract = {INTRODUCTION: Women are more vulnerable to Alzheimer's disease (AD) than men. The entorhinal cortex (EC) is one of the earliest structures affected in AD. We identified in cognitively intact elderly different molecular changes in the EC in relation to age.

METHODS: Changes in 12 characteristic molecules in relation to age were determined by quantitative immunohistochemistry or in situ hybridization in the EC. They were arbitrarily grouped into sex steroid-related molecules, markers of neuronal activity, neurotransmitter-related molecules, and cholinergic activity-related molecules.

RESULTS: The changes in molecules indicated increasing local estrogenic and neuronal activity accompanied by a higher and faster hyperphosphorylated tau accumulation in women's EC in relation to age, versus a mainly stable local estrogenic/androgenic and neuronal activity in men's EC.

DISCUSSION: EC employs a different neurobiological strategy in women and men to maintain cognitive function, which seems to be accompanied by an earlier start of AD in women.

HIGHLIGHTS: Local estrogen system is activated with age only in women's entorhinal cortex (EC). EC neuronal activity increased with age only in elderly women with intact cognition. Men and women have different molecular strategies to retain cognition with aging. P-tau accumulation in the EC was higher and faster in cognitively intact elderly women.}, } @article {pmid36960172, year = {2023}, author = {Ma, Z and Wang, K and Xu, M and Yi, W and Xu, F and Ming, D}, title = {Transformed common spatial pattern for motor imagery-based brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1116721}, pmid = {36960172}, issn = {1662-4548}, abstract = {OBJECTIVE: The motor imagery (MI)-based brain-computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To address this problem, previous researchers often used a filter bank to decompose EEG signals into multiple frequency bands before applying the traditional CSP.

APPROACH: This study proposed a novel method, i.e., transformed common spatial pattern (tCSP), to extract the discriminant EEG features from multiple frequency bands after but not before CSP. To verify its effectiveness, we tested tCSP on a dataset collected by our team and a public dataset from BCI competition III. We also performed an online evaluation of the proposed method.

MAIN RESULTS: As a result, for the dataset collected by our team, the classification accuracy of tCSP was significantly higher than CSP by about 8% and filter bank CSP (FBCSP) by about 4.5%. The combination of tCSP and CSP further improved the system performance with an average accuracy of 84.77% and a peak accuracy of 100%. For dataset IVa in BCI competition III, the combination method got an average accuracy of 94.55%, which performed best among all the presented CSP-based methods. In the online evaluation, tCSP and the combination method achieved an average accuracy of 80.00 and 84.00%, respectively.

SIGNIFICANCE: The results demonstrate that the frequency band selection after CSP is better than before for MI-based BCIs. This study provides a promising approach for decoding MI EEG patterns, which is significant for the development of BCIs.}, } @article {pmid36959601, year = {2023}, author = {Velasco, I and Sipols, A and De Blas, CS and Pastor, L and Bayona, S}, title = {Motor imagery EEG signal classification with a multivariate time series approach.}, journal = {Biomedical engineering online}, volume = {22}, number = {1}, pages = {29}, pmid = {36959601}, issn = {1475-925X}, abstract = {BACKGROUND: Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysis RESULTS: Applying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables.

CONCLUSIONS: This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.}, } @article {pmid36951376, year = {2023}, author = {Saraswat, M and Dubey, AK}, title = {EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-22}, doi = {10.1080/10255842.2023.2187662}, pmid = {36951376}, issn = {1476-8259}, abstract = {Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in brain-computer interface (BCI) systems. Due to massive and myriads data, the signals are appealed in a non-stationary format that ends with a poor quality resolution. To overcome this existing issue, a new framework of enhanced deep learning methods is proposed. The source signals are collected and undergo feature extraction in four ways. Hence, the features are concatenated to enhance the performance. Subsequently, the concatenated features are given to probability ratio-based Reptile Search Algorithm (PR-RSA) to select the optimal features. Finally, the classification is conducted using Enhanced Bi-directional Long Short-Term Memory (EBi-LSTM), where the hyperparameters are optimized by PR-RSA. Throughout the result analysis, it is confirmed that the offered model obtains elevated classification accuracy, and thus tends to increase the performance.}, } @article {pmid36950505, year = {2023}, author = {Fang, H and Yang, Y}, title = {Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1119685}, pmid = {36950505}, issn = {1662-5188}, abstract = {INTRODUCTION: Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.

METHODS: Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.

RESULTS: We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.

DISCUSSION: Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.}, } @article {pmid36950147, year = {2023}, author = {Moly, A and Aksenov, A and Martel, F and Aksenova, T}, title = {Online adaptive group-wise sparse Penalized Recursive Exponentially Weighted N-way Partial Least Square for epidural intracranial BCI.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1075666}, pmid = {36950147}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor Brain-Computer Interfaces (BCIs) create new communication pathways between the brain and external effectors for patients with severe motor impairments. Control of complex effectors such as robotic arms or exoskeletons is generally based on the real-time decoding of high-resolution neural signals. However, high-dimensional and noisy brain signals pose challenges, such as limitations in the generalization ability of the decoding model and increased computational demands.

METHODS: The use of sparse decoders may offer a way to address these challenges. A sparsity-promoting penalization is a common approach to obtaining a sparse solution. BCI features are naturally structured and grouped according to spatial (electrodes), frequency, and temporal dimensions. Applying group-wise sparsity, where the coefficients of a group are set to zero simultaneously, has the potential to decrease computational time and memory usage, as well as simplify data transfer. Additionally, online closed-loop decoder adaptation (CLDA) is known to be an efficient procedure for BCI decoder training, taking into account neuronal feedback. In this study, we propose a new algorithm for online closed-loop training of group-wise sparse multilinear decoders using L p -Penalized Recursive Exponentially Weighted N-way Partial Least Square (PREW-NPLS). Three types of sparsity-promoting penalization were explored using L p with p = 0., 0.5, and 1.

RESULTS: The algorithms were tested offline in a pseudo-online manner for features grouped by spatial dimension. A comparison study was conducted using an epidural ECoG dataset recorded from a tetraplegic individual during long-term BCI experiments for controlling a virtual avatar (left/right-hand 3D translation). Novel algorithms showed comparable or better decoding performance than conventional REW-NPLS, which was achieved with sparse models. The proposed algorithms are compatible with real-time CLDA.

DISCUSSION: The proposed algorithm demonstrated good performance while drastically reducing the computational load and the memory consumption. However, the current study is limited to offline computation on data recorded with a single patient, with penalization restricted to the spatial domain only.}, } @article {pmid36948359, year = {2023}, author = {Gao, X and Zhang, S and Liu, K and Tan, Z and Zhao, G and Han, Y and Cheng, Y and Li, C and Li, F and Tian, Y and Li, P}, title = {An Adaptive Joint CCA-ICA Method for Ocular Artifact Removal and its Application to Emotion Classification.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109841}, doi = {10.1016/j.jneumeth.2023.109841}, pmid = {36948359}, issn = {1872-678X}, abstract = {BACKGROUND: The quality of Electroencephalogram (EEG) signals is critical for revealing the neural mechanism of emotions. However, ocular artifacts decreased the signal to noise ratio (SNR) and covered the inherent cognitive component of EEGs, which pose a great challenge in neuroscience research.

NEW METHOD: We proposed a novel unsupervised learning algorithm to adaptively remove the ocular artifacts by combining canonical correlation analysis (CCA), independent component analysis (ICA), higher-order statistics, empirical mode decomposition (EMD), and wavelet denoising techniques. Specifically, the combination of CCA and ICA aimed to improve the quality of source separation, while the higher-order statistics further located the source of ocular artifacts. Subsequently, these noised sources were further corrected by EMD and wavelet denoising to improve SNR of EEG signals.

RESULTS: We evaluated the performance of our proposed method with simulation studies and real EEG applications. The results of simulation study showed our proposed method could significantly improve the quality of signals under almost all noise conditions compared to four state-of-art methods. Consistently, the experiments of real EEG applications showed that the proposed methods could efficiently restrict the components of ocular artifacts and preserve the inherent information of cognition processing to improve the reliability of related analysis such as power spectral density (PSD) and emotion recognition.

Our proposed model outperforms the comparative methods in EEG recovery, which further improve the application performance such as PSD analysis and emotion recognition.

CONCLUSIONS: The superior performance of our proposed method suggests that it is promising for removing ocular artifacts from EEG signals, which offers an efficient EEG preprocessing technology for the development of brain computer interface such as emotion recognition.}, } @article {pmid36945749, year = {2023}, author = {Lebani, BR and Barcelos, ADS and Gouveia, DSES and Girotti, ME and Remaille, EP and Skaff, M and Almeida, FG}, title = {The role of transurethral resection of prostate (TURP) in patients with underactive bladder: 12 months follow-up in different grades of detrusor contractility.}, journal = {The Prostate}, volume = {}, number = {}, pages = {}, doi = {10.1002/pros.24526}, pmid = {36945749}, issn = {1097-0045}, abstract = {INTRODUCTION AND OBJECTIVE: Male detrusor underactivity (DUA) definition remains controversial and no effective treatment is consolidated. Transurethral resection of the prostate (TURP) is one of the cornerstones surgical treatments recommended in bladder outlet obstruction (BOO). However, the role of prostatic surgery in male DUA is not clear. The primary endpoint was the clinical and voiding improvement based on IPSS and the maximum flow rate in uroflowmetry (Qmax) within 12 months.

MATERIALS AND METHODS: We analyzed an ongoing prospective database that embraces benign prostata hyperplasia (BPH) male patients with lower urinary tract symptoms who have undergone to TURP. All patients were evaluated pre and postoperatively based on IPSS questionnaires, prostate volume measured by ultrasound, postvoid residual urine volume (PVR), Prostate Specific Antigen measurement and urodynamic study (UDS) before the procedure. After surgery, all patients were evaluated at 1-, 3-, 6- and 12-months. Patients were categorized in 3 groups: Group 1-Detrusor Underactive (Bladder Contractility Index (BCI) [BCI] < 100 and BOO index [BOOI] < 40); Group 2-Detrusor Underactive and BOO (BCI < 100 and BOOI ≥ 40); Group 3-BOO (BCI ≥ 100 and BOOI ≥ 0).

RESULTS: It was included 158 patients underwent monopolar or bipolar TURP since November 2015 to March 2021. According to UDS, patients were categorized in: group 1 (n = 39 patients); group 2 (n = 41 patients); group 3 (n = 77 patients). Preoperative IPSS was similar between groups (group 1-24.9 ± 6.33; group 2-24.8 ± 7.33; group 3-24.5 ± 6.23). Qmax was statistically lower in the group 2 (group 1-5.43 ± 3.69; group 2-3.91 ± 2.08; group 3-6.3 ± 3.18) as well as greater PVR. The 3 groups presented similar outcomes regard to IPSS score during the follow-up. There was a significant increase in Qmax in the 3 groups. However, group 1 presented the lowest Qmax improvement.

CONCLUSION: There were different objective outcomes depending on the degree of DUA at 12 months follow-up. Patients with DUA had similar IPSS improvement. However, DUA patients had worst Qmax improvement than men with normal bladder contraction.}, } @article {pmid36939855, year = {2023}, author = {Karoly, HC and Drennan, ML and Prince, MA and Zulic, L and Dooley, G}, title = {Consuming oral cannabidiol prior to a standard alcohol dose has minimal effect on breath alcohol level and subjective effects of alcohol.}, journal = {Psychopharmacology}, volume = {}, number = {}, pages = {}, pmid = {36939855}, issn = {1432-2072}, support = {UL1 TR002535/TR/NCATS NIH HHS/United States ; K23AA028238/AA/NIAAA NIH HHS/United States ; }, abstract = {RATIONALE: Cannabidiol (CBD) is found in the cannabis plant and has garnered attention as a potential treatment for alcohol use disorder (AUD). CBD reduces alcohol consumption and other markers of alcohol dependence in rodents, but human research on CBD and alcohol is limited. It is unknown whether CBD reduces drinking in humans, and mechanisms through which CBD could impact behavioral AUD phenotypes are unknown.

OBJECTIVES: This study explores effects of oral CBD on breath alcohol level (BrAC), and subjective effects of alcohol in human participants who report heavy drinking.

METHODS: In this placebo-controlled, crossover study, participants consumed 30 mg CBD, 200 mg CBD, or placebo CBD before receiving a standardized alcohol dose. Participants were blind to which CBD dose they received at each session and completed sessions in random order. Thirty-six individuals completed at least one session and were included in analyses.

RESULTS: Differences in outcomes across the three conditions and by sex were explored using multilevel structural equation models. BrAC fell fastest in the placebo condition, followed by 30 mg and 200 mg CBD. Stimulation decreased more slowly in the 200 mg CBD condition than in placebo (b =  - 2.38, BCI [- 4.46, - .03]). Sedation decreased more slowly in the 30 mg CBD condition than in placebo (b =  - 2.41, BCI [- 4.61, - .09]). However, the magnitude of condition differences in BrAC and subjective effects was trivial.

CONCLUSIONS: CBD has minimal influence on BrAC and subjective effects of alcohol. Further research is needed to test whether CBD impacts alcohol consumption in humans, and if so, what mechanism(s) may explain this effect.}, } @article {pmid36938361, year = {2023}, author = {Hanna, J and Flöel, A}, title = {An accessible and versatile deep learning-based sleep stage classifier.}, journal = {Frontiers in neuroinformatics}, volume = {17}, number = {}, pages = {1086634}, pmid = {36938361}, issn = {1662-5196}, abstract = {Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.}, } @article {pmid36937688, year = {2023}, author = {Sawai, S and Murata, S and Fujikawa, S and Yamamoto, R and Shima, K and Nakano, H}, title = {Effects of neurofeedback training combined with transcranial direct current stimulation on motor imagery: A randomized controlled trial.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1148336}, pmid = {36937688}, issn = {1662-4548}, abstract = {INTRODUCTION: Neurofeedback (NFB) training and transcranial direct current stimulation (tDCS) have been shown to individually improve motor imagery (MI) abilities. However, the effect of combining both of them with MI has not been verified. Therefore, the aim of this study was to examine the effect of applying tDCS directly before MI with NFB.

METHODS: Participants were divided into an NFB group (n = 10) that performed MI with NFB and an NFB + tDCS group (n = 10) that received tDCS for 10 min before MI with NFB. Both groups performed 60 MI trials with NFB. The MI task was performed 20 times without NFB before and after training, and μ-event-related desynchronization (ERD) and vividness MI were evaluated.

RESULTS: μ-ERD increased significantly in the NFB + tDCS group compared to the NFB group. MI vividness significantly increased before and after training.

DISCUSSION: Transcranial direct current stimulation and NFB modulate different processes with respect to MI ability improvement; hence, their combination might further improve MI performance. The results of this study indicate that the combination of NFB and tDCS for MI is more effective in improving MI abilities than applying them individually.}, } @article {pmid36937679, year = {2023}, author = {Lai, D and Wan, Z and Lin, J and Pan, L and Ren, F and Zhu, J and Zhang, J and Wang, Y and Hao, Y and Xu, K}, title = {Neuronal representation of bimanual arm motor imagery in the motor cortex of a tetraplegia human, a pilot study.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1133928}, pmid = {36937679}, issn = {1662-4548}, abstract = {INTRODUCTION: How the human brain coordinates bimanual movements is not well-established.

METHODS: Here, we recorded neural signals from a paralyzed individual's left motor cortex during both unimanual and bimanual motor imagery tasks and quantified the representational interaction between arms by analyzing the tuning parameters of each neuron.

RESULTS: We found a similar proportion of neurons preferring each arm during unimanual movements, however, when switching to bimanual movements, the proportion of contralateral preference increased to 71.8%, indicating contralateral lateralization. We also observed a decorrelation process for each arm's representation across the unimanual and bimanual tasks. We further confined that these changes in bilateral relationships are mainly caused by the alteration of tuning parameters, such as the increased bilateral preferred direction (PD) shifts and the significant suppression in bilateral modulation depths (MDs), especially the ipsilateral side.

DISCUSSION: These results contribute to the knowledge of bimanual coordination and thus the design of cutting-edge bimanual brain-computer interfaces.}, } @article {pmid36936191, year = {2023}, author = {Islam, MK and Rastegarnia, A}, title = {Editorial: Recent advances in EEG (non-invasive) based BCI applications.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1151852}, pmid = {36936191}, issn = {1662-5188}, } @article {pmid36935358, year = {2023}, author = {Gerasimov, JY and Tu, D and Hitaishi, V and Harikesh, PC and Yang, CY and Abrahamsson, T and Rad, M and Donahue, MJ and Ejneby, MS and Berggren, M and Forchheimer, R and Fabiano, S}, title = {A Biologically Interfaced Evolvable Organic Pattern Classifier.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2207023}, doi = {10.1002/advs.202207023}, pmid = {36935358}, issn = {2198-3844}, support = {ERC-2018-ADG/ERC_/European Research Council/International ; }, abstract = {Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process these data into meaningful information, and translate that information into a format that can be interpreted by living systems. Here, the first example of interfacing a hardware-based pattern classifier with a biological nerve is reported. The classifier implements the Widrow-Hoff learning algorithm on an array of evolvable organic electrochemical transistors (EOECTs). The EOECTs' channel conductance is modulated in situ by electropolymerizing the semiconductor material within the channel, allowing for low voltage operation, high reproducibility, and an improvement in state retention by two orders of magnitude over state-of-the-art OECT devices. The organic classifier is interfaced with a biological nerve using an organic electrochemical spiking neuron to translate the classifier's output to a simulated action potential. The latter is then used to stimulate muscle contraction selectively based on the input pattern, thus paving the way for the development of adaptive neural interfaces for closed-loop therapeutic systems.}, } @article {pmid36933706, year = {2023}, author = {Wang, A and Fan, Z and Zhang, Y and Wang, J and Zhang, X and Wang, P and Mu, W and Zhan, G and Wang, M and Zhang, L and Gan, Z and Kang, X}, title = {Resting-state SEEG-Based Brain Network Analysis for the Detection of Epileptic Area.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109839}, doi = {10.1016/j.jneumeth.2023.109839}, pmid = {36933706}, issn = {1872-678X}, abstract = {BACKGROUND: Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions.

NEW METHOD: The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes.

RESULTS: By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p<0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94 ± 0.08 for the epilepsy zone.

CONCLUSIONS AND SIGNIFICANCE: The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.}, } @article {pmid36931795, year = {2023}, author = {Johnson, KA and Worbe, Y and Foote, KD and Butson, CR and Gunduz, A and Okun, MS}, title = {Neurosurgical lesioning for Tourette syndrome - Authors' reply.}, journal = {The Lancet. Neurology}, volume = {22}, number = {4}, pages = {292-293}, doi = {10.1016/S1474-4422(23)00079-0}, pmid = {36931795}, issn = {1474-4465}, } @article {pmid36930206, year = {2023}, author = {Wang, F and Chen, Y and Lin, Y and Wang, X and Li, K and Han, Y and Wu, J and Shi, X and Zhu, Z and Long, C and Hu, X and Duan, S and Gao, Z}, title = {A parabrachial to hypothalamic pathway mediates defensive behavior.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {36930206}, issn = {2050-084X}, abstract = {Defensive behaviors are critical for animal's survival. Both the paraventricular nucleus of the hypothalamus (PVN) and the parabrachial nucleus (PBN) have been shown to be involved in defensive behaviors. However, whether there are direct connections between them to mediate defensive behaviors remains unclear. Here, by retrograde and anterograde tracing, we uncover that cholecystokinin (CCK)-expressing neurons in the lateral PBN (LPB[CCK]) directly project to the PVN. By in vivo fiber photometry recording, we find that LPB[CCK] neurons actively respond to various threat stimuli. Selective photoactivation of LPB[CCK] neurons promotes aversion and defensive behaviors. Conversely, photoinhibition of LPB[CCK] neurons attenuates rat or looming stimuli-induced flight responses. Optogenetic activation of LPB[CCK] axon terminals within the PVN or PVN glutamatergic neurons promotes defensive behaviors. Whereas chemogenetic and pharmacological inhibition of local PVN neurons prevent LPB[CCK]-PVN pathway activation-driven flight responses. These data suggest that LPB[CCK] neurons recruit downstream PVN neurons to actively engage in flight responses. Our study identifies a previously unrecognized role for the LPB[CCK]-PVN pathway in controlling defensive behaviors.}, } @article {pmid36928694, year = {2023}, author = {Berger, CC and Coppi, S and Ehrsson, HH}, title = {Synchronous motor imagery and visual feedback of finger movement elicit the moving rubber hand illusion, at least in illusion-susceptible individuals.}, journal = {Experimental brain research}, volume = {}, number = {}, pages = {}, pmid = {36928694}, issn = {1432-1106}, abstract = {Recent evidence suggests that imagined auditory and visual sensory stimuli can be integrated with real sensory information from a different sensory modality to change the perception of external events via cross-modal multisensory integration mechanisms. Here, we explored whether imagined voluntary movements can integrate visual and proprioceptive cues to change how we perceive our own limbs in space. Participants viewed a robotic hand wearing a glove repetitively moving its right index finger up and down at a frequency of 1 Hz, while they imagined executing the corresponding movements synchronously or asynchronously (kinesthetic-motor imagery); electromyography (EMG) from the participants' right index flexor muscle confirmed that the participants kept their hand relaxed while imagining the movements. The questionnaire results revealed that the synchronously imagined movements elicited illusory ownership and a sense of agency over the moving robotic hand-the moving rubber hand illusion-compared with asynchronously imagined movements; individuals who affirmed experiencing the illusion with real synchronous movement also did so with synchronous imagined movements. The results from a proprioceptive drift task further demonstrated a shift in the perceived location of the participants' real hand toward the robotic hand in the synchronous versus the asynchronous motor imagery condition. These results suggest that kinesthetic motor imagery can be used to replace veridical congruent somatosensory feedback from a moving finger in the moving rubber hand illusion to trigger illusory body ownership and agency, but only if the temporal congruence rule of the illusion is obeyed. This observation extends previous studies on the integration of mental imagery and sensory perception to the case of multisensory bodily awareness, which has potentially important implications for research into embodiment of brain-computer interface controlled robotic prostheses and computer-generated limbs in virtual reality.}, } @article {pmid36927003, year = {2023}, author = {Hou, Y and Ling, Y and Wang, Y and Wang, M and Chen, Y and Li, X and Hou, X}, title = {Learning from the Brain: Bioinspired Nanofluidics.}, journal = {The journal of physical chemistry letters}, volume = {}, number = {}, pages = {2891-2900}, doi = {10.1021/acs.jpclett.2c03930}, pmid = {36927003}, issn = {1948-7185}, abstract = {The human brain completes intelligent behaviors such as the generation, transmission, and storage of neural signals by regulating the ionic conductivity of ion channels in neuron cells, which provides new inspiration for the development of ion-based brain-like intelligence. Against the backdrop of the gradual maturity of neuroscience, computer science, and micronano materials science, bioinspired nanofluidic iontronics, as an emerging interdisciplinary subject that focuses on the regulation of ionic conductivity of nanofluidic systems to realize brain-like functionalities, has attracted the attention of many researchers. This Perspective provides brief background information and the state-of-the-art progress of nanofluidic intelligent systems. Two main categories are included: nanofluidic transistors and nanofluidic memristors. The prospects of nanofluidic iontronics' interdisciplinary progress in future artificial intelligence fields such as neuromorphic computing or brain-computer interfaces are discussed. This Perspective aims to give readers a clear understanding of the concepts and prospects of this emerging interdisciplinary field.}, } @article {pmid36925628, year = {2023}, author = {Carino-Escobar, RI and Rodríguez-García, ME and Carrillo-Mora, P and Valdés-Cristerna, R and Cantillo-Negrete, J}, title = {Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1015464}, pmid = {36925628}, issn = {1662-5218}, abstract = {INTRODUCTION: Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.

METHODS: An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback.

RESULTS: Significantly higher BCI performance (65.4 ± 17.9% with the continuous and 62.1 ± 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback.

DISCUSSION: It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.}, } @article {pmid36924669, year = {2023}, author = {Wei, L and Qi, X and Yu, X and Zheng, Y and Luo, X and Wei, Y and Ni, P and Zhao, L and Wang, Q and Ma, X and Deng, W and Guo, W and Hu, X and Li, T}, title = {3,4-Dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide analogs act as potential AMPA receptor potentiators with antidepressant activity.}, journal = {European journal of medicinal chemistry}, volume = {251}, number = {}, pages = {115252}, doi = {10.1016/j.ejmech.2023.115252}, pmid = {36924669}, issn = {1768-3254}, abstract = {Major depressive disorder is a common psychiatric disorder, with ∼30% of patients suffering from treatment-resistant depression. Based on preclinical studies on ketamine, α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) activation may be a promising therapeutic approach. In this study, we synthesized a series of novel 3,4-dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide analogs and analyzed their potential as AMPAR potentiators. Compounds 5aa and 7k exhibited high potentiation with little agonist activity in a high-throughput screen using a calcium influx assay in cultured hippocampal primary neurons. In rats, compound 7k had better pharmacokinetic properties and oral bioavailability (F = 67.19%); it also exhibited an acceptable safety profile in vital internal organs based on hematoxylin and eosin staining. We found that 7k produced a rapid antidepressant-like effect in chronic restraint stress-induced mice 1 h after intraperitoneal administration. Our study presented a series of novel AMPAR potentiators and identified 7k as a promising drug-like candidate against major depressive disorders.}, } @article {pmid36923936, year = {2023}, author = {Kong, L and Zhang, D and Huang, S and Lai, J and Lu, L and Zhang, J and Hu, S}, title = {Extracellular Vesicles in Mental Disorders: A State-of-art Review.}, journal = {International journal of biological sciences}, volume = {19}, number = {4}, pages = {1094-1109}, pmid = {36923936}, issn = {1449-2288}, abstract = {Extracellular vesicles (EVs) are nanoscale particles with various physiological functions including mediating cellular communication in the central nervous system (CNS), which indicates a linkage between these particles and mental disorders such as schizophrenia, bipolar disorder, major depressive disorder, etc. To date, known characteristics of mental disorders are mainly neuroinflammation and dysfunctions of homeostasis in the CNS, and EVs are proven to be able to regulate these pathological processes. In addition, studies have found that some cargo of EVs, especially miRNAs, were significantly up- or down-regulated in patients with mental disorders. For many years, interest has been generated in exploring new diagnostic and therapeutic methods for mental disorders, but scale assessment and routine drug intervention are still the first-line applications so far. Therefore, underlying the downstream functions of EVs and their cargo may help uncover the pathogenetic mechanisms of mental disorders as well as provide novel biomarkers and therapeutic candidates. This review aims to address the connection between EVs and mental disorders, and discuss the current strategies that focus on EVs-related psychiatric detection and therapy.}, } @article {pmid36922925, year = {2023}, author = {Khodaei, F and Sadati, SH and Doost, M and Lashgari, R}, title = {LFP polarity changes across cortical and eccentricity in primary visual cortex.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1138602}, pmid = {36922925}, issn = {1662-4548}, abstract = {Local field potentials (LFPs) can evaluate neural population activity in the cortex and their interaction with other cortical areas. Analyzing current source density (CSD) rather than LFPs is very significant due to the reduction of volume conduction effects. Current sinks are construed as net inward transmembrane currents, while current sources are net outward ones. Despite extensive studies of LFPs and CSDs, their morphology in different cortical layers and eccentricities are still largely unknown. Because LFP polarity changes provide a measure of neural activity, they can be useful in implanting brain-computer interface (BCI) chips and effectively communicating the BCI devices to the brain. We hypothesize that sinks and sources analyses could be a way to quantitatively achieve their characteristics in response to changes in stimulus size and layer-dependent differences with increasing eccentricities. In this study, we show that stimulus properties play a crucial role in determining the flow. The present work focusses on the primary visual cortex (V1). In this study, we investigate a map of the LFP-CSD in V1 area by presenting different stimulus properties (e.g., size and type) in the visual field area of Macaque monkeys. Our aim is to use the morphology of sinks and sources to measure the input and output information in different layers as well as different eccentricities. According to the value of CSDs, the results show that the stimuli smaller than RF's size had lower strength than the others and the larger RF's stimulus size showed smaller strength than the optimized stimulus size, which indicated the suppression phenomenon. Additionally, with the increased eccentricity, CSD's strengths were increased across cortical layers.}, } @article {pmid36921432, year = {2023}, author = {Rokai, J and Ulbert, I and Márton, G}, title = {Edge computing on TPU for brain implant signal analysis.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {162}, number = {}, pages = {212-224}, doi = {10.1016/j.neunet.2023.02.036}, pmid = {36921432}, issn = {1879-2782}, abstract = {The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain-computer interfaces.}, } @article {pmid36925573, year = {2021}, author = {Lehnertz, K and Rings, T and Bröhl, T}, title = {Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks.}, journal = {Frontiers in network physiology}, volume = {1}, number = {}, pages = {755016}, pmid = {36925573}, issn = {2674-0109}, abstract = {Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.}, } @article {pmid36918388, year = {2023}, author = {Heerspink, HJL and Inker, LA and Tighiouart, H and Collier, WH and Haaland, B and Luo, J and Appel, GB and Chan, TM and Estacio, RO and Fervenza, F and Floege, J and Imai, E and Jafar, TH and Lewis, JB and Kam-Tao Li, P and Locatelli, F and Maes, BD and Perna, A and Perrone, RD and Praga, M and Schena, FP and Wanner, C and Xie, D and Greene, T}, title = {Change in Albuminuria and GFR Slope as Joint Surrogate Endpoints for Kidney Failure: Implications for Phase 2 Clinical Trials in CKD.}, journal = {Journal of the American Society of Nephrology : JASN}, volume = {}, number = {}, pages = {}, doi = {10.1681/ASN.0000000000000117}, pmid = {36918388}, issn = {1533-3450}, abstract = {BACKGROUND: Change in log urinary albumin to creatinine ratio (UACR) and GFR slope are individually used as surrogate endpoints in clinical trials of CKD progression. Whether combining these surrogate endpoints might strengthen inferences about clinical benefit is unknown.

METHODS: Using Bayesian meta-regressions across 41 randomized trials of CKD progression, we characterized the combined relationship between the treatment effects on the clinical endpoint (sustained doubling of serum creatinine, GFR<15 ml/min per 1.73m2, or kidney failure) and treatment effects on UACR change and chronic GFR slope after 3 months. We applied the results to the design of phase 2 trials based on UACR change and chronic GFR slope in combination.

RESULTS: Treatment effects on the clinical endpoint were strongly associated with the combination of treatment effects on UACR change and chronic slope. The posterior median meta-regression coefficients for treatment effects were -0.41 (95% Baysian confidence interval [BCI], -0.64 to -0.17) per 1 ml/min per 1.73m2 per year for the treatment effect on GFR slope and -0.06 (95% BCI, -0.90 to 0.77) for the treatment effect on UACR change. The predicted probability of clinical benefit when considering both surrogates was determined primarily by estimated treatment effects on UACR when sample size was small (approximately 60 patients per treatment arm) and follow-up brief (approximately 1 year), with the importance of GFR slope increasing for larger sample sizes and longer follow-up.

CONCLUSIONS: In phase 2 trials of CKD with sample sizes of 100 to 200 patients per arm and follow-up between 1 and 2 years, combining information from treatment effects on UACR change and GFR slope improved prediction of treatment effects on clinical endpoints.}, } @article {pmid36915907, year = {2023}, author = {Zhu, Y and Wu, L and Ye, S and Fu, Y and Huang, H and Lai, J and Shi, C and Hu, S}, title = {The Chinese Version of Oxford Depression Questionnaire: A Validation Study in Patients with Mood Disorders.}, journal = {Neuropsychiatric disease and treatment}, volume = {19}, number = {}, pages = {547-556}, pmid = {36915907}, issn = {1176-6328}, abstract = {BACKGROUND: Emotional blunting is prevalent in patients with mood disorders and adversely affects the overall treatment outcome. The Oxford Depression Questionnaire is a validated psychometric instrument for assessing emotional blunting. We aimed to evaluate the reliability and validity of the Chinese version of the ODQ (ODQ) in Chinese patients with mood disorders.

METHODS: 136 mood disorders patients and 95 healthy control participants were recruited at the First Affiliated Hospital of Zhejiang University, School of Medicine. Patients were assessed using the ODQ, Beck Depression Inventory-II (BDI-II), and Montgomery-Asberg Depression Rating Scale (MADRS). Internal consistency reliability and test-retest reliability were analyzed. Confirmatory factor analysis and correlation analysis were used to evaluate construct and convergent validity.

RESULTS: A total of 136 patients with mood disorders and 95 healthy controls participated in this study. Cronbach α values were 0.928 (ODQ-20) and 0.945 (ODQ-26). Test-retest reliability coefficients were 0.798 (ODQ-20) and 0.836 (ODQ-26) (p<0.05); intraclass correlation coefficient values were 0.777 (ODQ-20) and 0.781 (ODQ-26) (p<0.01). The score of ODQ was positively correlated with BDI-II and MADRS (r=0.326~0.719, 0.235~0.537, p<0.01). The differences in the ODQ scores between the patient and control groups were statistically significant.

CONCLUSION: The reliability, structural validity, and criterion validity of the ODQ applied to patients with mood disorders meet the psychometric requirements, and the scale can be used to assess emotional blunting in Chinese patients with mood disorders.}, } @article {pmid36915631, year = {2023}, author = {McDermott, EJ and Metsomaa, J and Belardinelli, P and Grosse-Wentrup, M and Ziemann, U and Zrenner, C}, title = {Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.}, journal = {Virtual reality}, volume = {27}, number = {1}, pages = {347-369}, pmid = {36915631}, issn = {1359-4338}, abstract = {Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time-frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.}, } @article {pmid36914265, year = {2023}, author = {Dougherty, LL and Dutta, S and Avasthi, P}, title = {The ERK activator, BCI, inhibits ciliogenesis and causes defects in motor behavior, ciliary gating, and cytoskeletal rearrangement.}, journal = {Life science alliance}, volume = {6}, number = {6}, pages = {}, doi = {10.26508/lsa.202301899}, pmid = {36914265}, issn = {2575-1077}, abstract = {MAPK pathways are well-known regulators of the cell cycle, but they have also been found to control ciliary length in a wide variety of organisms and cell types from Caenorhabditis elegans neurons to mammalian photoreceptors through unknown mechanisms. ERK1/2 is a MAP kinase in human cells that is predominantly phosphorylated by MEK1/2 and dephosphorylated by the phosphatase DUSP6. We have found that the ERK1/2 activator/DUSP6 inhibitor, (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI), inhibits ciliary maintenance in Chlamydomonas and hTERT-RPE1 cells and assembly in Chlamydomonas These effects involve inhibition of total protein synthesis, microtubule organization, membrane trafficking, and KAP-GFP motor dynamics. Our data provide evidence for various avenues for BCI-induced ciliary shortening and impaired ciliogenesis that gives mechanistic insight into how MAP kinases can regulate ciliary length.}, } @article {pmid36913120, year = {2023}, author = {Zhang, Y and Zeng, H and Lou, F and Tan, X and Zhang, X and Chen, G}, title = {SLC45A3 Serves as a Potential Therapeutic Biomarker to Attenuate White Matter Injury After Intracerebral Hemorrhage.}, journal = {Translational stroke research}, volume = {}, number = {}, pages = {}, pmid = {36913120}, issn = {1868-601X}, abstract = {Intracerebral hemorrhage (ICH) is a severe cerebrovascular disease, which impairs patients' white matter even after timely clinical interventions. Indicated by studies in the past decade, ICH-induced white matter injury (WMI) is closely related to neurological deficits; however, its underlying mechanism and pertinent treatment are yet insufficient. We gathered two datasets (GSE24265 and GSE125512), and by taking an intersection among interesting genes identified by weighted gene co-expression networks analysis, we determined target genes after differentially expressing genes in two datasets. Additional single-cell RNA-seq analysis (GSE167593) helped locate the gene in cell types. Furthermore, we established ICH mice models induced by autologous blood or collagenase. Basic medical experiments and diffusion tensor imaging were applied to verify the function of target genes in WMI after ICH. Through intersection and enrichment analysis, gene SLC45A3 was identified as the target one, which plays a key role in the regulation of oligodendrocyte differentiation involving in fatty acid metabolic process, etc. after ICH, and single-cell RNA-seq analysis also shows that it mainly locates in oligodendrocytes. Further experiments verified overexpression of SLC45A3 ameliorated brain injury after ICH. Therefore, SLC45A3 might serve as a candidate therapeutic biomarker for ICH-induced WMI, and overexpression of it may be a potential approach for injury attenuation.}, } @article {pmid36911809, year = {2023}, author = {Tao, QQ and Lin, RR and Wu, ZY}, title = {Early Diagnosis of Alzheimer's Disease: Moving Toward a Blood-Based Biomarkers Era.}, journal = {Clinical interventions in aging}, volume = {18}, number = {}, pages = {353-358}, pmid = {36911809}, issn = {1178-1998}, } @article {pmid36909601, year = {2023}, author = {Prescott, RA and Pankow, AP and de Vries, M and Crosse, K and Patel, RS and Alu, M and Loomis, C and Torres, V and Koralov, S and Ivanova, E and Dittmann, M and Rosenberg, BR}, title = {A comparative study of in vitro air-liquid interface culture models of the human airway epithelium evaluating cellular heterogeneity and gene expression at single cell resolution.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.02.27.530299}, pmid = {36909601}, abstract = {The airway epithelium is composed of diverse cell types with specialized functions that mediate homeostasis and protect against respiratory pathogens. Human airway epithelial cultures at air-liquid interface (HAE) are a physiologically relevant in vitro model of this heterogeneous tissue, enabling numerous studies of airway disease [1â€"7] . HAE cultures are classically derived from primary epithelial cells, the relatively limited passage capacity of which can limit experimental methods and study designs. BCi-NS1.1, a previously described and widely used basal cell line engineered to express hTERT, exhibits extended passage lifespan while retaining capacity for differentiation to HAE [5] . However, gene expression and innate immune function in HAE derived from BCi-NS1.1 versus primary cells have not been fully characterized. Here, combining single cell RNA-Seq (scRNA-Seq), immunohistochemistry, and functional experimentation, we confirm at high resolution that BCi-NS1.1 and primary HAE cultures are largely similar in morphology, cell type composition, and overall transcriptional patterns. While we observed cell-type specific expression differences of several interferon stimulated genes in BCi-NS1.1 HAE cultures, we did not observe significant differences in susceptibility to infection with influenza A virus and Staphylococcus aureus . Taken together, our results further support BCi-NS1.1-derived HAE cultures as a valuable tool for the study of airway infectious disease.}, } @article {pmid36908799, year = {2023}, author = {Du, P and Li, P and Cheng, L and Li, X and Su, J}, title = {Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1132290}, pmid = {36908799}, issn = {1662-4548}, abstract = {INTRODUCTION: Currently, it is still a challenge to detect single-trial P300 from electroencephalography (EEG) signals. In this paper, to address the typical problems faced by existing single-trial P300 classification, such as complex, time-consuming and low accuracy processes, a single-trial P300 classification algorithm based on multiplayer data fusion convolutional neural network (CNN) is proposed to construct a centralized collaborative brain-computer interfaces (cBCI) for fast and highly accurate classification of P300 EEG signals.

METHODS: In this paper, two multi-person data fusion methods (parallel data fusion and serial data fusion) are used in the data pre-processing stage to fuse multi-person EEG information stimulated by the same task instructions, and then the fused data is fed as input to the CNN for classification. In building the CNN network for single-trial P300 classification, the Conv layer was first used to extract the features of single-trial P300, and then the Maxpooling layer was used to connect the Flatten layer for secondary feature extraction and dimensionality reduction, thereby simplifying the computation. Finally batch normalisation is used to train small batches of data in order to better generalize the network and speed up single-trial P300 signal classification.

RESULTS: In this paper, the above new algorithms were tested on the Kaggle dataset and the Brain-Computer Interface (BCI) Competition III dataset, and by analyzing the P300 waveform features and EEG topography and the four standard evaluation metrics, namely Accuracy, Precision, Recall and F1-score,it was demonstrated that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed other classification algorithms.

DISCUSSION: The results show that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed the single-person model, and that the single-trial P300 classification algorithm with two multi-person data fusion CNNs involves smaller models, fewer training parameters, higher classification accuracy and improves the overall P300-cBCI classification rate and actual performance more effectively with a small amount of sample information compared to other algorithms.}, } @article {pmid36908782, year = {2023}, author = {Zhang, R and Chen, Y and Xu, Z and Zhang, L and Hu, Y and Chen, M}, title = {Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1129049}, pmid = {36908782}, issn = {1662-4548}, abstract = {Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.}, } @article {pmid36908763, year = {2023}, author = {Zuccaroli, I and Lucke-Wold, B and Palla, A and Eremiev, A and Sorrentino, Z and Zakare-Fagbamila, R and McNulty, J and Christie, C and Chandra, V and Mampre, D}, title = {Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections.}, journal = {OBM neurobiology}, volume = {7}, number = {1}, pages = {}, pmid = {36908763}, issn = {2573-4407}, abstract = {Reported neuro-modulation schemes in the literature are typically classified as closed-loop or open-loop. A novel group of recently developed neuro-modulation devices may be better described as a neural bypass, which attempts to transmit neural data from one location of the nervous system to another. The most common form of neural bypasses in the literature utilize EEG recordings of cortical information paired with functional electrical stimulation for effector muscle output, most commonly for assistive applications and rehabilitation in spinal cord injury or stroke. Other neural bypass locations that have also been described, or may soon be in development, include cortical-spinal bypasses, cortical-cortical bypasses, autonomic bypasses, peripheral-central bypasses, and inter-subject bypasses. The most common recording devices include EEG, ECoG, and microelectrode arrays, while stimulation devices include both invasive and noninvasive electrodes. Several devices are in development to improve the temporal and spatial resolution and biocompatibility for neuronal recording and stimulation. A major barrier to entry includes neuroplasticity and current decoding mechanisms that regularly require retraining. Neural bypasses are a unique class of neuro-modulation. Continued advancement of neural recording and stimulating devices with high spatial and temporal resolution, combined with decoding mechanisms uninhibited by neuroplasticity, can expand the therapeutic capability of neural bypassing. Overall, neural bypasses are a promising modality to improve the treatment of common neurologic disorders, including stroke, spinal cord injury, peripheral nerve injury, brain injury and more.}, } @article {pmid36907708, year = {2023}, author = {Gavaret, M and Iftimovici, A and Pruvost-Robieux, E}, title = {EEG: Current relevance and promising quantitative analyses.}, journal = {Revue neurologique}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neurol.2022.12.008}, pmid = {36907708}, issn = {0035-3787}, abstract = {Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.}, } @article {pmid36905065, year = {2023}, author = {Tao, T and Gao, Y and Jia, Y and Chen, R and Li, P and Xu, G}, title = {A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, pmid = {36905065}, issn = {1424-8220}, abstract = {An error-related potential (ErrP) occurs when people's expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain-computer interfaces.}, } @article {pmid36908334, year = {2022}, author = {Huggins, JE and Krusienski, D and Vansteensel, MJ and Valeriani, D and Thelen, A and Stavisky, S and Norton, JJS and Nijholt, A and Müller-Putz, G and Kosmyna, N and Korczowski, L and Kapeller, C and Herff, C and Halder, S and Guger, C and Grosse-Wentrup, M and Gaunt, R and Dusang, AN and Clisson, P and Chavarriaga, R and Anderson, CW and Allison, BZ and Aksenova, T and Aarnoutse, E}, title = {Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.}, journal = {Brain computer interfaces (Abingdon, England)}, volume = {9}, number = {2}, pages = {69-101}, pmid = {36908334}, issn = {2326-263X}, abstract = {The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.}, } @article {pmid36905004, year = {2023}, author = {Saibene, A and Caglioni, M and Corchs, S and Gasparini, F}, title = {EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, doi = {10.3390/s23052798}, pmid = {36905004}, issn = {1424-8220}, abstract = {In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.}, } @article {pmid36904950, year = {2023}, author = {Collazos-Huertas, DF and Álvarez-Meza, AM and Cárdenas-Peña, DA and Castaño-Duque, GA and Castellanos-Domínguez, CG}, title = {Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, doi = {10.3390/s23052750}, pmid = {36904950}, issn = {1424-8220}, abstract = {Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of "poor skill" subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.}, } @article {pmid36904683, year = {2023}, author = {Oikonomou, VP and Georgiadis, K and Kalaganis, F and Nikolopoulos, S and Kompatsiaris, I}, title = {A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, doi = {10.3390/s23052480}, pmid = {36904683}, issn = {1424-8220}, abstract = {In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).}, } @article {pmid36904629, year = {2023}, author = {Oikonomou, VP}, title = {Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {5}, pages = {}, doi = {10.3390/s23052425}, pmid = {36904629}, issn = {1424-8220}, abstract = {Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.}, } @article {pmid36899699, year = {2023}, author = {Niu, K and An, Z and Yao, Z and Chen, C and Yang, L and Xiong, J}, title = {Effects of Different Bedding Materials on Production Performance, Lying Behavior and Welfare of Dairy Buffaloes.}, journal = {Animals : an open access journal from MDPI}, volume = {13}, number = {5}, pages = {}, doi = {10.3390/ani13050842}, pmid = {36899699}, issn = {2076-2615}, abstract = {Different bedding materials have important effects on the behavioristics, production performance and welfare of buffalo. This study aimed to compare the effects of two bedding materials on lying behavior, production performance and animal welfare of dairy buffaloes. More than 40 multiparous lactating buffaloes were randomly divided into two groups, which were raised on fermented manure bedding (FMB) and chaff bedding (CB). The results showed that the application of FMB improved the lying behavior of buffaloes, the average daily lying time (ADLT) of buffaloes in FMB increased by 58 min compared to those in CB, with a significant difference (p < 0.05); the average daily standing time (ADST) decreased by 30 min, with a significant difference (p < 0.05); and the buffalo comfort index (BCI) increased, but the difference was not significant (p > 0.05). The average daily milk yield of buffaloes in FMB increased by 5.78% compared to buffaloes in CB. The application of FMB improved the hygiene of buffaloes. The locomotion score and hock lesion score were not significantly different between the two groups and all buffaloes did not show moderate and severe lameness. The price of FMB was calculated to be 46% of CB, which greatly reduced the cost of bedding material. In summary, FMB has significantly improved the lying behavior, production performance and welfare of buffaloes and significantly reduce the cost of bedding material.}, } @article {pmid36899597, year = {2023}, author = {Zhao, SN and Cui, Y and He, Y and He, Z and Diao, Z and Peng, F and Cheng, C}, title = {Teleoperation control of a wheeled mobile robot based on Brain-machine Interface.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {2}, pages = {3638-3660}, doi = {10.3934/mbe.2023170}, pmid = {36899597}, issn = {1551-0018}, abstract = {This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.}, } @article {pmid36899543, year = {2023}, author = {Yang, L and Shi, T and Lv, J and Liu, Y and Dai, Y and Zou, L}, title = {A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {2}, pages = {2482-2500}, doi = {10.3934/mbe.2023116}, pmid = {36899543}, issn = {1551-0018}, abstract = {To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.}, } @article {pmid36899504, year = {2023}, author = {Gan, L and Yin, X and Huang, J and Jia, B}, title = {Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {2}, pages = {1695-1715}, doi = {10.3934/mbe.2023077}, pmid = {36899504}, issn = {1551-0018}, abstract = {Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute nonfatal event and is characterized by high morbidity, disability, and mortality. Transcranial Doppler (TCD) ultrasonography is a non-invasive method for the diagnosis of cerebrovascular disease that uses the Doppler effect to detect the hemodynamic and physiological parameters of the major intracranial basilar arteries. It can provide important hemodynamic information that cannot be measured by other diagnostic imaging techniques for cerebrovascular disease. And the result parameters of TCD ultrasonography such as blood flow velocity and beat index can reflect the type of cerebrovascular disease and serve as a basis to assist physicians in the treatment of cerebrovascular diseases. Artificial intelligence (AI) is a branch of computer science which is used in a wide range of applications in agriculture, communications, medicine, finance, and other fields. In recent years, there are much research devoted to the application of AI to TCD. The review and summary of related technologies is an important work to promote the development of this field, which can provide an intuitive technical summary for future researchers. In this paper, we first review the development, principles, and applications of TCD ultrasonography and other related knowledge, and briefly introduce the development of AI in the field of medicine and emergency medicine. Finally, we summarize in detail the applications and advantages of AI technology in TCD ultrasonography including the establishment of an examination system combining brain computer interface (BCI) and TCD ultrasonography, the classification and noise cancellation of TCD ultrasonography signals using AI algorithms, and the use of intelligent robots to assist physicians in TCD ultrasonography and discuss the prospects for the development of AI in TCD ultrasonography.}, } @article {pmid36896512, year = {2023}, author = {Wu, Z and She, Q and Hou, Z and Li, Z and Tian, K and Ma, Y}, title = {Multi-source online transfer algorithm based on source domain selection for EEG classification.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {3}, pages = {4560-4573}, doi = {10.3934/mbe.2023211}, pmid = {36896512}, issn = {1551-0018}, abstract = {The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.}, } @article {pmid36883755, year = {2023}, author = {Greenwell, D and Vanderkolff, S and Feigh, J}, title = {Understanding De Novo Learning for Brain Machine Interfaces.}, journal = {Journal of neurophysiology}, volume = {}, number = {}, pages = {}, doi = {10.1152/jn.00496.2022}, pmid = {36883755}, issn = {1522-1598}, abstract = {De novo motor learning is a form of motor learning characterized by the development of an entirely new and distinct motor controller to accommodate a novel motor demand. Inversely, adaptation is a form of motor learning characterized by rapid, unconscious modifications in a previously established motor controller to accommodate small deviations in task demands. Since most of motor learning involves the adaption of previously established motor controllers, de novo learning can be challenging to isolate and observe. The recent publication from Haith et al. (2022) details a novel method to investigate de novo learning using a complex bimanual cursor control task. This research is especially important in the context of future brain machine interface devices that will present users with an entirely novel motor learning demand, requiring de novo learning.}, } @article {pmid36878831, year = {2023}, author = {Mathon, B and Navarro, V and Lecas, S and Roussel, D and Charpier, S and Carpentier, A}, title = {Safety Profile of Low-Intensity Pulsed Ultrasound-Induced Blood-Brain Barrier Opening in Non-epileptic Mice and in a Mouse Model of Mesial Temporal Lobe Epilepsy.}, journal = {Ultrasound in medicine & biology}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.ultrasmedbio.2023.02.002}, pmid = {36878831}, issn = {1879-291X}, abstract = {OBJECTIVE: It is unknown whether ultrasound-induced blood-brain barrier (BBB) disruption can promote epileptogenesis and how BBB integrity changes over time after sonication.

METHODS: To gain more insight into the safety profile of ultrasound (US)-induced BBB opening, we determined BBB permeability as well as histological modifications in C57BL/6 adult control mice and in the kainate (KA) model for mesial temporal lobe epilepsy in mice after sonication with low-intensity pulsed ultrasound (LIPU). Microglial and astroglial changes in ipsilateral hippocampus were examined at different time points following BBB disruption by respectively analyzing Iba1 and glial fibrillary acidic protein immunoreactivity. Using intracerebral EEG recordings, we further studied the possible electrophysiological repercussions of a repeated disrupted BBB for seizure generation in nine non-epileptic mice.

RESULTS: LIPU-induced BBB opening led to transient albumin extravasation and reversible mild astrogliosis, but not to microglial activation in the hippocampus of non-epileptic mice. In KA mice, the transient albumin extravasation into the hippocampus mediated by LIPU-induced BBB opening did not aggravate inflammatory processes and histologic changes that characterize the hippocampal sclerosis. Three LIPU-induced BBB opening did not induce epileptogenicity in non-epileptic mice implanted with depth EEG electrodes.

CONCLUSION: Our experiments in mice provide persuasive evidence of the safety of LIPU-induced BBB opening as a therapeutic modality for neurological diseases.}, } @article {pmid36878727, year = {2023}, author = {Ma, J and Hu, Z and Yue, H and Luo, Y and Wang, C and Wu, X and Gu, Y and Wang, L}, title = {GRM2 regulates functional integration of adult-born DGCs by paradoxically modulating MEK/ERK1/2 pathway.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.1886-22.2023}, pmid = {36878727}, issn = {1529-2401}, abstract = {Metabotropic glutamate receptor 2 (GRM2) is highly expressed in hippocampal dentate granule cells (DGCs), regulating synaptic transmission and hippocampal functions. Newborn DGCs are continuously generated throughout life, and express GRM2 when they are mature. However, it remained unclear whether and how GRM2 regulates the development and integration of these newborn neurons. We discovered that the expression of GRM2 in adult-born DGCs increased with neuronal development in mice of both sexes. Lack of GRM2 caused developmental defects of DGCs and impaired hippocampus-dependent cognitive functions. Intriguingly, our data showed that knockdown of Grm2 resulted in decreased b/c-Raf kinases, and paradoxically led to an excessive activation of MEK/ERK1/2 pathway. Inhibition of MEK ameliorated the developmental defects caused by Grm2 knockdown. Together, our results indicate that GRM2 is necessary for the development and functional integration of newborn DGCs in the adult hippocampus through regulating the phosphorylation and activation state of MEK/ERK1/2 pathway.SIGNIFICANCE STATEMENT:Metabotropic glutamate receptor 2 (GRM2) is highly expressed in mature dentate granule cells (DGCs) in the hippocampus. It remains unclear whether GRM2 is required for the development and integration of adult-born DGCs. We provided a series of in vivo and in vitro evidence to show GRM2 regulates the development of adult-born DGCs and their integration into existing hippocampal circuits. Lack of GRM2 in a cohort of newborn DGCs impaired object-to-location memory in mice. Moreover, we revealed that GRM2 knockdown paradoxically upregulated MEK/ERK1/2 pathway by suppressing b/c-Raf in developing neurons, which is likely a common mechanism underlying the regulation of the development of neurons expressing GRM2. Thus, Raf/MEK/ERK1/2 pathway could be a potential target for brain diseases related to GRM2 abnormality.}, } @article {pmid36877853, year = {2023}, author = {Qi, HX and Reed, JL and Liao, CC and Kaas, JH}, title = {Regressive changes in sizes of somatosensory cuneate nucleus after sensory loss in primates.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {120}, number = {11}, pages = {e2222076120}, doi = {10.1073/pnas.2222076120}, pmid = {36877853}, issn = {1091-6490}, abstract = {Neurons in the early stages of processing sensory information suffer transneuronal atrophy when deprived of their activating inputs. For over 40 y, members of our laboratory have studied the reorganization of the somatosensory cortex during and after recovering from different types of sensory loss. Here, we took advantage of the preserved histological material from these studies of the cortical effects of sensory loss to evaluate the histological consequences in the cuneate nucleus of the lower brainstem and the adjoining spinal cord. The neurons in the cuneate nucleus are activated by touch on the hand and arm, and relay this activation to the contralateral thalamus, and from the thalamus to the primary somatosensory cortex. Neurons deprived of activating inputs tend to shrink and sometimes die. We considered the effects of differences in species, type and extent of sensory loss, recovery time after injury, and age at the time of injury on the histology of the cuneate nucleus. The results indicate that all injuries that deprived part or all of the cuneate nucleus of sensory activation result in some atrophy of neurons as reflected by a decrease in nucleus size. The extent of the atrophy is greater with greater sensory loss and with longer recovery times. Based on supporting research, atrophy appears to involve a reduction in neuron size and neuropil, with little or no neuron loss. Thus, the potential exists for restoring the hand to cortex pathway with brain-machine interfaces, for bionic prosthetics, or biologically with hand replacement surgery.}, } @article {pmid36877440, year = {2023}, author = {He, C and Duan, S}, title = {Novel Insight into Glial Biology and Diseases.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36877440}, issn = {1995-8218}, } @article {pmid36876801, year = {2023}, author = {Kumar, A and Sah, DK and Khanna, K and Rai, Y and Yadav, AK and Ansari, MS and Bhatt, AN}, title = {A calcium and zinc composite alginate hydrogel for pre-hospital hemostasis and wound care.}, journal = {Carbohydrate polymers}, volume = {299}, number = {}, pages = {120186}, doi = {10.1016/j.carbpol.2022.120186}, pmid = {36876801}, issn = {1879-1344}, abstract = {We developed, characterized, and examined the hemostatic potential of sodium alginate-based Ca[2+] and Zn[2+] composite hydrogel (SA-CZ). SA-CZ hydrogel showed substantial in-vitro efficacy, as observed by the significant reduction in coagulation time with better blood coagulation index (BCI) and no evident hemolysis in human blood. SA-CZ significantly reduced bleeding time (≈60 %) and mean blood loss (≈65 %) in the tail bleeding and liver incision in the mice hemorrhage model (p ≤ 0.001). SA-CZ also showed enhanced cellular migration (1.58-fold) in-vitro and improved wound closure (≈70 %) as compared with betadine (≈38 %) and saline (≈34 %) at the 7th-day post-wound creation in-vivo (p < 0.005). Subcutaneous implantation and intra-venous gamma-scintigraphy of hydrogel revealed ample body clearance and non-considerable accumulation in any vital organ, proving its non-thromboembolic nature. Overall, SA-CZ showed good biocompatibility along with efficient hemostasis and wound healing qualities, making it suitable as a safe and effective aid for bleeding wounds.}, } @article {pmid36875646, year = {2023}, author = {Porcaro, C and Avanaki, K and Arias-Carrion, O and Mørup, M}, title = {Editorial: Combined EEG in research and diagnostics: Novel perspectives and improvements.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1152394}, pmid = {36875646}, issn = {1662-4548}, } @article {pmid36875236, year = {2023}, author = {Zhang, J and Gao, S and Zhou, K and Cheng, Y and Mao, S}, title = {An online hybrid BCI combining SSVEP and EOG-based eye movements.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1103935}, pmid = {36875236}, issn = {1662-5161}, abstract = {Hybrid brain-computer interface (hBCI) refers to a system composed of a single-modality BCI and another system. In this paper, we propose an online hybrid BCI combining steady-state visual evoked potential (SSVEP) and eye movements to improve the performance of BCI systems. Twenty buttons corresponding to 20 characters are evenly distributed in the five regions of the GUI and flash at the same time to arouse SSVEP. At the end of the flash, the buttons in the four regions move in different directions, and the subject continues to stare at the target with eyes to generate the corresponding eye movements. The CCA method and FBCCA method were used to detect SSVEP, and the electrooculography (EOG) waveform was used to detect eye movements. Based on the EOG features, this paper proposes a decision-making method based on SSVEP and EOG, which can further improve the performance of the hybrid BCI system. Ten healthy students took part in our experiment, and the average accuracy and information transfer rate of the system were 94.75% and 108.63 bits/min, respectively.}, } @article {pmid36868861, year = {2023}, author = {Wang, Y and Lin, J and Li, J and Yan, L and Li, W and He, X and Ma, H}, title = {Chronic neuronal inactivity utilizes the mTOR-TFEB pathway to drive transcription-dependent autophagy for homeostatic up-scaling.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.0146-23.2023}, pmid = {36868861}, issn = {1529-2401}, abstract = {Activity-dependent changes in protein expression are critical for neuronal plasticity, a fundamental process for the processing and storage of information in the brain. Among the various forms of plasticity, homeostatic synaptic up-scaling is unique in that it is induced primarily by neuronal inactivity. However, precisely how the turnover of synaptic proteins occurs in this homeostatic process remains unclear. Here, we report that chronically inhibiting neuronal activity in primary cortical neurons prepared from E18 Sprague-Dawley rats (both sexes) induces autophagy, thereby regulating key synaptic proteins for up-scaling. Mechanistically, chronic neuronal inactivity causes dephosphorylation of ERK and mTOR, which induces TFEB-mediated cytonuclear signaling and drives transcription-dependent autophagy to regulate αCaMKII and PSD95 during synaptic up-scaling. Together, these findings suggest that mTOR-dependent autophagy, which is often triggered by metabolic stressors such as starvation, is recruited and sustained during neuronal inactivity in order to maintain synaptic homeostasis, a process that ensures proper brain function and if impaired can cause neuropsychiatric disorders such as autism.SIGNIFICANCE STATEMENT:In the mammalian brain, protein turnover is tightly controlled by neuronal activation to ensure key neuronal functions during long-lasting synaptic plasticity. However, a long-standing question is how this process occurs during synaptic up-scaling, a process that requires protein turnover but is induced by neuronal inactivation. Here, we report that mTOR-dependent signaling-which is often triggered by metabolic stressors such as starvation-is "hijacked" by chronic neuronal inactivation, which then serves as a nucleation point for TFEB cytonuclear signaling that drives transcription-dependent autophagy for up-scaling. These results provide the first evidence of a physiological role of mTOR-dependent autophagy in enduing neuronal plasticity, thereby connecting major themes in cell biology and neuroscience via a servo loop that mediates autoregulation in the brain.}, } @article {pmid36868167, year = {2023}, author = {Yang, Y and Zhang, F and Gao, X and Feng, L and Xu, K}, title = {Progressive alterations in electrophysiological and epileptic network properties during the development of temporal lobe epilepsy in rats.}, journal = {Epilepsy & behavior : E&B}, volume = {141}, number = {}, pages = {109120}, doi = {10.1016/j.yebeh.2023.109120}, pmid = {36868167}, issn = {1525-5069}, abstract = {OBJECTIVE: Refractory temporal lobe epilepsy (TLE) with recurring seizures causing continuing pathological changes in neural reorganization. There is an incomplete understanding of how spatiotemporal electrophysiological characteristics changes during the development of TLE. Long-term multi-site epilepsy patients' data is hard to obtain. Thus, our study relied on animal models to reveal the changes in electrophysiological and epileptic network characteristics systematically.

METHODS: Long-term local field potentials (LFPs) were recorded over a period of 1 to 4 months from 6 pilocarpine-treated TLE rats. We compared variations of seizure onset zone (SOZ), seizure onset pattern (SOP), the latency of seizure onsets, and functional connectivity network from 10-channel LFPs between the early and late stages. Moreover, three machine learning classifiers trained by early-stage data were used to test seizure detection performance in the late stage.

RESULTS: Compared to the early stage, the earliest seizure onset was more frequently detected in hippocampus areas in the late stage. The latency of seizure onsets between electrodes became shorter. Low-voltage fast activity (LVFA) was the most common SOP and the proportion of it increased in the late stage. Different brain states were observed during seizures using Granger causality (GC). Moreover, seizure detection classifiers trained by early-stage data were less accurate when tested in late-stage data.

SIGNIFICANCE: Neuromodulation especially closed-loop deep brain stimulation (DBS) is effective in the treatment of refractory TLE. Although the frequency or amplitude of the stimulation is generally adjusted in existing closed-loop DBS devices in clinical usage, the adjustment rarely considers the pathological progression of chronic TLE. This suggests that an important factor affecting the therapeutic effect of neuromodulation may have been overlooked. The present study reveals time-varying electrophysiological and epileptic network properties in chronic TLE rats and indicates that classifiers of seizure detection and neuromodulation parameters might be designed to adapt to the current state dynamically with the progression of epilepsy.}, } @article {pmid36866539, year = {2023}, author = {Chen, S and Guan, X and Xie, L and Liu, C and Li, C and He, M and Hu, J and Fan, H and Li, Q and Xie, L and Yang, M and Zhang, X and Xiao, S and Tang, J}, title = {Aloe-emodin targets multiple signaling pathways by blocking ubiquitin-mediated degradation of DUSP1 in nasopharyngeal carcinoma cells.}, journal = {Phytotherapy research : PTR}, volume = {}, number = {}, pages = {}, doi = {10.1002/ptr.7793}, pmid = {36866539}, issn = {1099-1573}, abstract = {Aloe-emodin (AE) has been shown to inhibit the proliferation of several cancer cell lines, including human nasopharyngeal carcinoma (NPC) cell lines. In this study, we confirmed that AE inhibited malignant biological behaviors, including cell viability, abnormal proliferation, apoptosis, and migration of NPC cells. Western blotting analysis revealed that AE upregulated the expression of DUSP1, an endogenous inhibitor of multiple cancer-associated signaling pathways, resulting in blockage of the extracellular signal-regulated kinase (ERK)-1/2, protein kinase B (AKT), and p38-mitogen activated protein kinase（p38-MAPK） signaling pathways in NPC cell lines. Moreover, the selective inhibitor of DUSP1, BCI-hydrochloride, partially reversed the AE-induced cytotoxicity and blocked the aforementioned signaling pathways in NPC cells. In addition, the binding between AE and DUSP1 was predicted via molecular docking analysis using AutoDock-Vina software and further verified via a microscale thermophoresis assay. The binding amino acid residues were adjacent to the predicted ubiquitination site (Lys192) of DUSP1. Immunoprecipitation with the ubiquitin antibody, ubiquitinated DUSP1 was shown to be upregulated by AE. Our findings revealed that AE can stabilize DUSP1 by blocking its ubiquitin-proteasome-mediated degradation and proposed an underlying mechanism by which AE-upregulated DUSP1 may potentially target multiple pathways in NPC cells.}, } @article {pmid36866306, year = {2023}, author = {Patel, HH and Berlinberg, EJ and Nwachukwu, B and Williams, RJ and Mandelbaum, B and Sonkin, K and Forsythe, B}, title = {Quadriceps Weakness is Associated with Neuroplastic Changes Within Specific Corticospinal Pathways and Brain Areas After Anterior Cruciate Ligament Reconstruction: Theoretical Utility of Motor Imagery-Based Brain-Computer Interface Technology for Rehabilitation.}, journal = {Arthroscopy, sports medicine, and rehabilitation}, volume = {5}, number = {1}, pages = {e207-e216}, pmid = {36866306}, issn = {2666-061X}, abstract = {UNLABELLED: Persistent quadriceps weakness is a problematic sequela of anterior cruciate ligament reconstruction (ACLR). The purposes of this review are to summarize neuroplastic changes after ACL reconstruction; provide an overview of a promising interventions, motor imagery (MI), and its utility in muscle activation; and propose a framework using a brain-computer interface (BCI) to augment quadriceps activation. A literature review of neuroplastic changes, MI training, and BCI-MI technology in postoperative neuromuscular rehabilitation was conducted in PubMed, Embase, and Scopus. Combinations of the following search terms were used to identify articles: "quadriceps muscle," "neurofeedback," "biofeedback," "muscle activation," "motor learning," "anterior cruciate ligament," and "cortical plasticity." We found that ACLR disrupts sensory input from the quadriceps, which results in reduced sensitivity to electrochemical neuronal signals, an increase in central inhibition of neurons regulating quadriceps control and dampening of reflexive motor activity. MI training consists of visualizing an action, without physically engaging in muscle activity. Imagined motor output during MI training increases the sensitivity and conductivity of corticospinal tracts emerging from the primary motor cortex, which helps "exercise" the connections between the brain and target muscle tissues. Motor rehabilitation studies using BCI-MI technology have demonstrated increased excitability of the motor cortex, corticospinal tract, spinal motor neurons, and disinhibition of inhibitory interneurons. This technology has been validated and successfully applied in the recovery of atrophied neuromuscular pathways in stroke patients but has yet to be investigated in peripheral neuromuscular insults, such as ACL injury and reconstruction. Well-designed clinical studies may assess the impact of BCI on clinical outcomes and recovery time. Quadriceps weakness is associated with neuroplastic changes within specific corticospinal pathways and brain areas. BCI-MI shows strong potential for facilitating recovery of atrophied neuromuscular pathways after ACLR and may offer an innovative, multidisciplinary approach to orthopaedic care.

LEVEL OF EVIDENCE: V, expert opinion.}, } @article {pmid36865207, year = {2023}, author = {Forenzo, D and Liu, Y and Kim, J and Ding, Y and Yoon, T and He, B}, title = {Integrating simultaneous motor imagery and spatial attention for EEG-BCI control.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.02.20.529307}, pmid = {36865207}, abstract = {OBJECTIVE: EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control.

METHODS: We evaluated a cohort of 25 human subjects' ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI).

RESULTS: Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), statistically outperforms MI alone (42%), and was higher, but not statistically significant, than OSA alone (45%). MI+OSA had a similar performance to each subject's best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA.

CONCLUSION: Integrating MI and OSA leads to improved performance over MI alone at the group level and is the best BCI paradigm option for some subjects.

SIGNIFICANCE: This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users' BCI performance.}, } @article {pmid36863014, year = {2023}, author = {Li, G and Liu, Y and Chen, Y and Li, M and Song, J and Li, K and Zhang, Y and Hu, L and Qi, X and Wan, X and Liu, J and He, Q and Zhou, H}, title = {Polyvinyl alcohol/polyacrylamide double-network hydrogel-based semi-dry electrodes for robust electroencephalography recording at hairy scalp for noninvasive brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acc098}, pmid = {36863014}, issn = {1741-2552}, abstract = {OBJECTIVE: Reliable and user-friendly electrodes can continuously and real-time capture the electroencephalography signals, which is essential for real-life brain-computer interfaces (BCIs). This study develops a flexible, durable, and low-contact-impedance polyvinyl alcohol/polyacrylamide double-network hydrogel (PVA/PAM DNH)-based semi-dry electrode for robust electroencephalography recording at hairy scalp.

APPROACH: The PVA/PAM DNHs are developed using a cyclic freeze-thaw strategy and used as a saline reservoir for semi-dry electrodes. The PVA/PAM DNHs steadily deliver trace amounts of saline onto the scalp, enabling low and stable electrode-scalp impedance. The hydrogel also conforms well to the wet scalp, stabilizing the electrode-scalp interface. The feasibility of the real-life BCIs is validated by conducting four classic BCI paradigms on 16 participants.

MAIN RESULTS: The results show that the PVA/PAM DNHs with 7.5%wt% PVA achieve a satisfactory trade-off between the saline load-unloading capacity and the compressive strength. The proposed semi-dry electrode exhibits a low contact impedance (18 ± 8.9 kΩ at 10 Hz), a small offset potential (0.46 mV), and negligible potential drift (1.5 ± 0.4 μV/min). The temporal cross-correlation between the semi-dry and wet electrodes is 0.91, and the spectral coherence is higher than 0.90 at frequencies below 45 Hz. Furthermore, no significant differences are present in BCI classification accuracy between these two typical electrodes.

SIGNIFICANCE: Based on the durability, rapid setup, wear-comfort, and robust signals of the developed hydrogel, PVA/PAM DNH-based semi-dry electrodes are a promising alternative to wet electrodes in real-life BCIs. .}, } @article {pmid36780560, year = {2023}, author = {Chen, X and Ma, R and Zhang, W and Zeng, GQ and Wu, Q and Yimiti, A and Xia, X and Cui, J and Liu, Q and Meng, X and Bu, J and Chen, Q and Pan, Y and Yu, NX and Wang, S and Deng, ZD and Sack, AT and Laughlin, MM and Zhang, X}, title = {Alpha oscillatory activity is causally linked to working memory retention.}, journal = {PLoS biology}, volume = {21}, number = {2}, pages = {e3001999}, doi = {10.1371/journal.pbio.3001999}, pmid = {36780560}, issn = {1545-7885}, abstract = {Although previous studies have reported correlations between alpha oscillations and the "retention" subprocess of working memory (WM), causal evidence has been limited in human neuroscience due to the lack of delicate modulation of human brain oscillations. Conventional transcranial alternating current stimulation (tACS) is not suitable for demonstrating the causal evidence for parietal alpha oscillations in WM retention because of its inability to modulate brain oscillations within a short period (i.e., the retention subprocess). Here, we developed an online phase-corrected tACS system capable of precisely correcting for the phase differences between tACS and concurrent endogenous oscillations. This system permits the modulation of brain oscillations at the target stimulation frequency within a short stimulation period and is here applied to empirically demonstrate that parietal alpha oscillations causally relate to WM retention. Our experimental design included both in-phase and anti-phase alpha-tACS applied to participants during the retention subprocess of a modified Sternberg paradigm. Compared to in-phase alpha-tACS, anti-phase alpha-tACS decreased both WM performance and alpha activity. These findings strongly support a causal link between alpha oscillations and WM retention and illustrate the broad application prospects of phase-corrected tACS.}, } @article {pmid36862765, year = {2023}, author = {Mao, C and Xiao, P and Tao, XN and Qin, J and He, QT and Zhang, C and Guo, SC and Du, YQ and Chen, LN and Shen, DD and Yang, ZS and Zhang, HQ and Huang, SM and He, YH and Cheng, J and Zhong, YN and Shang, P and Chen, J and Zhang, DL and Wang, QL and Liu, MX and Li, GY and Guo, Y and Xu, HE and Wang, C and Zhang, C and Feng, S and Yu, X and Zhang, Y and Sun, JP}, title = {Unsaturated bond recognition leads to biased signal in a fatty acid receptor.}, journal = {Science (New York, N.Y.)}, volume = {}, number = {}, pages = {eadd6220}, doi = {10.1126/science.add6220}, pmid = {36862765}, issn = {1095-9203}, abstract = {Individual free fatty acids (FAs) play important roles in metabolic homeostasis, many via engagement with more than 40 GPCRs. Searching for receptors to sense beneficial ω-3 FAs of fish oil enabled the identification of GPR120, involving with a spectrum of metabolic diseases. Here, we report six cryo-EM structures of GPR120 in complex with FA hormones or TUG891 and Gi or Giq trimers. Aromatic residues inside the GPR120 ligand pocket were responsible for recognizing different double-bond positions of these FAs and connect ligand recognition to distinct effector coupling. We also investigated synthetic ligand selectivity and the structural basis of missense single nucleotide polymorphisms. We reveal how GPR120 differentiates rigid double bonds and flexible single bonds and may facilitate rational drug design targeting to GPR120.}, } @article {pmid36861900, year = {2023}, author = {Shah, AM}, title = {New development in brain-computer interface platforms: 1-year results from the SWITCH trial.}, journal = {Artificial organs}, volume = {}, number = {}, pages = {}, doi = {10.1111/aor.14511}, pmid = {36861900}, issn = {1525-1594}, abstract = {Synchron publishes SWITCH trial results demonstrating the safety and efficacy of stentrode™ device. The stentrode™ is an endovascularly implanted brain-computer interface communication device capable of relaying neural activity from the motor cortex of paralyzed patients. The platform has been used to recover speech.}, } @article {pmid36861042, year = {2023}, author = {Valeriani, D and Cecotti, H and Thelen, A and Herff, C}, title = {Editorial: Translational brain-computer interfaces: From research labs to the market and back.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1152466}, pmid = {36861042}, issn = {1662-5161}, } @article {pmid36860620, year = {2023}, author = {Huang, G and Zhao, Z and Zhang, S and Hu, Z and Fan, J and Fu, M and Chen, J and Xiao, Y and Wang, J and Dan, G}, title = {Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1122661}, pmid = {36860620}, issn = {1662-4548}, abstract = {INTRODUCTION: Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal.

METHODS: To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives.

RESULTS: Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks.

DISCUSSION: All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.}, } @article {pmid36860616, year = {2023}, author = {Ivanov, N and Chau, T}, title = {Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1108889}, pmid = {36860616}, issn = {1662-5188}, abstract = {Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.}, } @article {pmid36856917, year = {2023}, author = {Song, Y and Sun, Z and Sun, W and Luo, M and Du, Y and Jing, J and Wang, Y}, title = {Neuroplasticity Following Stroke from a Functional Laterality Perspective: A fNIRS Study.}, journal = {Brain topography}, volume = {}, number = {}, pages = {}, pmid = {36856917}, issn = {1573-6792}, abstract = {To explore alterations of resting-state functional connectivity (rsFC) in sensorimotor cortex following strokes with left or right hemiplegia considering the lateralization and neuroplasticity. Seventy-three resting-state functional near-infrared spectroscopy (fNIRS) files were selected, including 26 from left hemiplegia (LH), 21 from right hemiplegia (RH) and 26 from normal controls (NC) group. Whole-brain analyses matching the Pearson correlation were used for rsFC calculations. For right-handed normal controls, rsFC of motor components (M1 and M2) in the left hemisphere displayed a prominent intensity in comparison with the right hemisphere (p < 0.05), while for stroke groups, this asymmetry has disappeared. Additionally, RH rather than LH showed stronger rsFC between left S1 and left M1 in contrast to normal controls (p < 0.05), which correlated inversely with motor function (r = - 0.53, p < 0.05). Regarding M1, rsFC within ipsi-lesioned M1 has a negative correlation with motor function of the affected limb (r = - 0.60 for the RH group and - 0.43 for the LH group, p < 0.05). The rsFC within contra-lesioned M1 that innervates the normal side was weakened compared with that of normal controls (p < 0.05). Stronger rsFC of motor components in left hemisphere was confirmed by rs-fNIRS as the "secret of dominance" for the first time, while post-stroke hemiplegia broke this cortical asymmetry. Meanwhile, a statistically strengthened rsFC between left S1 and M1 only in right-hemiplegia group may act as a compensation for the impairment of the dominant side. This research has implications for brain-computer interfaces synchronizing sensory feedback with motor performance and transcranial magnetic regulation for cortical excitability to induce cortical plasticity.}, } @article {pmid36855290, year = {2023}, author = {Çevik Saldıran, T and Kara, İ and Dinçer, E and Öztürk, Ö and Çakıcı, R and Burroughs, T}, title = {Cross-cultural adaptation and validation of Diabetes Quality of Life Brief Clinical Inventory in Turkish patients with type 2 diabetes mellitus.}, journal = {Disability and rehabilitation}, volume = {}, number = {}, pages = {1-10}, doi = {10.1080/09638288.2023.2182917}, pmid = {36855290}, issn = {1464-5165}, abstract = {PURPOSE: To translate and culturally adapt the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) into Turkish and assess the psychometric properties of the translated version.

METHODS: A forward-backward translation process was conducted in conformity with international guidelines. A total of 150 patients with type 2 diabetes mellitus (T2DM) completed the Turkish version of DQoL-BCI (DQoL-BCI-Tr). The factor structure, test-retest reliability, and construct validity were evaluated.

RESULTS: In the DQoL-BCI-Tr, the three-factor structure was found optimal and explained 68.7% of the variance. The DQoL-BCI-Tr showed excellent internal consistency (Cronbach's alpha = 0.90) and test-retest reliability (ICC = 0.98). Cronbach's alpha values ranged from 0.85 to 0.91 for subscales (satisfaction, worry, impact). A negative correlation was found between the total scores of the DQoL-BCI-Tr and the EuroQoL-5 dimensions (EQ-5D) indexes (r= -0.22, p < 0.01). The DQoL-BCI-Tr total score and satisfaction and worry subscale scores differentiated between groups defined by glycated hemoglobin (HbA1c>9%) and the use of insulin.

CONCLUSIONS: The study results showed that the DQoL-BCI-Tr can be served as a reliable and valid instrument to obtain information from Turkish patients with T2DM diagnosis, including satisfaction with treatment, the impact of the disease, and worry about the social/vocational issues.Implications for rehabilitationThe Turkish version of the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) is a valid and reliable instrument.The DQoL-BCI Questionnaire in Turkish (DQoL-BCI-Tr) is an easy and quick way to determine satisfaction with treatment, impact of disease, and worry about the social/vocational issues.The DQoL-BCI-Tr is a reliable instrument for assessing disease-specific effects, emotional loads, and satisfaction of Turkish patients with type 2 diabetes in clinical and research settings.}, } @article {pmid36854561, year = {2023}, author = {Zheng, C and Liu, Y and Xiao, X and Zhou, X and Xu, F and Xu, M and Ming, D}, title = {[Advances in brain-computer interface based on high-frequency steady-state visual evoked potential].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {40}, number = {1}, pages = {155-162}, doi = {10.7507/1001-5515.202205090}, pmid = {36854561}, issn = {1001-5515}, abstract = {Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.}, } @article {pmid36854262, year = {2022}, author = {Dadarlat, MC and Canfield, RA and Orsborn, AL}, title = {Neural Plasticity in Sensorimotor Brain-Machine Interfaces.}, journal = {Annual review of biomedical engineering}, volume = {}, number = {}, pages = {}, doi = {10.1146/annurev-bioeng-110220-110833}, pmid = {36854262}, issn = {1545-4274}, abstract = {Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 25 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.}, } @article {pmid36854181, year = {2023}, author = {Zhang, Y and Qiu, S and He, H}, title = {Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbfdf}, pmid = {36854181}, issn = {1741-2552}, abstract = {A motor imagery-based brain-computer interface (MI-BCI) translates spontaneous movement intention from the brain to outside devices. MI-BCI systems based on a single modality have been widely researched in recent decades. Lately, along with the development of neuroimaging methods, multimodal MI-BCI studies that use multiple neural signals have been proposed, which are promising for enhancing the decoding accuracy of MI-BCI. Multimodal MI data contain rich common and complementary information. Effective feature representations are helpful to promote the performance of classification tasks. Thus, it is very important to explore and extract features with higher separability and robustness from the rich information in multimodal data. Approach: In this study, a five-class motor imagery experiment was designed. Electroencephalography and functional near infrared spectroscopy data were collected simultaneously. A multimodal MI decoding neural network was proposed. In this network, to enhance the feature representation, the heterogeneous data of different modalities in the spatial dimension were aligned through the proposed spatial alignment losses. Also, the multimodal features were aligned and fused in the temporal dimension by an attention-based modality fusion module. Main results and Significance: The collected dataset was analyzed from temporal, spatial and frequency perspectives. The results showed that the multimodal data contain visually separable motor imagery patterns. The experimental results show that the proposed decoding method achieved the highest decoding accuracy among the compared methods on the self-collected dataset and a public dataset. Ablation results show that each part of the proposed method is effective. Compared with single-modality decoding, the proposed method obtained 4.6% higher decoding accuracy on the self-collected dataset. This indicates that the proposed method can improve the performance of multimodal MI decoding. This study provides a new approach for capturing the rich information in multimodal MI data and enhancing multimodal MI-BCI decoding accuracy. .}, } @article {pmid36851970, year = {2023}, author = {Amiri, M and Nazari, S and Jafari, AH and Makkiabadi, B}, title = {A new full closed-loop brain-machine interface approach based on neural activity: A study based on modeling and experimental studies.}, journal = {Heliyon}, volume = {9}, number = {3}, pages = {e13766}, pmid = {36851970}, issn = {2405-8440}, abstract = {BACKGROUND: The bidirectional brain-machine interfaces algorithms are machines that decode neural response in order to control the external device and encode position of artificial limb to proper electrical stimulation, so that the interface between brain and machine closes. Most BMI researchers typically consider four basic elements: recording technology to extract brain activity, decoding algorithm to translate brain activity to the predicted movement of the external device, external device (prosthetic limb such as a robotic arm), and encoding interface to convert the motion of the external machine to set of the electrical stimulation of the brain.

NEW METHOD: In this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for sensory cortex (S1) connected to the neural network model of motor cortex (M1) considering the topographic mapping between S1 and M1. We use 4-box model in S1 and 4-box in M1 so that each box contains 500 neurons. Individual boxes include inhibitory and excitatory neurons and synapses. Next, we develop a new BMI algorithm based on neural activity. The main concept of this BMI algorithm is to close the loop between brain and mechaical external device.

RESULTS: The sensory interface as encoding algorithm convert the location of the external device (artificial limb) into the electrical stimulation which excite the S1 model. The motor interface as decoding algorithm convert neural recordings from the M1 model into a force which causes the movement of the external device. We present the simulation results for the on line BMI which means that there is a real time information exchange between 9 boxes and 4 boxes of S1-M1 network model and the external device. Also, off line information exchange between brain of five anesthetized rats and externnal device was performed. The proposed BMI algorithm has succeeded in controlling the movement of the mechanical arm towards the target area on simulation and experimental data, so that the BMI algorithm shows acceptable WTPE and the average number of iterations of the algorithm in reaching artificial limb to the target region.Comparison with existing methods and Conclusions: In order to confirm the simulation results the 9-box model of S1-M1 network was developed and the valid "spike train" algorithm, which has good results on real data, is used to compare the performance accuracy of the proposed BMI algorithm versus "spike train" algorithm on simulation and off line experimental data of anesthetized rats. Quantitative and qualitative results confirm the proper performance of the proposed algorithm compared to algorithm "spike train" on simulations and experimental data.}, } @article {pmid36851960, year = {2023}, author = {Lin, CL and Chen, LT}, title = {Improvement of brain-computer interface in motor imagery training through the designing of a dynamic experiment and FBCSP.}, journal = {Heliyon}, volume = {9}, number = {3}, pages = {e13745}, pmid = {36851960}, issn = {2405-8440}, abstract = {Motor imagery (MI) can produce a specific brain pattern when the subject imagines performing a particular action without any actual body movements. According to related previous research, the improvement of the training of MI brainwaves can be adopted by feedback methods in which the analysis of brainwave characteristics is very important. The aim of this study was to improve the subject's MI and the accuracy of classification. In order to ameliorate the accuracy of the MI of the left and right hand, the present study designed static and dynamic visual stimuli in experiments so as to evaluate which one can improve subjects' imagination training. Additionally, the filter bank common spatial pattern (FBCSP) method was used to divide the frequency band range of the brainwaves into multiple segments, following which linear discriminant analysis (LDA) was adopted for classification. The results revealed that the averaged false positive rate (FPR) under FBCSP-LDA in the dynamic MI experiment was the lowest FPR (23.76%). As such, this study suggested that a combination of the dynamic MI experiment and the FBCSP-LDA method improved the overall prediction error rate and ameliorated the performance of the MI brain-computer interface.}, } @article {pmid36850850, year = {2023}, author = {Ghodousi, M and Pousson, JE and Bernhofs, V and Griškova-Bulanova, I}, title = {Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {4}, pages = {}, pmid = {36850850}, issn = {1424-8220}, abstract = {A Brain-Computer Music Interface (BCMI) system may be designed to harness electroencephalography (EEG) signals for control over musical outputs in the context of emotionally expressive performance. To develop a real-time BCMI system, accurate and computationally efficient emotional biomarkers should first be identified. In the current study, we evaluated the ability of various features to discriminate between emotions expressed during music performance with the aim of developing a BCMI system. EEG data was recorded while subjects performed simple piano music with contrasting emotional cues and rated their success in communicating the intended emotion. Power spectra and connectivity features (Magnitude Square Coherence (MSC) and Granger Causality (GC)) were extracted from the signals. Two different approaches of feature selection were used to assess the contribution of neutral baselines in detection accuracies; 1- utilizing the baselines to normalize the features, 2- not taking them into account (non-normalized features). Finally, the Support Vector Machine (SVM) has been used to evaluate and compare the capability of various features for emotion detection. Best detection accuracies were obtained from the non-normalized MSC-based features equal to 85.57 ± 2.34, 84.93 ± 1.67, and 87.16 ± 0.55 for arousal, valence, and emotional conditions respectively, while the power-based features had the lowest accuracies. Both connectivity features show acceptable accuracy while requiring short processing time and thus are potential candidates for the development of a real-time BCMI system.}, } @article {pmid36850667, year = {2023}, author = {Siribunyaphat, N and Punsawad, Y}, title = {Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {4}, pages = {}, pmid = {36850667}, issn = {1424-8220}, abstract = {Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.}, } @article {pmid36850530, year = {2023}, author = {Xie, Y and Oniga, S}, title = {Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {4}, pages = {}, pmid = {36850530}, issn = {1424-8220}, abstract = {In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time-frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.}, } @article {pmid36848679, year = {2023}, author = {Letner, JG and Patel, PR and Hsieh, JC and Smith Flores, IM and Della Valle, E and Walker, LA and Weiland, JD and Chestek, CA and Cai, D}, title = {Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbf78}, pmid = {36848679}, issn = {1741-2552}, abstract = {OBJECTIVE: Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship.

APPROACH: Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8 µm diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12 weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50 µm radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates.

MAIN RESULTS: Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7±4.6 µm, X̄±S) in layer V motor cortex.

SIGNIFICANCE: Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.}, } @article {pmid36848586, year = {2023}, author = {Gupta, A and Daniel, R and Rao, A and Roy, PP and Chandra, S and Kim, BG}, title = {Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.}, journal = {Big data}, volume = {}, number = {}, pages = {}, doi = {10.1089/big.2021.0204}, pmid = {36848586}, issn = {2167-647X}, abstract = {With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.}, } @article {pmid36847833, year = {2023}, author = {Yang, Y and Garringer, HJ and Shi, Y and Lövestam, S and Peak-Chew, S and Zhang, X and Kotecha, A and Bacioglu, M and Koto, A and Takao, M and Spillantini, MG and Ghetti, B and Vidal, R and Murzin, AG and Scheres, SHW and Goedert, M}, title = {New SNCA mutation and structures of α-synuclein filaments from juvenile-onset synucleinopathy.}, journal = {Acta neuropathologica}, volume = {}, number = {}, pages = {}, pmid = {36847833}, issn = {1432-0533}, support = {MC_UP_A025-1013/MRC_/Medical Research Council/United Kingdom ; MC_U105184291/MRC_/Medical Research Council/United Kingdom ; }, abstract = {A 21-nucleotide duplication in one allele of SNCA was identified in a previously described disease with abundant α-synuclein inclusions that we now call juvenile-onset synucleinopathy (JOS). This mutation translates into the insertion of MAAAEKT after residue 22 of α-synuclein, resulting in a protein of 147 amino acids. Both wild-type and mutant proteins were present in sarkosyl-insoluble material that was extracted from frontal cortex of the individual with JOS and examined by electron cryo-microscopy. The structures of JOS filaments, comprising either a single protofilament, or a pair of protofilaments, revealed a new α-synuclein fold that differs from the folds of Lewy body diseases and multiple system atrophy (MSA). The JOS fold consists of a compact core, the sequence of which (residues 36-100 of wild-type α-synuclein) is unaffected by the mutation, and two disconnected density islands (A and B) of mixed sequences. There is a non-proteinaceous cofactor bound between the core and island A. The JOS fold resembles the common substructure of MSA Type I and Type II dimeric filaments, with its core segment approximating the C-terminal body of MSA protofilaments B and its islands mimicking the N-terminal arm of MSA protofilaments A. The partial similarity of JOS and MSA folds extends to the locations of their cofactor-binding sites. In vitro assembly of recombinant wild-type α-synuclein, its insertion mutant and their mixture yielded structures that were distinct from those of JOS filaments. Our findings provide insight into a possible mechanism of JOS fibrillation in which mutant α-synuclein of 147 amino acids forms a nucleus with the JOS fold, around which wild-type and mutant proteins assemble during elongation.}, } @article {pmid36845071, year = {2023}, author = {Chen, D and Liu, K and Guo, J and Bi, L and Xiang, J}, title = {Editorial: Brain-computer interface and its applications.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1140508}, doi = {10.3389/fnbot.2023.1140508}, pmid = {36845071}, issn = {1662-5218}, } @article {pmid36844572, year = {2023}, author = {Chen, PW and Ji, DH and Zhang, YS and Lee, C and Yeh, MY}, title = {Electroactive and Stretchable Hydrogels of 3,4-Ethylenedioxythiophene/thiophene Copolymers.}, journal = {ACS omega}, volume = {8}, number = {7}, pages = {6753-6761}, pmid = {36844572}, issn = {2470-1343}, abstract = {Hydrogels are conductive and stretchable, allowing for their use in flexible electronic devices, such as electronic skins, sensors, human motion monitoring, brain-computer interface, and so on. Herein, we synthesized the copolymers having various molar ratios of 3,4-ethylenedioxythiophene (EDOT) to thiophene (Th), which served as conductive additives. With doping engineering and incorporation with P(EDOT-co-Th) copolymers, hydrogels have presented excellent physical/chemical/electrical properties. It was found that the mechanical strength, adhesion ability, and conductivity of hydrogels were highly dependent on the molar ratio of EDOT to Th of the copolymers. The more the EDOT, the stronger the tensile strength and the greater the conductivity, but the lower the elongation break tends to be. By comprehensively evaluating the physical/chemical/electrical properties and cost of material use, the hydrogel incorporated with a 7:3 molar ratio P(EDOT-co-Th) copolymer was an optimal formulation for soft electronic devices.}, } @article {pmid36844419, year = {2022}, author = {Song, M and Huang, Y and Shen, Y and Shi, C and Breeschoten, A and Konijnenburg, M and Visser, H and Romme, J and Dutta, B and Alavi, MS and Bachmann, C and Liu, YH}, title = {A 1.66Gb/s and 5.8pJ/b Transcutaneous IR-UWB Telemetry System with Hybrid Impulse Modulation for Intracortical Brain-Computer Interfaces.}, journal = {Digest of technical papers. IEEE International Solid-State Circuits Conference}, volume = {2022}, number = {}, pages = {394-396}, pmid = {36844419}, issn = {0193-6530}, } @article {pmid36843389, year = {2023}, author = {Branco, MP and Geukes, SH and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ}, title = {Nine decades of electrocorticography: a comparison between epidural and subdural recordings.}, journal = {The European journal of neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1111/ejn.15941}, pmid = {36843389}, issn = {1460-9568}, abstract = {In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact, but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.}, } @article {pmid36842495, year = {2023}, author = {Hua, SS and Ding, JJ and Sun, TC and Guo, C and Zhang, Y and Yu, ZH and Cao, YQ and Zhong, LH and Wu, Y and Guo, LY and Luo, JH and Cui, YH and Qiu, S}, title = {NMDAR-dependent synaptic potentiation via APPL1 signaling is required for the accessibility of a prefrontal neuronal assembly in retrieving fear extinction.}, journal = {Biological psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.biopsych.2023.02.013}, pmid = {36842495}, issn = {1873-2402}, abstract = {BACKGROUND: The ventromedial prefrontal cortex (vmPFC) has been viewed as a locus to store and recall extinction memory. However, the synaptic and cellular mechanisms underlying this process remain elusive.

METHODS: We combined transgenic mice, electrophysiological recording, activity-dependent cell labeling, and chemogenetic manipulation to analyze the role of adaptor protein APPL1 in the vmPFC for fear extinction retrieval.

RESULTS: We found that both constitutive and conditional APPL1 knockout decreases NMDA receptor (NMDAR) function in the vmPFC and impairs fear extinction retrieval. Moreover, APPL1 undergoes nuclear translocation during extinction retrieval. Blocking APPL1 nucleocytoplasmic translocation reduces NMDAR currents and disrupts extinction retrieval. We further identified a prefrontal neuronal ensemble that is both necessary and sufficient for the storage of extinction memory. Inducible APPL1 knockout in this ensemble abolishes NMDAR-dependent synaptic potentiation and disrupts extinction retrieval, while simultaneously chemogenetic activation of this ensemble rescues the impaired behaviors.

CONCLUSIONS: Therefore, our results indicate that a prefrontal neuronal ensemble stores extinction memory, and APPL1 signaling supports these neurons to retrieve extinction memory via controlling NMDAR-dependent potentiation.}, } @article {pmid36842221, year = {2022}, author = {Liu, Z and Wang, L and Xu, S and Lu, K}, title = {A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification.}, journal = {Computers in biology and medicine}, volume = {155}, number = {}, pages = {106196}, doi = {10.1016/j.compbiomed.2022.106196}, pmid = {36842221}, issn = {1879-0534}, abstract = {Brain-computer Interface (BCI) system based on motor imagery (MI) heavily relies on electroencephalography (EEG) recognition with high accuracy. However, modeling and classification of MI EEG signals remains a challenging task due to the non-linear and non-stationary characteristics of the signals. In this paper, a new time-varying modeling framework combining multiwavelet basis functions and regularized orthogonal forward regression (ROFR) algorithm is proposed for the characterization and classification of MI EEG signals. Firstly, the time-varying coefficients of the time-varying autoregressive (TVAR) model are precisely approximated with the multiwavelet basis functions. Then a powerful ROFR algorithm is employed to dramatically alleviate the redundant model structure and accurately recover the relevant time-varying model parameters to obtain high resolution power spectral density (PSD) features. Finally, the features are sent to different classifiers for the classification task. To effectively improve the accuracy of classification, a principal component analysis (PCA) algorithm is utilized to determine the best feature subset and Bayesian optimization algorithm is performed to obtain the optimal parameters of the classifier. The proposed method achieves satisfactory classification accuracy on the public BCI Competition II Dataset III, which proves that this method potentially improves the recognition accuracy of MI EEG signals, and has great significance for the construction of BCI system based on MI.}, } @article {pmid36839377, year = {2023}, author = {De Rubis, G and Paudel, KR and Manandhar, B and Singh, SK and Gupta, G and Malik, R and Shen, J and Chami, A and MacLoughlin, R and Chellappan, DK and Oliver, BGG and Hansbro, PM and Dua, K}, title = {Agarwood Oil Nanoemulsion Attenuates Cigarette Smoke-Induced Inflammation and Oxidative Stress Markers in BCi-NS1.1 Airway Epithelial Cells.}, journal = {Nutrients}, volume = {15}, number = {4}, pages = {}, pmid = {36839377}, issn = {2072-6643}, abstract = {Chronic obstructive pulmonary disease (COPD) is an irreversible inflammatory respiratory disease characterized by frequent exacerbations and symptoms such as cough and wheezing that lead to irreversible airway damage and hyperresponsiveness. The primary risk factor for COPD is chronic cigarette smoke exposure, which promotes oxidative stress and a general pro-inflammatory condition by stimulating pro-oxidant and pro-inflammatory pathways and, simultaneously, inactivating anti-inflammatory and antioxidant detoxification pathways. These events cause progressive damage resulting in impaired cell function and disease progression. Treatments available for COPD are generally aimed at reducing the symptoms of exacerbation. Failure to regulate oxidative stress and inflammation results in lung damage. In the quest for innovative treatment strategies, phytochemicals, and complex plant extracts such as agarwood essential oil are promising sources of molecules with antioxidant and anti-inflammatory activity. However, their clinical use is limited by issues such as low solubility and poor pharmacokinetic properties. These can be overcome by encapsulating the therapeutic molecules using advanced drug delivery systems such as polymeric nanosystems and nanoemulsions. In this study, agarwood oil nanoemulsion (agarwood-NE) was formulated and tested for its antioxidant and anti-inflammatory potential in cigarette smoke extract (CSE)-treated BCi-NS1.1 airway basal epithelial cells. The findings suggest successful counteractivity of agarwood-NE against CSE-mediated pro-inflammatory effects by reducing the expression of the pro-inflammatory cytokines IL-1α, IL-1β, IL-8, and GDF-15. In addition, agarwood-NE induced the expression of the anti-inflammatory mediators IL-10, IL-18BP, TFF3, GH, VDBP, relaxin-2, IFN-γ, and PDGF. Furthermore, agarwood-NE also induced the expression of antioxidant genes such as GCLC and GSTP1, simultaneously activating the PI3K pro-survival signalling pathway. This study provides proof of the dual anti-inflammatory and antioxidant activity of agarwood-NE, highlighting its enormous potential for COPD treatment.}, } @article {pmid36822385, year = {2023}, author = {Zhang, Y and Zou, J and Ding, N}, title = {Acoustic correlates of the syllabic rhythm of speech: Modulation spectrum or local features of the temporal envelope.}, journal = {Neuroscience and biobehavioral reviews}, volume = {147}, number = {}, pages = {105111}, doi = {10.1016/j.neubiorev.2023.105111}, pmid = {36822385}, issn = {1873-7528}, abstract = {The syllable is a perceptually salient unit in speech. Since both the syllable and its acoustic correlate, i.e., the speech envelope, have a preferred range of rhythmicity between 4 and 8 Hz, it is hypothesized that theta-band neural oscillations play a major role in extracting syllables based on the envelope. A literature survey, however, reveals inconsistent evidence about the relationship between speech envelope and syllables, and the current study revisits this question by analyzing large speech corpora. It is shown that the center frequency of speech envelope, characterized by the modulation spectrum, reliably correlates with the rate of syllables only when the analysis is pooled over minutes of speech recordings. In contrast, in the time domain, a component of the speech envelope is reliably phase-locked to syllable onsets. Based on a speaker-independent model, the timing of syllable onsets explains about 24% variance of the speech envelope. These results indicate that local features in the speech envelope, instead of the modulation spectrum, are a more reliable acoustic correlate of syllables.}, } @article {pmid36837864, year = {2023}, author = {Lin, A and Wang, T and Li, C and Pu, F and Abdelrahman, Z and Jin, M and Yang, Z and Zhang, L and Cao, X and Sun, K and Hou, T and Liu, Z and Chen, L and Chen, Z}, title = {Association of Sarcopenia with Cognitive Function and Dementia Risk Score: A National Prospective Cohort Study.}, journal = {Metabolites}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/metabo13020245}, pmid = {36837864}, issn = {2218-1989}, abstract = {The relationship between skeletal muscle and cognitive disorders has drawn increasing attention. This study aims to examine the associations of sarcopenia with cognitive function and dementia risk score. Data on 1978 participants (aged 65 years and older) from the 2011 wave of the China Health and Retirement Longitudinal Study, with four follow-up waves to 2018, were used. Cognitive function was assessed by four dimensions, with a lower score indicating lower cognitive function. Dementia risk was assessed by a risk score using the Rotterdam Study Basic Dementia Risk Model (BDRM), with a higher score indicating a greater risk. Sarcopenia was defined when low muscle mass plus low muscle strength or low physical performance were met. We used generalized estimating equations to examine the associations of sarcopenia. In the fully adjusted models, sarcopenia was significantly associated with lower cognitive function (standardized, β = -0.15; 95% CIs: -0.26, -0.04) and a higher BDRM score (standardized, β = 0.42; 95% CIs: 0.29, 0.55). Our findings may provide a new avenue for alleviating the burden of cognitive disorders by preventing sarcopenia.}, } @article {pmid36837157, year = {2023}, author = {Liu, Q and Deng, WY and Zhang, LY and Liu, CX and Jie, WW and Su, RX and Zhou, B and Lu, LM and Liu, SW and Huang, XG}, title = {Modified Bamboo Charcoal as a Bifunctional Material for Methylene Blue Removal.}, journal = {Materials (Basel, Switzerland)}, volume = {16}, number = {4}, pages = {}, doi = {10.3390/ma16041528}, pmid = {36837157}, issn = {1996-1944}, abstract = {Biomass-derived raw bamboo charcoal (BC), NaOH-impregnated bamboo charcoal (BC-I), and magnetic bamboo charcoal (BC-IM) were fabricated and used as bio-adsorbents and Fenton-like catalysts for methylene blue removal. Compared to the raw biochar, a simple NaOH impregnation process significantly optimized the crystal structure, pore size distribution, and surface functional groups and increase the specific surface area from 1.4 to 63.0 m[2]/g. Further magnetization of the BC-I sample not only enhanced the surface area to 84.7 m[2]/g, but also improved the recycling convenience due to the superparamagnetism. The maximum adsorption capacity of BC, BC-I, and BC-IM for methylene blue at 328 K was 135.13, 220.26 and 497.51 mg/g, respectively. The pseudo-first-order rate constants k at 308 K for BC, BC-I, and BC-IM catalytic degradation in the presence of H2O2 were 0.198, 0.351, and 1.542 h[-1], respectively. A synergistic mechanism between adsorption and radical processes was proposed.}, } @article {pmid36836747, year = {2023}, author = {Omejc, N and Peskar, M and Miladinović, A and Kavcic, V and Džeroski, S and Marusic, U}, title = {On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features.}, journal = {Life (Basel, Switzerland)}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/life13020391}, pmid = {36836747}, issn = {2075-1729}, abstract = {The utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain-computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals' performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice.}, } @article {pmid36836659, year = {2023}, author = {Vasilyev, AN and Yashin, AS and Shishkin, SL}, title = {Quasi-Movements and "Quasi-Quasi-Movements": Does Residual Muscle Activation Matter?.}, journal = {Life (Basel, Switzerland)}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/life13020303}, pmid = {36836659}, issn = {2075-1729}, abstract = {Quasi-movements (QM) are observed when an individual minimizes a movement to an extent that no related muscle activation is detected. Likewise to imaginary movements (IM) and overt movements, QMs are accompanied by the event-related desynchronization (ERD) of EEG sensorimotor rhythms. Stronger ERD was observed under QMs compared to IMs in some studies. However, the difference could be caused by the remaining muscle activation in QMs that could escape detection. Here, we re-examined the relation between the electromyography (EMG) signal and ERD in QM using sensitive data analysis procedures. More trials with signs of muscle activation were observed in QMs compared with a visual task and IMs. However, the rate of such trials was not correlated with subjective estimates of actual movement. Contralateral ERD did not depend on the EMG but still was stronger in QMs compared with IMs. These results suggest that brain mechanisms are common for QMs in the strict sense and "quasi-quasi-movements" (attempts to perform the same task accompanied by detectable EMG elevation) but differ between them and IMs. QMs could be helpful in research aimed at better understanding motor action and at modeling the use of attempted movements in the brain-computer interfaces with healthy participants.}, } @article {pmid36831864, year = {2023}, author = {Lakshminarayanan, K and Ramu, V and Rajendran, J and Chandrasekaran, KP and Shah, R and Daulat, SR and Moodley, V and Madathil, D}, title = {The Effect of Tactile Imagery Training on Reaction Time in Healthy Participants.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020321}, pmid = {36831864}, issn = {2076-3425}, abstract = {BACKGROUND: Reaction time is an important measure of sensorimotor performance and coordination and has been shown to improve with training. Various training methods have been employed in the past to improve reaction time. Tactile imagery (TI) is a method of mentally simulating a tactile sensation and has been used in brain-computer interface applications. However, it is yet unknown whether TI can have a learning effect and improve reaction time.

OBJECTIVE: The purpose of this study was to investigate the effect of TI on reaction time in healthy participants.

METHODS: We examined the reaction time to vibratory stimuli before and after a TI training session in an experimental group and compared the change in reaction time post-training with pre-training in the experimental group as well as the reaction time in a control group. A follow-up evaluation of reaction time was also conducted.

RESULTS: The results showed that TI training significantly improved reaction time after TI compared with before TI by approximately 25% (pre-TI right-hand mean ± SD: 456.62 ± 124.26 ms, pre-TI left-hand mean ± SD: 448.82 ± 124.50 ms, post-TI right-hand mean ± SD: 340.32 ± 65.59 ms, post-TI left-hand mean ± SD: 335.52 ± 59.01 ms). Furthermore, post-training reaction time showed significant reduction compared with the control group and the improved reaction time had a lasting effect even after four weeks post-training.

CONCLUSION: These findings indicate that TI training may serve as an alternate imagery strategy for improving reaction time without the need for physical practice.}, } @article {pmid36831857, year = {2023}, author = {Peketi, S and Dhok, SB}, title = {Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020315}, pmid = {36831857}, issn = {2076-3425}, abstract = {Joint attention skills deficiency in Autism spectrum disorder (ASD) hinders individuals from communicating effectively. The P300 Electroencephalogram (EEG) signal-based brain-computer interface (BCI) helps these individuals in neurorehabilitation training to overcome this deficiency. The detection of the P300 signal is more challenging in ASD as it is noisy, has less amplitude, and has a higher latency than in other individuals. This paper presents a novel application of the variational mode decomposition (VMD) technique in a BCI system involving ASD subjects for P300 signal identification. The EEG signal is decomposed into five modes using VMD. Thirty linear and non-linear time and frequency domain features are extracted for each mode. Synthetic minority oversampling technique data augmentation is performed to overcome the class imbalance problem in the chosen dataset. Then, a comparative analysis of three popular machine learning classifiers is performed for this application. VMD's fifth mode with a support vector machine (fine Gaussian kernel) classifier gave the best performance parameters, namely accuracy, F1-score, and the area under the curve, as 91.12%, 91.18%, and 96.6%, respectively. These results are better when compared to other state-of-the-art methods.}, } @article {pmid36831846, year = {2023}, author = {Cattan, GH and Quemy, A}, title = {Case-Based and Quantum Classification for ERP-Based Brain-Computer Interfaces.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020303}, pmid = {36831846}, issn = {2076-3425}, abstract = {Low transfer rates are a major bottleneck for brain-computer interfaces based on electroencephalography (EEG). This problem has led to the development of more robust and accurate classifiers. In this study, we investigated the performance of variational quantum, quantum-enhanced support vector, and hypergraph case-based reasoning classifiers in the binary classification of EEG data from a P300 experiment. On the one hand, quantum classification is a promising technology to reduce computational time and improve learning outcomes. On the other hand, case-based reasoning has an excellent potential to simplify the preprocessing steps of EEG analysis. We found that the balanced training (prediction) accuracy of each of these three classifiers was 56.95 (51.83), 83.17 (50.25), and 71.10% (52.04%), respectively. In addition, case-based reasoning performed significantly lower with a simplified (49.78%) preprocessing pipeline. These results demonstrated that all classifiers were able to learn from the data and that quantum classification of EEG data was implementable; however, more research is required to enable a greater prediction accuracy because none of the classifiers were able to generalize from the data. This could be achieved by improving the configuration of the quantum classifiers (e.g., increasing the number of shots) and increasing the number of trials for hypergraph case-based reasoning classifiers through transfer learning.}, } @article {pmid36831811, year = {2023}, author = {Liang, X and Liu, Y and Yu, Y and Liu, K and Liu, Y and Zhou, Z}, title = {Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain-Computer Interfaces.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020268}, pmid = {36831811}, issn = {2076-3425}, abstract = {Convolutional neural networks (CNNs) have shown great potential in the field of brain-computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram (EEG) signals without artificial feature extraction. Some CNNs have achieved better classification accuracy than that of traditional methods. Raw EEG signals are usually represented as a two-dimensional (2-D) matrix composed of channels and time points, ignoring the spatial topological information of electrodes. Our goal is to make a CNN that takes raw EEG signals as inputs have the ability to learn spatial topological features and improve its classification performance while basically maintaining its original structure. We propose an EEG topographic representation module (TRM). This module consists of (1) a mapping block from raw EEG signals to a 3-D topographic map and (2) a convolution block from the topographic map to an output with the same size as the input. According to the size of the convolutional kernel used in the convolution block, we design two types of TRMs, namely TRM-(5,5) and TRM-(3,3). We embed the two TRM types into three widely used CNNs (ShallowConvNet, DeepConvNet and EEGNet) and test them on two publicly available datasets (the Emergency Braking During Simulated Driving Dataset (EBDSDD) and the High Gamma Dataset (HGD)). Results show that the classification accuracies of all three CNNs are improved on both datasets after using the TRMs. With TRM-(5,5), the average classification accuracies of DeepConvNet, EEGNet and ShallowConvNet are improved by 6.54%, 1.72% and 2.07% on the EBDSDD and by 6.05%, 3.02% and 5.14% on the HGD, respectively; with TRM-(3,3), they are improved by 7.76%, 1.71% and 2.17% on the EBDSDD and by 7.61%, 5.06% and 6.28% on the HGD, respectively. We improve the classification performance of three CNNs on both datasets through the use of TRMs, indicating that they have the capability to mine spatial topological EEG information. More importantly, since the output of a TRM has the same size as the input, CNNs with raw EEG signals as inputs can use this module without changing their original structures.}, } @article {pmid36831784, year = {2023}, author = {Arı, E and Taçgın, E}, title = {Input Shape Effect on Classification Performance of Raw EEG Motor Imagery Signals with Convolutional Neural Networks for Use in Brain-Computer Interfaces.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020240}, pmid = {36831784}, issn = {2076-3425}, abstract = {EEG signals are interpreted, analyzed and classified by many researchers for use in brain-computer interfaces. Although there are many different EEG signal acquisition methods, one of the most interesting is motor imagery signals. Many different signal processing methods, machine learning and deep learning models have been developed for the classification of motor imagery signals. Among these, Convolutional Neural Network models generally achieve better results than other models. Because the size and shape of the data is important for training Convolutional Neural Network models and discovering the right relationships, researchers have designed and experimented with many different input shape structures. However, no study has been found in the literature evaluating the effect of different input shapes on model performance and accuracy. In this study, the effects of different input shapes on model performance and accuracy in the classification of EEG motor imagery signals were investigated, which had not been specifically studied before. In addition, signal preprocessing methods, which take a long time before classification, were not used; rather, two CNN models were developed for training and classification using raw data. Two different datasets, BCI Competition IV 2A and 2B, were used in classification processes. For different input shapes, 53.03-89.29% classification accuracy and 2-23 s epoch time were obtained for 2A dataset, 64.84-84.94% classification accuracy and 4-10 s epoch time were obtained for 2B dataset. This study showed that the input shape has a significant effect on the classification performance, and when the correct input shape is selected and the correct CNN architecture is developed, feature extraction and classification can be done well by the CNN architecture without any signal preprocessing.}, } @article {pmid36831764, year = {2023}, author = {Hu, H and Yue, K and Guo, M and Lu, K and Liu, Y}, title = {Subject Separation Network for Reducing Calibration Time of MI-Based BCI.}, journal = {Brain sciences}, volume = {13}, number = {2}, pages = {}, doi = {10.3390/brainsci13020221}, pmid = {36831764}, issn = {2076-3425}, abstract = {Motor imagery brain-computer interface (MI-based BCIs) have demonstrated great potential in various applications. However, to well generalize classifiers to new subjects, a time-consuming calibration process is necessary due to high inter-subject variabilities of EEG signals. This process is costly and tedious, hindering the further expansion of MI-based BCIs outside of the laboratory. To reduce the calibration time of MI-based BCIs, we propose a novel domain adaptation framework that adapts multiple source subjects' labeled data to the unseen trials of target subjects. Firstly, we train one Subject Separation Network(SSN) for each of the source subjects in the dataset. Based on adversarial domain adaptation, a shared encoder is constructed to learn similar representations for both domains. Secondly, to model the factors that cause subject variabilities and eliminate the correlated noise existing in common feature space, private feature spaces orthogonal to the shared counterpart are learned for each subject. We use a shared decoder to validate that the model is actually learning from task-relevant neurophysiological information. At last, an ensemble classifier is built by the integration of the SSNs using the information extracted from each subject's task-relevant characteristics. To quantify the efficacy of the framework, we analyze the accuracy-calibration cost trade-off in MI-based BCIs, and theoretically guarantee a generalization bound on the target error. Visualizations of the transformed features illustrate the effectiveness of domain adaptation. The experimental results on the BCI Competition IV-IIa dataset prove the effectiveness of the proposed framework compared with multiple classification methods. We infer from our results that users could learn to control MI-based BCIs without a heavy calibration process. Our study further shows how to design and train Neural Networks to decode task-related information from different subjects and highlights the potential of deep learning methods for inter-subject EEG decoding.}, } @article {pmid36829694, year = {2023}, author = {Nawaz, R and Wood, G and Nisar, H and Yap, VV}, title = {Exploring the Effects of EEG-Based Alpha Neurofeedback on Working Memory Capacity in Healthy Participants.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {2}, pages = {}, doi = {10.3390/bioengineering10020200}, pmid = {36829694}, issn = {2306-5354}, abstract = {Neurofeedback, an operant conditioning neuromodulation technique, uses information from brain activities in real-time via brain-computer interface (BCI) technology. This technique has been utilized to enhance the cognitive abilities, including working memory performance, of human beings. The aims of this study are to investigate how alpha neurofeedback can improve working memory performance in healthy participants and to explore the underlying neural mechanisms in a working memory task before and after neurofeedback. Thirty-six participants divided into the NFT group and the control group participated in this study. This study was not blinded, and both the participants and the researcher were aware of their group assignments. Increasing power in the alpha EEG band was used as a neurofeedback in the eyes-open condition only in the NFT group. The data were collected before and after neurofeedback while they were performing the N-back memory task (N = 1 and N = 2). Both groups showed improvement in their working memory performance. There was an enhancement in the power of their frontal alpha and beta activities with increased working memory load (i.e., 2-back). The experimental group showed improvements in their functional connections between different brain regions at the theta level. This effect was absent in the control group. Furthermore, brain hemispheric lateralization was found during the N-back task, and there were more intra-hemisphere connections than inter-hemisphere connections of the brain. These results suggest that healthy participants can benefit from neurofeedback and from having their brain networks changed after the training.}, } @article {pmid36829681, year = {2023}, author = {Lee, PL and Chen, SH and Chang, TC and Lee, WK and Hsu, HT and Chang, HH}, title = {Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {2}, pages = {}, doi = {10.3390/bioengineering10020186}, pmid = {36829681}, issn = {2306-5354}, abstract = {The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that enables users to communicate with external environments through their minds. However, current MI-BCI systems ask naïve subjects to perform unfamiliar MI tasks with simple textual instruction or a visual/auditory cue. The unclear instruction for MI execution not only results in large inter-subject variability in the measured EEG patterns but also causes the difficulty of grouping cross-subject data for big-data training. In this study, we designed an BCI training method in a virtual reality (VR) environment. Subjects wore a head-mounted device (HMD) and executed action observation (AO) concurrently with MI (i.e., AO + MI) in VR environments. EEG signals recorded in AO + MI task were used to train an initial model, and the initial model was continually improved by the provision of EEG data in the following BCI training sessions. We recruited five healthy subjects, and each subject was requested to participate in three kinds of tasks, including an AO + MI task, an MI task, and the task of MI with visual feedback (MI-FB) three times. This study adopted a transformer- based spatial-temporal network (TSTN) to decode the user's MI intentions. In contrast to other convolutional neural network (CNN) or recurrent neural network (RNN) approaches, the TSTN extracts spatial and temporal features, and applies attention mechanisms along spatial and temporal dimensions to perceive the global dependencies. The mean detection accuracies of TSTN were 0.63, 0.68, 0.75, and 0.77 in the MI, first MI-FB, second MI-FB, and third MI-FB sessions, respectively. This study demonstrated the AO + MI gave an easier way for subjects to conform their imagery actions, and the BCI performance was improved with the continual learning of the MI-FB training process.}, } @article {pmid36827704, year = {2023}, author = {Ming, G and Zhong, H and Pei, W and Gao, X and Wang, Y}, title = {A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbee0}, pmid = {36827704}, issn = {1741-2552}, abstract = {Objective.The traditional uniform flickering stimulation pattern shows strong steady-state visual evoked potential (SSVEP) responses and poor user experience with intense flicker perception. To achieve a balance between performance and comfort in SSVEP-based brain-computer interface (BCI) systems, this study proposed a new grid stimulation pattern with reduced stimulation area and low spatial contrast.Approach.A spatial contrast scanning experiment was conducted first to clarify the relationship between the SSVEP characteristics and the signs and values of spatial contrast. Four stimulation patterns were involved in the experiment: the ON and OFF grid stimulation patterns that separately activated the positive or negative contrast information processing pathways, the ON-OFF grid stimulation pattern that simultaneously activated both pathways, and the uniform flickering stimulation pattern that served as a control group. The contrast-intensity and contrast-user experience curves were obtained for each stimulation pattern. Accordingly, the optimized stimulation schemes with low spatial contrast (the ON-50% grid stimulus, the OFF-50% grid stimulus, and the Flicker-30% stimulus) were applied in a 12-target and a 40-target BCI speller and compared with the traditional uniform flickering stimulus (the Flicker-500% stimulus) in the evaluation of BCI performance and subjective experience.Main results.The OFF-50% grid stimulus showed comparable online performance (12-target, 2 s: 69.87±0.74 vs. 69.76±0.58 bits min[-1], 40-target, 4 s: 57.02±2.53 vs. 60.79±1.08 bits min[-1]) and improved user experience (better comfortable level, weaker flicker perception and higher preference level) compared to the traditional Flicker-500% stimulus in both multi-targets BCI spellers.Significance.Selective activation of the negative contrast information processing pathway using the new OFF-50% grid stimulus evoked robust SSVEP responses. On this basis, high-performance and user-friendly SSVEP-based BCIs have been developed and implemented, which has important theoretical significance and application value in promoting the development of the visual BCI technology.}, } @article {pmid36827271, year = {2023}, author = {McCaffrey, KR and Balaguera-Reina, SA and Falk, BG and Gati, EV and Cole, JM and Mazzotti, FJ}, title = {How to estimate body condition in large lizards? Argentine black and white tegu (Salvator merianae, Duméril and Bibron, 1839) as a case study.}, journal = {PloS one}, volume = {18}, number = {2}, pages = {e0282093}, doi = {10.1371/journal.pone.0282093}, pmid = {36827271}, issn = {1932-6203}, abstract = {Body condition is a measure of the health and fitness of an organism represented by available energy stores, typically fat. Direct measurements of fat are difficult to obtain non-invasively, thus body condition is usually estimated by calculating body condition indices (BCIs) using mass and length. The utility of BCIs is contingent on the relationship of BCIs and fat, thereby validation studies should be performed to select the best performing BCI before application in ecological investigations. We evaluated 11 BCIs in 883 Argentine black and white tegus (Salvator merianae) removed from their non-native range in South Florida, United States. Because the length-mass relationship in tegus is allometric, a segmented linear regression model was fit to the relationship between mass and length to define size classes. We evaluated percent, residual, and scaled fat and determined percent fat was the best measure of fat, because it was the least-associated with snout-vent length (SVL). We evaluated performance of BCIs with the full dataset and within size classes and identified Fulton's K as the best performing BCI for our sampled population, explaining up to 19% of the variation in fat content. Overall, we found that BCIs: 1) maintained relatively weak relationships with measures of fat and 2) splitting data into size classes reduced the strength of the relationship (i.e., bias) between percent fat and SVL but did not improve the performance of BCIs. We postulate that the weak performance of BCIs in our dataset was likely due to the weak association of fat with SVL, the body plan and life-history traits of tegus, and potentially inadequate accounting of available energy resources. We caution against assuming that BCIs are strong indicators of body condition across species and suggest that validation studies be implemented, or that alternative or complimentary measures of health or fitness should be considered.}, } @article {pmid36825130, year = {2023}, author = {Jatupornpoonsub, T and Thimachai, P and Supasyndh, O and Wongsawat, Y}, title = {QEEG characteristics associated with malnutrition-inflammation complex syndrome.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {944988}, doi = {10.3389/fnhum.2023.944988}, pmid = {36825130}, issn = {1662-5161}, abstract = {End-stage renal disease (ESRD) has been linked to cerebral complications due to the comorbidity of malnutrition and inflammation, which is referred to as malnutrition-inflammation complex syndrome (MICS). The severity of this condition is clinically assessed with the malnutrition-inflammation score (MIS), and a cutoff of five is used to optimally distinguish patients with and without MICS. However, this tool is still invasive and inconvenient, because it combines medical records, physical examination, and laboratory results. These steps require clinicians and limit MIS usage on a regular basis. Cerebral diseases in ESRD patients can be evaluated reliably and conveniently by using quantitative electroencephalogram (QEEG), which possibly reflects the severity of MICS likewise. Given the links between kidney and brain abnormalities, we hypothesized that some QEEG patterns might be associated with the severity of MICS and could be used to distinguish ESRD patients with and without MICS. Hence, we recruited 62 ESRD participants and divided them into two subgroups: ESRD with MICS (17 women (59%), age 60.31 ± 7.79 years, MIS < 5) and ESRD without MICS (20 women (61%), age 62.03 ± 9.29 years, MIS ≥ 5). These participants willingly participated in MIS and QEEG assessments. We found that MICS-related factors may alter QEEG characteristics, including the absolute power of the delta, theta, and beta 1 bands, the relative power of the theta and beta 3 subbands, the coherence of the delta and theta bands, and the amplitude asymmetry of the beta 1 band, in certain brain regions. Although most of these QEEG patterns are significantly correlated with MIS, the delta absolute power, beta 1 amplitude asymmetry, and theta coherence are the optimal inputs for the logistic regression model, which can accurately classify ESRD patients with and without MICS (90.0 ± 5.7% area under the receiver operating characteristic curve). We suggest that these QEEG features can be used not only to evaluate the severity of cerebral disorders in ESRD patients but also to noninvasively monitor MICS in clinical practice.}, } @article {pmid36825118, year = {2023}, author = {Wang, X and Xing, K and He, M and He, T and Xiang, X and Chen, T and Zhang, L and Li, H}, title = {Time-restricted feeding is an intervention against excessive dark-phase sleepiness induced by obesogenic diet.}, journal = {National science review}, volume = {10}, number = {1}, pages = {nwac222}, doi = {10.1093/nsr/nwac222}, pmid = {36825118}, issn = {2053-714X}, abstract = {High-fat diet (HFD)-induced obesity is a growing epidemic and major health concern. While excessive daytime sleepiness (EDS) is a common symptom of HFD-induced obesity, preliminary findings suggest that reduced wakefulness could be improved with time-restricted feeding (TRF). At present, however, the underlying neural mechanisms remain largely unknown. The paraventricular thalamic nucleus (PVT) plays a role in maintaining wakefulness. We found that chronic HFD impaired the activity of PVT neurons. Notably, inactivation of the PVT was sufficient to reduce and fragment wakefulness during the active phase in lean mice, similar to the sleep-wake alterations observed in obese mice with HFD-induced obesity. On the other hand, enhancing PVT neuronal activity consolidated wakefulness in mice with HFD-induced obesity. We observed that the fragmented wakefulness could be eliminated and reversed by TRF. Furthermore, TRF prevented the HFD-induced disruptions on synaptic transmission in the PVT, in a feeding duration-dependent manner. Collectively, our findings demonstrate that ad libitum access to a HFD results in inactivation of the PVT, which is critical to impaired nocturnal wakefulness and increased sleep, while TRF can prevent and reverse diet-induced PVT dysfunction and excessive sleepiness. We establish a link between TRF and neural activity, through which TRF can potentially serve as a lifestyle intervention against diet/obesity-related EDS.}, } @article {pmid36824667, year = {2023}, author = {Chen, M and Chen, Z and Xiao, X and Zhou, L and Fu, R and Jiang, X and Pang, M and Xia, J}, title = {Corticospinal circuit neuroplasticity may involve silent synapses: Implications for functional recovery facilitated by neuromodulation after spinal cord injury.}, journal = {IBRO neuroscience reports}, volume = {14}, number = {}, pages = {185-194}, doi = {10.1016/j.ibneur.2022.08.005}, pmid = {36824667}, issn = {2667-2421}, abstract = {Spinal cord injury (SCI) leads to devastating physical consequences, such as severe sensorimotor dysfunction even lifetime disability, by damaging the corticospinal system. The conventional opinion that SCI is intractable due to the poor regeneration of neurons in the adult central nervous system (CNS) needs to be revisited as the CNS is capable of considerable plasticity, which underlie recovery from neural injury. Substantial spontaneous neuroplasticity has been demonstrated in the corticospinal motor circuitry following SCI. Some of these plastic changes appear to be beneficial while others are detrimental toward locomotor function recovery after SCI. The beneficial corticospinal plasticity in the spared corticospinal circuits can be harnessed therapeutically by multiple contemporary neuromodulatory approaches, especially the electrical stimulation-based modalities, in an activity-dependent manner to improve functional outcomes in post-SCI rehabilitation. Silent synapse generation and unsilencing contribute to profound neuroplasticity that is implicated in a variety of neurological disorders, thus they may be involved in the corticospinal motor circuit neuroplasticity following SCI. Exploring the underlying mechanisms of silent synapse-mediated neuroplasticity in the corticospinal motor circuitry that may be exploited by neuromodulation will inform a novel direction for optimizing therapeutic repair strategies and rehabilitative interventions in SCI patients.}, } @article {pmid36822277, year = {2023}, author = {Gan, A and Gong, A and Ding, P and Yuan, X and Chen, M and Fu, Y and Cheng, Y}, title = {Computer-aided diagnosis of schizophrenia based on node2vec and Transformer.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109824}, doi = {10.1016/j.jneumeth.2023.109824}, pmid = {36822277}, issn = {1872-678X}, abstract = {OBJECTIVE: Compared with the healthy control(HC) group, the brain structure and function of schizophrenia(SZ) patients are significantly abnormal, so brain imaging methods can be used to achieve the aided diagnosis of SZ. However, a brain network based on brain imaging data is non-Euclidean, and its intrinsic features cannot be learned effectively by general deep learning models. Furthermore, in the majority of existing studies, brain network features were manually specified as the input of machine learning models.

METHODS: In this study, brain functional network constructed from the subject's fMRI data is analyzed, and its small-world value is calculated and t-tested; the node2vec algorithm in graph embedding is introduced to transform the constructed brain network into low-dimensional dense vectors, and the brain network's non-Euclidean spatial structure characteristics are retained to the greatest extent, so that its intrinsic features can be extracted by deep learning models; GridMask is used to randomly mask part of the information in the vectors to enhance the data; and then features can be extracted using the Transformer model to identify SZ.

RESULTS: It is again shown that the small-world value of the brain network in SZ is significantly lower than that in HC by t-test (p=0.014¡0.05). 97.78% classification accuracy is achieved by the proposed methods (node2vec + GridMask + Transformer) in 30 SZ patients and 30 healthy people.

CONCLUSION: The experiment shows that the node2vec used in this paper can effectively solve the problem of brain network features being difficult to learn by general deep learning models. The high-precision computer-aided diagnosis of SZ can be obtained by combining node2vec with Transformer and GridMask.

SIGNIFICANCE: The proposed methods in the paper are expected to be used for aided diagnosis of SZ.}, } @article {pmid36821640, year = {2023}, author = {Jeunet, C and N'Kaoua, B and Subramanian, S and Hachet, M and Lotte, F}, title = {Correction: Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.}, journal = {PloS one}, volume = {18}, number = {2}, pages = {e0282281}, doi = {10.1371/journal.pone.0282281}, pmid = {36821640}, issn = {1932-6203}, abstract = {[This corrects the article DOI: 10.1371/journal.pone.0143962.].}, } @article {pmid36821578, year = {2023}, author = {Goodrich, JA and Walker, DI and He, J and Lin, X and Baumert, BO and Hu, X and Alderete, TL and Chen, Z and Valvi, D and Fuentes, ZC and Rock, S and Wang, H and Berhane, K and Gilliland, FD and Goran, MI and Jones, DP and Conti, DV and Chatzi, L}, title = {Metabolic Signatures of Youth Exposure to Mixtures of Per- and Polyfluoroalkyl Substances: A Multi-Cohort Study.}, journal = {Environmental health perspectives}, volume = {131}, number = {2}, pages = {27005}, doi = {10.1289/EHP11372}, pmid = {36821578}, issn = {1552-9924}, abstract = {BACKGROUND: Exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous and has been associated with an increased risk of several cardiometabolic diseases. However, the metabolic pathways linking PFAS exposure and human disease are unclear.

OBJECTIVE: We examined associations of PFAS mixtures with alterations in metabolic pathways in independent cohorts of adolescents and young adults.

METHODS: Three hundred twelve overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) and 137 young adults from the Southern California Children's Health Study (CHS) were included in the analysis. Plasma PFAS and the metabolome were determined using liquid-chromatography/high-resolution mass spectrometry. A metabolome-wide association study was performed on log-transformed metabolites using Bayesian regression with a g-prior specification and g-computation for modeling exposure mixtures to estimate the impact of exposure to a mixture of six ubiquitous PFAS (PFOS, PFHxS, PFHpS, PFOA, PFNA, and PFDA). Pathway enrichment analysis was performed using Mummichog and Gene Set Enrichment Analysis. Significance across cohorts was determined using weighted Z-tests.

RESULTS: In the SOLAR and CHS cohorts, PFAS exposure was associated with alterations in tyrosine metabolism (meta-analysis p=0.00002) and de novo fatty acid biosynthesis (p=0.03), among others. For example, when increasing all PFAS in the mixture from low (∼30th percentile) to high (∼70th percentile), thyroxine (T4), a thyroid hormone related to tyrosine metabolism, increased by 0.72 standard deviations (SDs; equivalent to a standardized mean difference) in the SOLAR cohort (95% Bayesian credible interval (BCI): 0.00, 1.20) and 1.60 SD in the CHS cohort (95% BCI: 0.39, 2.80). Similarly, when going from low to high PFAS exposure, arachidonic acid increased by 0.81 SD in the SOLAR cohort (95% BCI: 0.37, 1.30) and 0.67 SD in the CHS cohort (95% BCI: 0.00, 1.50). In general, no individual PFAS appeared to drive the observed associations.

DISCUSSION: Exposure to PFAS is associated with alterations in amino acid metabolism and lipid metabolism in adolescents and young adults. https://doi.org/10.1289/EHP11372.}, } @article {pmid36821341, year = {2023}, author = {Chen, Y and Chen, S and Zhang, X and Zhang, S and Jia, K and Anderson, BA and Gong, M}, title = {Reward history modulates attention based on feature relationship.}, journal = {Journal of experimental psychology. General}, volume = {}, number = {}, pages = {}, doi = {10.1037/xge0001384}, pmid = {36821341}, issn = {1939-2222}, abstract = {Prioritizing attention to reward-predictive items is critical for survival, but challenging because these items rarely appear in the same feature or within the same environment. However, whether attention selection can be adaptively tuned to items that matched the context-dependent, relative feature of previously rewarded items remains largely unknown. In four experiments (N = 40 per experiment), we trained participants to learn the color-reward association and then adopted visual search tasks in which the color of a singleton distractor matched either the feature value (e.g., red or yellow) or feature relationship (i.e., redder or yellower) of previously rewarded colors. We consistently found enhanced attentional capture by a singleton distractor when it was relationally matched to the high reward compared with the low reward relationship, in addition to observing the typical effect of learned value on singletons matching the previously rewarded colors. Our findings provide novel evidence for the flexibility of value-driven attention via feature relationship, which is particularly useful given the changeable sensory inputs in real-world searches. (PsycInfo Database Record (c) 2023 APA, all rights reserved).}, } @article {pmid36820790, year = {2022}, author = {Bretton-Granatoor, Z and Stealey, H and Santacruz, SR and Lewis-Peacock, JA}, title = {Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data.}, journal = {IEEE Biomedical Circuits and Systems Conference : healthcare technology : [proceedings]. IEEE Biomedical Circuits and Systems Conference}, volume = {2022}, number = {}, pages = {650-654}, pmid = {36820790}, abstract = {Feature selection, or dimensionality reduction, has become a standard step in reducing large-scale neural datasets into usable signals for brain-machine interface and neurofeedback decoders. Current techniques in fMRI data reduce the number of voxels (features) by performing statistics on individual voxels or using traditional techniques that utilize linear combinations of features (e.g., principal component analysis (PCA)). However, these methods often do not account for the cross-correlations found across voxels and do not sufficiently reduce the feature space to support efficient real-time feedback. To overcome these limitations, we propose using factor analysis on fMRI data. This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs). Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of the total feature space to build our multinomial classifier. In one NHP subject, the average accuracy of classifying eight target locations over 64 sessions was 62.43% (+/-6.19%) compared to a PCA-based classifier with 60.26% (+/-6.02%). In healthy fMRI subjects, we reduced the feature space to an average of 0.33% of the initial space. Group average (n=5) accuracy of FA-based category classification was 74.33% (+/- 4.91%) compared to a PCA-based classifier with 68.42% (+/-4.79%). FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders. Importantly, FA-based methods allow researchers to address specific hypotheses about how underlying neural activity relates to behavior.}, } @article {pmid36817868, year = {2023}, author = {Xiao, HY and Chai, JY and Fang, YY and Lai, YS}, title = {The spatial-temporal risk profiling of Clonorchis sinensis infection over 50 years implies the effectiveness of control programs in South Korea: a geostatistical modeling study.}, journal = {The Lancet regional health. Western Pacific}, volume = {33}, number = {}, pages = {100697}, pmid = {36817868}, issn = {2666-6065}, abstract = {BACKGROUND: Over the past 50 years, two national control programs on Clonorchis sinensis infection have been conducted in South Korea. Spatial-temporal profiles of infection risk provide useful information on assessing the effectiveness of the programs and planning spatial-targeted control strategies.

METHODS: Advanced Bayesian geostatistical joint models with spatial-temporal random effects were developed to analyze disease data collecting by a systematic review with potential influencing factors, and to handle issues of preferential sampling and data heterogeneities. Changes of the infection risk were analyzed.

FINDINGS: We presented the first spatial-temporal risk maps of C. sinensis infection at 5 × 5 km[2] resolution from 1970 to 2020 in South Korea. Moderate-to-high risk areas were shrunk, but temporal variances were shown in different areas. The population-adjusted estimated prevalence across the country was 5.99% (95% BCI: 5.09-7.01%) in 1970, when the first national deworming campaign began. It declined to 3.95% (95% BCI: 2.88-3.95%) in 1995, when the campaign suspended, and increased to 4.73% (95% BCI: 4.00-5.42%) in 2004, just before the Clonorchiasis Eradication Program (CEP). The population-adjusted prevalence was estimated at 2.77% (95% BCI: 1.67-4.34%) in 2020, 15 years after CEP started, corresponding to 1.42 (95% BCI: 0.85-2.23) million infected people.

INTERPRETATION: The first nationwide campaign and the CEP showed effectiveness on control of C. sinensis infection. Moderate-to-high risk areas identified by risk maps should be prioritized for control and intervention.

FUNDING: The National Natural Science Foundation of China (project no. 82073665) and the Natural Science Foundation of Guangdong Province (project no. 2022A1515010042).}, } @article {pmid36817607, year = {2023}, author = {Shang, YF and Shen, YY and Zhang, MC and Lv, MC and Wang, TY and Chen, XQ and Lin, J}, title = {Progress in salivary glands: Endocrine glands with immune functions.}, journal = {Frontiers in endocrinology}, volume = {14}, number = {}, pages = {1061235}, pmid = {36817607}, issn = {1664-2392}, abstract = {The production and secretion of saliva is an essential function of the salivary glands. Saliva is a complicated liquid with different functions, including moistening, digestion, mineralization, lubrication, and mucosal protection. This review focuses on the mechanism and neural regulation of salivary secretion, and saliva is secreted in response to various stimuli, including odor, taste, vision, and mastication. The chemical and physical properties of saliva change dynamically during physiological and pathophysiological processes. Moreover, the central nervous system modulates salivary secretion and function via various neurotransmitters and neuroreceptors. Smell, vision, and taste have been investigated for the connection between salivation and brain function. The immune and endocrine functions of the salivary glands have been explored recently. Salivary glands play an essential role in innate and adaptive immunity and protection. Various immune cells such as B cells, T cells, macrophages, and dendritic cells, as well as immunoglobins like IgA and IgG have been found in salivary glands. Evidence supports the synthesis of corticosterone, testosterone, and melatonin in salivary glands. Saliva contains many potential biomarkers derived from epithelial cells, gingival crevicular fluid, and serum. High level of matrix metalloproteinases and cytokines are potential markers for oral carcinoma, infectious disease in the oral cavity, and systemic disease. Further research is required to monitor and predict potential salivary biomarkers for health and disease in clinical practice and precision medicine.}, } @article {pmid36817318, year = {2023}, author = {Zhang, Z and Li, D and Zhao, Y and Fan, Z and Xiang, J and Wang, X and Cui, X}, title = {A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1101726}, pmid = {36817318}, issn = {1662-5188}, abstract = {INTRODUCTION: Speller is the best way to express the performance of the brain-computer interface (BCI) paradigm. Due to its advantages of short analysis time and high accuracy, the SSVEP paradigm has been widely used in the BCI speller system based on the wet electrode. It is widely known that the wet electrode operation is cumbersome and that the subjects have a poor experience. In addition, in the asynchronous SSVEP system based on threshold analysis, the system flickers continuously from the beginning to the end of the experiment, which leads to visual fatigue. The dry electrode has a simple operation and provides a comfortable experience for subjects. The EOG signal can avoid the stimulation of SSVEP for a long time, thus reducing fatigue.

METHODS: This study first designed the brain-controlled switch based on continuous blinking EOG signal and SSVEP signal to improve the flexibility of the BCI speller. Second, in order to increase the number of speller instructions, we designed the time-space frequency conversion (TSFC) SSVEP stimulus paradigm by constantly changing the time and space frequency of SSVEP sub-stimulus blocks, and designed a speller in a dry electrode environment.

RESULTS: Seven subjects participated and completed the experiments. The results showed that the accuracy of the brain-controlled switch designed in this study was up to 94.64%, and all the subjects could use the speller flexibly. The designed 60-character speller based on the TSFC-SSVEP stimulus paradigm has an accuracy rate of 90.18% and an information transmission rate (ITR) of 117.05 bits/min. All subjects can output the specified characters in a short time.

DISCUSSION: This study designed and implemented a multi-instruction SSVEP speller based on dry electrode. Through the combination of EOG and SSVEP signals, the speller can be flexibly controlled. The frequency of SSVEP stimulation sub-block is recoded in time and space by TSFC-SSVEP stimulation paradigm, which greatly improves the number of output instructions of BCI system in dry electrode environment. This work only uses FBCCA algorithm to test the stimulus paradigm, which requires a long stimulus time. In the future, we will use trained algorithms to study stimulus paradigm to improve its overall performance.}, } @article {pmid36816135, year = {2023}, author = {Wang, M and Zhou, H and Li, X and Chen, S and Gao, D and Zhang, Y}, title = {Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1113593}, pmid = {36816135}, issn = {1662-4548}, abstract = {Motor imagery (MI) electroencephalogram (EEG) signals have a low signal-to-noise ratio, which brings challenges in feature extraction and feature selection with high classification accuracy. In this study, we proposed an approach that combined an improved lasso with relief-f to extract the wavelet packet entropy features and the topological features of the brain function network. For signal denoising and channel filtering, raw MI EEG was filtered based on an R[2] map, and then the wavelet soft threshold and one-to-one multi-class score common spatial pattern algorithms were used. Subsequently, the relative wavelet packet entropy and corresponding topological features of the brain network were extracted. After feature fusion, mutcorLasso and the relief-f method were applied for feature selection, followed by three classifiers and an ensemble classifier, respectively. The experiments were conducted on two public EEG datasets (BCI Competition III dataset IIIa and BCI Competition IV dataset IIa) to verify this proposed method. The results showed that the brain network topology features and feature selection methods can retain the information of EEG more effectively and reduce the computational complexity, and the average classification accuracy for both public datasets was above 90%; hence, this algorithms is suitable in MI-BCI and has potential applications in rehabilitation and other fields.}, } @article {pmid36813927, year = {2023}, author = {Shang, Y and Gao, X and An, A}, title = {Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.}, journal = {Medical & biological engineering & computing}, volume = {}, number = {}, pages = {}, pmid = {36813927}, issn = {1741-0444}, abstract = {Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.}, } @article {pmid36812763, year = {2023}, author = {Barrier, ML and Myszor, IT and Sahariah, P and Sigurdsson, S and Carmena-Bargueño, M and Pérez-Sánchez, H and Gudmundsson, GH}, title = {Aroylated phenylenediamine HO53 modulates innate immunity, histone acetylation and metabolism.}, journal = {Molecular immunology}, volume = {155}, number = {}, pages = {153-164}, doi = {10.1016/j.molimm.2023.02.003}, pmid = {36812763}, issn = {1872-9142}, abstract = {In the current context of antibiotic resistance, the need to find alternative treatment strategies is urgent. Our research aimed to use synthetized aroylated phenylenediamines (APDs) to induce the expression of cathelicidin antimicrobial peptide gene (CAMP) to minimize the necessity of antibiotic use during infection. One of these compounds, HO53, showed promising results in inducing CAMP expression in bronchial epithelium cells (BCi-NS1.1 hereafter BCi). Thus, to decipher the cellular effects of HO53 on BCi cells, we performed RNA sequencing (RNAseq) analysis after 4, 8 and 24 h treatment of HO53. The number of differentially expressed transcripts pointed out an epigenetic modulation. Yet, the chemical structure and in silico modeling indicated HO53 as a histone deacetylase (HDAC) inhibitor. When exposed to a histone acetyl transferase (HAT) inhibitor, BCi cells showed a decreased expression of CAMP. Inversely, when treated with a specific HDAC3 inhibitor (RGFP996), BCi cells showed an increased expression of CAMP, indicating acetylation status in cells as determinant for the induction of the expression of the gene CAMP expression. Interestingly, a combination treatment with both HO53 and HDAC3 inhibitor RGFP966 leads to a further increase of CAMP expression. Moreover, HDAC3 inhibition by RGFP966 leads to increased expression of STAT3 and HIF1A, both previously demonstrated to be involved in pathways regulating CAMP expression. Importantly, HIF1α is considered as a master regulator in metabolism. A significant number of genes of metabolic enzymes were detected in our RNAseq data with enhanced expression conveying a shift toward enhanced glycolysis. Overall, we are demonstrating that HO53 might have a translational value against infections in the future through a mechanism leading to innate immunity strengthening involving HDAC inhibition and shifting the cells towards an immunometabolism, which further favors innate immunity activation.}, } @article {pmid36808912, year = {2023}, author = {Li, R and Hu, H and Zhao, X and Wang, Z and Xu, G}, title = {A static paradigm based on illusion-induced VEP for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbdc0}, pmid = {36808912}, issn = {1741-2552}, abstract = {OBJECTIVE: Visual evoked potentials (VEPs) have been commonly applied in brain-computer interfaces (BCIs) due to their satisfactory classification performance recently. However, most existing methods with flickering or oscillating stimuli will induce visual fatigue under long-term training, thus restricting the implementation of VEP-based BCIs. To address this issue, a novel paradigm adopting static motion illusion based on illusion-induced visual evoked potential (IVEP) is proposed for BCIs to enhance visual experience and practicality.

APPROACH: This study explored the responses to baseline and illusion tasks including the Rotating-Tilted-Lines (RTL) illusion and Rotating-Snakes (RS) illusion. The distinguishable features were examined between different illusions by analyzing the event-related potentials (ERPs) and amplitude modulation of evoked oscillatory responses.

MAIN RESULTS: The illusion stimuli elicited VEPs in an early time window encompassing a negative component (N1) from 110 to 200 ms and a positive component (P2) between 210 and 300 ms. Based on the feature analysis, a filter bank was designed to extract discriminative signals. The task-related component analysis (TRCA) was used to evaluate the binary classification task performance of the proposed method. Then the highest accuracy of 86.67% was achieved with a data length of 0.6 s.

SIGNIFICANCE: The results of this study demonstrate that the static motion illusion paradigm has the feasibility of implementation and is promising for VEP-based BCI applications.}, } @article {pmid36805270, year = {2023}, author = {Liu, X and Whalen, AJ and Ryu, SB and Lee, SW and Fried, SI and Kim, K and Cai, C and Lauritzen, M and Bertram, N and Chang, B and Yu, T and Han, A}, title = {MEMS micro-coils for magnetic neurostimulation.}, journal = {Biosensors & bioelectronics}, volume = {227}, number = {}, pages = {115143}, doi = {10.1016/j.bios.2023.115143}, pmid = {36805270}, issn = {1873-4235}, abstract = {Micro-coil magnetic stimulation of brain tissue presents new challenges for MEMS micro-coil probe fabrication. The main challenges are threefold; (i) low coil resistance for high power efficiency, (ii) low leak current from the probe into the in vitro experimental set-up, (iii) adaptive MEMS process technology because of the dynamic research area, which requires agile design changes. Taking on these challenges, we present a MEMS fabrication process that has three main features; (i) multilayer resist lift-off process to pattern up to 1800-nm-thick metal films, and special care is taken to obtain high conductivity thin-films by physical vapor deposition, and (ii) all micro-coil Al wires are encapsulated in at least 200 nm of ALD alumina and 6-μm-thick parylene C such the leak resistance is high (>210 GΩ), (iii) combining a multi-step DRIE process and maskless photolithography for adaptive design and device fabrication. The entire process requires four lithography steps. Because we avoided SOI wafers and lithography mask fabrication, the design-to-device time is shortened significantly. The resulting probes are 4-mm-long, 60-μm-thick, and down to 150 μm-wide. Selected MEMS coil devices were validated in vivo using mice and compared to previous work.}, } @article {pmid36805091, year = {2023}, author = {Zhang, Y and Wang, Y and Li, Z and Wang, Z and Cheng, J and Bai, X and Hsu, YC and Sun, Y and Li, S and Shi, J and Sui, B and Bai, R}, title = {Vascular-water-exchange MRI (VEXI) enables the detection of subtle AXR alterations in Alzheimer's disease without MRI contrast agent, which may relate to BBB integrity.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119951}, doi = {10.1016/j.neuroimage.2023.119951}, pmid = {36805091}, issn = {1095-9572}, abstract = {Blood-brain barrier (BBB) impairment is an important pathophysiological process in Alzheimer's disease (AD) and a potential biomarker for early diagnosis of AD. However, most current neuroimaging methods assessing BBB function need the injection of exogenous contrast agents (or tracers), which limits the application of these methods in a large population. In this study, we aim to explore the feasibility of vascular water exchange MRI (VEXI), a diffusion-MRI-based method proposed to assess the BBB permeability to water molecules without using a contrast agent, in the detection of the BBB breakdown in AD. We tested VEXI on a 3T MRI scanner on three groups: AD patients (AD group), mild cognitive impairment (MCI) patients due to AD (MCI group), and the age-matched normal cognition subjects (NC group). Interestingly, we find that the apparent water exchange across the BBB (AXRBBB) measured by VEXI shows higher values in MCI compared with NC, and this higher AXRBBB happens specifically in the hippocampus. This increase in AXRBBB value increase gets larger and extends to more brain regions (medial orbital frontal cortex and thalamus) from MCI group to the AD group. Furthermore, we find that the AXRBBB of these three regions detected by VEXI is correlated significantly with impairment of respective cognitive domains independent of age, sex and education. These results suggest VEXI is a promising method to assess the BBB breakdown in AD.}, } @article {pmid36801814, year = {2023}, author = {Lakshminarayanan, K and Shah, R and Yao, Y and Madathil, D}, title = {The Effects of Subthreshold Vibratory Noise on Cortical Activity During Motor Imagery.}, journal = {Motor control}, volume = {}, number = {}, pages = {1-14}, doi = {10.1123/mc.2022-0061}, pmid = {36801814}, issn = {1087-1640}, abstract = {Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain-computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery-based brain-computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion-extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery-related event-related desynchronization and improved task classification performance.}, } @article {pmid36801453, year = {2023}, author = {Zhao, K and Zhu, J and Yang, L and Shang, Z and Wan, H}, title = {Goal given moment modulates the time period of gamma oscillations in nidopallium caudolaterale during the goal-directed behavior of pigeon.}, journal = {Brain research}, volume = {}, number = {}, pages = {148288}, doi = {10.1016/j.brainres.2023.148288}, pmid = {36801453}, issn = {1872-6240}, abstract = {The cognitive processes of goal-directed navigation are believed to be organized around and serve the identification and selection of goals. Differences in LFP signals in avian nidopallium caudolaterale (NCL) under different goal location/distance information in the goal-directed behavior have been studied. However, for goals that are multifarious constructs that include various information, the modulation of goal time information on the LFP of NCL during goal-directed behavior remains unclear. In this study, we recorded the LFP activity from the NCL of eight pigeons as they performed two goal-directed decision-making tasks in a plus-maze. During the two tasks with different goal time information, spectral analysis revealed significant LFP power selectively enhanced in the slow gamma band (40-60 Hz), while the slow gamma band of LFP which could effectively decode the behavioral goal of the pigeons existed in different time periods. These findings suggest that the LFP activity in the gamma band correlates with the goal-time information, and help to shed light on the contribution of the gamma rhythm recorded from the NCL in goal-directed behavior.}, } @article {pmid36801435, year = {2023}, author = {Chikhi, S and Matton, N and Sanna, M and Blanchet, S}, title = {Mental strategies and resting state EEG: effect on high alpha amplitude modulation by neurofeedback in healthy young adults.}, journal = {Biological psychology}, volume = {}, number = {}, pages = {108521}, doi = {10.1016/j.biopsycho.2023.108521}, pmid = {36801435}, issn = {1873-6246}, abstract = {Neurofeedback (NFB) is a brain-computer interface which allows individuals to modulate their brain activity. Despite the self-regulatory nature of NFB, the effectiveness of strategies used during NFB training has been little investigated. In a single session of NFB training (6*3min training blocks) with healthy young participants, we experimentally tested if providing a list of mental strategies (list group, N = 46), compared with a group receiving no strategies (no list group, N = 39), affected participants' neuromodulation ability of high alpha (10-12Hz) amplitude. We additionally asked participants to verbally report the mental strategies used to enhance high alpha amplitude. The verbatim was then classified in pre-established categories in order to examine the effect of type of mental strategy on high alpha amplitude. First, we found that giving a list to the participants did not promote the ability to neuromodulate high alpha activity. However, our analysis of the specific strategies reported by learners during training blocks revealed that cognitive effort and recalling memories were associated with higher high alpha amplitude. Furthermore, the resting amplitude of trained high alpha frequency predicted an amplitude increase during training, a factor that may optimize inclusion in NFB protocols. The present results also corroborate the interrelation with other frequency bands during NFB training. Although these findings are based on a single NFB session, our study represents a further step towards developing effective protocols for high alpha neuromodulation by NFB.}, } @article {pmid36801241, year = {2023}, author = {Gu, L and Jiang, J and Han, H and Gan, JQ and Wang, H}, title = {Recognition of unilateral lower limb movement based on EEG signals with ERP-PCA analysis.}, journal = {Neuroscience letters}, volume = {800}, number = {}, pages = {137133}, doi = {10.1016/j.neulet.2023.137133}, pmid = {36801241}, issn = {1872-7972}, abstract = {It has been confirmed that motor imagery (MI) and motor execution (ME) share a subset of mechanisms underlying motor cognition. In contrast to the well-studied laterality of upper limb movement, the laterality hypothesis of lower limb movement also exists, but it needs to be characterized by further investigation. This study used electroencephalographic (EEG) recordings of 27 subjects to compare the effects of bilateral lower limb movement in the MI and ME paradigms. Event-related potential (ERP) recorded was decomposed into meaningful and useful representatives of the electrophysiological components, such as N100 and P300. Principal components analysis (PCA) was used to trace the characteristics of ERP components temporally and spatially, respectively. The hypothesis of this study is that the functional opposition of unilateral lower limbs of MI and ME should be reflected in the different alterations of the spatial distribution of lateralized activity. Meanwhile, the significant ERP-PCA components of the EEG signals as identifiable feature sets were applied with support vector machine to identify left and right lower limb movement tasks. The average classification accuracy over all subjects is up to 61.85% for MI and 62.94% for ME. The proportion of subjects with significant results are 51.85% for MI and 59.26% for ME, respectively. Therefore, a potential new classification model for lower limb movement can be applied on brain computer interface (BCI) systems in the future.}, } @article {pmid36800979, year = {2023}, author = {Hambridge, T and Coffeng, LE and de Vlas, SJ and Richardus, JH}, title = {Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach.}, journal = {Infectious diseases of poverty}, volume = {12}, number = {1}, pages = {12}, pmid = {36800979}, issn = {2049-9957}, abstract = {BACKGROUND: Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type.

METHODS: Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors.

RESULTS: For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: -1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14-2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08-2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review.

CONCLUSIONS: The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.}, } @article {pmid36800288, year = {2023}, author = {Shu, X and Wei, C and Tu, WY and Zhong, K and Qi, S and Wang, A and Bai, L and Zhang, SX and Luo, B and Xu, ZZ and Zhang, K and Shen, C}, title = {Negative regulation of TREM2-mediated C9orf72 poly-GA clearance by the NLRP3 inflammasome.}, journal = {Cell reports}, volume = {42}, number = {2}, pages = {112133}, doi = {10.1016/j.celrep.2023.112133}, pmid = {36800288}, issn = {2211-1247}, abstract = {Expansion of the hexanucleotide repeat GGGGCC in the C9orf72 gene is the most common genetic factor in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Poly-Gly-Ala (poly-GA), one form of dipeptide repeat proteins (DPRs) produced from GGGGCC repeats, tends to form neurotoxic protein aggregates. The C9orf72 GGGGCC repeats and microglial receptor TREM2 are both associated with risk for ALS/FTD. The role and regulation of TREM2 in C9orf72-ALS/FTD remain unclear. Here, we found that poly-GA proteins activate the microglial NLRP3 inflammasome to produce interleukin-1β (IL-1β), which promotes ADAM10-mediated TREM2 cleavage and inhibits phagocytosis of poly-GA. The inhibitor of the NLRP3 inflammasome, MCC950, reduces the TREM2 cleavage and poly-GA aggregates, resulting in the alleviation of motor deficits in poly-GA mice. Our study identifies a crosstalk between NLRP3 and TREM2 signaling, suggesting that targeting the NLRP3 inflammasome to sustain TREM2 is an approach to treat C9orf72-ALS/FTD.}, } @article {pmid36799296, year = {2023}, author = {Liu, Y and Shen, X and Zhang, Y and Zheng, X and Cepeda, C and Wang, Y and Duan, S and Tong, X}, title = {Interactions of glial cells with neuronal synapses, from astrocytes to microglia and oligodendrocyte lineage cells.}, journal = {Glia}, volume = {}, number = {}, pages = {}, doi = {10.1002/glia.24343}, pmid = {36799296}, issn = {1098-1136}, abstract = {The mammalian brain is a complex organ comprising neurons, glia, and more than 1 × 10[14] synapses. Neurons are a heterogeneous group of electrically active cells, which form the framework of the complex circuitry of the brain. However, glial cells, which are primarily divided into astrocytes, microglia, oligodendrocytes (OLs), and oligodendrocyte precursor cells (OPCs), constitute approximately half of all neural cells in the mammalian central nervous system (CNS) and mainly provide nutrition and tropic support to neurons in the brain. In the last two decades, the concept of "tripartite synapses" has drawn great attention, which emphasizes that astrocytes are an integral part of the synapse and regulate neuronal activity in a feedback manner after receiving neuronal signals. Since then, synaptic modulation by glial cells has been extensively studied and substantially revised. In this review, we summarize the latest significant findings on how glial cells, in particular, microglia and OL lineage cells, impact and remodel the structure and function of synapses in the brain. Our review highlights the cellular and molecular aspects of neuron-glia crosstalk and provides additional information on how aberrant synaptic communication between neurons and glia may contribute to neural pathologies.}, } @article {pmid36799225, year = {2023}, author = {Tu, WY and Xu, W and Zhang, J and Qi, S and Bai, L and Shen, C and Zhang, K}, title = {C9orf72 poly-GA proteins impair neuromuscular transmission.}, journal = {Zoological research}, volume = {44}, number = {2}, pages = {331-340}, doi = {10.24272/j.issn.2095-8137.2022.356}, pmid = {36799225}, issn = {2095-8137}, abstract = {Amyotrophic lateral sclerosis (ALS) is a devastating motoneuron disease, in which lower motoneurons lose control of skeletal muscles. Degeneration of neuromuscular junctions (NMJs) occurs at the initial stage of ALS. Dipeptide repeat proteins (DPRs) from G4C2 repeat-associated non-ATG (RAN) translation are known to cause C9orf72-associated ALS (C9-ALS). However, DPR inclusion burdens are weakly correlated with neurodegenerative areas in C9-ALS patients, indicating that DPRs may exert cell non-autonomous effects, in addition to the known intracellular pathological mechanisms. Here, we report that poly-GA, the most abundant form of DPR in C9-ALS, is released from cells. Local administration of poly-GA proteins in peripheral synaptic regions causes muscle weakness and impaired neuromuscular transmission in vivo. The NMJ structure cannot be maintained, as evidenced by the fragmentation of postsynaptic acetylcholine receptor (AChR) clusters and distortion of presynaptic nerve terminals. Mechanistic study demonstrated that extracellular poly-GA sequesters soluble Agrin ligands and inhibits Agrin-MuSK signaling. Our findings provide a novel cell non-autonomous mechanism by which poly-GA impairs NMJs in C9-ALS. Thus, targeting NMJs could be an early therapeutic intervention for C9-ALS.}, } @article {pmid36798295, year = {2023}, author = {Pollmann, EH and Yin, H and Uguz, I and Dubey, A and Wingel, KE and Choi, JS and Moazeni, S and Gilhotra, Y and Pavlovsky, VA and Banees, A and Boominathan, V and Robinson, J and Veeraraghavan, A and Pieribone, VA and Pesaran, B and Shepard, KL}, title = {Subdural CMOS optical probe (SCOPe) for bidirectional neural interfacing.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.02.07.527500}, pmid = {36798295}, abstract = {Optical neurotechnologies use light to interface with neurons and can monitor and manipulate neural activity with high spatial-temporal precision over large cortical extents. While there has been significant progress in miniaturizing microscope for head-mounted configurations, these existing devices are still very bulky and could never be fully implanted. Any viable translation of these technologies to human use will require a much more noninvasive, fully implantable form factor. Here, we leverage advances in microelectronics and heterogeneous optoelectronic packaging to develop a transformative, ultrathin, miniaturized device for bidirectional optical stimulation and recording: the subdural CMOS Optical Probe (SCOPe). By being thin enough to lie entirely within the subdural space of the primate brain, SCOPe defines a path for the eventual human translation of a new generation of brain-machine interfaces based on light.}, } @article {pmid36795966, year = {2023}, author = {Ghoreifi, A and Seyedian, SL and Piatti, P and Chew, YC and Jara, B and Sanossian, L and Bhasin, JM and Yamada, T and Fuchs, G and Bhanvadia, S and Sotelo, R and Hung, A and Aron, M and Desai, M and Gill, I and Daneshmand, S and Liang, G and Djaladat, H}, title = {A Urine-Based DNA Methylation Marker Test to Detect Upper Tract Urothelial Carcinoma: A Prospective Cohort Study.}, journal = {The Journal of urology}, volume = {}, number = {}, pages = {101097JU0000000000003188}, doi = {10.1097/JU.0000000000003188}, pmid = {36795966}, issn = {1527-3792}, abstract = {PURPOSE: To explore the accuracy of a urine-based epigenetic test for detecting upper tract urothelial carcinoma (UTUC).

MATERIALS AND METHODS: Under an IRB-approved protocol, urine samples were prospectively collected from primary UTUC patients before radical nephroureterectomy (RNU), ureterectomy, or ureteroscopy (URS) between December 2019 and March 2022. Samples were analyzed with Bladder CARE, a urine-based test that measures the methylation levels of 3 cancer biomarkers (TRNA-Cys, SIM2, and NKX1-1) and 2 internal control loci using methylation-sensitive restriction enzymes coupled with qPCR. Results were reported as the Bladder CARE Index (BCI) score and quantitatively categorized as positive (BCI >5), high-risk (BCI 2.5-5), or negative (BCI <2.5). The findings were compared with those of 1:1 sex/age-matched cancer-free healthy individuals.

RESULTS: Fifty patients (40 RNU, 7 ureterectomy, and 3 URS) with a median (IQR) age of 72 (64-79) years were included. BCI results were positive in 47, high-risk in one, and negative in 2 patients. A significant correlation was found between BCI values and tumor size. Urine cytology was available for 35 patients, of whom 22 (63%) were false-negative. UTUC patients had significantly higher BCI values compared to the controls (mean BCI 189.3 vs 1.6, respectively; P <.001). The sensitivity, specificity, positive predictive value, and negative predictive value of the Bladder CARE test for detecting UTUC were 96%, 88%, 89%, and 96%, respectively.Conclusions:Bladder CARE is an accurate urine-based epigenetic test for the diagnosis of UTUC, with much higher sensitivity than standard urine cytology.}, } @article {pmid36792239, year = {2023}, author = {Ziafati, A and Maleki, A}, title = {Genetic algorithm based ensemble system using MLR and MsetCCA methods for SSVEP frequency recognition.}, journal = {Medical engineering & physics}, volume = {111}, number = {}, pages = {103945}, doi = {10.1016/j.medengphy.2022.103945}, pmid = {36792239}, issn = {1873-4030}, abstract = {BCI systems provide a direct communication channel between the human and the machine using brain signals. Among the various methods of steady-state visual evoked potential (SSVEP) stimulation frequency detection, multiple linear regression (MLR), and multiset canonical correlation analysis (MsetCCA) methods have achieved high accurate results in recent studies. The purpose of this study is to utilize both approaches and benefit from them using a genetic algorithm (GA). This algorithm leads to high-performance optimization due to its large number of regulatory parameters. Signal analysis was performed for the windows with 0.5 to 4 s duration length and with 0.5-second incremental steps. In this paper, we were able to achieve 100% accuracy of recognition for 2-second time-windows using the genetic algorithm to optimally ensemble SSVEP stimulation frequency detection methods. The accuracy of the proposed system indicates a significant improvement in detection compared to either MLR or MsetCCA alone and indicates that the ensemble system is correctly optimized using the genetic algorithm. Genetic algorithm is one of the most widely used algorithms because of its high regulatory parameters leading to its high flexibility. The improvement in detection of the proposed system is due to the use of the strengths of both two methods, and the optimal choice of the system response to visual stimuli.}, } @article {pmid36789010, year = {2023}, author = {Matsuki, E and Kawamoto, S and Morikawa, Y and Yahagi, N}, title = {The Impact of Cold Ambient Temperature in the Pattern of Influenza Virus Infection.}, journal = {Open forum infectious diseases}, volume = {10}, number = {2}, pages = {ofad039}, pmid = {36789010}, issn = {2328-8957}, abstract = {BACKGROUND: Prior literature suggests that cold temperature strongly influences the immune function of animals and human behaviors, which may allow for the transmission of respiratory viral infections. However, information on the impact of cold stimuli, especially the impact of temporal change in the ambient temperature on influenza virus transmission, is limited.

METHODS: A susceptible-infected-recovered-susceptible model was applied to evaluate the effect of temperature change on influenza virus transmission.

RESULTS: The mean temperature of the prior week was positively associated with the number of newly diagnosed cases (0.107 [95% Bayesian credible interval {BCI}, .106-.109]), whereas the mean difference in the temperature of the prior week was negatively associated (-0.835 [95% BCI, -.840 to -.830]). The product of the mean temperature and mean difference in the temperature of the previous week were also negatively associated with the number of newly diagnosed cases (-0.192 [95% BCI, -.197 to -.187]).

CONCLUSIONS: The mean temperature and the mean difference in temperature affected the number of newly diagnosed influenza cases differently. Our data suggest that high ambient temperature and a drop in the temperature and their interaction increase the risk of infection. Therefore, the highest risk of infection is attributable to a steep fall in temperature in a relatively warm environment.}, } @article {pmid36788214, year = {2023}, author = {Yu, B and Zhang, Q and Lin, L and Zhou, X and Ma, W and Wen, S and Li, C and Wang, W and Wu, Q and Wang, X and Li, XM}, title = {Molecular and cellular evolution of the amygdala across species analyzed by single-nucleus transcriptome profiling.}, journal = {Cell discovery}, volume = {9}, number = {1}, pages = {19}, pmid = {36788214}, issn = {2056-5968}, abstract = {The amygdala, or an amygdala-like structure, is found in the brains of all vertebrates and plays a critical role in survival and reproduction. However, the cellular architecture of the amygdala and how it has evolved remain elusive. Here, we generated single-nucleus RNA-sequencing data for more than 200,000 cells in the amygdala of humans, macaques, mice, and chickens. Abundant neuronal cell types from different amygdala subnuclei were identified in all datasets. Cross-species analysis revealed that inhibitory neurons and inhibitory neuron-enriched subnuclei of the amygdala were well-conserved in cellular composition and marker gene expression, whereas excitatory neuron-enriched subnuclei were relatively divergent. Furthermore, LAMP5[+] interneurons were much more abundant in primates, while DRD2[+] inhibitory neurons and LAMP5[+]SATB2[+] excitatory neurons were dominant in the human central amygdalar nucleus (CEA) and basolateral amygdalar complex (BLA), respectively. We also identified CEA-like neurons and their species-specific distribution patterns in chickens. This study highlights the extreme cell-type diversity in the amygdala and reveals the conservation and divergence of cell types and gene expression patterns across species that may contribute to species-specific adaptations.}, } @article {pmid36787644, year = {2023}, author = {Nardelli, M and Citi, L and Barbieri, R and Valenza, G}, title = {Characterization of autonomic states by complex sympathetic and parasympathetic dynamics.}, journal = {Physiological measurement}, volume = {}, number = {}, pages = {}, doi = {10.1088/1361-6579/acbc07}, pmid = {36787644}, issn = {1361-6579}, abstract = {Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises specially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.}, } @article {pmid36786985, year = {2022}, author = {Bobrova, EV and Reshetnikova, VV and Vershinina, EA and Grishin, AA and Isaev, MR and Bobrov, PD and Gerasimenko, YP}, title = {Dependence of Brain-Computer Interface Control Training on Personality Traits.}, journal = {Doklady. Biochemistry and biophysics}, volume = {507}, number = {1}, pages = {273-277}, pmid = {36786985}, issn = {1608-3091}, abstract = {Personality traits (PTs) are predictors of the success of control of brain-computer interfaces (BCIs); however, it is unknown how the PTs that are optimal for BCI control changes during training. The paper for the first time analyzes the correlations between PTs and the accuracy of the classification (AC) of brain states in imagining the movements of the hands, feet, and locomotion during 10-day training of ten volunteers in BCI control. In the first 3 days of training, the AC is higher for more stressed and anxious volunteers; in the last days, for calmer ones. In the middle of the training period, AC is higher in low-demonstrativeness persons, it is more pronounced when imagining foot movements. Correlations of low demonstrativeness, as well as of foresight and self-control with AC when imagining foot movements are revealed significantly more often than when imagining hand movements and locomotions. During almost the entire period of training, AC with locomotion imagination is higher in individualists. The results make it possible to propose individually-oriented recommendations for the use of BCI based on the imagination of movements for the rehabilitation of patients with motor disorders.}, } @article {pmid36780814, year = {2023}, author = {Mattheiss, JP and Breyta, R and Kurath, G and LaDeau, SL and Páez, DJ and Ferguson, PFB}, title = {Coproduction and modeling spatial contact networks prevent bias about infectious hematopoietic necrosis virus transmission for Snake River Basin salmonids.}, journal = {Journal of environmental management}, volume = {334}, number = {}, pages = {117415}, doi = {10.1016/j.jenvman.2023.117415}, pmid = {36780814}, issn = {1095-8630}, abstract = {Much remains unknown about variation in pathogen transmission across the geographic range of a free-ranging fish or animal species and about the influence of movement (associated with husbandry practices or animal behavior) on pathogen transmission. Salmonid hatcheries are an ideal system in which to study these processes. Salmonid hatcheries are managed for endangered species recovery, supplementation of threatened or at-risk fish stocks, support of fisheries, and ecosystem stability. Infectious hematopoietic necrosis virus (IHNV) is a rhabdovirus of significant concern to salmon aquaculture. Landscape IHNV transmission dynamics previously had been estimated only for salmonid hatcheries in the Lower Columbia River Basin (LCRB). The objectives of this study were to estimate IHNV transmission dynamics in a unique geographic region, the Snake River Basin (SRB), and to quantitatively estimate the effect of model coproduction on inference because previous assessments of coproduction have been qualitative. In contrast to the LCRB, the SRB has hatchery complexes consisting of a main hatchery and ≥1 satellite facility. Knowledge about hatchery complexes was held by a subset of project researchers but would not have been available to project modelers without coproduction. Project modelers generated and tested multiple versions of Bayesian susceptible-exposedinfected models to realistically represent the SRB and estimate the effect of coproduction. Models estimated the frequency of transmission routes, route-specific infection probabilities, and infection probabilities for combinations of salmonid hosts and IHNV lineages. Model results indicated that in the SRB, avoiding exposure to IHNV-positive adult salmonids is the most important action to prevent juvenile infections. Migrating adult salmonids exposed juvenile cohort-sites most frequently, and the infection probability was greatest following exposure to migrating adults. Without coproduction, the frequency of exposure by migrating adults would have been overestimated by 70 cohort-sites, and the infection probability following exposure to migrating adults would have been underestimated by∼0.09. The coproduced model had less uncertainty in the infection probability if no transmission route could be identified (Bayesian credible interval (BCI) width = 0.12) compared to the model without coproduction (BCI width = 0.34). Evidence for virus lineage MD specialization on steelhead and rainbow trout (both Oncorhynchus mykiss) was apparent without model coproduction. In the SRB, we found a greater probability of virus lineage UC infection in Chinook salmon (Oncorhynchus tshawytscha) compared to in O. mykiss, whereas in the LCRB, UC more clearly exhibited a generalist approach. Coproduction influenced estimates that depended on transmission routes, which operated differently at main hatcheries and satellite sites within hatchery complexes. Hatchery complexes are found outside of the SRB and are not specific to salmonid hatcheries alone. There is great potential for coproduction and modeling spatial contact networks to advance understanding about infectious disease transmission in complex production systems and surrounding free-ranging animal populations.}, } @article {pmid36778458, year = {2023}, author = {Wilson, GH and Willett, FR and Stein, EA and Kamdar, F and Avansino, DT and Hochberg, LR and Shenoy, KV and Druckmann, S and Henderson, JM}, title = {Long-term unsupervised recalibration of cursor BCIs.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.02.03.527022}, pmid = {36778458}, abstract = {Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in free use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms the state of the art in large-scale, closed-loop simulations over two months and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales; only target-inference methods appear capable of enabling long-term unsupervised recalibration. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.}, } @article {pmid36778360, year = {2023}, author = {Chen, K and Cambi, F and Kozai, TDY}, title = {Pro-myelinating Clemastine administration improves recording performance of chronically implanted microelectrodes and nearby neuronal health.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.01.31.526463}, pmid = {36778360}, abstract = {Intracortical microelectrodes have become a useful tool in neuroprosthetic applications in the clinic and to understand neurological disorders in basic neurosciences. Many of these brain-machine interface technology applications require successful long-term implantation with high stability and sensitivity. However, the intrinsic tissue reaction caused by implantation remains a major failure mechanism causing loss of recorded signal quality over time. Oligodendrocytes remain an underappreciated intervention target to improve chronic recording performance. These cells can accelerate action potential propagation and provides direct metabolic support for neuronal health and functionality. However, implantation injury causes oligodendrocyte degeneration and leads to progressive demyelination in surrounding brain tissue. Previous work highlighted that healthy oligodendrocytes are necessary for greater electrophysiological recording performance and the prevention of neuronal silencing around implanted microelectrodes over chronic implantation. Thus, we hypothesize that enhancing oligodendrocyte activity with a pharmaceutical drug, Clemastine, will prevent the chronic decline of microelectrode recording performance. Electrophysiological evaluation showed that the promyelination Clemastine treatment significantly elevated the signal detectability and quality, rescued the loss of multi-unit activity, and increased functional interlaminar connectivity over 16-weeks of implantation. Additionally, post-mortem immunohistochemistry showed that increased oligodendrocyte density and myelination coincided with increased survival of both excitatory and inhibitory neurons near the implant. Overall, we showed a positive relationship between enhanced oligodendrocyte activity and neuronal health and functionality near the chronically implanted microelectrode. This study shows that therapeutic strategy that enhance oligodendrocyte activity is effective for integrating the functional device interface with brain tissue over chronic implantation period.}, } @article {pmid36776946, year = {2022}, author = {Chen, XJ and Collins, LM and Mainsah, BO}, title = {Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication.}, journal = {Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics}, volume = {2022}, number = {}, pages = {1642-1647}, pmid = {36776946}, issn = {1062-922X}, abstract = {Brain-computer interfaces (BCIs), such as the P300 speller, can provide a means of communication for individuals with severe neuromuscular limitations. BCIs interpret electroencephalography (EEG) signals in order to translate embedded information about a user's intent into executable commands to control external devices. However, EEG signals are inherently noisy and nonstationary, posing a challenge to extended BCI use. Conventionally, a BCI classifier is trained via supervised learning in an offline calibration session; once trained, the classifier is deployed for online use and is not updated. As the statistics of a user's EEG data change over time, the performance of a static classifier may decline with extended use. It is therefore desirable to automatically adapt the classifier to current data statistics without requiring offline recalibration. In an existing semi-supervised learning approach, the classifier is trained on labeled EEG data and is then updated using incoming unlabeled EEG data and classifier-predicted labels. To reduce the risk of learning from incorrect predictions, a threshold is imposed to exclude unlabeled data with low-confidence label predictions from the expanded training set when retraining the adaptive classifier. In this work, we propose the use of a language model for spelling error correction and disambiguation to provide information about label correctness during semi-supervised learning. Results from simulations with multi-session P300 speller user EEG data demonstrate that our language-guided semi-supervised approach significantly improves spelling accuracy relative to conventional BCI calibration and threshold-based semi-supervised learning.}, } @article {pmid36776560, year = {2023}, author = {Li, S and Al-Sheikh, U and Chen, Y and Kang, L}, title = {Nematode homologs of the sour taste receptor Otopetrin1 are evolutionarily conserved acid-sensitive proton channels.}, journal = {Frontiers in cell and developmental biology}, volume = {11}, number = {}, pages = {1133890}, pmid = {36776560}, issn = {2296-634X}, abstract = {Numerous taste receptors and related molecules have been identified in vertebrates and invertebrates. Otopetrin1 has recently been identified as mammalian sour taste receptor which is essential for acid sensation. However, whether other Otopetrin proteins are involved in PH-sensing remains unknown. In C. elegans, there are eight otopetrin homologous genes but their expression patterns and functions have not been reported so far. Through heterologous expression in HEK293T cells, we found that ceOTOP1a can be activated by acid in NMDG[+] solution without conventional cations, which generated inward currents and can be blocked by zinc ions. Moreover, we found that Otopetrin channels are widely expressed in numerous tissues, especially in sensory neurons in the nematode. These results suggest that the biophysical characteristics of the Otopetrin channels in nematodes are generally conserved. However, a series of single gene mutations of otopetrins, which were constructed by CRISPR-Cas9 method, did not affect either calcium responses in ASH polymodal sensory neurons to acid stimulation or acid avoidance behaviors, suggesting that Otopetrin channels might have diverse functions among species. This study reveals that nematode Otopetrins are evolutionarily conserved acid-sensitive proton channels, and provides a framework for further revealing the function and mechanisms of Otopetrin channels in both invertebrates and vertebrates.}, } @article {pmid36776220, year = {2022}, author = {Cajigas, I and Davis, KC and Prins, NW and Gallo, S and Naeem, JA and Fisher, L and Ivan, ME and Prasad, A and Jagid, JR}, title = {Brain-Computer interface control of stepping from invasive electrocorticography upper-limb motor imagery in a patient with quadriplegia.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1077416}, pmid = {36776220}, issn = {1662-5161}, abstract = {Introduction: Most spinal cord injuries (SCI) result in lower extremities paralysis, thus diminishing ambulation. Using brain-computer interfaces (BCI), patients may regain leg control using neural signals that actuate assistive devices. Here, we present a case of a subject with cervical SCI with an implanted electrocorticography (ECoG) device and determined whether the system is capable of motor-imagery-initiated walking in an assistive ambulator. Methods: A 24-year-old male subject with cervical SCI (C5 ASIA A) was implanted before the study with an ECoG sensing device over the sensorimotor hand region of the brain. The subject used motor-imagery (MI) to train decoders to classify sensorimotor rhythms. Fifteen sessions of closed-loop trials followed in which the subject ambulated for one hour on a robotic-assisted weight-supported treadmill one to three times per week. We evaluated the stability of the best-performing decoder over time to initiate walking on the treadmill by decoding upper-limb (UL) MI. Results: An online bagged trees classifier performed best with an accuracy of 84.15% averaged across 9 weeks. Decoder accuracy remained stable following throughout closed-loop data collection. Discussion: These results demonstrate that decoding UL MI is a feasible control signal for use in lower-limb motor control. Invasive BCI systems designed for upper-extremity motor control can be extended for controlling systems beyond upper extremity control alone. Importantly, the decoders used were able to use the invasive signal over several weeks to accurately classify MI from the invasive signal. More work is needed to determine the long-term consequence between UL MI and the resulting lower-limb control.}, } @article {pmid36772822, year = {2023}, author = {Arioka, M and Koyano, K and Nakao, Y and Ozaki, M and Nakamura, S and Kiuchi, H and Okada, H and Itoh, S and Murao, K and Kusaka, T}, title = {Quantitative effects of bilirubin structural photoisomers on the measurement of direct bilirubin via the vanadate oxidation method.}, journal = {Annals of clinical biochemistry}, volume = {}, number = {}, pages = {45632231154748}, doi = {10.1177/00045632231154748}, pmid = {36772822}, issn = {1758-1001}, abstract = {BACKGROUND: Exposing blood serum samples to ambient white light-emitting diode (WLED) light may accelerate bilirubin photoisomer production. We previously demonstrated the quantitative effect of bilirubin configurational isomers (BCI) on direct bilirubin (DB) value using the vanadate oxidation method. However, the effects of bilirubin structural photoisomers (BSI) remain unclear.

METHODS: In Study 1, the relationship between WLED irradiation time and BSI production was examined. Serum samples from five neonates were irradiated with WLED light for 0, 10, 30, 60 and 180 min. Bilirubin isomer concentration and BSI production rates were calculated. In Study 2, we performed quantitative investigation of BSI effect on DB values: Differences in DB, BCI and BSI values before and after irradiation were calculated as ⊿DB, ⊿BCI and ⊿BSI, respectively. Assuming the coefficient of BCI affecting DB values was 'a', relational expression was ⊿DB = a*⊿BSI + 0.19*⊿BCI. Serum samples from 15 neonates were irradiated with green LED light for 10 and 30 s. The respective bilirubin isomer levels were measured, and the coefficient was derived.

RESULTS: In Study 1, the median BSI production rate was 0.022 mg/dL per min in specimens with an unconjugated bilirubin concentration of 10.88 mg/dL. In Study 2, assuming that ⊿DB-0.19*⊿BCI was Y and ⊿BSI was X, the relational expression was Y = 0.34X-0.03 (R[2] = 0.87; p < .01) and a = 0.34.

CONCLUSIONS: Under ambient WLED light, serum sample generated 1.3 mg/dL BSIs in 1 h. Approximately 34% (0.44 mg/dL) of BSI concentrations was measured as DB when using the vanadate oxidation method according to the above equation.}, } @article {pmid36772744, year = {2023}, author = {Yang, L and Van Hulle, MM}, title = {Real-Time Navigation in Google Street View[[®]] Using a Motor Imagery-Based BCI.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031704}, pmid = {36772744}, issn = {1424-8220}, abstract = {Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair-the latter raises safety concerns-thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View[[®]]. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes' lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.}, } @article {pmid36772731, year = {2023}, author = {Ranieri, A and Pichiorri, F and Colamarino, E and de Seta, V and Mattia, D and Toppi, J}, title = {Parallel Factorization to Implement Group Analysis in Brain Networks Estimation.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031693}, pmid = {36772731}, issn = {1424-8220}, abstract = {When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average connectivity matrices from both simulated and real datasets. The PARAFAC approach was solved using three different numbers of rank-one tensors (PAR-FACT). Synthetic data were parametrized according to different levels of three parameters: network dimension (NODES), number of observations (SAMPLE-SIZE), and noise (SWAP-CON) in order to investigate the way they affect the grand average estimation. PARAFAC was then tested on a real connectivity dataset, derived from EEG data of 17 healthy subjects performing wrist extension with left and right hand separately. Findings on both synthetic and real data revealed the potential of the PARAFAC algorithm as a useful tool for grand average extraction. As expected, the best performances in terms of FPR, FNR, and AUC were achieved for great values of sample size and low noise level. A crucial role has been revealed for the PAR-FACT parameter, revealing that an increase in the number of rank-one tensors solving the PARAFAC problem leads to an increase in FPR values and, thus, to a worse grand average estimation.}, } @article {pmid36772444, year = {2023}, author = {Li, M and Qiu, M and Kong, W and Zhu, L and Ding, Y}, title = {Fusion Graph Representation of EEG for Emotion Recognition.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031404}, pmid = {36772444}, issn = {1424-8220}, abstract = {Various relations existing in Electroencephalogram (EEG) data are significant for EEG feature representation. Thus, studies on the graph-based method focus on extracting relevancy between EEG channels. The shortcoming of existing graph studies is that they only consider a single relationship of EEG electrodes, which results an incomprehensive representation of EEG data and relatively low accuracy of emotion recognition. In this paper, we propose a fusion graph convolutional network (FGCN) to extract various relations existing in EEG data and fuse these extracted relations to represent EEG data more comprehensively for emotion recognition. First, the FGCN mines brain connection features on topology, causality, and function. Then, we propose a local fusion strategy to fuse these three graphs to fully utilize the valuable channels with strong topological, causal, and functional relations. Finally, the graph convolutional neural network is adopted to represent EEG data for emotion recognition better. Experiments on SEED and SEED-IV demonstrate that fusing different relation graphs are effective for improving the ability in emotion recognition. Furthermore, the emotion recognition accuracy of 3-class and 4-class is higher than that of other state-of-the-art methods.}, } @article {pmid36772343, year = {2023}, author = {Ron-Angevin, R and Fernández-Rodríguez, Á and Dupont, C and Maigrot, J and Meunier, J and Tavard, H and Lespinet-Najib, V and André, JM}, title = {Comparison of Two Paradigms Based on Stimulation with Images in a Spelling Brain-Computer Interface.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031304}, pmid = {36772343}, issn = {1424-8220}, abstract = {A P300-based speller can be used to control a home automation system via brain activity. Evaluation of the visual stimuli used in a P300-based speller is a common topic in the field of brain-computer interfaces (BCIs). The aim of the present work is to compare, using the usability approach, two types of stimuli that have provided high performance in previous studies. Twelve participants controlled a BCI under two conditions, which varied in terms of the type of stimulus employed: a red famous face surrounded by a white rectangle (RFW) and a range of neutral pictures (NPs). The usability approach included variables related to effectiveness (accuracy and information transfer rate), efficiency (stress and fatigue), and satisfaction (pleasantness and System Usability Scale and Affect Grid questionnaires). The results indicated that there were no significant differences in effectiveness, but the system that used NPs was reported as significantly more pleasant. Hence, since satisfaction variables should also be considered in systems that potential users are likely to employ regularly, the use of different NPs may be a more suitable option than the use of a single RFW for the development of a home automation system based on a visual P300-based speller.}, } @article {pmid36772275, year = {2023}, author = {Yedukondalu, J and Sharma, LD}, title = {Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031235}, pmid = {36772275}, issn = {1424-8220}, abstract = {Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.}, } @article {pmid36772115, year = {2023}, author = {Lin, CF and Lin, HC}, title = {IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {3}, pages = {}, doi = {10.3390/s23031078}, pmid = {36772115}, issn = {1424-8220}, abstract = {This study aims to extract the energy feature distributions in the form of marginal frequency (MF) and Hilbert spectrum (HS) in the intrinsic mode functions (IMF) domain for actual movement (AM)-based and motor imagery (MI)-based electroencephalogram (EEG) signals using the Hilbert-Huang transformation (HHT) time frequency (TF) analysis method. Accordingly, F5 and F6 EEG signal TF energy feature distributions in delta (0.5-4 Hz) rhythm are explored. We propose IMF-based and residue function (RF)-based MF and HS feature information extraction methods with IMFRFERDD (IMFRF energy refereed distribution density), IMFRFMFERDD (IMFRF MF energy refereed distribution density), and IMFRFHSERDD (IMFRF HS energy refereed distribution density) parameters using HHT with application to AM, MI EEG F5, and F6 signals in delta rhythm. The AM and MI tasks involve simultaneously opening fists and feet, as well as simultaneously closing fists and feet. Eight samples (32 in total) with a time duration of 1000 ms are extracted for analyzing F5AM, F5MI, F6AM, and F6MI EEG signals, which are decomposed into five IMFs and one RF. The maximum average IMFRFERDD values of IMF4 are 3.70, 3.43, 3.65, and 3.69 for F5AM, F5MI, F6 AM, and F6MI, respectively. The maximum average IMFRFMFERDD values of IMF4 in the delta rhythm are 21.50, 20.15, 21.02, and 17.30, for F5AM, F5MI, F6AM, and F6MI, respectively. Additionally, the maximum average IMFRFHSERDD values of IMF4 in delta rhythm are 39,21, 39.14, 36.29, and 33.06 with time intervals of 500-600, 800-900, 800-900, and 500-600 ms, for F5AM, F5MI, F6AM, and F6MI, respectively. The results of this study, advance our understanding of meaningful feature information of F5MM, F5MI, F6MM, and F6MI, enabling the design of MI-based brain-computer interface assistive devices for disabled persons.}, } @article {pmid36767088, year = {2023}, author = {Alvarado, C and Castillo-Aguilar, M and Villegas, V and Estrada Goic, C and Harris, K and Barria, P and Moraes, MM and Mendes, TT and Arantes, RME and Valdés-Badilla, P and Núñez-Espinosa, C}, title = {Physical Activity, Seasonal Sensitivity and Psychological Well-Being of People of Different Age Groups Living in Extreme Environments.}, journal = {International journal of environmental research and public health}, volume = {20}, number = {3}, pages = {}, doi = {10.3390/ijerph20031719}, pmid = {36767088}, issn = {1660-4601}, abstract = {Physical activity can prevent many organic and mental pathologies. For people living in extreme southern high-latitude environments, weather conditions can affect these activities, altering their psychological well-being and favoring the prevalence of seasonal sensitivity (SS). This study aims to determine the relationships between the practice of physical activity, seasonal sensitivity and well-being in people living in high southern latitudes. A cross-sectional study was conducted, using the Seasonal Pattern Assessment Questionnaire (SPAQ), applying a psychological well-being scale, and determining sports practice according to the recommendations of the World Health Organization (WHO) for the 370 male (n = 209; 55%) and female (n = 173; 45%) participants. The main results indicated that 194 people (52 ± 7.7 years) reported physical activity. High-intensity physical activity practitioners recorded a significantly lower proportion of SS. In terms of psychological well-being, an adverse effect was found between the Seasonal Score Index (SSI) and five subcategories of the Ryff well-being scale. In conclusion, those who perform high-intensity physical activity have a lower SS, and those who have a higher SS have a lower psychological well-being.}, } @article {pmid36766680, year = {2023}, author = {Küçükakarsu, M and Kavsaoğlu, AR and Alenezi, F and Alhudhaif, A and Alwadie, R and Polat, K}, title = {A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain's Electrical Activity Signals.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {13}, number = {3}, pages = {}, doi = {10.3390/diagnostics13030575}, pmid = {36766680}, issn = {2075-4418}, abstract = {This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.}, } @article {pmid36765392, year = {2023}, author = {Nair, L and Winkle, B and Senanayake, E}, title = {Managing blunt cardiac injury.}, journal = {Journal of cardiothoracic surgery}, volume = {18}, number = {1}, pages = {71}, pmid = {36765392}, issn = {1749-8090}, abstract = {Blunt cardiac injury (BCI) encompasses a spectrum of pathologies ranging from clinically silent, transient arrhythmias to deadly cardiac wall rupture. Of diagnosed BCIs, cardiac contusion is most common. Suggestive symptoms may be unrelated to BCI, while some injuries may be clinically asymptomatic. Cardiac rupture is the most devastating complication of BCI. Most patients who sustain rupture of a heart chamber do not reach the emergency department alive. The incidence of BCI following blunt thoracic trauma remains variable and no gold standard exists to either diagnose cardiac injury or provide management. Diagnostic tests should be limited to identifying those patients who are at risk of developing cardiac complications as a result of cardiac in jury. Therapeutic interventions should be directed to treat the complications of cardiac injury. Prompt, appropriate and well-orchestrated surgical treatment is invaluable in the management of the unstable patients.}, } @article {pmid36765285, year = {2023}, author = {Bae, CM and Cho, JY and Jung, H and Son, SA}, title = {Serum pro-B-type natriuretic peptide levels and cardiac index as adjunctive tools of blunt cardiac injury.}, journal = {BMC cardiovascular disorders}, volume = {23}, number = {1}, pages = {81}, pmid = {36765285}, issn = {1471-2261}, abstract = {BACKGROUND: Blunt cardiac injury (BCI) has a variety of symptoms that may be a potentially life-threatening injury that can lead to death. Depending on the diagnosis of BCI, treatment direction and length of stay may vary. In addition, the utility of other diagnostic tests for cardiac disease as diagnostic tools for BCI remain unclear. The purpose of this study was to investigate the competence of N-terminal pro-B-type natriuretic peptide (NT pro-BNP) and cardiac index (C.I) as adjunctive diagnostic tools for BCI.

METHODS: From January 2018 to March 2020, severe trauma patients with sternum fracture who were admitted to the traumatic intensive care unit (TICU) were included this study. Patients with sternum fracture, 18 years of age or older, and with an injury severity score > 16 who required intensive care were included. Invasive measurement for the analysis of the pulse contour for C.I monitoring and intravenous blood sampling for NT pro-BNP measurement were performed. Sampling and 12-lead electrocardiogram were performed at different time points as follows: immediately after TICU admission and at 24 h and 48 h after trauma.

RESULTS: Among 103; 33 patients with factors that could affect NT pro-BNP were excluded; therefore, 63 patients were included in this study. According to the American Association for the Surgery of Trauma Cardiac Injury Scale, 33 patients were diagnosed with non-BCI, and 30 patients constituted with BCI. The median ages of the patients were 58 (52-69), and 60 (45-69) years in the non-BCI and BCI groups, respectively (p = 0.77). The median NT pro-BNP values were higher in the BCI group on admission, hospital day (HD) 2, and HD 3, however, no statistical difference was observed (125 (49-245) vs. 130 (47-428) pg/mL, p = 0.08, 124 (68-224) vs. 187 (55-519) pg/mL, p = 0.09, and 121(59-225) vs. 133 (56-600) pg/mL, p = 0.17, respectively). On the contrary, significantly lower values were observed in the median C.I measurement on admission and HD 3 in the BCI group (3.2 (2.8-3.5) vs. 2.6 (2.3-3.5) L/min/m[2], p < 0.01 and 3.2 (3.1-3.9) vs. 2.9 (2.4-3.2) L/min/m[2], p < 0.01, respectively); however, no significant difference was observed on HD 2 (3.4 (3.0-3.7) vs. 2.6 (2.4-3.4) L/min/m[2], p = 0.17), Furthermore, The median lactate levels in the BCI group upon admission, HD 2, and HD 3 were significantly higher than those in the non-BCI group (1.8 (1.1-2.6) vs. 3.1 (2.1-4.4) mmol/L, p < 0.01; 1.3 (0.8-2.3) vs. 3.0 (2.2-4.7) mmol/L, p < 0.01; and 1.5 (0.9-1.5) vs. 2.2 (1.3-3.7) mmol/L, p < 0.01, respectively).

CONCLUSION: Consecutive values of NT pro-BNP and C.I show no correlation with ECG-based BCI diagnosis. However, lactate level measurement may help in the early recognition of BCI as an adjunctive tool. It should be noted that this is a hypothesis-generating study for BCI diagnosis. Further studies should be conducted in larger populations with a prospective approach.}, } @article {pmid36765121, year = {2023}, author = {Hinss, MF and Jahanpour, ES and Somon, B and Pluchon, L and Dehais, F and Roy, RN}, title = {Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications.}, journal = {Scientific data}, volume = {10}, number = {1}, pages = {85}, pmid = {36765121}, issn = {2052-4463}, abstract = {Brain-Computer Interfaces and especially passive Brain-Computer interfaces (pBCI), with their ability to estimate and monitor user mental states, are receiving increasing attention from both the fundamental research and the applied research and development communities. Testing new pipelines and benchmarking classifiers and feature extraction algorithms is central to further research within this domain. Unfortunately, data sharing in pBCI research is still scarce. The COG-BCI database encompasses the recordings of 29 participants over 3 separate sessions with 4 different tasks (MATB, N-Back, PVT, Flanker) designed to elicit different mental states, for a total of over 100 hours of open EEG data. This dataset was validated on a subjective, behavioral and physiological level, to ensure its usefulness to the pBCI community. Furthermore, a proof of concept is given with an example of mental workload estimation pipeline and results, to ensure that the data can be used for the design and evaluation of pBCI pipelines. This body of work presents a large effort to promote the use of pBCIs in an open science framework.}, } @article {pmid36764125, year = {2023}, author = {Kong, LZ and Shen, YT and Zhang, DH and Lai, JB and Hu, SH}, title = {Free long-acting injectables for patients with psychosis: A step forward.}, journal = {Asian journal of psychiatry}, volume = {83}, number = {}, pages = {103476}, doi = {10.1016/j.ajp.2023.103476}, pmid = {36764125}, issn = {1876-2026}, } @article {pmid36763992, year = {2023}, author = {Chen, J and Wang, D and Yi, W and Xu, M and Tan, X}, title = {Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acbb2c}, pmid = {36763992}, issn = {1741-2552}, abstract = {Objective.Motor Imagery Brain-Computer Interface (MI-BCI) is an active Brain-Computer Interface (BCI) paradigm focusing on the identification of motor intention, which is one of the most important non-invasive BCI paradigms. In MI-BCI studies, deep learning-based methods (especially lightweight networks) have attracted more attention in recent years, but the decoding performance still needs further improving.Approach.To solve this problem, we designed a filter bank structure with sinc-convolutional layers for spatio-temporal feature extraction of MI-EEG in four motor rhythms. The Channel Self-Attention method was introduced for feature selection based on both global and local information, so as to build a model called Filter Bank Sinc-convolutional Network with Channel Self-Attention (FB-Sinc-SCANet) for high performance MI-decoding. Also, we proposed a data augmentation method based on Multivariate Empirical Mode Decomposition (MEMD) to improve the generalization capability of the model.Main results.We performed an intra-subject evaluation experiment on the unseen data of three open MI datasets. The proposed method achieved mean accuracy of 78.20% (4-class scenario) on BCI Competition IV IIa, 87.34% (2-class scenario) on BCI Competition IV IIb, and 72.03% (2-class scenario) on OpenBMI dataset, which are significantly higher than those of compared deep learning-based methods by at least 3.05% (p=0.0469), 3.18% (p=0.0371), and 2.27% (p=0.0024) respectively.Significance.This work provides a new option for deep learning-based MI decoding, which can be employed for building BCI systems for motor rehabilitation.}, } @article {pmid36763058, year = {2022}, author = {Ma, D and Zhong, L and Yan, Z and Yao, J and Zhang, Y and Ye, F and Huang, Y and Lai, D and Yang, W and Hou, P and Guo, J}, title = {Structural mechanisms for the activation of human cardiac KCNQ1 channel by electro-mechanical coupling enhancers.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {119}, number = {45}, pages = {e2207067119}, doi = {10.1073/pnas.2207067119}, pmid = {36763058}, issn = {1091-6490}, abstract = {The cardiac KCNQ1 potassium channel carries the important IKs current and controls the heart rhythm. Hundreds of mutations in KCNQ1 can cause life-threatening cardiac arrhythmia. Although KCNQ1 structures have been recently resolved, the structural basis for the dynamic electro-mechanical coupling, also known as the voltage sensor domain-pore domain (VSD-PD) coupling, remains largely unknown. In this study, utilizing two VSD-PD coupling enhancers, namely, the membrane lipid phosphatidylinositol 4,5-bisphosphate (PIP2) and a small-molecule ML277, we determined 2.5-3.5 Å resolution cryo-electron microscopy structures of full-length human KCNQ1-calmodulin (CaM) complex in the apo closed, ML277-bound open, and ML277-PIP2-bound open states. ML277 binds at the "elbow" pocket above the S4-S5 linker and directly induces an upward movement of the S4-S5 linker and the opening of the activation gate without affecting the C-terminal domain (CTD) of KCNQ1. PIP2 binds at the cleft between the VSD and the PD and brings a large structural rearrangement of the CTD together with the CaM to activate the PD. These findings not only elucidate the structural basis for the dynamic VSD-PD coupling process during KCNQ1 gating but also pave the way to develop new therapeutics for anti-arrhythmia.}, } @article {pmid36760796, year = {2022}, author = {Xu, F and Zhao, J and Liu, M and Yu, X and Wang, C and Lou, Y and Shi, W and Liu, Y and Gao, L and Yang, Q and Zhang, B and Lu, S and Tang, J and Leng, J}, title = {Exploration of sleep function connection and classification strategies based on sub-period sleep stages.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1088116}, pmid = {36760796}, issn = {1662-4548}, abstract = {BACKGROUND: As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas.

METHODS: Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages.

RESULTS: The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%.

CONCLUSION: The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.}, } @article {pmid36760718, year = {2022}, author = {Singh, AK and Krishnan, S}, title = {Trends in EEG signal feature extraction applications.}, journal = {Frontiers in artificial intelligence}, volume = {5}, number = {}, pages = {1072801}, pmid = {36760718}, issn = {2624-8212}, abstract = {This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis.}, } @article {pmid36750362, year = {2023}, author = {Esparza-Iaizzo, M and Vigué-Guix, I and Ruzzoli, M and Torralba, M and Soto-Faraco, S}, title = {Long-range alpha-synchronisation as control signal for BCI: A feasibility study.}, journal = {eNeuro}, volume = {}, number = {}, pages = {}, doi = {10.1523/ENEURO.0203-22.2023}, pmid = {36750362}, issn = {2373-2822}, abstract = {Shifts in spatial attention are associated with variations in alpha-band (α, 8-14 Hz) activity, specifically in inter-hemispheric imbalance. The underlying mechanism is attributed to local α-synchronisation, which regulates local inhibition of neural excitability, and fronto-parietal synchronisation reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCI). In the present study, we explored whether long-range α-synchronisation presents lateralised patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data shows a lateralised pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronisation with support vector machines (SVM) was at chance-level. The present findings suggest EEG may not be capable of detecting long-range α-synchronisation in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control.SIGNIFICANCE STATEMENTCognitive neuroscience advances should ideally have a real-world impact, with an obvious avenue for transference being BCI applications. The hope is to faithfully translate user-generated brain endogenous states into control signals to actuate devices. A paramount challenge for transfer is to move from group-level, multi-trial average approaches to single-trial level. Here, we evaluated the feasibility of single-trial estimation of phase synchrony across distant brain regions. Although many studies link attention to long-range synchrony modulation, this metric has never been used to control BCI. We present a first attempt of a synchrony-based BCI that, albeit unsuccessful, should help break new ground to map endogenous attention shifts to real-time control of brain-computer actuated systems.}, } @article {pmid36750151, year = {2023}, author = {Kikkert, S and Sonar, HA and Freund, P and Paik, J and Wenderoth, N}, title = {Hand and face somatotopy shown using MRI-safe vibrotactile stimulation with a novel Soft Pneumatic Actuator (SPA)-Skin interface.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119932}, doi = {10.1016/j.neuroimage.2023.119932}, pmid = {36750151}, issn = {1095-9572}, abstract = {The exact somatotopy of the human facial representation in the primary somatosensory cortex (S1) remains debated. One reason that progress has been hampered is due to the methodological challenge of how to apply automated vibrotactile stimuli to face areas in a manner that is: 1) reliable despite differences in the curvatures of face locations; and 2) MR-compatible and free of MR-interference artefacts when applied in the MR head-coil. Here we overcome this challenge by using soft pneumatic actuator (SPA) technology. SPAs are made of a soft silicon material and can be in- or deflated by means of airflow, have a small diameter, and are flexible in structure, enabling good skin contact even on curved body surfaces (as on the face). To validate our approach, we first mapped the well-characterised S1 finger layout using this novel device and confirmed that tactile stimulation of the fingers elicited characteristic somatotopic finger activations in S1. We then used the device to automatically and systematically deliver somatosensory stimulation to different face locations. We found that the forehead representation was least distant from the representation of the hand. Within the face representation, we found that the lip representation is most distant from the forehead representation, with the chin represented in between. Together, our results demonstrate that this novel MR compatible device produces robust and clear somatotopic representational patterns using vibrotactile stimulation through SPA-technology.}, } @article {pmid36749989, year = {2023}, author = {Massaeli, F and Bagheri, M and Power, SD}, title = {EEG-based detection of modality-specific visual and auditory sensory processing.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb9be}, pmid = {36749989}, issn = {1741-2552}, abstract = {OBJECTIVE: A passive brain-computer interface (pBCI) is a system that enhances a human-machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e., the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the "level" of cognitive resources required (e.g., high vs. low), but we argue that having information regarding the specific "type" of resources (e.g., visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level.

APPROACH: 15 participants performed carefully designed visual and auditory tasks while EEG data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks.

MAIN RESULTS: The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance.

SIGNIFICANCE: These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.}, } @article {pmid36749645, year = {2023}, author = {Ziemba, AM and Woodson, MCC and Funnell, JL and Wich, D and Balouch, B and Rende, D and Amato, DN and Bao, J and Oprea, I and Cao, D and Bajalo, N and Ereifej, ES and Capadona, JR and Palermo, EF and Gilbert, RJ}, title = {Development of a Slow-Degrading Polymerized Curcumin Coating for Intracortical Microelectrodes.}, journal = {ACS applied bio materials}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsabm.2c00969}, pmid = {36749645}, issn = {2576-6422}, abstract = {Intracortical microelectrodes are used with brain-computer interfaces to restore lost limb function following nervous system injury. While promising, recording ability of intracortical microelectrodes diminishes over time due, in part, to neuroinflammation. As curcumin has demonstrated neuroprotection through anti-inflammatory activity, we fabricated a 300 nm-thick intracortical microelectrode coating consisting of a polyurethane copolymer of curcumin and polyethylene glycol (PEG), denoted as poly(curcumin-PEG1000 carbamate) (PCPC). The uniform PCPC coating reduced silicon wafer hardness by two orders of magnitude and readily absorbed water within minutes, demonstrating that the coating is soft and hydrophilic in nature. Using an in vitro release model, curcumin eluted from the PCPC coating into the supernatant over 1 week; the majority of the coating was intact after an 8-week incubation in buffer, demonstrating potential for longer term curcumin release and softness. Assessing the efficacy of PCPC within a rat intracortical microelectrode model in vivo, there were no significant differences in tissue inflammation, scarring, neuron viability, and myelin damage between the uncoated and PCPC-coated probes. As the first study to implant nonfunctional probes with a polymerized curcumin coating, we have demonstrated the biocompatibility of a PCPC coating and presented a starting point in the design of poly(pro-curcumin) polymers as coating materials for intracortical electrodes.}, } @article {pmid36745927, year = {2023}, author = {Li, B and Zhang, S and Hu, Y and Lin, Y and Gao, X}, title = {Assembling global and local spatial-temporal filters to extract discriminant information of EEG in RSVP task.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb96f}, pmid = {36745927}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-computer interface (BCI) system has developed rapidly in the past decade. And rapid serial visual presentation (RSVP) is an important BCI paradigm to detect the targets in high-speed image streams. For decoding EEG in RSVP task, the ensemble-model methods have better performance than the single-model ones.

APPROACH: This study proposed a method based on ensemble learning to extract discriminant information of EEG. An extreme gradient boosting (XGB) framework was utilized to sequentially generate the sub models, including one global spatial-temporal filter and a group of local ones. EEG was reconstructed in 3-dimensional form (2-D electrode space × time series) to learn the spatial-temporal features from real local cortical space.

MAIN RESULTS: A benchmark RSVP EEG dataset was utilized to evaluate the performance of the proposed method, where EEG data of 63 subjects were analyzed. Compared with several state-of-the-art methods, the spatial-temporal patterns of proposed method were more consistent with P300, and the proposed method can provide significantly better classification performance.

SIGNIFICANCE: The ensemble model in this study was end-to-end optimized, which can avoid error accumulation. The sub models optimized by gradient boosting theory can extract discriminant information complementarily and non-redundantly.}, } @article {pmid36745911, year = {2023}, author = {Adhikary, S and Jain, K and Saha, B and Chowdhury, D}, title = {Optimized EEG Based Mood Detection with Signal Processing and Deep Neural Networks for Brain-Computer Interface.}, journal = {Biomedical physics & engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/acb942}, pmid = {36745911}, issn = {2057-1976}, abstract = {Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration. Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.}, } @article {pmid36743394, year = {2022}, author = {Song, M and Huang, Y and Visser, HJ and Romme, J and Liu, YH}, title = {An Energy-Efficient and High-Data-Rate IR-UWB Transmitter for Intracortical Neural Sensing Interfaces.}, journal = {IEEE journal of solid-state circuits}, volume = {57}, number = {12}, pages = {3656-3668}, pmid = {36743394}, issn = {0018-9200}, abstract = {This paper presents an implantable impulse-radio ultra-wideband (IR-UWB) wireless telemetry system for intracortical neural sensing interfaces. A 3-dimensional (3-D) hybrid impulse modulation that comprises phase shift keying (PSK), pulse position modulation (PPM) and pulse amplitude modulation (PAM) is proposed to increase modulation order without significantly increasing the demodulation requirement, thus leading to a high data rate of 1.66 Gbps and an increased air-transmission range. Operating in 6 - 9 GHz UWB band, the presented transmitter (TX) supports the proposed hybrid modulation with a high energy efficiency of 5.8 pJ/bit and modulation quality (EVM< -21 dB). A low-noise injection-locked ring oscillator supports 8-PSK with a phase error of 2.6°. A calibration free delay generator realizes a 4-PPM with only 115 μW and avoids potential cross-modulation between PPM and PSK. A switch-cap power amplifier with an asynchronous pulse-shaping performs 4-PAM with high energy efficiency and linearity. The TX is implemented in 28 nm CMOS technology, occupying 0.155mm[2] core area. The wireless module including a printed monopole antenna has a module area of only 1.05 cm[2]. The transmitter consumes in total 9.7 mW when transmitting -41.3 dBm/MHz output power. The wireless telemetry module has been validated ex-vivo with a 15-mm multi-layer porcine tissue, and achieves a communication (air) distance up to 15 cm, leading to at least 16× improvement in distance-moralized energy efficiency of 45 pJ/bit/meter compared to state-of-the-art.}, } @article {pmid36741783, year = {2022}, author = {Shibu, CJ and Sreedharan, S and Arun, KM and Kesavadas, C and Sitaram, R}, title = {Explainable artificial intelligence model to predict brain states from fNIRS signals.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1029784}, pmid = {36741783}, issn = {1662-5161}, abstract = {Objective: Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI) system that can decompose the Deep Learning mode's output onto the input variables for fNIRS signals is described here. Approach: We propose an xAI-fNIRS system that consists of a classification module and an explanation module. The classification module consists of two separately trained sliding window-based classifiers, namely, (i) 1-D Convolutional Neural Network (CNN); and (ii) Long Short-Term Memory (LSTM). The explanation module uses SHAP (SHapley Additive exPlanations) to explain the CNN model's output in terms of the model's input. Main results: We observed that the classification module was able to classify two types of datasets: (a) Motor task (MT), acquired from three subjects; and (b) Motor imagery (MI), acquired from 29 subjects, with an accuracy of over 96% for both CNN and LSTM models. The explanation module was able to identify the channels contributing the most to the classification of MI or MT and therefore identify the channel locations and whether they correspond to oxy- or deoxy-hemoglobin levels in those locations. Significance: The xAI-fNIRS system can distinguish between the brain states related to overt and covert motor imagery from fNIRS signals with high classification accuracy and is able to explain the signal features that discriminate between the brain states of interest.}, } @article {pmid36741671, year = {2023}, author = {Almosallam, W and Aljoujou, AA and Ayoubi, HR and Alzoubi, H}, title = {Evaluation of the Effect of Antihypertensive Drugs on the Values of Dental Pulp Oxygen Saturation in Hypertension Patients: A Case-Control Study.}, journal = {Cureus}, volume = {15}, number = {1}, pages = {e33245}, pmid = {36741671}, issn = {2168-8184}, abstract = {Purpose This study aimed to know about the positive or negative effect of antihypertensive drugs of different groups on the values of dental pulp oxygen saturation in hypertension patients. Materials and Methods A case-control study to evaluate the impact of the antihypertensive drugs on the values of dental pulp oxygen saturation in hypertension patients. The studied sample consisted of 40 participants, and they were distributed into two groups: Group I (n=20): Hypertension patients treated with antihypertensive drugs, and Group II (n=20): Healthy participants. A finger pulse oximeter was recorded after a rest period of 15 minutes by BCI® Advisor® vital signs monitor. The patient was then asked to use a chlorhexidine digluconate mouth rinse for five minutes, and the two dental pulp pulse oximeters for the central upper incisors were also recorded for all participants. Data were analyzed using the Mann-Whitney U test. Results The results showed that there was no significant difference between the finger pulse oximeters of the two studied groups (P-value = 0.421). The two dental pulp oxygen saturation was higher than the control group with statistically significant (P-value = 0.043, P-value = 0.002). Conclusions Within the limitation of this study, it can be concluded that antihypertensive drugs increase the dental pulp oxygen saturation in patients with hypertension who are treated with antihypertensive drugs, and thus there is a positive effect of these drugs in stimulating the dental pulp.}, } @article {pmid36740976, year = {2023}, author = {Yao, S and Shi, S and Zhou, Q and Wang, J and Du, X and Takahata, T and Roe, AW}, title = {Functional topography of pulvinar-visual cortex networks in macaques revealed by INS-fMRI.}, journal = {The Journal of comparative neurology}, volume = {}, number = {}, pages = {}, doi = {10.1002/cne.25456}, pmid = {36740976}, issn = {1096-9861}, abstract = {The pulvinar in the macaque monkey contains three divisions: the medial pulvinar (PM), the lateral pulvinar (PL), and the inferior pulvinar (PI). Anatomical studies have shown that connections of PM are preferentially distributed to higher association areas, those of PL are biased toward the ventral visual pathway, and those of PI are biased with the dorsal visual pathway. To study functional connections of the pulvinar at mesoscale, we used a novel method called INS-fMRI (infrared neural stimulation and functional magnetic resonance imaging). This method permits studies and comparisons of multiple pulvinar networks within single animals. As previously revealed, stimulations of different sites in PL and PI produced topographically organized focal activations in visual areas V1, V2, and V3. In contrast, PM stimulation elicited little or diffuse response. The relative activations of areas V1, V2, V3A, V3d, V3v, V4, MT, and MST revealed that connections of PL are biased to ventral pathway areas, and those of PI are biased to dorsal areas. Different statistical values of activated blood-oxygen-level-dependent responses produced the same center of activation, indicating stability of connectivity; it also suggests possible dynamics of broad to focal responses from single stimulation sites. These results demonstrate that infrared neural stimulation-induced connectivity is largely consistent with previous anatomical connectivity studies, thereby demonstrating validity of our novel method. In addition, it suggests additional interpretations of functional connectivity to complement anatomical studies.}, } @article {pmid36742108, year = {2021}, author = {Jee, S}, title = {Brain Oscillations and Their Implications for Neurorehabilitation.}, journal = {Brain & NeuroRehabilitation}, volume = {14}, number = {1}, pages = {e7}, pmid = {36742108}, issn = {2383-9910}, abstract = {Neural oscillation is rhythmic or repetitive neural activities, which can be observed at all levels of the central nervous system (CNS). The large-scale oscillations measured by electroencephalography have long been used in clinical practice and may have a potential for the usage in neurorehabilitation for people with various CNS disorders. The recent advancement of computational neuroscience has opened up new opportunities to explore clinical application of the results of neural oscillatory activity analysis to evaluation and diagnosis; monitoring the rehab progress; prognostication; and personalized rehabilitation planning in neurorehabilitation. In addition, neural oscillation is catching more attention to its role as a target of noninvasive neuromodulation in neurological disorders.}, } @article {pmid36738734, year = {2023}, author = {Cui, Q and Bi, H and Lv, Z and Wu, Q and Hua, J and Gu, B and Huo, C and Tang, M and Chen, Y and Chen, C and Chen, S and Zhang, X and Wu, Z and Lao, Z and Sheng, N and Shen, C and Zhang, Y and Wu, ZY and Jin, Z and Yang, P and Liu, H and Li, J and Bai, G}, title = {Diverse CMT2 neuropathies are linked to aberrant G3BP interactions in stress granules.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2022.12.046}, pmid = {36738734}, issn = {1097-4172}, abstract = {Complex diseases often involve the interplay between genetic and environmental factors. Charcot-Marie-Tooth type 2 neuropathies (CMT2) are a group of genetically heterogeneous disorders, in which similar peripheral neuropathology is inexplicably caused by various mutated genes. Their possible molecular links remain elusive. Here, we found that upon environmental stress, many CMT2-causing mutant proteins adopt similar properties by entering stress granules (SGs), where they aberrantly interact with G3BP and integrate into SG pathways. For example, glycyl-tRNA synthetase (GlyRS) is translocated from the cytoplasm into SGs upon stress, where the mutant GlyRS perturbs the G3BP-centric SG network by aberrantly binding to G3BP. This disrupts SG-mediated stress responses, leading to increased stress vulnerability in motoneurons. Disrupting this aberrant interaction rescues SG abnormalities and alleviates motor deficits in CMT2D mice. These findings reveal a stress-dependent molecular link across diverse CMT2 mutants and provide a conceptual framework for understanding genetic heterogeneity in light of environmental stress.}, } @article {pmid36736668, year = {2023}, author = {Deng, J and Sun, J and Lu, S and Yue, K and Liu, W and Shi, H and Zou, L}, title = {Exploring neural activity in inflammatory bowel diseases using functional connectivity and DKI-fMRI fusion.}, journal = {Behavioural brain research}, volume = {}, number = {}, pages = {114325}, doi = {10.1016/j.bbr.2023.114325}, pmid = {36736668}, issn = {1872-7549}, abstract = {Although MRI has made considerable progress in Inflammatory bowel disease (IBD), most studies have concentrated on data information from a single modality, and a better understanding of the interplay between brain function and structure, as well as appropriate clinical aids to diagnosis, is required. We calculated functional connectivity through fMRI time series using resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) data from 27 IBD patients and 29 healthy controls. Through the DKI data of each subject, its unique structure map is obtained, and the relevant indicators are projected onto the structure map corresponding to each subject by using the graph Fourier transform in the grasp signal processing (GSP) technology. After the features are optimized, a classical support vector machine is used to classify the features. IBD patients have altered functional connectivity in the default mode network (DMN) and subcortical network (SCN). At the same time, compared with the traditional brain network analysis, in the test of some indicators, the average classification accuracy produced by the framework method is 12.73% higher than that of the traditional analysis method. This paper found that the brain network structure of IBD patients in DMN and SCN has changed. Simultaneously, the application of GSP technology to fuse functional information and structural information is superior to the traditional framework in classification, providing a new perspective for subsequent clinical auxiliary diagnosis.}, } @article {pmid36736571, year = {2023}, author = {Yan, K and Tao, R and Huang, X and Zhang, E}, title = {Influence of advisees' facial feedback on subsequent advice-giving by advisors: Evidence from the behavioral and neurophysiological approach.}, journal = {Biological psychology}, volume = {}, number = {}, pages = {108506}, doi = {10.1016/j.biopsycho.2023.108506}, pmid = {36736571}, issn = {1873-6246}, abstract = {Previous work has demonstrated the interpersonal implications of advisees' decisions (acceptance or rejection) on advisors' advice-giving behavior in subsequent exchanges. Here, using an ERP technique, we investigated how advisees' facial feedback (smiling, neutral, or frowning) accompanying their decisions (acceptance or rejection) influenced advisors' feedback evaluation from advisees and their advice-giving in subsequent exchanges. Behaviorally, regardless of whether the advice was accepted or rejected, advisors who received smiling-expression feedback would show higher willingness rates in subsequent advice-giving decisions, while advisors who received frowning-expression feedback would show lower willingness rates. On the neural level, in the feedback evaluation stage, the FRN and P3 responses were not sensitive to facial feedback. In contrast, frowning-expression feedback elicited a larger LPC amplitude than neutral- and smiling-expression feedback, regardless of whether the advice was accepted or rejected. In the advice decision stage, advisors who received neutral-expression feedback showed a larger N2 in making decisions than advisors who received frowning-expression feedback only after the advice was rejected. Additionally, Advisors who received smiling- and neutral-expression feedback showed a larger P3 in making decisions than advisors who received frowning-expression feedback only after the advice was accepted. In sum, the current findings extended previous research findings by showing that the effect of advisees' facial expressions on the advisors' advice-giving existed in multiple stages, including both the feedback evaluation stage and the advice decision stage.}, } @article {pmid36736001, year = {2023}, author = {Mao, J and Qiu, S and Wei, W and He, H}, title = {Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {161}, number = {}, pages = {65-82}, doi = {10.1016/j.neunet.2023.01.009}, pmid = {36736001}, issn = {1879-2782}, abstract = {Rapid Serial Visual Presentation (RSVP) based Brain-Computer Interface (BCI) facilities the high-throughput detection of rare target images by detecting evoked event-related potentials (ERPs). At present, the decoding accuracy of the RSVP-based BCI system limits its practical applications. This study introduces eye movements (gaze and pupil information), referred to as EYE modality, as another useful source of information to combine with EEG-based BCI and forms a novel target detection system to detect target images in RSVP tasks. We performed an RSVP experiment, recorded the EEG signals and eye movements simultaneously during a target detection task, and constructed a multi-modal dataset including 20 subjects. Also, we proposed a cross-modal guiding and fusion network to fully utilize EEG and EYE modalities and fuse them for better RSVP decoding performance. In this network, a two-branch backbone was built to extract features from these two modalities. A Cross-Modal Feature Guiding (CMFG) module was proposed to guide EYE modality features to complement the EEG modality for better feature extraction. A Multi-scale Multi-modal Reweighting (MMR) module was proposed to enhance the multi-modal features by exploring intra- and inter-modal interactions. And, a Dual Activation Fusion (DAF) was proposed to modulate the enhanced multi-modal features for effective fusion. Our proposed network achieved a balanced accuracy of 88.00% (±2.29) on the collected dataset. The ablation studies and visualizations revealed the effectiveness of the proposed modules. This work implies the effectiveness of introducing the EYE modality in RSVP tasks. And, our proposed network is a promising method for RSVP decoding and further improves the performance of RSVP-based target detection systems.}, } @article {pmid36733372, year = {2023}, author = {Gams, A and Naik, GR}, title = {Editorial: Neurorobotics explores gait movement in the sporting community.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1127994}, pmid = {36733372}, issn = {1662-5218}, } @article {pmid36731812, year = {2023}, author = {Soroush, PZ and Herff, C and Ries, SK and Shih, JJ and Schultz, T and Krusienski, DJ}, title = {The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119913}, doi = {10.1016/j.neuroimage.2023.119913}, pmid = {36731812}, issn = {1095-9572}, abstract = {Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech modes, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate the existence of a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.}, } @article {pmid36731770, year = {2023}, author = {Pan, L and Ping, A and Schriver, KE and Roe, AW and Zhu, J and Xu, K}, title = {Infrared neural stimulation in human cerebral cortex.}, journal = {Brain stimulation}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.brs.2023.01.1678}, pmid = {36731770}, issn = {1876-4754}, abstract = {BACKGROUND: Modulation of brain circuits by electrical stimulation has led to exciting and powerful therapies for diseases such as Parkinson's. Because human brain organization is based in mesoscale (millimeter-scale) functional nodes, having a method that can selectively target such nodes could enable more precise, functionally specific stimulation therapies. Infrared Neural Stimulation (INS) is an emerging stimulation technology that stimulates neural tissue via delivery of tiny heat pulses. In nonhuman primates, this optical method provides focal intensity-dependent stimulation of the brain without tissue damage. However, whether INS application to the human central nervous system (CNS) is similarly effective is unknown.

OBJECTIVE: To examine the effectiveness of INS on human cerebral cortex in intraoperative setting and to evaluate INS damage threshholds.

METHODS: Five epileptic subjects undergoing standard lobectomy for epilepsy consented to this study. Cortical response to INS was assessed by intrinsic signal optical imaging (OI, a method that detects changes in tissue reflectance due to neuronal activity). A custom integrated INS and OI system was developed specifically for short-duration INS and OI acquisition during surgical procedures. Single pulse trains of INS with intensities from 0.2 to 0.8 J/cm[2] were delivered to the somatosensory cortex and responses were recorded via optical imaging. Following tissue resection, histological analysis was conducted to evaluate damage threshholds.

RESULTS: As assessed by OI, and similar to results in monkeys, INS induced responses in human cortex were highly focal (millimeter sized) and led to relative suppression of nearby cortical sites. Intensity dependence was observed at both stimulated and functionally connected sites. Histological analysis of INS-stimulated human cortical tissue provided damage threshold estimates.

CONCLUSION: This is the first study demonstrating application of INS to human CNS and shows feasibility for stimulating single cortical nodes and associated sites and provided INS damage threshold estimates for cortical tissue. Our results suggest that INS is a promising tool for stimulation of functionally selective mesoscale circuits in the human brain, and may lead to advances in the future of precision medicine.}, } @article {pmid36731636, year = {2023}, author = {Jin, J and Chen, X and Zhang, D and Liang, Z}, title = {Editorial for the Special Issue "Visual Evoked Brain Computer Interface Studies".}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109806}, doi = {10.1016/j.jneumeth.2023.109806}, pmid = {36731636}, issn = {1872-678X}, } @article {pmid36729587, year = {2023}, author = {Rimbert, S and Lelarge, J and Guerci, P and Bidgoli, SJ and Meistelman, C and Cheron, G and Cebolla Alvarez, AM and Schmartz, D}, title = {Detection of Motor Cerebral Activity After Median Nerve Stimulation During General Anesthesia (STIM-MOTANA): Protocol for a Prospective Interventional Study.}, journal = {JMIR research protocols}, volume = {12}, number = {}, pages = {e43870}, doi = {10.2196/43870}, pmid = {36729587}, issn = {1929-0748}, abstract = {BACKGROUND: Accidental awareness during general anesthesia (AAGA) is defined as an unexpected awareness of the patient during general anesthesia. This phenomenon occurs in 1%-2% of high-risk practice patients and can cause physical suffering and psychological after-effects, called posttraumatic stress disorder. In fact, no monitoring techniques are satisfactory enough to effectively prevent AAGA; therefore, new alternatives are needed. Because the first reflex for a patient during an AAGA is to move, but cannot do so because of the neuromuscular blockers, we believe that it is possible to design a brain-computer interface (BCI) based on the detection of movement intention to warn the anesthetist. To do this, we propose to describe and detect the changes in terms of motor cortex oscillations during general anesthesia with propofol, while a median nerve stimulation is performed. We believe that our results could enable the design of a BCI based on median nerve stimulation, which could prevent AAGA.

OBJECTIVE: To our knowledge, no published studies have investigated the detection of electroencephalographic (EEG) patterns in relation to peripheral nerve stimulation over the sensorimotor cortex during general anesthesia. The main objective of this study is to describe the changes in terms of event-related desynchronization and event-related synchronization modulations, in the EEG signal over the motor cortex during general anesthesia with propofol while a median nerve stimulation is performed.

METHODS: STIM-MOTANA is an interventional and prospective study conducted with patients scheduled for surgery under general anesthesia, involving EEG measurements and median nerve stimulation at two different times: (1) when the patient is awake before surgery (2) and under general anesthesia. A total of 30 patients will receive surgery under complete intravenous anesthesia with a target-controlled infusion pump of propofol.

RESULTS: The changes in event-related desynchronization and event-related synchronization during median nerve stimulation according to the various propofol concentrations for 30 patients will be analyzed. In addition, we will apply 4 different offline machine learning algorithms to detect the median nerve stimulation at the cerebral level. Recruitment began in December 2022. Data collection is expected to conclude in June 2024.

CONCLUSIONS: STIM-MOTANA will be the first protocol to investigate median nerve stimulation cerebral motor effect during general anesthesia for the detection of intraoperative awareness. Based on strong practical and theoretical scientific reasoning from our previous studies, our innovative median nerve stimulation-based BCI would provide a way to detect intraoperative awareness during general anesthesia.

TRIAL REGISTRATION: Clinicaltrials.gov NCT05272202; https://clinicaltrials.gov/ct2/show/NCT05272202.

PRR1-10.2196/43870.}, } @article {pmid36729246, year = {2023}, author = {Knopf, S and Frahm, N and M Pfotenhauer, S}, title = {How Neurotech Start-Ups Envision Ethical Futures: Demarcation, Deferral, Delegation.}, journal = {Science and engineering ethics}, volume = {29}, number = {1}, pages = {4}, pmid = {36729246}, issn = {1471-5546}, abstract = {Like many ethics debates surrounding emerging technologies, neuroethics is increasingly concerned with the private sector. Here, entrepreneurial visions and claims of how neurotechnology innovation will revolutionize society-from brain-computer-interfaces to neural enhancement and cognitive phenotyping-are confronted with public and policy concerns about the risks and ethical challenges related to such innovations. But while neuroethics frameworks have a longer track record in public sector research such as the U.S. BRAIN Initiative, much less is known about how businesses-and especially start-ups-address ethics in tech development. In this paper, we investigate how actors in the field frame and enact ethics as part of their innovative R&D processes and business models. Drawing on an empirical case study on direct-to-consumer (DTC) neurotechnology start-ups, we find that actors engage in careful boundary-work to anticipate and address public critique of their technologies, which allows them to delineate a manageable scope of their ethics integration. In particular, boundaries are drawn around four areas: the technology's actual capability, purpose, safety and evidence-base. By drawing such lines of demarcation, we suggest that start-ups make their visions of ethical neurotechnology in society more acceptable, plausible and desirable, favoring their innovations while at the same time assigning discrete responsibilities for ethics. These visions establish a link from the present into the future, mobilizing the latter as promissory place where a technology's benefits will materialize and to which certain ethical issues can be deferred. In turn, the present is constructed as a moment in which ethical engagement could be delegated to permissive regulatory standards and scientific authority. Our empirical tracing of the construction of 'ethical realities' in and by start-ups offers new inroads for ethics research and governance in tech industries beyond neurotechnology.}, } @article {pmid36726940, year = {2023}, author = {Liu, Y and Xu, S and Yang, Y and Zhang, K and He, E and Liang, W and Luo, J and Wu, Y and Cai, X}, title = {Nanomaterial-based microelectrode arrays for in vitro bidirectional brain-computer interfaces: a review.}, journal = {Microsystems & nanoengineering}, volume = {9}, number = {}, pages = {13}, pmid = {36726940}, issn = {2055-7434}, abstract = {A bidirectional in vitro brain-computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.}, } @article {pmid36726556, year = {2022}, author = {Hossain, KM and Islam, MA and Hossain, S and Nijholt, A and Ahad, MAR}, title = {Status of deep learning for EEG-based brain-computer interface applications.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1006763}, pmid = {36726556}, issn = {1662-5188}, abstract = {In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.}, } @article {pmid36723288, year = {2023}, author = {Yang, T and Wang, SC and Ye, L and Maimaitiyiming, Y and Naranmandura, H}, title = {Targeting Viral Proteins for Restraining SARS-CoV-2: Focusing Lens on Viral Proteins Beyond Spike for Discovering New Drug Targets.}, journal = {Expert opinion on drug discovery}, volume = {}, number = {}, pages = {}, doi = {10.1080/17460441.2023.2175812}, pmid = {36723288}, issn = {1746-045X}, abstract = {INTRODUCTION: Emergence of highly infectious SARS-CoV-2 variants are reducing protection provided by the current vaccines, requiring constant updates in antiviral approaches. As a member of the Coronaviridae family, SARS-CoV-2 encodes four structural and sixteen nonstructural proteins which participate in various aspects of the viral life cycle including genome replication and transcription, virion assembly, release and entry into cells, as well as compromising host cellular defenses. As alien proteins to host cells, many viral proteins represent potential targets for combating the SARS-CoV-2.

AREAS COVERED: Based on literature from PubMed and Web of Science databases, the authors summarize the typical characteristics of SARS-CoV-2 from the whole viral particle to the individual viral proteins as well as their corresponding functions in virus life cycle. The authors also discuss the potential and emerging targeted interventions to curb virus replication and spread in detail to provide unique insights into the rapidly spreading SARS-CoV-2 infection and countermeasures against it.

EXPERT OPINION: Our comprehensive analysis highlights the rationale and need to focus on non-spike viral proteins that are less mutated but has important functions. Examples of this include: structural proteins (e.g., nucleocapsid protein, envelope protein) and extensively-concerned nonstructural proteins (e.g., NSP3, NSP5, NSP12) as well as the ones with relatively less attention (e.g., NSP1, NSP10, NSP14 and NSP16), for developing novel drugs to overcome resistance of SARS-CoV-2 variants to preexisting vaccines and antibody-based treatments.}, } @article {pmid36721006, year = {2023}, author = {Li, Z and Zheng, Y and Diao, X and Li, R and Sun, N and Xu, Y and Li, X and Duan, S and Gong, W and Si, K}, title = {Robust and adjustable dynamic scattering compensation for high-precision deep tissue optogenetics.}, journal = {Communications biology}, volume = {6}, number = {1}, pages = {128}, doi = {10.1038/s42003-023-04487-w}, pmid = {36721006}, issn = {2399-3642}, abstract = {The development of high-precision optogenetics in deep tissue is limited due to the strong optical scattering induced by biological tissue. Although various wavefront shaping techniques have been developed to compensate the scattering, it is still a challenge to non-invasively characterize the dynamic scattered optical wavefront inside the living tissue. Here, we present a non-invasive scattering compensation system with fast multidither coherent optical adaptive technique (fCOAT), which allows the rapid wavefront correction and stable focusing in dynamic scattering medium. We achieve subcellular-resolution focusing through 500-μm-thickness brain slices, or even three pieces overlapped mouse skulls after just one iteration with a 589 nm CW laser. Further, focusing through dynamic scattering medium such as live rat ear is also successfully achieved. The formed focus can maintain longer than 60 s, which satisfies the requirements of stable optogenetics manipulation. Moreover, the focus size is adjustable from subcellular level to tens of microns to freely match the various manipulation targets. With the specially designed fCOAT system, we successfully achieve single-cellular optogenetic manipulation through the brain tissue, with a stimulation efficiency enhancement up to 300% compared with that of the speckle.}, } @article {pmid36720854, year = {2023}, author = {Duan, J and Xu, P and Zhang, H and Luan, X and Yang, J and He, X and Mao, C and Shen, DD and Ji, Y and Cheng, X and Jiang, H and Jiang, Y and Zhang, S and Zhang, Y and Xu, HE}, title = {Mechanism of hormone and allosteric agonist mediated activation of follicle stimulating hormone receptor.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {519}, pmid = {36720854}, issn = {2041-1723}, abstract = {Follicle stimulating hormone (FSH) is an essential glycoprotein hormone for human reproduction, which functions are mediated by a G protein-coupled receptor, FSHR. Aberrant FSH-FSHR signaling causes infertility and ovarian hyperstimulation syndrome. Here we report cryo-EM structures of FSHR in both inactive and active states, with the active structure bound to FSH and an allosteric agonist compound 21 f. The structures of FSHR are similar to other glycoprotein hormone receptors, highlighting a conserved activation mechanism of hormone-induced receptor activation. Compound 21 f formed extensive interactions with the TMD to directly activate FSHR. Importantly, the unique residue H615[7.42] in FSHR plays an essential role in determining FSHR selectivity for various allosteric agonists. Together, our structures provide a molecular basis of FSH and small allosteric agonist-mediated FSHR activation, which could inspire the design of FSHR-targeted drugs for the treatment of infertility and controlled ovarian stimulation for in vitro fertilization.}, } @article {pmid36720164, year = {2023}, author = {Li, Z and Zhang, G and Wang, L and Wei, J and Dang, J}, title = {Emotion recognition using spatial-temporal EEG features through convolutional graph attention network.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb79e}, pmid = {36720164}, issn = {1741-2552}, abstract = {OBJECTIVE: Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is of great significance for realizing emotional brain computer interaction and improving machine intelligence.

APPROACH: In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG. After that, a novel convolutional graph attention network was used to fuse the DE and FC features and further extract higher-level graph structural information with sufficient expressive power for emotion recognition. Furthermore, we introduced a multi-headed attention mechanism in graph neural networks to improve the generalization ability of the model.

MAIN RESULTS: We evaluated the emotion recognition performance of our proposed model on the public SEED and DEAP datasets, which achieved a classification accuracy of 99.11±0.83% and 94.83±3.41% in subject-dependent and subject-independent experiments on SEED dataset, and achieved an accuracy of 91.19±1.24% and 92.03±4.57% for discrimination of arousal and valence in subject-independent experiments on DEAP dataset. Notably, our model achieved state-of-the-art (SOTA) performance on cross-subject emotion recognition task for both datasets. In addition, we gained an insight into the proposed frame by both the ablation experiments and the analysis of spatial patterns of FC and DE features.

SIGNIFICANCE: All these results prove the effectiveness of the STFCGAT architecture for emotion recognition and also indicate that there are significant differences in the spatial-temporal characteristics of the brain under different emotional states.}, } @article {pmid36720162, year = {2023}, author = {Remakanthakurup Sindhu, K and Ngo, D and Ombao, H and Olaya, JE and Shrey, DW and Lopour, BA}, title = {A novel method for dynamically altering the surface area of intracranial EEG electrodes.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb79f}, pmid = {36720162}, issn = {1741-2552}, abstract = {Intracranial EEG (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain. We first present a theoretical model and an in vitro validation of the method. We then report the results of an in vivo implementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e., epileptic spikes. We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike. Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.}, } @article {pmid36719563, year = {2023}, author = {Dong, Y and Wang, L and Li, M}, title = {Applying correlation analysis to electrode optimization in source domain.}, journal = {Medical & biological engineering & computing}, volume = {}, number = {}, pages = {}, pmid = {36719563}, issn = {1741-0444}, abstract = {In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.}, } @article {pmid36716553, year = {2023}, author = {Fan, C and Zha, R and Liu, Y and Wei, Z and Wang, Y and Song, H and Lv, W and Ren, J and Hong, W and Gou, H and Zhang, P and Chen, Y and Zhou, Y and Pan, Y and Zhang, X}, title = {Altered white matter functional network in nicotine addiction.}, journal = {Psychiatry research}, volume = {321}, number = {}, pages = {115073}, doi = {10.1016/j.psychres.2023.115073}, pmid = {36716553}, issn = {1872-7123}, abstract = {Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.}, } @article {pmid36716494, year = {2023}, author = {Delisle-Rodriguez, D and Silva, L and Bastos Filho, TF}, title = {EEG changes during passive movements improve the motor imagery feature extraction in BCIs-based sensory feedback calibration.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb73b}, pmid = {36716494}, issn = {1741-2552}, abstract = {OBJECTIVE: This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.

METHOD: After filtering the raw EEG, a two-step method for spatial feature extraction by using the Riemannian Covariance Matrices (RCM) method and Common Spatial Patterns (CSP) is proposed here. It uses electroencephalogram (EEG) data from trials providing feedback, in an intermediate step composed of both kth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.

RESULTS: The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.

SIGNIFICANCE: Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.}, } @article {pmid36711591, year = {2023}, author = {Willett, F and Kunz, E and Fan, C and Avansino, D and Wilson, G and Choi, EY and Kamdar, F and Hochberg, LR and Druckmann, S and Shenoy, KV and Henderson, JM}, title = {A high-performance speech neuroprosthesis.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.01.21.524489}, pmid = {36711591}, abstract = {Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text [1,2] or sound [3,4] .Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary [1â€"5] . Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI [2]) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI [6] and begins to approach the speed of natural conversation (160 words per minute [7]). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.}, } @article {pmid36711163, year = {2023}, author = {Cho, YK and Koh, CS and Lee, Y and Park, M and Kim, TJ and Jung, HH and Chang, JW and Jun, SB}, title = {Somatosensory ECoG-based brain-machine interface with electrical stimulation on medial forebrain bundle.}, journal = {Biomedical engineering letters}, volume = {13}, number = {1}, pages = {85-95}, pmid = {36711163}, issn = {2093-985X}, abstract = {Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.}, } @article {pmid36711161, year = {2023}, author = {Valencia, D and Alimohammad, A}, title = {Partially binarized neural networks for efficient spike sorting.}, journal = {Biomedical engineering letters}, volume = {13}, number = {1}, pages = {73-83}, pmid = {36711161}, issn = {2093-985X}, abstract = {While brain-implantable neural spike sorting can be realized using efficient algorithms, the presence of noise may make it difficult to maintain high-peformance sorting using conventional techniques. In this article, we explore the use of partially binarized neural networks (PBNNs), to the best of our knowledge for the first time, for sorting of neural spike feature vectors. It is shown that compared to the waveform template-based methods, PBNNs offer robust spike sorting over various datasets and noise levels. The ASIC implementation of the PBNN-based spike sorting system in a standard 180-nm CMOS process is presented. The post place and route simulations results show that the synthesized PBNN consumes only 0.59 μ W of power from a 1.8 V supply while operating at 24 kHz and occupies 0.15 mm 2 of silicon area. It is shown that the designed PBNN-based spike sorting system not only offers comparable accuracy to the state-of-the-art spike sorting systems over various noise levels and datasets, it also occupies a smaller silicon area and consumes less power and energy. This makes PBNNs a viable alternative towards the implementation of brain-implantable spike sorting systems.}, } @article {pmid36711153, year = {2022}, author = {Sohn, WJ and Lim, J and Wang, PT and Pu, H and Malekzadeh-Arasteh, O and Shaw, SJ and Armacost, M and Gong, H and Kellis, S and Andersen, RA and Liu, CY and Heydari, P and Nenadic, Z and Do, AH}, title = {Benchtop and bedside validation of a low-cost programmable cortical stimulator in a testbed for bi-directional brain-computer-interface research.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1075971}, pmid = {36711153}, issn = {1662-4548}, abstract = {INTRODUCTION: Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback.

METHODS: A custom programmable direct cortical stimulator (DCS) capable of eliciting artificial sensorimotor response was integrated into an embedded BCI system to form a safe, independent, wireless, and battery powered testbed to explore BD-BCI concepts at a low cost. The BD-BCI stimulator output was tested in phantom brain tissue by assessing its ability to deliver electrical stimulation equivalent to an FDA-approved commercial electrical cortical stimulator. Subsequently, the stimulator was tested in an epilepsy patient with subcortical electrocorticographic (ECoG) implants covering the sensorimotor cortex to assess its ability to elicit equivalent responses as the FDA-approved counterpart. Additional safety features (impedance monitoring, artifact mitigation, and passive and active charge balancing mechanisms) were also implemeneted and tested in phantom brain tissue. Finally, concurrent operation with interleaved stimulation and BCI decoding was tested in a phantom brain as a proof-of-concept operation of BD-BCI system.

RESULTS: The benchtop prototype BD-BCI stimulator's basic output features (current amplitude, pulse frequency, pulse width, train duration) were validated by demonstrating the output-equivalency to an FDA-approved commercial cortical electrical stimulator (R [2] > 0.99). Charge-neutral stimulation was demonstrated with pulse-width modulation-based correction algorithm preventing steady state voltage deviation. Artifact mitigation achieved a 64.5% peak voltage reduction. Highly accurate impedance monitoring was achieved with R [2] > 0.99 between measured and actual impedance, which in-turn enabled accurate charge density monitoring. An online BCI decoding accuracy of 93.2% between instructional cues and decoded states was achieved while delivering interleaved stimulation. The brain stimulation mapping via ECoG grids in an epilepsy patient showed that the two stimulators elicit equivalent responses.

SIGNIFICANCE: This study demonstrates clinical validation of a fully-programmable electrical stimulator, integrated into an embedded BCI system. This low-cost BD-BCI system is safe and readily applicable as a testbed for BD-BCI research. In particular, it provides an all-inclusive hardware platform that approximates the limitations in a near-future implantable BD-BCI. This successful benchtop/human validation of the programmable electrical stimulator in a BD-BCI system is a critical milestone toward fully-implantable BD-BCI systems.}, } @article {pmid36711141, year = {2022}, author = {Li, H and Liu, M and Yu, X and Zhu, J and Wang, C and Chen, X and Feng, C and Leng, J and Zhang, Y and Xu, F}, title = {Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1097660}, pmid = {36711141}, issn = {1662-4548}, abstract = {BACKGROUND: Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients.

METHODS: According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group.

RESULTS: The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%.

CONCLUSION: The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.}, } @article {pmid36710855, year = {2022}, author = {Sajno, E and Bartolotta, S and Tuena, C and Cipresso, P and Pedroli, E and Riva, G}, title = {Machine learning in biosignals processing for mental health: A narrative review.}, journal = {Frontiers in psychology}, volume = {13}, number = {}, pages = {1066317}, pmid = {36710855}, issn = {1664-1078}, abstract = {Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.}, } @article {pmid36709613, year = {2023}, author = {Cai, J and Xie, M and Zhao, L and Li, X and Liang, S and Deng, W and Guo, W and Ma, X and Sham, PC and Wang, Q and Li, T}, title = {White matter changes and its relationship with clinical symptom in medication-naive first-episode early onset schizophrenia.}, journal = {Asian journal of psychiatry}, volume = {82}, number = {}, pages = {103482}, doi = {10.1016/j.ajp.2023.103482}, pmid = {36709613}, issn = {1876-2026}, abstract = {Previous studies have highlighted the role of white matter (WM) alterations as biomarkers of the disease state and prognosis of schizophrenia. However, less is known about WM abnormalities in the rarely occurring adolescent early onset schizophrenia (EOS). In this study, T1-weighted and diffusion-weighted images were collected in 56 medication-naive first-episode participants with EOS and 43 healthy controls (HCs). Using Tract-based Spatial Statistics, we calculate case-control differences in scalar diffusion measures, i.e. fractional anisotropy (FA) and mean diffusivity (MD), and investigated their association with clinical feature in participants with EOS. Compared with HCs, decreased MD was found in EOS group most notably in the inferior longitudinal fasciculus, anterior thalamic radiation, inferior fronto-occipital fasciculus and corticospinal tract in the right hemisphere. No significant difference was found in FA between these two groups. The FA values of the forceps minor and the right superior longitudinal fasciculus were suggested to be related to the severity of clinical symptom in participants with EOS. These results provide clues about the neural basis of schizophrenia and a potential biomarker for clinical studies.}, } @article {pmid36707885, year = {2023}, author = {Angerhöfer, C and Vermehren, M and Colucci, A and Nann, M and Koßmehl, P and Niedeggen, A and Kim, WS and Chang, WK and Paik, NJ and Hömberg, V and Soekadar, SR}, title = {The Berlin Bimanual Test for Tetraplegia (BeBiTT): development, psychometric properties, and sensitivity to change in assistive hand exoskeleton application.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {17}, pmid = {36707885}, issn = {1743-0003}, support = {759370/ERC_/European Research Council/International ; }, abstract = {BACKGROUND: Assistive hand exoskeletons are promising tools to restore hand function after cervical spinal cord injury (SCI) but assessing their specific impact on bimanual hand and arm function is limited due to lack of reliable and valid clinical tests. Here, we introduce the Berlin Bimanual Test for Tetraplegia (BeBiTT) and demonstrate its psychometric properties and sensitivity to assistive hand exoskeleton-related improvements in bimanual task performance.

METHODS: Fourteen study participants with subacute cervical SCI performed the BeBiTT unassisted (baseline). Thereafter, participants repeated the BeBiTT while wearing a brain/neural hand exoskeleton (B/NHE) (intervention). Online control of the B/NHE was established via a hybrid sensorimotor rhythm-based brain-computer interface (BCI) translating electroencephalographic (EEG) and electrooculographic (EOG) signals into open/close commands. For reliability assessment, BeBiTT scores were obtained by four independent observers. Besides internal consistency analysis, construct validity was assessed by correlating baseline BeBiTT scores with the Spinal Cord Independence Measure III (SCIM III) and Quadriplegia Index of Function (QIF). Sensitivity to differences in bimanual task performance was assessed with a bootstrapped paired t-test.

RESULTS: The BeBiTT showed excellent interrater reliability (intraclass correlation coefficients > 0.9) and internal consistency (α = 0.91). Validity of the BeBiTT was evidenced by strong correlations between BeBiTT scores and SCIM III as well as QIF. Wearing a B/NHE (intervention) improved the BeBiTT score significantly (p < 0.05) with high effect size (d = 1.063), documenting high sensitivity to intervention-related differences in bimanual task performance.

CONCLUSION: The BeBiTT is a reliable and valid test for evaluating bimanual task performance in persons with tetraplegia, suitable to assess the impact of assistive hand exoskeletons on bimanual function.}, } @article {pmid36706879, year = {2023}, author = {Li, H and Shen, S and Yu, K and Wang, H and Fu, J}, title = {Construction of porous structure-based carboxymethyl chitosan/sodium alginate/tea polyphenols for wound dressing.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {123404}, doi = {10.1016/j.ijbiomac.2023.123404}, pmid = {36706879}, issn = {1879-0003}, abstract = {Polysaccharide-based materials with porous structure were selected as the basic skeleton to prepare a flexible and biodegradable wound dressing. The carboxymethyl chitosan/sodium alginate/tea polyphenols (CC/SA/TP) with two-layer porous structure exhibits a variety of performances. The specific combined structure with ordered and lamellar porous structure was constructed by high-speed homogenized foaming, Ca[2+] crosslinking and two-step freeze-drying methods. Moreover, the CC/SA/TP porous structure owns a better shape retention and recovery because of the 3D network with "egg-box" structure formed by impregnation. Tea polyphenols are efficiently encapsulated into porous structure and released in a sustained pattern. After storing for 60 days, the CC/SA/TP porous structure still exhibits great suitable water vapor transmittance, efficient antibacterial activity and ultrarapid antioxidant activity. Meanwhile, the relatively low differential blood clotting index (BCI) and cytotoxicity of the CC/SA/TP porous structure indicate that it possesses the possibility for adjusting and controlling wound bleeding. The test results reveal that the CC/SA/TP porous structure might be expected to play a great potential role in biomedical applications of wound dressing.}, } @article {pmid36705845, year = {2023}, author = {Zhao, ZD and Zhang, L and Xiang, X and Kim, D and Li, H and Cao, P and Shen, WL}, title = {Neurocircuitry of Predatory Hunting.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1007/s12264-022-01018-1}, pmid = {36705845}, issn = {1995-8218}, abstract = {Predatory hunting is an important type of innate behavior evolutionarily conserved across the animal kingdom. It is typically composed of a set of sequential actions, including prey search, pursuit, attack, and consumption. This behavior is subject to control by the nervous system. Early studies used toads as a model to probe the neuroethology of hunting, which led to the proposal of a sensory-triggered release mechanism for hunting actions. More recent studies have used genetically-trackable zebrafish and rodents and have made breakthrough discoveries in the neuroethology and neurocircuits underlying this behavior. Here, we review the sophisticated neurocircuitry involved in hunting and summarize the detailed mechanism for the circuitry to encode various aspects of hunting neuroethology, including sensory processing, sensorimotor transformation, motivation, and sequential encoding of hunting actions. We also discuss the overlapping brain circuits for hunting and feeding and point out the limitations of current studies. We propose that hunting is an ideal behavioral paradigm in which to study the neuroethology of motivated behaviors, which may shed new light on epidemic disorders, including binge-eating, obesity, and obsessive-compulsive disorders.}, } @article {pmid36704636, year = {2023}, author = {Lyu, X and Ding, P and Li, S and Dong, Y and Su, L and Zhao, L and Gong, A and Fu, Y}, title = {Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {1}, pages = {105-118}, pmid = {36704636}, issn = {1871-4080}, abstract = {Existing brain-computer interface (BCI) research has made great progress in improving the accuracy and information transfer rate (ITR) of BCI systems. However, the practicability of BCI is still difficult to achieve. One of the important reasons for this difficulty is that human factors are not fully considered in the research and development of BCI. As a result, BCI systems have not yet reached users' expectations. In this study, we investigate a BCI system of motor imagery for lower limb synchronous rehabilitation as an example. From the perspective of human factors engineering of BCI, a comprehensive evaluation method of BCI system development is proposed based on the concept of human-centered design and evaluation. Subjects' satisfaction ratings for BCI sensors, visual analog scale (VAS), subjects' satisfaction rating of the BCI system, and the mental workload rating for subjects manipulating the BCI system, as well as interview/follow-up comprehensive evaluation of motor imagery of BCI (MI-BCI) system satisfaction were used. The methods and concepts proposed in this study provide useful insights for the design of personalized MI-BCI. We expect that the human factors engineering of BCI could be applied to the design and satisfaction evaluation of MI-BCI, so as to promote the practical application of this kind of BCI.}, } @article {pmid36704625, year = {2023}, author = {Cui, Z and Lin, J and Fu, X and Zhang, S and Li, P and Wu, X and Wang, X and Chen, W and Zhu, S and Li, Y}, title = {Construction of the dynamic model of SCI rehabilitation using bidirectional stimulation and its application in rehabilitating with BCI.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {1}, pages = {169-181}, pmid = {36704625}, issn = {1871-4080}, abstract = {UNLABELLED: Patients with complete spinal cord injury have a complete loss of motor and sensory functions below the injury plane, leading to a complete loss of function of the nerve pathway in the injured area. Improving the microenvironment in the injured area of patients with spinal cord injury, promoting axon regeneration of the nerve cells is challenging research fields. The brain-computer interface rehabilitation system is different from the other rehabilitation techniques. It can exert bidirectional stimulation on the spinal cord injury area, and can make positively rehabilitation effects of the patient with complete spinal cord injury. A dynamic model was constructed for the patient with spinal cord injury under-stimulation therapy, and the mechanism of the brain-computer interface in rehabilitation training was explored. The effects of the three current rehabilitation treatment methods on the microenvironment in a microscopic nonlinear model were innovatively unified and a complex system mapping relationship from the microscopic axon growth to macroscopic motor functions was constructed. The basic structure of the model was determined by simulating and fitting the data of the open rat experiments. A clinical rehabilitation experiment of spinal cord injury based on brain-computer interface was built, recruiting a patient with complete spinal cord injury, and the rehabilitation training and follow-up were conducted. The changes in the motor function of the patient was simulated and predicted through the constructed model, and the trend in the motor function improvement was successfully predicted over time. This proposed model explores the mechanism of brain-computer interface in rehabilitating patients with complete spinal cord injury, and it is also an application of complex system theory in rehabilitation medicine.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09804-3.}, } @article {pmid36704007, year = {2022}, author = {de Oliveira, IH and Rodrigues, AC}, title = {Empirical comparison of deep learning methods for EEG decoding.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1003984}, pmid = {36704007}, issn = {1662-4548}, abstract = {Electroencephalography (EEG) is a technique that can be used in non-invasive brain-machine interface (BMI) systems to register brain electrical activity. The EEG signals are non-linear and non-stationary, making the decoding procedure a complex task. Deep learning techniques have been successfully applied in several research fields, often improving the results compared with traditional approaches. Therefore, it is believed that these techniques can also improve the process of decoding brain signals in BMI systems. In this work, we present the implementation of two deep learning-based decoders and we compared the results with other state of art deep learning methods. The first decoder uses long short-term memory (LSTM) recurrent neural network and the second, entitled EEGNet-LSTM, combines a well-known neural decoder based on convolutional neural networks, called EEGNet, with some LSTM layers. The decoders have been tested using data set 2a from BCI Competition IV, and the results showed that the EEGNet-LSTM decoder has been approximately 23% better than the competition-winning decoder. A Wilcoxon t-test showed a significant difference between the two decoders (Z = 2.524, p = 0.012). The LSTM-based decoder has been approximately 9% higher than the best decoder from the same competition. However, there was no significant difference (Z = 1.540, p = 0.123). In order to verify the replication of the EEGNet-LSTM decoder on another data, we performed a test with PhysioNet's Physiobank EEG Motor Movement/Imagery dataset. The EEGNet-LSTM presented a higher performance (0.85 accuracy) than the EEGNet (0.82 accuracy). The results of this work can be important for the development of new research, as well as EEG-based BMI systems, which can benefit from the high precision of neural decoders.}, } @article {pmid36699986, year = {2023}, author = {Shang, Q and Ma, H and Wang, C and Gao, L}, title = {Effects of Background Fitting of e-Commerce Live Streaming on Consumers' Purchase Intentions: A Cognitive-Affective Perspective.}, journal = {Psychology research and behavior management}, volume = {16}, number = {}, pages = {149-168}, pmid = {36699986}, issn = {1179-1578}, abstract = {PURPOSE: The purpose of this paper is to explore the effects of the background fitting of e-commerce live streaming on consumers' purchase intentions and the relevant internal psychological mechanism from the cognitive-affective perspective.

METHODS: In this study, a theoretical framework model of SOR comprising six variables is established. SPSS and SmartPLS are used to test the model and analyze data collected from a comprehensive questionnaire survey of 424 Chinese online consumers.

RESULTS: Results demonstrate that the impact of background fitting in e-commerce live streaming on consumers' purchase intentions can be divided into three stages. In the first stage, background fitting (comprised of both product-background fit and anchor-background fit) positively affect consumer cognitive process (perceived trust and perceived value). Perceived trust is mainly affected by anchor-background fit, while perceived value is mainly affected by product-background fit. In the second stage, consumers' cognitive process subsequently affects their affective process (perceived pleasure). Perceived value also has a greater positive effect on consumers' perceived pleasure than perceived trust, although perceived trust is a prerequisite for improving perceived value. In the third stage, the affective process further promotes consumers' purchase intentions.

CONCLUSION: Combining both SOR theory and cognitive-affective perspective, this study reveals that the internal influence mechanism of background fitting in e-commerce live streaming on consumers' purchase intentions is divided into three stages. Theoretically, this study not only expands the application of SOR theory in the research field of e-commerce live streaming from the perspective of external background stimulation, but also importantly contributes to the application of cognitive-emotional perspective in e-commerce live streaming. Practically, the study suggests optimizing background fitting as an effective way to improve consumer purchase intention in e-commerce live streaming, and it is better to optimize background fitting from the perspective of improving perceived trust, perceived value, and perceived pleasure.}, } @article {pmid36699541, year = {2022}, author = {Hu, J and Wang, Y and Tong, Y and Lin, G and Li, Y and Chen, J and Xu, D and Wang, L and Bai, R}, title = {Thalamic structure and anastomosis in different hemispheres of moyamoya disease.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1058137}, pmid = {36699541}, issn = {1662-4548}, abstract = {OBJECTIVE: The progression of the asymptomatic hemisphere of moyamoya disease (MMD) is largely unknown. In this study, we investigated the differences in subcortical gray matter structure and angiographic features between asymptomatic and symptomatic hemispheres in patients with MMD.

METHODS: We retrospectively reviewed patients with MMD in consecutive cases in our center. We compared subcortical gray matter volume and three types of collaterals (lenticulostriate anastomosis, thalamic anastomosis, and choroidal anastomosis) between symptomatic and asymptomatic hemispheres. Symptomatic hemispheres were classified as ischemic hemisphere (i-hemisphere) and hemorrhagic hemisphere (h-hemisphere). Asymptomatic hemispheres were classified as contralateral asymptomatic hemisphere of i-hemisphere (ai-hemisphere), contralateral asymptomatic hemisphere of h-hemisphere (ah-hemisphere), bilateral asymptomatic hemispheres in asymptomatic group (aa-hemisphere).

RESULTS: A total of 117 MMD patients were reviewed, and 49 of them met the inclusion criteria, with 98 hemispheres being analyzed. The thalamic volume was found to differ significantly between the i- and ai-hemispheres (P = 0.010), between the i- and ah-hemispheres (P = 0.004), as well as between the h- and ai-hemispheres (P = 0.002), between the h- and ah-hemispheres (P < 0.001). There was a higher incidence of thalamic anastomosis in the ai-hemispheres than i-hemispheres (31.3% vs. 6.3%, P = 0.070), and in the ah-hemispheres than h-hemispheres (29.6% vs. 11.1%, P = 0.088). Additionally, the hemispheres with thalamic anastomosis had a significantly greater volume than those without thalamic anastomosis (P = 0.024). Univariate and multivariate logistic regression analysis showed that thalamic volume was closely associated with thalamic anastomosis.

CONCLUSION: The thalamic volume and the incidence of thalamic anastomosis increase in asymptomatic hemispheres and decrease in symptomatic hemispheres. Combining these two characteristics may be helpful in assessing the risk of stroke in the asymptomatic hemispheres of MMD as well as understanding the pathological evolution of the disease.}, } @article {pmid36699533, year = {2022}, author = {Li, Y and Zhang, X and Ming, D}, title = {Early-stage fusion of EEG and fNIRS improves classification of motor imagery.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1062889}, pmid = {36699533}, issn = {1662-4548}, abstract = {INTRODUCTION: Many research papers have reported successful implementation of hybrid brain-computer interfaces by complementarily combining EEG and fNIRS, to improve classification performance. However, modality or feature fusion of EEG and fNIRS was usually designed for specific user cases, which were generally customized and hard to be generalized. How to effectively utilize information from the two modalities was still unclear.

METHODS: In this paper, we conducted a study to investigate the stage of bi-modal fusion based on EEG and fNIRS. A Y-shaped neural network was proposed and evaluated on an open dataset, which fuses the bimodal information in different stages.

RESULTS: The results suggests that the early-stage fusion of EEG and fNIRS have significantly higher performance compared to middle-stage and late-stage fusion network configuration (N = 57, P < 0.05). With the proposed framework, the average accuracy of 29 participants reaches 76.21% in the left-or-right hand motor imagery task in leave-one-out cross-validation, using bi-modal data as network inputs respectively, which is in the same level as the state-of-the-art hybrid BCI methods based on EEG and fNIRS data.}, } @article {pmid36698872, year = {2022}, author = {Zanona, AF and Piscitelli, D and Seixas, VM and Scipioni, KRDDS and Bastos, MSC and de Sá, LCK and Monte-Silva, K and Bolivar, M and Solnik, S and De Souza, RF}, title = {Brain-computer interface combined with mental practice and occupational therapy enhances upper limb motor recovery, activities of daily living, and participation in subacute stroke.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1041978}, pmid = {36698872}, issn = {1664-2295}, abstract = {BACKGROUND: We investigated the effects of brain-computer interface (BCI) combined with mental practice (MP) and occupational therapy (OT) on performance in activities of daily living (ADL) in stroke survivors.

METHODS: Participants were randomized into two groups: experimental (n = 23, BCI controlling a hand exoskeleton combined with MP and OT) and control (n = 21, OT). Subjects were assessed with the functional independence measure (FIM), motor activity log (MAL), amount of use (MAL-AOM), and quality of movement (MAL-QOM). The box and blocks test (BBT) and the Jebsen hand functional test (JHFT) were used for the primary outcome of performance in ADL, while the Fugl-Meyer Assessment was used for the secondary outcome. Exoskeleton activation and the degree of motor imagery (measured as event-related desynchronization) were assessed in the experimental group. For the BCI, the EEG electrodes were placed on the regions of FC3, C3, CP3, FC4, C4, and CP4, according to the international 10-20 EEG system. The exoskeleton was placed on the affected hand. MP was based on functional tasks. OT consisted of ADL training, muscle mobilization, reaching tasks, manipulation and prehension, mirror therapy, and high-frequency therapeutic vibration. The protocol lasted 1 h, five times a week, for 2 weeks.

RESULTS: There was a difference between baseline and post-intervention analysis for the experimental group in all evaluations: FIM (p = 0.001, d = 0.56), MAL-AOM (p = 0.001, d = 0.83), MAL-QOM (p = 0.006, d = 0.84), BBT (p = 0.004, d = 0.40), and JHFT (p = 0.001, d = 0.45). Within the experimental group, post-intervention improvements were detected in the degree of motor imagery (p < 0.001) and the amount of exoskeleton activations (p < 0.001). For the control group, differences were detected for MAL-AOM (p = 0.001, d = 0.72), MAL-QOM (p = 0.013, d = 0.50), and BBT (p = 0.005, d = 0.23). Notably, the effect sizes were larger for the experimental group. No differences were detected between groups at post-intervention.

CONCLUSION: BCI combined with MP and OT is a promising tool for promoting sensorimotor recovery of the upper limb and functional independence in subacute post-stroke survivors.}, } @article {pmid36698168, year = {2023}, author = {Lim, CG and Soh, CP and Lim, SSY and Fung, DSS and Guan, C and Lee, TS}, title = {Home-based brain-computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial.}, journal = {Child and adolescent psychiatry and mental health}, volume = {17}, number = {1}, pages = {15}, pmid = {36698168}, issn = {1753-2000}, abstract = {BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a prevalent child neurodevelopmental disorder that is treated in clinics and in schools. Previous trials suggested that our brain-computer interface (BCI)-based attention training program could improve ADHD symptoms. We have since developed a tablet version of the training program which can be paired with wireless EEG headsets. In this trial, we investigated the feasibility of delivering this tablet-based BCI intervention at home.

METHODS: Twenty children diagnosed with ADHD, who did not receive any medication for the preceding month, were randomised to receive the 8-week tablet-based BCI intervention either in the clinic or at home. Those in the home intervention group received instructions before commencing the program and got reminders if they were lagging on the training sessions. The ADHD Rating Scale was completed by a blinded clinician at baseline and at week 8. Adverse events were monitored during any contact with the child throughout the trial and at week 8.

RESULTS: Children in both groups could complete the tablet-based intervention easily on their own with minimal support from the clinic therapist or their parents (at home). The intervention was safe with few reported adverse effects. Clinician-rated inattentive symptoms on the ADHD-Rating Scale reduced by 3.2 (SD 6.20) and 3.9 (SD 5.08) for the home-based and clinic-based groups respectively, suggesting that home-based intervention was comparable to clinic-based intervention.

CONCLUSIONS: This trial demonstrated that the tablet version of our BCI-based attention training program can be safely delivered to children in the comfort of their own home. Trial registration This trial is registered at clinicaltrials.gov as NCT01344044.}, } @article {pmid36696073, year = {2023}, author = {Öztürk, S and Devecioğlu, İ and Güçlü, B}, title = {Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex.}, journal = {Journal of computational neuroscience}, volume = {}, number = {}, pages = {}, pmid = {36696073}, issn = {1573-6873}, abstract = {Decoding of sensorimotor information is essential for brain-computer interfaces (BCIs) as well as in normal functioning organisms. In this study, Bayesian models were developed for the prediction of binary decisions of 10 awake freely-moving male/female rats based on neural activity in a vibrotactile yes/no detection task. The vibrotactile stimuli were 40-Hz sinusoidal displacements (amplitude: 200 µm, duration: 0.5 s) applied on the glabrous skin. The task was to depress the right lever for stimulus detection and left lever for stimulus-off condition. Spike activity was recorded from 16-channel microwire arrays implanted in the hindlimb representation of primary somatosensory cortex (S1), overlapping also with the associated representation in the primary motor cortex (M1). Single-/multi-unit average spike rate (Rd) within the stimulus analysis window was used as the predictor of the stimulus state and the behavioral response at each trial based on a Bayesian network model. Due to high neural and psychophysical response variability for each rat and also across subjects, mean Rd was not correlated with hit and false alarm rates. Despite the fluctuations in the neural data, the Bayesian model for each rat generated moderately good accuracy (0.60-0.90) and good class prediction scores (recall, precision, F1) and was also tested with subsets of data (e.g. regular vs. fast spike groups). It was generally observed that the models were better for rats with lower psychophysical performance (lower sensitivity index A'). This suggests that Bayesian inference and similar machine learning techniques may be especially helpful during the training phase of BCIs or for rehabilitation with neuroprostheses.}, } @article {pmid36693374, year = {2023}, author = {Fan, Z and Chang, J and Liang, Y and Zhu, H and Zhang, C and Zheng, D and Wang, J and Xu, Y and Li, QJ and Hu, H}, title = {Neural mechanism underlying depressive-like state associated with social status loss.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2022.12.033}, pmid = {36693374}, issn = {1097-4172}, abstract = {Downward social mobility is a well-known mental risk factor for depression, but its neural mechanism remains elusive. Here, by forcing mice to lose against their subordinates in a non-violent social contest, we lower their social ranks stably and induce depressive-like behaviors. These rank-decline-associated depressive-like behaviors can be reversed by regaining social status. In vivo fiber photometry and single-unit electrophysiological recording show that forced loss, but not natural loss, generates negative reward prediction error (RPE). Through the lateral hypothalamus, the RPE strongly activates the brain's anti-reward center, the lateral habenula (LHb). LHb activation inhibits the medial prefrontal cortex (mPFC) that controls social competitiveness and reinforces retreats in contests. These results reveal the core neural mechanisms mutually promoting social status loss and depressive behaviors. The intertwined neuronal signaling controlling mPFC and LHb activities provides a mechanistic foundation for the crosstalk between social mobility and psychological disorder, unveiling a promising target for intervention.}, } @article {pmid36693292, year = {2023}, author = {Santamaría-Vázquez, E and Martínez-Cagigal, V and Marcos-Martínez, D and Rodríguez-González, V and Pérez-Velasco, S and Moreno-Calderón, S and Hornero, R}, title = {MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research.}, journal = {Computer methods and programs in biomedicine}, volume = {230}, number = {}, pages = {107357}, doi = {10.1016/j.cmpb.2023.107357}, pmid = {36693292}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations.

METHODS: We followed strict development practices to optimize MEDUSA© for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages.

RESULTS: MEDUSA© provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility.

CONCLUSIONS: MEDUSA© is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.}, } @article {pmid36693278, year = {2023}, author = {Johnston, R and Abbass, M and Corrigan, B and Martinez-Trujillo, J and Sachs, A}, title = {Decoding spatial locations from primate lateral prefrontal cortex neural activity during virtual navigation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb5c2}, pmid = {36693278}, issn = {1741-2552}, abstract = {OBJECTIVE: Decoding the intended trajectories from brain signals using a brain-computer interface system could be used to improve the mobility of patients with disabilities.

APPROACH: Neuronal activity associated with spatial locations was examined while macaques performed a navigation task within a virtual environment.

MAIN RESULTS: Here, we provide proof of principle that multi-unit spiking activity recorded from the lateral prefrontal cortex of non-human primates can be used to predict the location of a subject in a virtual maze during a navigation task. The spatial positions within the maze that require a choice or are associated with relevant task events can be better predicted than the locations where no relevant events occur. Importantly, within a task epoch of a single trial, multiple locations along the maze can be independently identified using a support vector machine model.

SIGNIFICANCE: Considering that the lateral prefrontal cortex of macaques and humans share similar properties, our results suggest that this area could be a valuable implant location for an intracortical brain computer interface system used for spatial navigation in patients with disabilities.}, } @article {pmid36689427, year = {2023}, author = {Pattisapu, S and Ray, S}, title = {Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier.}, journal = {PloS one}, volume = {18}, number = {1}, pages = {e0279881}, doi = {10.1371/journal.pone.0279881}, pmid = {36689427}, issn = {1932-6203}, abstract = {Stimulus-induced narrow-band gamma oscillations (30-70 Hz) in human electro-encephalograph (EEG) have been linked to attentional and memory mechanisms and are abnormal in mental health conditions such as autism, schizophrenia and Alzheimer's Disease. However, since the absolute power in EEG decreases rapidly with increasing frequency following a "1/f" power law, and the gamma band includes line noise frequency, these oscillations are highly susceptible to instrument noise. Previous studies that recorded stimulus-induced gamma oscillations used expensive research-grade EEG amplifiers to address this issue. While low-cost EEG amplifiers have become popular in Brain Computer Interface applications that mainly rely on low-frequency oscillations (< 30 Hz) or steady-state-visually-evoked-potentials, whether they can also be used to measure stimulus-induced gamma oscillations is unknown. We recorded EEG signals using a low-cost, open-source amplifier (OpenBCI) and a traditional, research-grade amplifier (Brain Products GmbH), both connected to the OpenBCI cap, in male (N = 6) and female (N = 5) subjects (22-29 years) while they viewed full-screen static gratings that are known to induce two distinct gamma oscillations: slow and fast gamma, in a subset of subjects. While the EEG signals from OpenBCI were considerably noisier, we found that out of the seven subjects who showed a gamma response in Brain Products recordings, six showed a gamma response in OpenBCI as well. In spite of the noise in the OpenBCI setup, the spectral and temporal profiles of these responses in alpha (8-13 Hz) and gamma bands were highly correlated between OpenBCI and Brain Products recordings. These results suggest that low-cost amplifiers can potentially be used in stimulus-induced gamma response detection.}, } @article {pmid36683147, year = {2023}, author = {Jin, J and Xu, Z and Zhang, L and Zhang, C and Zhao, X and Mao, Y and Zhang, H and Liang, X and Wu, J and Yang, Y and Zhang, J}, title = {Gut-derived β-amyloid: Likely a centerpiece of the gut-brain axis contributing to Alzheimer's pathogenesis.}, journal = {Gut microbes}, volume = {15}, number = {1}, pages = {2167172}, doi = {10.1080/19490976.2023.2167172}, pmid = {36683147}, issn = {1949-0984}, abstract = {Peripheral β-amyloid (Aβ), including those contained in the gut, may contribute to the formation of Aβ plaques in the brain, and gut microbiota appears to exert an impact on Alzheimer's disease (AD) via the gut-brain axis, although detailed mechanisms are not clearly defined. The current study focused on uncovering the potential interactions among gut-derived Aβ in aging, gut microbiota, and AD pathogenesis. To achieve this goal, the expression levels of Aβ and several key proteins involved in Aβ metabolism were initially assessed in mouse gut, with key results confirmed in human tissue. The results demonstrated that a high level of Aβ was detected throughout the gut in both mice and human, and gut Aβ42 increased with age in wild type and mutant amyloid precursor protein/presenilin 1 (APP/PS1) mice. Next, the gut microbiome of mice was characterized by 16S rRNA sequencing, and we found the gut microbiome altered significantly in aged APP/PS1 mice and fecal microbiota transplantation (FMT) of aged APP/PS1 mice increased gut BACE1 and Aβ42 levels. Intra-intestinal injection of isotope or fluorescence labeled Aβ combined with vagotomy was also performed to investigate the transmission of Aβ from gut to brain. The data showed that, in aged mice, the gut Aβ42 was transported to the brain mainly via blood rather than the vagal nerve. Furthermore, FMT of APP/PS1 mice induced neuroinflammation, a phenotype that mimics early AD pathology. Taken together, this study suggests that the gut is likely a critical source of Aβ in the brain, and gut microbiota can further upregulate gut Aβ production, thereby potentially contributing to AD pathogenesis.}, } @article {pmid36682180, year = {2023}, author = {Peng, G and Zhao, K and Zhang, H and Xu, D and Kong, X}, title = {Temporal relative transformer encoding cooperating with channel attention for EEG emotion analysis.}, journal = {Computers in biology and medicine}, volume = {154}, number = {}, pages = {106537}, doi = {10.1016/j.compbiomed.2023.106537}, pmid = {36682180}, issn = {1879-0534}, abstract = {Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-computer fusion. EEG signals have inherent temporal and spatial characteristics. However, existing studies did not fully consider the two properties. In addition, the position encoding mechanism in the vanilla transformer cannot effectively encode the continuous temporal character of the emotion. A temporal relative (TR) encoding mechanism is proposed to encode the temporal EEG signals for constructing the temporality self-attention in the transformer. To explore the contribution of each EEG channel corresponding to the electrode on the cerebral cortex to emotion analysis, a channel-attention (CA) mechanism is presented. The temporality self-attention mechanism cooperates with the channel-attention mechanism to utilize the temporal and spatial information of EEG signals simultaneously by preprocessing. Exhaustive experiments are conducted on the DEAP dataset, including the binary classification on valence, arousal, dominance, and liking. Furthermore, the discrete emotion category classification task is also conducted by mapping the dimensional annotations of DEAP into discrete emotion categories (5-class). Experimental results demonstrate that our model outperforms the advanced methods for all classification tasks.}, } @article {pmid36682005, year = {2023}, author = {Guo, B and Zheng, H and Jiang, H and Li, X and Guan, N and Zuo, Y and Zhang, Y and Yang, H and Wang, X}, title = {Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.}, journal = {Briefings in bioinformatics}, volume = {}, number = {}, pages = {}, doi = {10.1093/bib/bbac628}, pmid = {36682005}, issn = {1477-4054}, abstract = {Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.}, } @article {pmid36680589, year = {2023}, author = {Afreen, A and Ahmed, Z and Khalid, N and Ferheen, I and Ahmed, I}, title = {Optimization and cholesterol-lowering activity of exopolysaccharide from Lactiplantibacillus paraplantarum NCCP 962.}, journal = {Applied microbiology and biotechnology}, volume = {}, number = {}, pages = {}, pmid = {36680589}, issn = {1432-0614}, abstract = {Exopolysaccharides (EPSs) are biological polymers with unique structural features have gained particular interest in the fields of food, chemistry and medicine, and food industry. EPS from the food-grade lactic acid bacteria (LAB) can be used as a natural food additives to commercial ones in the processing and development of functional foods and nutraceuticals. The current study was aimed to explore the EPS-producing LAB from the dahi; to optimize the fermentation conditions through Plackett-Burman (PB) and response surface methodology (RSM); and to study its physicochemical, rheological, functional attributes, and cholesterol-lowering activity. Lactiplantibacillus paraplantarum NCCP 962 was isolated among the 08 strains screened at the initial stage. The PB design screened out four independent factors that had a significant positive effect, i.e., lactose, yeast extract, CaCl2, and tryptone, while the remaining seven had a non-significant effect. The RSM exhibited lactose, yeast extract, and CaCl2, significantly contributing to EPS yield. The maximum EPS yield (0.910 g/L) was obtained at 6.57% lactose, 0.047% yeast extract, 0.59% CaCl2, and 1.37% tryptone. The R[2] value above 97% explains the higher variability and depicts the model's validity. The resulted EPS was a heteropolysaccharide in nature with mannose, glucose, and galactose monosaccharides. FTIR spectrum reflected the presence of functional groups, i.e., O-H, C-H, C = O, C-O-H, and CH2. SEM revealed a porous and rough morphology of EPS, also found to be thermally stable and negligible weight loss, i.e., 14.0% at 257 °C and 35.4% at 292.9 °C was observed in the 1st and 2nd phases, respectively. Rheological attributes revealed that strain NCCP 962 had high viscosity by increasing the EPS concentration, low pH, and temperature with respectable water holding, oil capacities, foaming abilities, and stability. NCCP 962 EPS possessed up to 46.4% reduction in cholesterol concentration in the supernatant. Conclusively, these results suggested that strain NCCP 962 can be used in food processing applications and other medical fields. KEY POINTS: • The fermentation conditions affect EPS yield from L. paraplantarum and significantly increased yield to 0.910 g/L. • The EPS was heteropolysaccharide in nature and thermally stable with amorphous morphology. • Good cholesterol-lowering potential with the best rheological, emulsifying, and foaming capacities.}, } @article {pmid36679557, year = {2023}, author = {Lupenko, S and Butsiy, R and Shakhovska, N}, title = {Advanced Modeling and Signal Processing Methods in Brain-Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {2}, pages = {}, doi = {10.3390/s23020760}, pmid = {36679557}, issn = {1424-8220}, abstract = {In this study is substantiated the new mathematical model of vector of electroencephalographic signals, registered under the conditions of multiple repetitions of the mental control influences of brain-computer interface operator, in the form of a vector of cyclic rhythmically connected random processes, which, due to taking into account the stochasticity and cyclicity, the variability and commonality of the rhythm of the investigated signals have a number of advantages over the known models. This new model opens the way for the study of multidimensional distribution functions; initial, central, and mixed moment functions of higher order such as for each electroencephalographic signal separately; as well as for their respective compatible probabilistic characteristics, among which the most informative characteristics can be selected. This provides an increase in accuracy in the detection (classification) of mental control influences of the brain-computer interface operators. Based on the developed mathematical model, the statistical processing methods of vector of electroencephalographic signals are substantiated, which consist of statistical evaluation of its probabilistic characteristics and make it possible to conduct an effective joint statistical estimation of the probability characteristics of electroencephalographic signals. This provides the basis for coordinated integration of information from different sensors. The use of moment functions of higher order and their spectral images in the frequency domain, as informative characteristics in brain-computer interface systems, are substantiated. Their significant sensitivity to the mental controlling influence of the brain-computer interface operator is experimentally established. The application of Bessel's inequality to the problems of reducing the dimensions (from 500 to 20 numbers) of the vectors of informative features makes it possible to significantly reduce the computational complexity of the algorithms for the functioning of brain-computer interface systems. Namely, we experimentally established that only the first 20 values of the Fourier transform of the estimation of moment functions of higher-order electroencephalographic signals are sufficient to form the vector of informative features in brain-computer interface systems, because these spectral components make up at least 95% of the total energy of the corresponding statistical estimate of the moment functions of higher-order electroencephalographic signals.}, } @article {pmid36679501, year = {2023}, author = {Milanés-Hermosilla, D and Trujillo-Codorniú, R and Lamar-Carbonell, S and Sagaró-Zamora, R and Tamayo-Pacheco, JJ and Villarejo-Mayor, JJ and Delisle-Rodriguez, D}, title = {Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {2}, pages = {}, doi = {10.3390/s23020703}, pmid = {36679501}, issn = {1424-8220}, abstract = {The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.}, } @article {pmid36676348, year = {2023}, author = {Wen, J and Tang, L and Zhang, S and Zhan, Q and Wang, Y}, title = {Qualitative and Quantitative Investigations on the Failure Effect of Critical Fissures in Rock Specimens under Plane Strain Compression.}, journal = {Materials (Basel, Switzerland)}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/ma16020611}, pmid = {36676348}, issn = {1996-1944}, abstract = {To investigate the failure effects of critical fissures in rock specimens subjected to plane strain compression (PSC), five types of internal fissures in rock specimens were designed and twelve PSC tests were conducted for two lithologies based on the discrete element method (DEM). The results were analyzed in terms of the fracture mode, data characteristics, and crack evolution. The results indicated the following. (1) The rock samples with a critical fissure under PSC showed a weak face shear fracture mode, which was influenced by lithology, fissure angle, and fissure surface direction. (2) There were four critical expansion points (CEPs) of axial stress of the rocks under PSC, which were the stage signs of rock materials from local damage to complete fracture. The rock-bearing capacity index (RockBCI) was further proposed. (3) The bearing capacity of rock samples with horizontal fissures, fissures whose angles coincided with the fracture surface, and fissures whose surface was perpendicular to the lateral confine direction was the worst; their BCI[2] values were found to be 80.6%, 70.8%, and 56.9% of the rock samples without any fissures, respectively. The delayed fracture situation under PSC was identified and analyzed. (4) The crack evolution followed the unified law of localization, and the fissures in the rocks changed the mode of crack development and the path of the deepening and connecting of crack clusters, as well as affecting the time process from damage to collapse. This research innovatively investigated the behavior characteristics of rock samples with a fissure under PSC, and it qualitatively and quantitatively analyzed the bearing capacity of rock mass from local damage to fracture.}, } @article {pmid36675707, year = {2022}, author = {Ma, Y and Gong, A and Nan, W and Ding, P and Wang, F and Fu, Y}, title = {Personalized Brain-Computer Interface and Its Applications.}, journal = {Journal of personalized medicine}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/jpm13010046}, pmid = {36675707}, issn = {2075-4426}, abstract = {Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.}, } @article {pmid36675486, year = {2023}, author = {Morone, G and Pichiorri, F}, title = {Post-Stroke Rehabilitation: Challenges and New Perspectives.}, journal = {Journal of clinical medicine}, volume = {12}, number = {2}, pages = {}, doi = {10.3390/jcm12020550}, pmid = {36675486}, issn = {2077-0383}, abstract = {A stroke is determined by insufficient blood supply to the brain due to vessel occlusion (ischemic stroke) or rupture (hemorrhagic stroke), resulting in immediate neurological impairment to differing degrees [...].}, } @article {pmid36672726, year = {2023}, author = {Coelho, HRS and Neves, SCD and Menezes, JNDS and Antoniolli-Silva, ACMB and Oliveira, RJ}, title = {Mesenchymal Stromal Cell Therapy Reverses Detrusor Hypoactivity in a Chronic Kidney Patient.}, journal = {Biomedicines}, volume = {11}, number = {1}, pages = {}, doi = {10.3390/biomedicines11010218}, pmid = {36672726}, issn = {2227-9059}, abstract = {Detrusor hypoactivity (DH) is characterized by low detrusor pressure or a short contraction associated with low urinary flow. This condition can progress to chronic renal failure (CRF) and result in the need for dialysis. The present case report demonstrates that a patient diagnosed with DH and CRF who received two transplants with 2 × 10[6] autologous mesenchymal stromal cells at an interval of 30 days recovered the contractile strength of the bladder and normalized his renal function. The patient had a score of 19 on the ICIQ-SF before cell therapy, and that score was reduced to 1 after transplantation. These results demonstrate that there was an improvement in his voiding function, urinary stream and urine volume as evaluated by urofluxometry. In addition, a urodynamic study carried out after treatment showed an increase in the maximum flow from 2 mL/s to 23 mL/s, the detrusor pressure in the maximum flow from 21 cm H2O to 46 cm H2O and a BCI that went from 31 to 161, characterizing good detrusor contraction. Thus, in the present case, the transplantation of autologous mesenchymal stromal cells proved to be a viable therapeutic option to allow the patient to recover the contractile strength of the bladder, and reversed the CRF.}, } @article {pmid36672115, year = {2023}, author = {Zhao, ZP and Nie, C and Jiang, CT and Cao, SH and Tian, KX and Yu, S and Gu, JW}, title = {Modulating Brain Activity with Invasive Brain-Computer Interface: A Narrative Review.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010134}, pmid = {36672115}, issn = {2076-3425}, abstract = {Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.}, } @article {pmid36672052, year = {2022}, author = {Pepi, C and Mercier, M and Carfì Pavia, G and de Benedictis, A and Vigevano, F and Rossi-Espagnet, MC and Falcicchio, G and Marras, CE and Specchio, N and de Palma, L}, title = {Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010071}, pmid = {36672052}, issn = {2076-3425}, abstract = {OBJECTIVES: Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome.

METHODS: We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy.

RESULTS: Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified.

CONCLUSIONS: The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT.

SIGNIFICANCE: The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.}, } @article {pmid36672050, year = {2022}, author = {Gao, T and Hu, Y and Zhuang, J and Bai, Y and Lu, R}, title = {Repetitive Transcranial Magnetic Stimulation of the Brain Region Activated by Motor Imagery Involving a Paretic Wrist and Hand for Upper-Extremity Motor Improvement in Severe Stroke: A Preliminary Study.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010069}, pmid = {36672050}, issn = {2076-3425}, abstract = {Approximately two-thirds of stroke survivors experience chronic upper-limb paresis; however, treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) can enhance motor function recovery in stroke survivors, but its efficacy is controversial. We compared the efficacy of stimulating different targets in 10 chronic stroke patients with severe upper-limb motor impairment. Motor imagery-based brain-computer interface training augmented with virtual reality was used to induce neural activity in the brain region during an imagery task. Participants were then randomly assigned to two groups: an experimental group (received high-frequency rTMS delivered to the brain region activated earlier) and a comparison group (received low-frequency rTMS delivered to the contralesional primary motor cortex). Behavioural metrics and diffusion tensor imaging were compared pre- and post rTMS. After the intervention, participants in both groups improved somewhat. This preliminary study indicates that in chronic stroke patients with severe upper-limb motor impairment, inducing activation in specific brain regions during motor imagery tasks and selecting these regions as a target is feasible. Further studies are needed to explore the efficacy of this intervention.}, } @article {pmid36672046, year = {2022}, author = {Adama, S and Bogdan, M}, title = {Application of Soft-Clustering to Assess Consciousness in a CLIS Patient.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010065}, pmid = {36672046}, issn = {2076-3425}, abstract = {Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients' quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients' consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient's consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients' consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems.}, } @article {pmid36672038, year = {2022}, author = {Fu, J and Chen, S and Jia, J}, title = {Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010056}, pmid = {36672038}, issn = {2076-3425}, abstract = {Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.}, } @article {pmid36672034, year = {2022}, author = {Gao, X and Yang, Y and Zhang, F and Zhou, F and Zhu, J and Sun, J and Xu, K and Chen, Y}, title = {A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection Algorithm.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010052}, pmid = {36672034}, issn = {2076-3425}, abstract = {Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance.}, } @article {pmid36671894, year = {2022}, author = {Xu, M and Zhao, Y and Xu, G and Zhang, Y and Sun, S and Sun, Y and Wang, J and Pei, R}, title = {Recent Development of Neural Microelectrodes with Dual-Mode Detection.}, journal = {Biosensors}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/bios13010059}, pmid = {36671894}, issn = {2079-6374}, abstract = {Neurons communicate through complex chemical and electrophysiological signal patterns to develop a tight information network. A physiological or pathological event cannot be explained by signal communication mode. Therefore, dual-mode electrodes can simultaneously monitor the chemical and electrophysiological signals in the brain. They have been invented as an essential tool for brain science research and brain-computer interface (BCI) to obtain more important information and capture the characteristics of the neural network. Electrochemical sensors are the most popular methods for monitoring neurochemical levels in vivo. They are combined with neural microelectrodes to record neural electrical activity. They simultaneously detect the neurochemical and electrical activity of neurons in vivo using high spatial and temporal resolutions. This paper systematically reviews the latest development of neural microelectrodes depending on electrode materials for simultaneous in vivo electrochemical sensing and electrophysiological signal recording. This includes carbon-based microelectrodes, silicon-based microelectrode arrays (MEAs), and ceramic-based MEAs, focusing on the latest progress since 2018. In addition, the structure and interface design of various types of neural microelectrodes have been comprehensively described and compared. This could be the key to simultaneously detecting electrochemical and electrophysiological signals.}, } @article {pmid36669202, year = {2023}, author = {Ming, G and Pei, W and Gao, X and Wang, Y}, title = {A high-performance SSVEP-based BCI using imperceptible flickers.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb50e}, pmid = {36669202}, issn = {1741-2552}, abstract = {Objective.Existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) struggle to balance user experience and system performance. This study proposed an individualized space and phase modulation method to code imperceptible flickers at 60 Hz towards a user-friendly SSVEP-based BCI with high performance.Approach.The individualized customization of visual stimulation took the subject-to-subject variability in cortex geometry into account. An annulus global-stimulation was divided into local-stimulations of eight annular sectors and presented to subjects separately. The local-stimulation SSVEPs were superimposed to simulate global-stimulation SSVEPs with 4[7]space and phase coding combinations. A four-class phase-coded BCI diagram was used to evaluate the simulated classification performance. The performance ranking of all simulated global-stimulation SSVEPs were obtained and three performance levels (optimal, medium, worst) of individualized modulation groups were searched for each subject. The standard-modulation group conforming to the V1 'cruciform' geometry and the non-modulation group were involved as controls. A four-target phase-coded BCI system with SSVEPs at 60 Hz was implemented with the five modulation groups and questionnaires were used to evaluate user experience.Main results.The proposed individualized space and phase modulation method effectively modulated the SSVEP intensity without affecting the user experience. The online BCI system using the 60 Hz stimuli achieved mean information transfer rates of 52.8 ± 1.9 bits min[-1], 16.8 ± 2.4 bits min[-1], and 42.4 ± 3.0 bits min[-1]with individualized optimal-modulation, individualized worst-modulation, and non-modulation groups, respectively.Significance.Structural and functional characteristics of the human visual cortex were exploited to enhance the response intensity of SSVEPs at 60 Hz, resulting in a high-performance BCI system with good user experience. This study has important theoretical significance and application value for promoting the development of the visual BCI technology.}, } @article {pmid36662378, year = {2023}, author = {Li, J and Wang, J and Wang, T and Kong, W and Xi, X}, title = {Quantification of body ownership awareness induced by the visual movement illusion of the lower limbs: a study of electroencephalogram and surface electromyography.}, journal = {Medical & biological engineering & computing}, volume = {}, number = {}, pages = {}, pmid = {36662378}, issn = {1741-0444}, abstract = {The visual movement illusion (VMI) is a subjective experience. This illusion is produced by watching the subject's motion video. At the same time, VMI evokes awareness of body ownership. We applied the power spectral density (PSD) matrix and the partial directed correlation (PDC) matrix to build the PPDC matrix for the γ2 band (34-98.5 Hz), combining cerebral cortical and musculomotor cortical complexity and PPDC to quantify the degree of body ownership. Thirty-five healthy subjects were recruited to participate in this experiment. The subjects' electroencephalography (EEG) and surface electromyography (sEMG) data were recorded under resting conditions, observation conditions, illusion conditions, and actual seated front-kick movements. The results show the following: (1) VMI activates the cerebral cortex to some extent; (2) VMI enhances cortical muscle excitability in the rectus femoris and medial vastus muscles; (3) VMI induces a sense of body ownership; (4) the use of PPDC values, fuzzy entropy values of muscles, and fuzzy entropy values of the cerebral cortex can quantify whether VMI induces awareness of body ownership. These results illustrate that PPDC can be used as a biomarker to show that VMI affects changes in the cerebral cortex and as a quantitative tool to show whether body ownership awareness arises.}, } @article {pmid36662082, year = {2023}, author = {Karbalaei Akbari, M and Siraj Lopa, N and Shahriari, M and Najafzadehkhoee, A and Galusek, D and Zhuiykov, S}, title = {Functional Two-Dimensional Materials for Bioelectronic Neural Interfacing.}, journal = {Journal of functional biomaterials}, volume = {14}, number = {1}, pages = {}, doi = {10.3390/jfb14010035}, pmid = {36662082}, issn = {2079-4983}, abstract = {Realizing the neurological information processing by analyzing the complex data transferring behavior of populations and individual neurons is one of the fast-growing fields of neuroscience and bioelectronic technologies. This field is anticipated to cover a wide range of advanced applications, including neural dynamic monitoring, understanding the neurological disorders, human brain-machine communications and even ambitious mind-controlled prosthetic implant systems. To fulfill the requirements of high spatial and temporal resolution recording of neural activities, electrical, optical and biosensing technologies are combined to develop multifunctional bioelectronic and neuro-signal probes. Advanced two-dimensional (2D) layered materials such as graphene, graphene oxide, transition metal dichalcogenides and MXenes with their atomic-layer thickness and multifunctional capabilities show bio-stimulation and multiple sensing properties. These characteristics are beneficial factors for development of ultrathin-film electrodes for flexible neural interfacing with minimum invasive chronic interfaces to the brain cells and cortex. The combination of incredible properties of 2D nanostructure places them in a unique position, as the main materials of choice, for multifunctional reception of neural activities. The current review highlights the recent achievements in 2D-based bioelectronic systems for monitoring of biophysiological indicators and biosignals at neural interfaces.}, } @article {pmid36658415, year = {2023}, author = {Gu, J and Jiang, J and Ge, S and Wang, H}, title = {Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems.}, journal = {Medical & biological engineering & computing}, volume = {}, number = {}, pages = {}, pmid = {36658415}, issn = {1741-0444}, abstract = {The common spatial patterns (CSP) technique is an effective strategy for the classification of multichannel electroencephalogram (EEG) signals. However, the objective function expression of the conventional CSP algorithm is based on the L2-norm, which makes the performance of the method easily affected by outliers and noise. In this paper, we consider a new extension to CSP, which is termed capped L21-norm-based common spatial patterns (CCSP-L21), by using the capped L21-norm rather than the L2-norm for robust modeling. L21-norm considers the L1-norm sum which largely alleviates the influence of outliers and noise for the sake of robustness. The capped norm is further used to mitigate the effects of extreme outliers whose signal amplitude is much higher than that of the normal signal. Moreover, a non-greedy iterative procedure is derived to solve the proposed objective function. The experimental results show that the proposed method achieves the highest average recognition rates on the three real data sets of BCI competitions, which are 91.67%, 85.07%, and 82.04%, respectively. Capped L21-norm-based common spatial patterns-a robust model for EEG signals classification.}, } @article {pmid36657633, year = {2023}, author = {Perez-Garcia, G and Bicak, M and Haure-Mirande, JV and Perez, GM and Otero-Pagan, A and Gama Sosa, MA and De Gasperi, R and Sano, M and Barlow, C and Gage, FH and Readhead, B and Ehrlich, ME and Gandy, S and Elder, GA}, title = {BCI-838, an orally active mGluR2/3 receptor antagonist pro-drug, rescues learning behavior deficits in the PS19 MAPT[P301S] mouse model of tauopathy.}, journal = {Neuroscience letters}, volume = {}, number = {}, pages = {137080}, doi = {10.1016/j.neulet.2023.137080}, pmid = {36657633}, issn = {1872-7972}, abstract = {Tauopathies are a heterogeneous group of neurodegenerative disorders that are clinically and pathologically distinct from Alzheimer's disease (AD) having tau inclusions in neurons and/or glia as their most prominent neuropathological feature. BCI-838 (MGS00210) is a group II metabotropic glutamate receptor (mGluR2/3) antagonist pro-drug. Previously, we reported that orally administered BCI-838 improved learning behavior and reduced anxiety in Dutch (APP[E693Q]) transgenic mice, a model of the pathological accumulation of Aβ oligomers found in AD. Herein, we investigated effects of BCI-838 on PS19 male mice that express the tauopathy mutation MAPT[P301S] associated with human frontotemporal lobar degeneration (FTLD). These mice develop an aging-related tauopathy without amyloid accumulation. Mice were divided into three experimental groups: (1) non-transgenic wild type mice treated with vehicle, (2) PS19 mice treated with vehicle and (3) PS19 mice treated with 5 mg/kg BCI-838. Groups of 10-13 mice were utilized. Vehicle or BCI-838 was administered by oral gavage for 4 weeks. Behavioral testing consisting of a novel object recognition task was conducted after drug administration. Two studies were performed beginning treatment of mice at 3 or 7 months of age. One month of BCI-838 treatment rescued deficits in recognition memory in PS19 mice whether treatment was begun at 3 or 7 months of age. These studies extend the potential utility of BCI-838 to neurodegenerative conditions that have tauopathy as their underlying basis. They also suggest an mGluR2/3 dependent mechanism as a basis for the behavioral deficits in PS19 mice.}, } @article {pmid36657242, year = {2023}, author = {Proverbio, AM and Tacchini, M and Jiang, K}, title = {What do you have in mind? ERP markers of visual and auditory imagery.}, journal = {Brain and cognition}, volume = {166}, number = {}, pages = {105954}, doi = {10.1016/j.bandc.2023.105954}, pmid = {36657242}, issn = {1090-2147}, abstract = {This study aimed to investigate the psychophysiological markers of imagery processes through EEG/ERP recordings. Visual and auditory stimuli representing 10 different semantic categories were shown to 30 healthy participants. After a given interval and prompted by a light signal, participants were asked to activate a mental image corresponding to the semantic category for recording synchronized electrical potentials. Unprecedented electrophysiological markers of imagination were recorded in the absence of sensory stimulation. The following peaks were identified at specific scalp sites and latencies, during imagination of infants (centroparietal positivity, CPP, and late CPP), human faces (anterior negativity, AN), animals (anterior positivity, AP), music (P300-like), speech (N400-like), affective vocalizations (P2-like) and sensory (visual vs auditory) modality (PN300). Overall, perception and imagery conditions shared some common electro/cortical markers, but during imagery the category-dependent modulation of ERPs was long latency and more anterior, with respect to the perceptual condition. These ERP markers might be precious tools for BCI systems (pattern recognition, classification, or A.I. algorithms) applied to patients affected by consciousness disorders (e.g., in a vegetative or comatose state) or locked-in-patients (e.g., spinal or SLA patients).}, } @article {pmid36656873, year = {2023}, author = {Pang, J and Peng, S and Hou, C and Zhao, H and Fan, Y and Ye, C and Zhang, N and Wang, T and Cao, Y and Zhou, W and Sun, D and Wang, K and Rümmeli, MH and Liu, H and Cuniberti, G}, title = {Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles.}, journal = {ACS sensors}, volume = {}, number = {}, pages = {}, doi = {10.1021/acssensors.2c02790}, pmid = {36656873}, issn = {2379-3694}, abstract = {Graphene remains of great interest in biomedical applications because of biocompatibility. Diseases relating to human senses interfere with life satisfaction and happiness. Therefore, the restoration by artificial organs or sensory devices may bring a bright future by the recovery of senses in patients. In this review, we update the most recent progress in graphene based sensors for mimicking human senses such as artificial retina for image sensors, artificial eardrums, gas sensors, chemical sensors, and tactile sensors. The brain-like processors are discussed based on conventional transistors as well as memristor related neuromorphic computing. The brain-machine interface is introduced for providing a single pathway. Besides, the artificial muscles based on graphene are summarized in the means of actuators in order to react to the physical world. Future opportunities remain for elevating the performances of human-like sensors and their clinical applications.}, } @article {pmid36655886, year = {2022}, author = {Breen, JR and Pensini, P}, title = {Grounded by Mother Nature's Revenge.}, journal = {Experimental psychology}, volume = {69}, number = {5}, pages = {284-294}, doi = {10.1027/1618-3169/a000566}, pmid = {36655886}, issn = {2190-5142}, abstract = {Leisure air travel is a popular form of tourism, but its emissions are a major contributor to anthropogenic climate change. Restrictions to leisure air travel have previously received little support; however, the same restrictions to mitigate the spread of COVID-19 have been popular. This support is unlikely to persist in a postpandemic world, highlighting the need for alternative ways to improve support for reducing leisure air travel. Anthropomorphism of nature has consistently predicted proenvironmental behavior, which has been mediated by guilt felt for harm to the environment. This research is the first empirical study to explore this relationship in the context of COVID-19, where it examined support for restricting leisure air travel to help mitigate (1) COVID-19 and (2) climate change. In an experimental online study, Australian residents (N = 325, Mage = 54.48, SDage = 14.63, 62% women) were recruited through social media. Anthropomorphism of nature in the context of COVID-19 (AMP-19) was manipulated through exposure to a news article. Participants then completed measures of environmental guilt and support for restricting leisure air travel to mitigate COVID-19 (LAT-19) and to mitigate climate change (LAT-CC). A significant indirect effect was observed in both models, such that AMP-19 predicted environmental guilt which in turn predicted LAT-19 (f[2] = .26; BCI [0.66, 3.87]) and LAT-CC (f[2] = .45; BCI [0.84, 5.06]). The results imply that anthropomorphism of nature in the context of COVID-19 can improve attitudes toward this proenvironmental behavior, with greater support when this was to mitigate climate change. Implications are discussed.}, } @article {pmid36654858, year = {2023}, author = {Jin, S and Chen, X and Zheng, H and Cai, W and Lin, X and Kong, X and Ni, Y and Ye, J and Li, X and Shen, L and Guo, B and Abdelrahman, Z and Zhou, S and Mao, S and Wang, Y and Yao, C and Gu, X and Yu, B and Wang, Z and Wang, X}, title = {Downregulation of UBE4B promotes CNS axon regrowth and functional recovery after stroke.}, journal = {iScience}, volume = {26}, number = {1}, pages = {105885}, pmid = {36654858}, issn = {2589-0042}, abstract = {The limited intrinsic regrowth capacity of corticospinal axons impedes functional recovery after cortical stroke. Although the mammalian target of rapamycin (mTOR) and p53 pathways have been identified as the key intrinsic pathways regulating CNS axon regrowth, little is known about the key upstream regulatory mechanism by which these two major pathways control CNS axon regrowth. By screening genes that regulate ubiquitin-mediated degradation of the p53 proteins in mice, we found that ubiquitination factor E4B (UBE4B) represses axonal regrowth in retinal ganglion cells and corticospinal neurons. We found that axonal regrowth induced by UBE4B depletion depended on the cooperative activation of p53 and mTOR. Importantly, overexpression of UbV.E4B, a competitive inhibitor of UBE4B, in corticospinal neurons promoted corticospinal axon sprouting and facilitated the recovery of corticospinal axon-dependent function in a cortical stroke model. Thus, our findings provide a translatable strategy for restoring corticospinal tract-dependent functions after cortical stroke.}, } @article {pmid36654371, year = {2021}, author = {Zhu, J and Chen, F and Luo, L and Wu, W and Dai, J and Zhong, J and Lin, X and Chai, C and Ding, P and Liang, L and Wang, S and Ding, X and Chen, Y and Wang, H and Qiu, J and Wang, F and Sun, C and Zeng, Y and Fang, J and Jiang, X and Liu, P and Tang, G and Qiu, X and Zhang, X and Ruan, Y and Jiang, S and Li, J and Zhu, S and Xu, X and Li, F and Liu, Z and Cao, G and Chen, D}, title = {Single-cell atlas of domestic pig cerebral cortex and hypothalamus.}, journal = {Science bulletin}, volume = {66}, number = {14}, pages = {1448-1461}, doi = {10.1016/j.scib.2021.04.002}, pmid = {36654371}, issn = {2095-9281}, abstract = {The brain of the domestic pig (Sus scrofa domesticus) has drawn considerable attention due to its high similarities to that of humans. However, the cellular compositions of the pig brain (PB) remain elusive. Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB (frontal lobe, parietal lobe, temporal lobe, occipital lobe, and hypothalamus) and identified 21 cell subpopulations. The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution. Furthermore, we identified cell types and molecular pathways closely associated with neurological disorders, bridging the gap between gene mutations and pathogenesis. We reported, to our knowledge, the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species, providing extensive resources for future research regarding neural science, evolutionary developmental biology, and regenerative medicine.}, } @article {pmid36652620, year = {2023}, author = {Abbasi, J and Suran, M}, title = {From Thought to Text: How an Endovascular Brain-Computer Interface Could Help Patients With Severe Paralysis Communicate.}, journal = {JAMA}, volume = {}, number = {}, pages = {}, doi = {10.1001/jama.2022.24343}, pmid = {36652620}, issn = {1538-3598}, } @article {pmid36652475, year = {2023}, author = {Liang, W and Balasubramanian, K and Papadourakis, V and Hatsopoulos, NG}, title = {Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {120}, number = {4}, pages = {e2212227120}, doi = {10.1073/pnas.2212227120}, pmid = {36652475}, issn = {1091-6490}, support = {NIH R01 NS111982/GF/NIH HHS/United States ; }, abstract = {Propagating spatiotemporal neural patterns are widely evident across sensory, motor, and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details. Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis. We recorded local field potentials (LFPs) from multielectrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200 to 400 Hz) envelopes in the LFP amplitude. We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions. Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatiotemporal characteristics in refining brain-machine interfaces.}, } @article {pmid36650644, year = {2023}, author = {Jiang, J and Fu, Y and Tang, A and Gao, X and Zhang, D and Shen, Y and Mou, T and Hu, S and Gao, J and Lai, J}, title = {Sex difference in prebiotics on gut and blood-brain barrier dysfunction underlying stress-induced anxiety and depression.}, journal = {CNS neuroscience & therapeutics}, volume = {}, number = {}, pages = {}, doi = {10.1111/cns.14091}, pmid = {36650644}, issn = {1755-5949}, abstract = {BACKGROUND: Most of the previous studies have demonstrated the potential antidepressive and anxiolytic role of prebiotic supplement in male subjects, yet few have females enrolled. Herein, we explored whether prebiotics administration during chronic stress prevented depression-like and anxiety-like behavior in a sex-specific manner and the mechanism of behavioral differences caused by sex.

METHODS: Female and male C57 BL/J mice on normal diet were supplemented with or without a combination of fructo-oligosaccharides (FOS) and galacto-oligosaccharides (GOS) during 3- and 4-week chronic restraint stress (CRS) treatment, respectively. C57 BL/J mice on normal diet without CRS were used as controls. Behavior consequences, gut microbiota, dysfunction of gut and brain-blood barriers, and inflammatory profiles were measured.

RESULTS: In the 3rd week, FOS + GOS administration attenuated stress-induced anxiety-like behavior in female, but not in male mice, and the anxiolytic effects in males were observed until the 4th week. However, protective effects of prebiotics on CRS-induced depression were not observed. Changes in the gene expression of tight junction proteins in the distal colon and hippocampus, and decreased number of colon goblet cells following CRS were restored by prebiotics only in females. In both female and male mice, prebiotics alleviated stress-induced BBB dysfunction and elevation in pro-inflammatory cytokines levels, and modulated gut microbiota caused by stress. Furthermore, correlation analysis revealed that anxiety-like behaviors were significantly correlated with levels of pro-inflammatory cytokines and gene expression of tight junction proteins in the hippocampus of female mice, and the abundance of specific gut microbes was also correlated with anxiety-like behaviors, pro-inflammatory cytokines, and gene expression of tight junction proteins in the hippocampus of female mice.

CONCLUSION: Female mice were more vulnerable to stress and prebiotics than males. The gut microbiota, gut and blood-brain barrier, and inflammatory response may mediate the protective effects of prebiotics on anxiety-like behaviors in female mice.}, } @article {pmid36607323, year = {2023}, author = {Zhang, Y and Schriver, KE and Hu, JM and Roe, AW}, title = {Spatial frequency representation in V2 and V4 of macaque monkey.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, doi = {10.7554/eLife.81794}, pmid = {36607323}, issn = {2050-084X}, abstract = {Spatial frequency (SF) is an important attribute in the visual scene and is a defining feature of visual processing channels. However, there remain many unsolved questions about how extrastriate areas in primate visual cortex code this fundamental information. Here, using intrinsic signal optical imaging in visual areas of V2 and V4 of macaque monkeys, we quantify the relationship between SF maps and (1) visual topography and (2) color and orientation maps. We find that in orientation regions, low to high SF is mapped orthogonally to orientation; in color regions, which are reported to contain orthogonal axes of color and lightness, low SFs tend to be represented more frequently than high SFs. This supports a population-based SF fluctuation related to the 'color/orientation' organizations. We propose a generalized hypercolumn model across cortical areas, comprised of two orthogonal parameters with additional parameters.}, } @article {pmid36650410, year = {2023}, author = {Zhang, J and Wang, X and Xu, B and Wu, Y and Lou, X and Shen, X}, title = {An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern.}, journal = {Medical & biological engineering & computing}, volume = {}, number = {}, pages = {}, pmid = {36650410}, issn = {1741-0444}, abstract = {The motor imagery brain-computer interface (MI-BCI) provides an interactive control channel for spinal cord injury patients. However, the limitations of feature extraction algorithms may lead to low accuracy and instability in decoding electroencephalogram (EEG) signals. In this study, we examined the classification performance of an MI-BCI system by focusing on the distinction of the left and right foot kinaesthetic motor imagery tasks in five subjects. Feature extraction was performed using the common space pattern (CSP) and the Tikhonov regularisation CSP (TRCSP) spatial filters. TRCSP overcomes the CSP problems of noise sensitivity and overfitting. Moreover, support vector machine (SVM) and linear discriminant analysis (LDA) were used for classification and recognition. We constructed four combined classification methods (TRCSP-SVM, TRCSP-LDA, CSP-SVM, and CSP-LDA) and evaluated them by comparing their accuracies, kappa coefficients, and receiver operating characteristic (ROC) curves. The results showed that the TRCSP-SVM method performed significantly better than others (average accuracy 97%, average kappa coefficient 0.91, and average area under ROC curve (AUC) 0.98). Using TRCSP instead of standard CSP improved accuracy by up to 10%. This study provides insights into the classification of EEG signals. The results of this study can aid lower limb MI-BCI systems in rehabilitation training.}, } @article {pmid36645915, year = {2023}, author = {Li, M and Zuo, H and Zhou, H and Xu, G and Qi, E}, title = {A study of action difference on motor imagery based on delayed matching posture task.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb386}, pmid = {36645915}, issn = {1741-2552}, abstract = {OBJECTIVE: Motor imagery (MI)-based brain-computer interfaces (BCI) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively.

APPROACH: To explore action differences, this study proposes a novel paradigm named as Action Observation based Delayed Matching Posture Task (AO-DMPT). Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference.

MAIN RESULTS: Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p<0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28μV of grasping, 5.64μV and 5.25μV higher than the cutting and the writing, respectively (p<0.05).

SIGNIFICANCE: The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further.}, } @article {pmid36645913, year = {2023}, author = {Valencia, D and Leone, G and Keller, N and Mercier, PP and Alimohammad, A}, title = {Power-efficient in vivo brain-machine interfaces via brain-state estimation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb385}, pmid = {36645913}, issn = {1741-2552}, abstract = {OBJECTIVE: Advances in brain-machine interfaces (BMIs) can potentially improve the quality of life of millions of users with spinal cord injury or other neurological disorders by allowing them to interact with the physical environment at their will.

APPROACH: To reduce the power consumption of the brain-implanted interface, this article presents the first hardware realization of an in vivo intention-aware interface via brain-state estimation.

MAIN RESULTS: It is shown that incorporating brain-state estimation reduces the in vivo power consumption and reduces total energy dissipation by over 1.8x compared to those of the current systems, enabling longer batter life for implanted circuits. The synthesized application-specific integrated circuit (ASIC) of the designed intention-aware multi-unit spike detection system in a standard 180-nm CMOS process occupies 0.03 mm[2]of silicon area and consumes 0.63 μW of power per channel, which is the least power consumption among the current in vivo ASIC realizations.

SIGNIFICANCE: The proposed interface is the first practical approach towards realizing asynchronous BMIs while reducing the power consumption of the BMI interface and enhancing neural decoding performance compared to those of the conventional synchronous BMIs.}, } @article {pmid36644311, year = {2022}, author = {Sui, Y and Yu, H and Zhang, C and Chen, Y and Jiang, C and Li, L}, title = {Deep brain-machine interfaces: sensing and modulating the human deep brain.}, journal = {National science review}, volume = {9}, number = {10}, pages = {nwac212}, pmid = {36644311}, issn = {2053-714X}, abstract = {Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.}, } @article {pmid36643889, year = {2023}, author = {Alharbi, H}, title = {Identifying Thematics in a Brain-Computer Interface Research.}, journal = {Computational intelligence and neuroscience}, volume = {2023}, number = {}, pages = {2793211}, pmid = {36643889}, issn = {1687-5273}, abstract = {This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.}, } @article {pmid36534700, year = {2022}, author = {Zhang, L and Liu, C and Zhou, X and Zhou, H and Luo, S and Wang, Q and Yao, Z and Chen, JF}, title = {Neural representation and modulation of volitional motivation in response to escalating efforts.}, journal = {The Journal of physiology}, volume = {}, number = {}, pages = {}, doi = {10.1113/JP283915}, pmid = {36534700}, issn = {1469-7793}, abstract = {Task-dependent volitional control of the selected neural activity in the cortex is critical to neuroprosthetic learning to achieve reliable and robust control of the external device. The volitional control of neural activity is driven by a motivational factor (volitional motivation), which directly reinforces the target neurons via real-time biofeedback. However, in the absence of motor behaviour, how do we evaluate volitional motivation? Here, we defined the criterion (ΔF/F) of the calcium fluorescence signal in a volitionally controlled neural task, then escalated the efforts by progressively increasing the number of reaching the criterion or holding time after reaching the criterion. We devised calcium-based progressive threshold-crossing events (termed 'Calcium PTE') and calcium-based progressive threshold-crossing holding-time (termed 'Calcium PTH') for quantitative assessment of volitional motivation in response to progressively escalating efforts. Furthermore, we used this novel neural representation of volitional motivation to explore the neural circuit and neuromodulator bases for volitional motivation. As with behavioural motivation, chemogenetic activation and pharmacological blockade of the striatopallidal pathway decreased and increased, respectively, the breakpoints of the 'Calcium PTE' and 'Calcium PTH' in response to escalating efforts. Furthermore, volitional and behavioural motivation shared similar dopamine dynamics in the nucleus accumbens in response to trial-by-trial escalating efforts. In general, the development of a neural representation of volitional motivation may open a new avenue for smooth and effective control of brain-machine interface tasks. KEY POINTS: Volitional motivation is quantitatively evaluated by M1 neural activity in response to progressively escalating volitional efforts. The striatopallidal pathway and adenosine A2A receptor modulate volitional motivation in response to escalating efforts. Dopamine dynamics encode prediction signal for reward in response to repeated escalating efforts during motor and volitional conditioning. Mice learn to modulate neural activity to compensate for repeated escalating efforts in volitional control.}, } @article {pmid36639665, year = {2023}, author = {Pichiorri, F and Toppi, J and de Seta, V and Colamarino, E and Masciullo, M and Tamburella, F and Lorusso, M and Cincotti, F and Mattia, D}, title = {Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {5}, pmid = {36639665}, issn = {1743-0003}, abstract = {BACKGROUND: Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks.

METHODS: EEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups.

RESULTS: Between-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections' distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients.

CONCLUSIONS: High-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.}, } @article {pmid36639237, year = {2023}, author = {Rubin, DB and Ajiboye, AB and Barefoot, L and Bowker, M and Cash, SS and Chen, D and Donoghue, JP and Eskandar, EN and Friehs, G and Grant, C and Henderson, JM and Kirsch, RF and Marujo, R and Masood, M and Mernoff, ST and Miller, JP and Mukand, JA and Penn, RD and Shefner, J and Shenoy, KV and Simeral, JD and Sweet, JA and Walter, BL and Williams, ZM and Hochberg, LR}, title = {Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System.}, journal = {Neurology}, volume = {}, number = {}, pages = {}, doi = {10.1212/WNL.0000000000201707}, pmid = {36639237}, issn = {1526-632X}, abstract = {BACKGROUND AND OBJECTIVES: Brain computer interfaces (BCIs) are being developed to restore mobility, communication, and functional independence to people with paralysis. Though supported by decades of preclinical data, the safety of chronically implanted microelectrode array BCIs in humans is unknown. We report safety results from the prospective, open-label, non-randomized BrainGate feasibility study (NCT00912041), the largest and longest-running clinical trial of an implanted BCI.

METHODS: Adults aged 18-75 with quadriparesis from spinal cord injury, brainstem stroke, or motor neuron disease were enrolled through seven clinical sites in the United States. Participants underwent surgical implantation of one or two microelectrode arrays in the motor cortex of the dominant cerebral hemisphere. The primary safety outcome was device-related serious adverse events requiring device explanation or resulting in death or permanently increased disability during the one-year post-implant evaluation period. Secondary outcomes include the type and frequency of other adverse events as well as the feasibility of the BrainGate system for controlling a computer or other assistive technologies.

RESULTS: From 2004 - 2021, fourteen adults enrolled in the BrainGate trial had devices surgically implanted. The average duration of device implantation was 872 days, yielding 12,203 days of safety experience. There were 68 device-related adverse events, including 6 device-related serious adverse events. The most common device-related adverse event was skin irritation around the percutaneous pedestal. There were no safety events that required device explantation, no unanticipated adverse device events, no intracranial infections, and no participant deaths or adverse events resulting in permanently increased disability related to the investigational device.

DISCUSSION: The BrainGate Neural Interface system has a safety record comparable to other chronically implanted medical devices. Given rapid recent advances in this technology and continued performance gains, these data suggest a favorable risk/benefit ratio in appropriately selected individuals to support ongoing research and development.

ClinicalTrials.gov Identifier: NCT00912041.

CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that the neurosurgically placed BrainGate Neural Interface system is associated with a low rate of SAEs defined as those requiring device explanation, resulting in death, or resulting in permanently increased disability during the one-year post implant period.}, } @article {pmid36638268, year = {2023}, author = {Wu, J and Chen, C and Qin, C and Li, Y and Jiang, N and Yuan, Q and Duan, Y and Liu, M and Wei, X and Yu, Y and Zhuang, L and Wang, P}, title = {Mimicking the Biological Sense of Taste In Vitro Using a Taste Organoids-on-a-Chip System.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2206101}, doi = {10.1002/advs.202206101}, pmid = {36638268}, issn = {2198-3844}, abstract = {Thanks to the gustatory system, humans can experience the flavors in foods and drinks while avoiding the intake of some harmful substances. Although great advances in the fields of biotechnology, microfluidics, and nanotechnologies have been made in recent years, this astonishing recognition system can hardly be replaced by any artificial sensors designed so far. Here, taste organoids are coupled with an extracellular potential sensor array to form a novel bioelectronic organoid and developed a taste organoids-on-a-chip system (TOS) for highly mimicking the biological sense of taste ex vivo with high stability and repeatability. The taste organoids maintain key taste receptors expression after the third passage and high cell viability during 7 days of on-chip culture. Most importantly, the TOS not only distinguishs sour, sweet, bitter, and salt stimuli with great specificity, but also recognizes varying concentrations of the stimuli through an analytical method based on the extraction of signal features and principal component analysis. It is hoped that this bioelectronic tongue can facilitate studies in food quality controls, disease modelling, and drug screening.}, } @article {pmid36637269, year = {2023}, author = {Hu, J and Wang, Y and Zhu, Y and Li, Y and Chen, J and Zhang, Y and Xu, D and Bai, R and Wang, L}, title = {Preoperative Brain Functional Connectivity Improve Predictive Accuracy of Outcomes After Revascularization in Moyamoya Disease.}, journal = {Neurosurgery}, volume = {92}, number = {2}, pages = {344-352}, doi = {10.1227/neu.0000000000002205}, pmid = {36637269}, issn = {1524-4040}, abstract = {BACKGROUND: In patients with moyamoya disease (MMD), focal impairments in cerebral hemodynamics are often inconsistent with patients' clinical prognoses. Evaluation of entire brain functional networks may enable predicting MMD outcomes after revascularization.

OBJECTIVE: To investigate whether preoperative brain functional connectivity could predict outcomes after revascularization in MMD.

METHODS: We included 34 patients with MMD who underwent preoperative MRI scanning and combined revascularization surgery. We used region of interest analyses to explore the differences in functional connectivity for 90 paired brain regions between patients who had favorable outcomes 1 year after surgery (no recurrent stroke, with improved preoperative symptoms, or modified Rankin Scale [mRS]) and those who had unimproved outcomes (recurrent stroke, persistent symptoms, or declined mRS). Variables, including age, body mass index, mRS at admission, Suzuki stage, posterior cerebral artery involvement, and functional connectivity with significant differences between the groups, were included in the discriminant function analysis to predict patient outcomes.

RESULTS: Functional connectivity between posterior cingulate cortex and paracentral lobule within the right hemisphere, and interhemispheric connection between superior parietal gyrus and middle frontal gyrus, precuneus and middle cingulate cortex, cuneus and precuneus, differed significantly between the groups (P < .001, false discovery rate corrected) and had the greatest discriminant function in the prediction model. Although clinical characteristics of patients with MMD showed great accuracy in predicting outcomes (64.7%), adding information on functional connections improved accuracy to 91.2%.

CONCLUSION: Preoperative functional connectivity derived from rs-fMRI may be an early hallmark for predicting patients' prognosis after revascularization surgery for MMD.}, } @article {pmid36636754, year = {2023}, author = {Hudson, HM and Guggenmos, DJ and Azin, M and Vitale, N and McKenzie, KA and Mahnken, JD and Mohseni, P and Nudo, RJ}, title = {Broad Therapeutic Time Window for Driving Motor Recovery After TBI Using Activity-Dependent Stimulation.}, journal = {Neurorehabilitation and neural repair}, volume = {}, number = {}, pages = {15459683221145144}, doi = {10.1177/15459683221145144}, pmid = {36636754}, issn = {1552-6844}, abstract = {BACKGROUND: After an acquired injury to the motor cortex, the ability to generate skilled movements is impaired, leading to long-term motor impairment and disability. While rehabilitative therapy can improve outcomes in some individuals, there are no treatments currently available that are able to fully restore lost function.

OBJECTIVE: We previously used activity-dependent stimulation (ADS), initiated immediately after an injury, to drive motor recovery. The objective of this study was to determine if delayed application of ADS would still lead to recovery and if the recovery would persist after treatment was stopped.

METHODS: Rats received a controlled cortical impact over primary motor cortex, microelectrode arrays were implanted in ipsilesional premotor and somatosensory areas, and a custom brain-machine interface was attached to perform the ADS. Stimulation was initiated either 1, 2, or 3 weeks after injury and delivered constantly over a 4-week period. An additional group was monitored for 8 weeks after terminating ADS to assess persistence of effect. Results were compared to rats receiving no stimulation.

RESULTS: ADS was delayed up to 3 weeks from injury onset and still resulted in significant motor recovery, with maximal recovery occurring in the 1-week delay group. The improvements in motor performance persisted for at least 8 weeks following the end of treatment.

CONCLUSIONS: ADS is an effective method to treat motor impairments following acquired brain injury in rats. This study demonstrates the clinical relevance of this technique as it could be initiated in the post-acute period and could be explanted/ceased once recovery has occurred.}, } @article {pmid36636584, year = {2022}, author = {Truong, MT and Liu, YC and Kohn, J and Chinnadurai, S and Zopf, DA and Tribble, M and Tanner, PB and Sie, K and Chang, KW}, title = {Integrated microtia and aural atresia management.}, journal = {Frontiers in surgery}, volume = {9}, number = {}, pages = {944223}, pmid = {36636584}, issn = {2296-875X}, abstract = {OBJECTIVES: To present recommendations for the coordinated evaluation and management of the hearing and reconstructive needs of patients with microtia and aural atresia.

METHODS: A national working group of 9 experts on microtia and atresia evaluated a working document on the evaluation and treatment of patients. Treatment options for auricular reconstruction and hearing habilitation were reviewed and integrated into a coordinated care timeline.

RESULTS: Recommendations were created for children with microtia and atresia, including diagnostic considerations, surgical and non-surgical options for hearing management and auricular reconstruction, and the treatment timeline for each option. These recommendations are based on the collective opinion of the group and are intended for otolaryngologists, audiologists, plastic surgeons, anaplastologists, and any provider caring for a patient with microtia and ear canal atresia. Close communication between atresia/hearing reconstruction surgeon and microtia repair surgeon is strongly recommended.}, } @article {pmid36635340, year = {2023}, author = {Daly, I}, title = {Neural decoding of music from the EEG.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {624}, pmid = {36635340}, issn = {2045-2322}, abstract = {Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG). In this study we explore how functional magnetic resonance imaging (fMRI) can be combined with EEG to develop an accoustic decoder. Specifically, we first used a joint EEG-fMRI paradigm to record brain activity while participants listened to music. We then used fMRI-informed EEG source localisation and a bi-directional long-term short term deep learning network to first extract neural information from the EEG related to music listening and then to decode and reconstruct the individual pieces of music an individual was listening to. We further validated our decoding model by evaluating its performance on a separate dataset of EEG-only recordings. We were able to reconstruct music, via our fMRI-informed EEG source analysis approach, with a mean rank accuracy of 71.8% ([Formula: see text], [Formula: see text]). Using only EEG data, without participant specific fMRI-informed source analysis, we were able to identify the music a participant was listening to with a mean rank accuracy of 59.2% ([Formula: see text], [Formula: see text]). This demonstrates that our decoding model may use fMRI-informed source analysis to aid EEG based decoding and reconstruction of acoustic information from brain activity and makes a step towards building EEG-based neural decoders for other complex information domains such as other acoustic, visual, or semantic information.}, } @article {pmid36634598, year = {2022}, author = {Zhu, S and Hosni, SI and Huang, X and Wan, M and Borgheai, SB and McLinden, J and Shahriari, Y and Ostadabbas, S}, title = {A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces.}, journal = {Computers in biology and medicine}, volume = {153}, number = {}, pages = {106498}, doi = {10.1016/j.compbiomed.2022.106498}, pmid = {36634598}, issn = {1879-0534}, abstract = {Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain-computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics. Moreover, existing graph theoretic approaches are mostly used to reveal the topological patterns of brain functional networks based on synchronization between signals from distinctive spatial regions, instead of interdependence between states at different timestamps. In this study, we present a robust fold-wise hyperparameter optimization framework utilizing a series of conventional graph-based measurements combined with spectral graph features and investigate its discriminative performance on classification of a designed mental task in 6 participants with amyotrophic lateral sclerosis (ALS). Across all of our participants, we reached an average accuracy of 71.1%±4.5% for mental task classification by combining the global graph-based measurements and the spectral graph features, higher than the conventional non-graph based feature performance (67.1%±7.5%). Compared to using either one of the graphic features (66.3%±6.5% for the eigenvalues and 65.9%±5.2% for the global graph features), our feature combination strategy shows considerable improvement in both accuracy and robustness performance. Our results indicate the feasibility and advantage of the presented fold-wise optimization framework utilizing graph-based features in BCI systems targeted at end-users.}, } @article {pmid36633302, year = {2023}, author = {Sample, M and Sattler, S and Boehlen, W and Racine, E}, title = {Brain-computer interfaces, disability, and the stigma of refusal: A factorial vignette study.}, journal = {Public understanding of science (Bristol, England)}, volume = {}, number = {}, pages = {9636625221141663}, doi = {10.1177/09636625221141663}, pmid = {36633302}, issn = {1361-6609}, abstract = {As brain-computer interfaces are promoted as assistive devices, some researchers worry that this promise to "restore" individuals worsens stigma toward disabled people and fosters unrealistic expectations. In three web-based survey experiments with vignettes, we tested how refusing a brain-computer interface in the context of disability affects cognitive (blame), emotional (anger), and behavioral (coercion) stigmatizing attitudes (Experiment 1, N = 222) and whether the effect of a refusal is affected by the level of brain-computer interface functioning (Experiment 2, N = 620) or the risk of malfunctioning (Experiment 3, N = 620). We found that refusing a brain-computer interface increased blame and anger, while brain-computer interface functioning did change the effect of a refusal. Higher risks of device malfunctioning partially reduced stigmatizing attitudes and moderated the effect of refusal. This suggests that information about disabled people who refuse a technology can increase stigma toward them. This finding has serious implications for brain-computer interface regulation, media coverage, and the prevention of ableism.}, } @article {pmid36630716, year = {2023}, author = {Abrego, AM and Khan, W and Wright, CE and Islam, MR and Ghajar, MH and Bai, X and Tandon, N and Seymour, JP}, title = {Sensing local field potentials with a directional and scalable depth electrode array.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb230}, pmid = {36630716}, issn = {1741-2552}, abstract = {A variety of electrophysiology tools are available to the neurosurgeon for diagnosis, functional therapy, and neural prosthetics. However, no tool can currently address these three critical needs: (i) access to all cortical regions in a minimally invasive manner; (ii) recordings with microscale, mesoscale, and macroscale resolutions simultaneously; and (iii) access to spatially distant multiple brain regions that constitute distributed cognitive networks. We present a novel device for recording local field potentials (LFPs) with the form factor of a stereo-electroencephalographic electrode but combined with radially positioned microelectrodes and using the lead body to shield LFP sources, enabling directional sensitivity and scalability, referred to as the DISC array. As predicted by our electro-quasistatic models, DISC demonstrated significantly improved signal-to-noise ratio, directional sensitivity, and decoding accuracy from rat barrel cortex recordings during whisker stimulation. Critical for future translation, DISC demonstrated a higher SNR than virtual ring electrodes and a noise floor approaching that of large ring electrodes in an unshielded environment after common average referencing. DISC also revealed independent, stereoscopic current source density measures whose direction was verified after histology. Directional sensitivity of LFPs may significantly improve brain-computer interfaces and many diagnostic procedures, including epilepsy foci detection and deep brain targeting.}, } @article {pmid36630714, year = {2023}, author = {Guo, Z and Chen, F}, title = {Impacts of simplifying articulation movements imagery to speech imagery BCI performance.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb232}, pmid = {36630714}, issn = {1741-2552}, abstract = {OBJECTIVE: Speech imagery can be used as a reliable, natural, and user-friendly activation task for the development of brain-computer interface (BCI), which empowers individuals with severe disabilities to interact with their environment. The functional near-infrared spectroscopy (fNIRS) is advanced as one of the most suitable brain imaging methods for developing BCI systems owing to its advantages of being non-invasive, portable, insensitive to motion artifacts, and having relatively high spatial resolution.

APPROACH: To improve the classification performance of speech imagery BCI based on fNIRS, a novel paradigm was developed in this work by simplifying the articulation movements in speech imagery to make the articulation movement differences clearer between different words imagery tasks. A speech imagery BCI was proposed to directly answer questions by covertly rehearsing the word '' or '' ('yes' or 'no' in English), and an unconstrained rest task also was contained in this BCI. The articulation movements of speech imagery were simplified by retaining only the movements of the jaw and lips of vowels in Chinese Pinyin for words '' and ''.

MAIN RESULTS: Compared with conventional speech imagery, simplifying the articulation movements in speech imagery could generate more different brain activities among different tasks, which led to more differentiable temporal features and significantly higher classification performance. The average 3-class classification accuracies of the proposed paradigm across all 20 participants reached 69.6 % and 60.2% which were about 10.8% and 5.6% significantly higher than those of the conventional speech imagery paradigm operated in the 0-10 s and 0-2.5 s time windows, respectively.

SIGNIFICANCE: These results suggested that simplifying the articulation movements in speech imagery is promising for improving the classification performance of intuitive BCIs based on speech imagery.}, } @article {pmid36628907, year = {2022}, author = {Lai, JB and Kong, LZ and Chen, J and Hu, SH}, title = {From strict quarantine to an optimized policy: Are we psychologically prepared?.}, journal = {Asian journal of psychiatry}, volume = {81}, number = {}, pages = {103435}, doi = {10.1016/j.ajp.2022.103435}, pmid = {36628907}, issn = {1876-2026}, } @article {pmid36626112, year = {2023}, author = {Dos Santos, EM and San-Martin, R and Fraga, FJ}, title = {Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers.}, journal = {Medical & biological engineering & computing}, volume = {}, number = {}, pages = {}, pmid = {36626112}, issn = {1741-0444}, abstract = {Motor imagery brain-computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain-computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5-4 Hz), alpha (8-13 Hz), and beta+gamma (13-40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed "leave-one-subject-out" (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called "BCI illiteracy." Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.}, } @article {pmid36625869, year = {2023}, author = {Sprinzl, G and Toner, J and Koitschev, A and Berger, N and Keintzel, T and Rasse, T and Baumgartner, WD and Honeder, C and Magele, A and Plontke, S and Götze, G and Schmutzhard, J and Zelger, P and Corkill, S and Lenarz, T and Salcher, R}, title = {Multicentric study on surgical information and early safety and performance results with the Bonebridge BCI 602: an active transcutaneous bone conduction hearing implant.}, journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery}, volume = {}, number = {}, pages = {}, pmid = {36625869}, issn = {1434-4726}, abstract = {AIM: This European multicentric study aimed to prove safety and performance of the Bonebridge BCI 602 in children and adults suffering from either conductive hearing loss (CHL), mixed hearing loss (MHL), or single-sided sensorineural deafness (SSD).

METHODS: 33 patients (13 adults and 10 children with either CHL or MHL and 10 patients with SSD) in three study groups were included. Patients were their own controls (single-subject repeated measures), comparing the unaided or pre-operative to the 3-month post-operative outcomes. Performance was evaluated by sound field thresholds (SF), word recognition scores (WRS) and/or speech reception thresholds in quiet (SRT) and in noise (SNR). Safety was demonstrated with a device-specific surgical questionnaire, adverse event reporting and stable pure-tone measurements.

RESULTS: The Bonebridge BCI 602 significantly improved SF thresholds (+ 25.5 dB CHL/MHL/SSD), speech intelligibility in WRS (+ 68.0% CHL/MHL) and SRT in quiet (- 16.5 dB C/MHL) and in noise (- 3.51 dB SNR SSD). Air conduction (AC) and bone conduction (BC) thresholds remained stable over time. All adverse events were resolved, with none unanticipated. Mean audio processor wearing times in hours [h] per day for the CHL/MHL group were ~ 13 h for adults, ~ 11 h for paediatrics and ~ 6 h for the SSD group. The average surgical length was 57 min for the CHL/MHL group and 42 min for the SSD group. The versatility of the BCI 602 (reduced drilling depth and ability to bend the transition for optimal placement) allows for treatment of normal, pre-operated and malformed anatomies. All audiological endpoints were reached.

CONCLUSIONS: The Bonebridge BCI 602 significantly improved hearing thresholds and speech understanding. Since implant placement follows the patient's anatomy instead of the shape of the device and the duration of surgery is shorter than with its predecessor, implantation is easier with the BCI 602. Performance and safety were proven for adults and children as well as for the CHL/MHL and SSD indications 3 months post-operatively.}, } @article {pmid36624409, year = {2023}, author = {Rusé, J and Clenet, A and Vaiva, G and Debien, C and Arbus, C and Salles, J}, title = {The association between reattempted suicide and incoming calls to the brief contact intervention service, VigilanS: a study of the clinical profile of callers.}, journal = {BMC psychiatry}, volume = {23}, number = {1}, pages = {21}, pmid = {36624409}, issn = {1471-244X}, abstract = {BACKGROUND: Suicide is a major health problem globally. As attempted suicide is a major risk factor for suicide, specific prevention strategies have been designed for use thereafter. An example is the brief contact intervention (BCI). In this regard, France employs a composite BCI, VigilanS, which utilizes three types of contact: phone calls, postcards and a 'who to contact in a crisis' card. Previous studies have found that this system is effective at preventing suicide. Nevertheless, VigilanS was not effective in the same way for all the patients included. This observation raises the question of specific adaptation during follow-up for populations that were less receptive to the service. In consideration of this issue, we identified one study which found that incoming calls to the service were linked with a higher risk of suicide reattempts. However, this study did not document the profiles of the patients who made these calls. Better understanding of why this population is more at risk is important in terms of identifying factors that could be targeted to improve follow-up. This research therefore aims to bring together such data.

METHODS: We performed a retrospective analysis of 579 patients referred to VigilanS by Toulouse University Hospital (France). We examined the sociodemographics, clinical characteristics, and follow-ups in place and compared the patients who made incoming calls to the service versus those who did not. Subsequently, we conducted a regression analysis using the significantly associated element of patients calling VigilanS. Then, in order to better understand this association, we analyzed the factors, including such calls, that were linked to the risk of suicide reattempts.

RESULTS: We found that 22% of the patients in our sample called the VigilanS service. These individuals: were older, at 41.4 years versus 37.9 years for the non-callers; were more likely to have a borderline personality disorder (BPD) diagnosis (28.9% versus 19.3%); and had a history of suicide attempts (71.9% versus 54.6%). Our analysis confirmed that incoming calls to VigilanS (OR = 2.9) were associated with reattempted suicide, as were BPD (OR = 1.8) and a history of suicide attempts (OR = 1.7).

CONCLUSION: There was a high risk that the patients calling VigilanS would make another suicide attempt. However, this association was present regardless of the clinical profile. We postulate that this link between incoming calls and reattempted suicide may arise because this form of contact is, in fact, a way in which patients signal that a further attempt will be made.}, } @article {pmid36622685, year = {2023}, author = {Mitchell, P and Lee, SCM and Yoo, PE and Morokoff, A and Sharma, RP and Williams, DL and MacIsaac, C and Howard, ME and Irving, L and Vrljic, I and Williams, C and Bush, S and Balabanski, AH and Drummond, KJ and Desmond, P and Weber, D and Denison, T and Mathers, S and O'Brien, TJ and Mocco, J and Grayden, DB and Liebeskind, DS and Opie, NL and Oxley, TJ and Campbell, BCV}, title = {Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study.}, journal = {JAMA neurology}, volume = {}, number = {}, pages = {}, doi = {10.1001/jamaneurol.2022.4847}, pmid = {36622685}, issn = {2168-6157}, abstract = {IMPORTANCE: Brain-computer interface (BCI) implants have previously required craniotomy to deliver penetrating or surface electrodes to the brain. Whether a minimally invasive endovascular technique to deliver recording electrodes through the jugular vein to superior sagittal sinus is safe and feasible is unknown.

OBJECTIVE: To assess the safety of an endovascular BCI and feasibility of using the system to control a computer by thought.

The Stentrode With Thought-Controlled Digital Switch (SWITCH) study, a single-center, prospective, first in-human study, evaluated 5 patients with severe bilateral upper-limb paralysis, with a follow-up of 12 months. From a referred sample, 4 patients with amyotrophic lateral sclerosis and 1 with primary lateral sclerosis met inclusion criteria and were enrolled in the study. Surgical procedures and follow-up visits were performed at the Royal Melbourne Hospital, Parkville, Australia. Training sessions were performed at patients' homes and at a university clinic. The study start date was May 27, 2019, and final follow-up was completed January 9, 2022.

INTERVENTIONS: Recording devices were delivered via catheter and connected to subcutaneous electronic units. Devices communicated wirelessly to an external device for personal computer control.

MAIN OUTCOMES AND MEASURES: The primary safety end point was device-related serious adverse events resulting in death or permanent increased disability. Secondary end points were blood vessel occlusion and device migration. Exploratory end points were signal fidelity and stability over 12 months, number of distinct commands created by neuronal activity, and use of system for digital device control.

RESULTS: Of 4 patients included in analyses, all were male, and the mean (SD) age was 61 (17) years. Patients with preserved motor cortex activity and suitable venous anatomy were implanted. Each completed 12-month follow-up with no serious adverse events and no vessel occlusion or device migration. Mean (SD) signal bandwidth was 233 (16) Hz and was stable throughout study in all 4 patients (SD range across all sessions, 7-32 Hz). At least 5 attempted movement types were decoded offline, and each patient successfully controlled a computer with the BCI.

CONCLUSIONS AND RELEVANCE: Endovascular access to the sensorimotor cortex is an alternative to placing BCI electrodes in or on the dura by open-brain surgery. These final safety and feasibility data from the first in-human SWITCH study indicate that it is possible to record neural signals from a blood vessel. The favorable safety profile could promote wider and more rapid translation of BCI to people with paralysis.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03834857.}, } @article {pmid36620442, year = {2022}, author = {Mu, J and Grayden, DB and Tan, Y and Oetomo, D}, title = {Frequency set selection for multi-frequency steady-state visual evoked potential-based brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1057010}, pmid = {36620442}, issn = {1662-4548}, abstract = {OBJECTIVE: Multi-frequency steady-state visual evoked potential (SSVEP) stimulation and decoding methods enable the representation of a large number of visual targets in brain-computer interfaces (BCIs). However, unlike traditional single-frequency SSVEP, multi-frequency SSVEP is not yet widely used. One of the key reasons is that the redundancy in the input options requires an additional selection process to define an effective set of frequencies for the interface. This study investigates systematic frequency set selection methods.

METHODS: An optimization strategy based on the analysis of the frequency components in the resulting multi-frequency SSVEP is proposed, investigated and compared to existing methods, which are constructed based on the analysis of the stimulation (input) signals. We hypothesized that minimizing the occurrence of common sums in the multi-frequency SSVEP improves the performance of the interface, and that selection by pairs further increases the accuracy compared to selection by frequencies. An experiment with 12 participants was conducted to validate the hypotheses.

RESULTS: Our results demonstrated a statistically significant improvement in decoding accuracy with the proposed optimization strategy based on multi-frequency SSVEP features compared to conventional techniques. Both hypotheses were validated by the experiments.

CONCLUSION: Performing selection by pairs and minimizing the number of common sums in selection by pairs are effective ways to select suitable frequency sets that improve multi-frequency SSVEP-based BCI accuracies.

SIGNIFICANCE: This study provides guidance on frequency set selection in multi-frequency SSVEP. The proposed method in this study shows significant improvement in BCI performance (decoding accuracy) compared to existing methods in the literature.}, } @article {pmid36619242, year = {2022}, author = {Wang, L and Lan, Z and Wang, Q and Bai, X and Ma, F}, title = {An Adaptive EEG Classification Algorithm Based on CSSD and ELM_Kernel for Small Training Samples.}, journal = {Journal of healthcare engineering}, volume = {2022}, number = {}, pages = {4509612}, pmid = {36619242}, issn = {2040-2309}, abstract = {Rehabilitation technologies based on brain-computer interface (BCI) have become a promising approach for patients with dyskinesia to regain movement. In BCI experiment, there is often a necessary stage of calibration measurement before the feedback applications. To reduce the time required for initial training, it is of great importance to have a method which can learn to classify electroencephalogram (EEG) signals with a little amount of training data. In this paper, the novel combination of feature extraction and classification algorithm is proposed for classification of EEG signals with a small number of training samples. For feature extraction, the motor imagery EEG signals are pre-processed, and a relative distance criterion is defined to select the optimal combination of channels. Subsequently, common spatial subspace decomposition (CSSD) algorithm and extreme learning machine with kernel (ELM_Kernel) algorithm are used to perform the types of tasks classification of motor imagery EEG signals. Simulation results demonstrate that the proposed method produces a high average classification accuracy of 99.1% on BCI Competition III dataset IVa and 76.92% on BCI Competition IV dataset IIa outperforming state-of-the-art algorithms and obtains a good classification accuracy.}, } @article {pmid36619090, year = {2022}, author = {Chen, J and Zhao, Z and Shu, Q and Cai, G}, title = {Feature extraction based on microstate sequences for EEG-based emotion recognition.}, journal = {Frontiers in psychology}, volume = {13}, number = {}, pages = {1065196}, pmid = {36619090}, issn = {1664-1078}, abstract = {Recognizing emotion from Electroencephalography (EEG) is a promising and valuable research issue in the field of affective brain-computer interfaces (aBCI). To improve the accuracy of emotion recognition, an emotional feature extraction method is proposed based on the temporal information in the EEG signal. This study adopts microstate analysis as a spatio-temporal analysis for EEG signals. Microstates are defined as a series of momentary quasi-stable scalp electric potential topographies. Brain electrical activity could be modeled as being composed of a time sequence of microstates. Microstate sequences provide an ideal macroscopic window for observing the temporal dynamics of spontaneous brain activity. To further analyze the fine structure of the microstate sequence, we propose a feature extraction method based on k-mer. K-mer is a k-length substring of a given sequence. It has been widely used in computational genomics and sequence analysis. We extract features that are based on the D 2 ∗ statistic of k-mer. In addition, we also extract four parameters (duration, occurrence, time coverage, GEV) of each microstate class as features at the coarse level. We conducted experiments on the DEAP dataset to evaluate the performance of the proposed features. The experimental results demonstrate that the fusion of features in fine and coarse levels can effectively improve classification accuracy.}, } @article {pmid36618996, year = {2022}, author = {Pan, J and Chen, X and Ban, N and He, J and Chen, J and Huang, H}, title = {Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1077717}, pmid = {36618996}, issn = {1662-5161}, abstract = {A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.}, } @article {pmid36618992, year = {2022}, author = {Feng, J and Li, Y and Jiang, C and Liu, Y and Li, M and Hu, Q}, title = {Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1068165}, pmid = {36618992}, issn = {1662-5161}, abstract = {INTRODUCTION: Electroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of training samples of MI electroencephalogram (MI-EEG) for a single subject and the great individual differences of MI-EEG among different subjects, the generalization and accuracy of the model on the specific MI task may be poor.

METHODS: To solve these problems, an adaptive cross-subject transfer learning algorithm is proposed, which is based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) method. First, the common spatial pattern (CSP) is used to extract the spatial features. Then, in order to make the feature distribution more similar among different subjects, the KMM algorithm is used to compute a sample weight matrix for aligning the mean between source and target domains and reducing distribution differences among different subjects. Finally, the sample weight matrix from KMM is used as the initialization weight of TrAdaBoost, and then TrAdaBoost is used to adaptively select source domain samples that are closer to the target task distribution to assist in building a classification model.

RESULTS: In order to verify the effectiveness and feasibility of the proposed method, the algorithm is applied to BCI Competition IV datasets and in-house datasets. The results show that the average classification accuracy of the proposed method on the public datasets is 89.1%, and the average classification accuracy on the in-house datasets is 80.4%.

DISCUSSION: Compared with the existing methods, the proposed method effectively improves the classification accuracy of MI-EEG signals. At the same time, this paper also applies the proposed algorithm to the in-house dataset, the results verify the effectiveness of the algorithm again, and the results of this study have certain clinical guiding significance for brain rehabilitation.}, } @article {pmid36617977, year = {2023}, author = {Khan, NN and Ganai, NA and Ahmad, T and Shanaz, S and Majid, R and Mir, MA and Ahmad, SF}, title = {Morphometric indices of native sheep breeds of the Himalayan region of India using multivariate principal component analysis.}, journal = {Zygote (Cambridge, England)}, volume = {}, number = {}, pages = {1-6}, doi = {10.1017/S0967199422000636}, pmid = {36617977}, issn = {1469-8730}, abstract = {This study was performed to analyze the morphometric traits and indices in 3000 animals of five registered sheep breeds in the Himalayan region under a multivariate approach. Data were recorded under field conditions with equal coverage of the five breeds, viz., Karnah, Gurez, Poonchi, Bakerwal and Changthangi on body length (BL), height at withers (HW), chest girth (CG), ear length (EL), and tail length (TL). Furthermore, four derived traits (indices) were studied, which included an index of body frame (IBF), an index of thorax development (ITD), a Baron-Crevat index (BCI), and an index of body weight (IBW). Multivariate principal component analysis (PCA) was undertaken on nine morphometric traits. Kaiser's criterion was used to reduce the number of principal components for further analysis and interpretation. The adequacy of sampling was evaluated using Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity. The mean BL ranged from 52.15 (Changthangi) to 71.13 (Gurez). The estimates of HW, CG, EL and TL were highest in Gurez (63.49), Bakerwal (84.82), Bakerwal (7.26), and Karnah (8.18) breeds, respectively. Among the derived traits, the highest IBF was observed in the Gurez breed with an estimate of 112.22. Upon multivariate PCA on the dataset, the first four principal components were able to explain 92.117% of the total variance. The KMO test, Bartlett's test of sphericity and estimated communalities showed the appropriateness of PCA on the evaluated traits. Four eigenvalues were greater than one and were extracted for further analysis. Morphometric traits were highly correlated, except for EL and TL that showed lower correlation estimates with other traits. The Changthangi population showed the lowest estimates of BL, HW, CG and rectangular body frame. The present study ascertained important morphometric traits/indices that can help in developing selection criteria and formulating sustainable breeding and conservation plans vis-à-vis the unique sheep breeds of the temperate Himalayas.}, } @article {pmid36617798, year = {2022}, author = {Lee, YJ and Lee, HJ and Tae, KS}, title = {Classification of EEG signals related to real and imagery knee movements using deep learning for brain computer interfaces.}, journal = {Technology and health care : official journal of the European Society for Engineering and Medicine}, volume = {}, number = {}, pages = {}, doi = {10.3233/THC-220363}, pmid = {36617798}, issn = {1878-7401}, abstract = {BACKGROUND: Non-invasive Brain-Computer Interface (BCI) uses an electroencephalogram (EEG) to obtain information on brain neural activity. Because EEG can be contaminated by various artifacts during the collection process, it has primarily evolved into motor imagery (MI) with a low risk of contamination. However, MI has a disadvantage in that accurate data is difficult to obtain.

OBJECTIVE: The goal of this study was to determine which motor imagery and movement execution (ME) of the knee has the best classification performance.

METHODS: Ten subjects were selected to provide MI and ME data for four different types of knee exercise. The experiment was conducted to keep the left, right, and both knees extend or bend for five seconds, and there was a five seconds break between each movement. Each motion was performed 20 times and the MI was carried out in the same protocol. Motions were classified through a modified model of the Lenet-5 of CNN (Convolution Neural Network).

RESULTS: The deep learning data was classified, and a study discovered that ME (98.91%) could be classified significantly more accurately than MI (98.37%) (p< 0.001).

CONCLUSION: If future studies on other body movements are conducted, we anticipate that BCI can be further developed to be more accurate. And such advancements in BCI can be used to facilitate the patient's communication by analyzing the user's movement intention. These results can also be used for various controls such as robots using a combination of MI and ME.}, } @article {pmid35950925, year = {2022}, author = {Miskowiak, KW and Yalin, N and Seeberg, I and Burdick, KE and Balanzá-Martínez, V and Bonnin, CDM and Bowie, CR and Carvalho, AF and Dols, A and Douglas, K and Gallagher, P and Hasler, G and Kessing, LV and Lafer, B and Lewandowski, KE and López-Jaramillo, C and Martinez-Aran, A and McIntyre, RS and Porter, RJ and Purdon, SE and Schaffer, A and Sumiyoshi, T and Torres, IJ and Van Rheenen, TE and Yatham, LN and Young, AH and Vieta, E and Stokes, PRA}, title = {Can magnetic resonance imaging enhance the assessment of potential new treatments for cognitive impairment in mood disorders? A systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force.}, journal = {Bipolar disorders}, volume = {24}, number = {6}, pages = {615-636}, pmid = {35950925}, issn = {1399-5618}, mesh = {*Bipolar Disorder/diagnostic imaging/drug therapy ; Cognition ; *Cognitive Dysfunction/diagnostic imaging ; Humans ; Magnetic Resonance Imaging ; Mood Disorders/diagnostic imaging/drug therapy ; }, abstract = {BACKGROUND: Developing treatments for cognitive impairment is key to improving the functioning of people with mood disorders. Neuroimaging may assist in identifying brain-based efficacy markers. This systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force examines the evidence from neuroimaging studies of pro-cognitive interventions.

METHODS: We included magnetic resonance imaging (MRI) studies of candidate interventions in people with mood disorders or healthy individuals, following the procedures of the Preferred Reporting Items for Systematic reviews and Meta-Analysis 2020 statement. Searches were conducted on PubMed/MEDLINE, PsycInfo, EMBASE, Cochrane Library, and Clinicaltrials.gov from inception to 30th April 2021. Two independent authors reviewed the studies using the National Heart, Lung, Blood Institutes of Health Quality Assessment Tool for Controlled Intervention Studies and the quality of neuroimaging methodology assessment checklist.

RESULTS: We identified 26 studies (N = 702). Six investigated cognitive remediation or pharmacological treatments in mood disorders (N = 190). In healthy individuals, 14 studies investigated pharmacological interventions (N = 319), 2 cognitive training (N = 73) and 4 neuromodulatory treatments (N = 120). Methodologies were mostly rated as 'fair'. 77% of studies investigated effects with task-based fMRI. Findings varied but most consistently involved treatment-associated cognitive control network (CCN) activity increases with cognitive improvements, or CCN activity decreases with no cognitive change, and increased functional connectivity. In mood disorders, treatment-related default mode network suppression occurred.

CONCLUSIONS: Modulation of CCN and DMN activity is a putative efficacy biomarker. Methodological recommendations are to pre-declare intended analyses and use task-based fMRI, paradigms probing the CCN, longitudinal assessments, mock scanning, and out-of-scanner tests.}, } @article {pmid36617098, year = {2023}, author = {Alotaibi, FM and Fawad, }, title = {An AI-Inspired Spatio-Temporal Neural Network for EEG-Based Emotional Status.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {1}, pages = {}, doi = {10.3390/s23010498}, pmid = {36617098}, issn = {1424-8220}, abstract = {The accurate identification of the human emotional status is crucial for an efficient human-robot interaction (HRI). As such, we have witnessed extensive research efforts made in developing robust and accurate brain-computer interfacing models based on diverse biosignals. In particular, previous research has shown that an Electroencephalogram (EEG) can provide deep insight into the state of emotion. Recently, various handcrafted and deep neural network (DNN) models were proposed by researchers for extracting emotion-relevant features, which offer limited robustness to noise that leads to reduced precision and increased computational complexity. The DNN models developed to date were shown to be efficient in extracting robust features relevant to emotion classification; however, their massive feature dimensionality problem leads to a high computational load. In this paper, we propose a bag-of-hybrid-deep-features (BoHDF) extraction model for classifying EEG signals into their respective emotion class. The invariance and robustness of the BoHDF is further enhanced by transforming EEG signals into 2D spectrograms before the feature extraction stage. Such a time-frequency representation fits well with the time-varying behavior of EEG patterns. Here, we propose to combine the deep features from the GoogLeNet fully connected layer (one of the simplest DNN models) together with the OMTLBP_SMC texture-based features, which we recently developed, followed by a K-nearest neighbor (KNN) clustering algorithm. The proposed model, when evaluated on the DEAP and SEED databases, achieves a 93.83 and 96.95% recognition accuracy, respectively. The experimental results using the proposed BoHDF-based algorithm show an improved performance in comparison to previously reported works with similar setups.}, } @article {pmid36610247, year = {2022}, author = {Robinette, K and Sims, J and Pang, B and Babu, S}, title = {Transcutaneous versus percutaneous bone-anchored hearing aids: A quality of life comparison.}, journal = {American journal of otolaryngology}, volume = {44}, number = {2}, pages = {103758}, doi = {10.1016/j.amjoto.2022.103758}, pmid = {36610247}, issn = {1532-818X}, abstract = {PURPOSE: To determine whether patients have improved quality of life outcomes with percutaneous bone conduction implant (p-BCI) versus transcutaneous bone conduction implant (t-BCI).

MATERIALS & METHODS: Retrospective chart review of patients who have undergone placement of a BCI in the Ascension St John Providence Health System from 2013 to 2018. Patient satisfaction of t-BCI and p-BCI was measured using a questionnaire that incorporated the Glasgow Benefit Inventory (GBI) and BAHA, aesthetic, hygiene & use (BAHU) survey. Key outcome variables were separated into 2 categories: (1) evaluation of wound healing and implant-associated complications, and (2) quality of life improvements.

RESULTS: Comparative analysis of the 27 p-BCI patients and 10 t-BCI patients showed overall positive benefit with no statistically significant difference on quality of life improvement between the two groups. Total complication rates for p-BCI (48.1 %) vs t-BCI (10 %) was marginally significant (p = 0.056). Rate of revision for p-BCI versus t-BCI was 14.8 % vs 0 %, respectively.

CONCLUSION: This study provides a much-needed comparative insight in patient's experience with these two devices. Understanding which device is preferable in the patient's view will offer helpful information for guiding proper implant selection.}, } @article {pmid36610205, year = {2022}, author = {Tang, W and Shen, T and Huang, Y and Zhu, W and You, S and Zhu, C and Zhang, L and Ma, J and Wang, Y and Zhao, J and Li, T and Lai, HY}, title = {Exploring structural and functional alterations in drug-naïve obsessive-compulsive disorder patients: An ultrahigh field multimodal MRI study.}, journal = {Asian journal of psychiatry}, volume = {81}, number = {}, pages = {103431}, doi = {10.1016/j.ajp.2022.103431}, pmid = {36610205}, issn = {1876-2026}, abstract = {BACKGROUND: Brain structural and functional alterations have been reported in obsessive-compulsive disorder (OCD) patients; however, these findings were inconsistent across studies due to several limitations, including small sample sizes, different inclusion/exclusion criteria, varied demographic characteristics and symptom dimensions, comorbidity, and medication status. Prominent and replicable neuroimaging biomarkers remain to be discovered.

METHODS: This study explored the gray matter structure, neural activity, and white matter microstructure differences in 40 drug-naïve OCD patients and 57 matched healthy controls using ultrahigh field 7.0 T multimodal magnetic resonance imaging, which increased the spatial resolution and detection power. We also evaluated correlations among different modalities, imaging features and clinical symptoms.

RESULTS: Drug-naïve OCD patients exhibited significantly increased gray matter volume in the frontal cortex, especially in the orbitofrontal cortex, as well as volumetric reduction in the temporal lobe, occipital lobe and cerebellum. Increased neural activities were observed in the cingulate gyri and precuneus. Increased temporal-middle cingulate and posterior cingulate-precuneus functional connectivities and decreased frontal-middle cingulate connectivity were further detected. Decreased fractional anisotropy values were found in the cingulum-hippocampus gyrus and inferior fronto-occipital fascicle in OCD patients. Moreover, significantly altered imaging features were related to OCD symptom severity. Altered functional and structural neural connectivity might influence compulsive and obsessive features, respectively.

CONCLUSIONS: Altered structure and function of the classical cortico-striato-thalamo-cortical circuit, limbic system, default mode network, visual, language and sensorimotor networks play important roles in the neurophysiology of OCD.}, } @article {pmid36609445, year = {2023}, author = {Williams, JB and Cao, Q and Wang, W and Lee, YH and Qin, L and Zhong, P and Ren, Y and Ma, K and Yan, Z}, title = {Inhibition of histone methyltransferase Smyd3 rescues NMDAR and cognitive deficits in a tauopathy mouse model.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {91}, pmid = {36609445}, issn = {2041-1723}, abstract = {Pleiotropic mechanisms have been implicated in Alzheimer's disease (AD), including transcriptional dysregulation, protein misprocessing and synaptic dysfunction, but how they are mechanistically linked to induce cognitive deficits in AD is unclear. Here we find that the histone methyltransferase Smyd3, which catalyzes histone H3 lysine 4 trimethylation (H3K4me3) to activate gene transcription, is significantly elevated in prefrontal cortex (PFC) of AD patients and P301S Tau mice, a model of tauopathies. A short treatment with the Smyd3 inhibitor, BCI-121, rescues cognitive behavioral deficits, and restores synaptic NMDAR function and expression in PFC pyramidal neurons of P301S Tau mice. Fbxo2, which encodes an E3 ubiquitin ligase controlling the degradation of NMDAR subunits, is identified as a downstream target of Smyd3. Smyd3-induced upregulation of Fbxo2 in P301S Tau mice is linked to the increased NR1 ubiquitination. Fbxo2 knockdown in PFC leads to the recovery of NMDAR function and cognitive behaviors in P301S Tau mice. These data suggest an integrated mechanism and potential therapeutic strategy for AD.}, } @article {pmid36608342, year = {2023}, author = {Han, J and Xu, M and Xiao, X and Yi, W and Jung, TP and Ming, D}, title = {A high-speed hybrid brain-computer interface with more than 200 targets.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb105}, pmid = {36608342}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain's intentions. No studies have reported a BCI system with over 200 targets.

APPROACH: This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography (EEG) features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments.

MAIN RESULTS: The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37%±7.49% and 86.00%±5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83±39.20 bits/min and 204.47±37.56 bits/min, respectively. Notably, the peak ITR could reach up to 367.83 bits/min.

SIGNIFICANCE: This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI's application scenarios.}, } @article {pmid36608339, year = {2023}, author = {Tao, T and Jia, Y and Xu, G and Liang, R and Zhang, Q and Chen, L and Gao, Y and Chen, R and Zheng, X and Yu, Y}, title = {Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb102}, pmid = {36608339}, issn = {1741-2552}, abstract = {OBJECTIVE: Motor Imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).

APPROACH: A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.

MAIN RESULTS: The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization (ERD) modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after 3 MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after 6 experiments.

SIGNIFICANCE: Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only 3 training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.}, } @article {pmid36607454, year = {2023}, author = {LaMarca, K and Gevirtz, R and Lincoln, AJ and Pineda, JA}, title = {Brain-Computer Interface Training of mu EEG Rhythms in Intellectually Impaired Children with Autism: A Feasibility Case Series.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {36607454}, issn = {1573-3270}, abstract = {Prior studies show that neurofeedback training (NFT) of mu rhythms improves behavior and EEG mu rhythm suppression during action observation in children with autism spectrum disorder (ASD). However, intellectually impaired persons were excluded because of their behavioral challenges. We aimed to determine if intellectually impaired children with ASD, who were behaviorally prepared to take part in a mu-NFT study using conditioned auditory reinforcers, would show improvements in symptoms and mu suppression following mu-NFT. Seven children with ASD (ages 6-8; mean IQ 70.6 ± 7.5) successfully took part in mu-NFT. Four cases demonstrated positive learning trends (hit rates) during mu-NFT (learners), and three cases did not (non-learners). Artifact-creating behaviors were present during tests of mu suppression for all cases, but were more frequent in non-learners. Following NFT, learners showed behavioral improvements and were more likely to show evidence of a short-term increase in mu suppression relative to non-learners who showed little to no EEG or behavior improvements. Results support mu-NFT's application in some children who otherwise may not have been able to take part without enhanced behavioral preparations. Children who have more limitations in demonstrating learning during NFT, or in providing data with relatively low artifact during task-dependent EEG tests, may have less chance of benefiting from mu-NFT. Improving the identification of ideal mu-NFT candidates, mu-NFT learning rates, source analyses, EEG outcome task performance, population-specific artifact-rejection methods, and the theoretical bases of NFT protocols, could aid future BCI-based, neurorehabilitation efforts.}, } @article {pmid36606248, year = {2022}, author = {Kim, J and Jiang, X and Forenzo, D and Liu, Y and Anderson, N and Greco, CM and He, B}, title = {Immediate effects of short-term meditation on sensorimotor rhythm-based brain-computer interface performance.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1019279}, pmid = {36606248}, issn = {1662-5161}, abstract = {INTRODUCTION: Meditation has been shown to enhance a user's ability to control a sensorimotor rhythm (SMR)-based brain-computer interface (BCI). For example, prior work have demonstrated that long-term meditation practices and an 8-week mindfulness-based stress reduction (MBSR) training have positive behavioral and neurophysiological effects on SMR-based BCI. However, the effects of short-term meditation practice on SMR-based BCI control are still unknown.

METHODS: In this study, we investigated the immediate effects of a short, 20-minute meditation on SMR-based BCI control. Thirty-seven subjects performed several runs of one-dimensional cursor control tasks before and after two types of 20-minute interventions: a guided mindfulness meditation exercise and a recording of a narrator reading a journal article.

RESULTS: We found that there is no significant change in BCI performance and Electroencephalography (EEG) BCI control signal following either 20-minute intervention. Moreover, the change in BCI performance between the meditation group and the control group was found to be not significant.

DISCUSSION: The present results suggest that a longer period of meditation is needed to improve SMR-based BCI control.}, } @article {pmid36604821, year = {2023}, author = {Echtioui, A and Zouch, W and Ghorbel, M and Mhiri, C and Hamam, H}, title = {Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.}, journal = {Clinical EEG and neuroscience}, volume = {}, number = {}, pages = {15500594221148285}, doi = {10.1177/15500594221148285}, pmid = {36604821}, issn = {2169-5202}, abstract = {Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with the device by tackling a sequence of motor imagery tasks. However, the extracting user-specific features and increasing the accuracy of the classifier remain as difficult tasks in MI-based BCI. In this work, we propose a new method using artificial neural network (ANN) enhancing the performance of the motor imagery classification. Feature extraction techniques, like time domain parameters, band power features, signal power features, and wavelet packet decomposition (WPD), are studied and compared. Four classification algorithms are implemented which are Quadratic Discriminant Analysis, k-Nearest Neighbors, Linear Discriminant Analysis, and proposed ANN architecture. We added Batch Normalization layers to the proposed ANN architecture to improve the learning time and accuracy of the neural network. These layers also alleviate the effect of weight initialization and the addition of a regularization effect on the network. Our proposed method using ANN architecture achieves 0.5545 of kappa and 58.42% of accuracy on the BCI Competition IV-2a dataset. Our results show that the modified ANN method, with frequency and spatial features extracted by WPD and Common Spatial Pattern, respectively, offers a better classification compared to other current methods.}, } @article {pmid36604739, year = {2023}, author = {Sun, G and McCartin, M and Liu, W and Zhang, Q and Kenefati, G and Chen, ZS and Wang, J}, title = {Temporal pain processing in the primary somatosensory cortex and anterior cingulate cortex.}, journal = {Molecular brain}, volume = {16}, number = {1}, pages = {3}, pmid = {36604739}, issn = {1756-6606}, support = {GM115384/GM/NIGMS NIH HHS/United States ; NS121776/NS/NINDS NIH HHS/United States ; }, abstract = {Pain is known to have sensory and affective components. The sensory pain component is encoded by neurons in the primary somatosensory cortex (S1), whereas the emotional or affective pain experience is in large part processed by neural activities in the anterior cingulate cortex (ACC). The timing of how a mechanical or thermal noxious stimulus triggers activation of peripheral pain fibers is well-known. However, the temporal processing of nociceptive inputs in the cortex remains little studied. Here, we took two approaches to examine how nociceptive inputs are processed by the S1 and ACC. We simultaneously recorded local field potentials in both regions, during the application of a brain-computer interface (BCI). First, we compared event related potentials in the S1 and ACC. Next, we used an algorithmic pain decoder enabled by machine-learning to detect the onset of pain which was used during the implementation of the BCI to automatically treat pain. We found that whereas mechanical pain triggered neural activity changes first in the S1, the S1 and ACC processed thermal pain with a reasonably similar time course. These results indicate that the temporal processing of nociceptive information in different regions of the cortex is likely important for the overall pain experience.}, } @article {pmid36604186, year = {2023}, author = {Zhang, P and Zhang, D and Lai, J and Fu, Y and Wu, L and Huang, H and Pan, Y and Jiang, J and Xi, C and Che, Z and Song, X and Hu, S}, title = {Characteristics of the gut microbiota in bipolar depressive disorder patients with distinct weight.}, journal = {CNS neuroscience & therapeutics}, volume = {}, number = {}, pages = {}, doi = {10.1111/cns.14078}, pmid = {36604186}, issn = {1755-5949}, abstract = {BACKGROUND: Preliminary studies have indicated metabolic dysfunction and gut dysbiosis in patients with bipolar disorder (BD). In this study, we aimed to clarify the impact of the gut microbial composition and function on metabolic dysfunction in BD patients with an acute depressive episode.

METHODS: Fresh fecal samples were provided from 58 patients with BD depression, including 29 with normal weight (NW) and 29 with overweight/obesity (OW), and 31 healthy controls (HCs). The hypervariable region of 16 S rRNA gene (V3-V4) sequencing was performed using IonS5TMXL platform to evaluate the bacterial communities. Differences of microbial community and correlation to clinical parameters across different groups were analyzed.

RESULTS: Compared to NW and HCs, the OW group showed a decreased tendency in alpha diversity index. Beta diversity was markedly different among these groups (PERMANOVA: R[2]  = 0.034, p = 0.01) and was higher in patients versus HCs. A total number of 24 taxa displayed significantly different abundance among OW, NW, and HCs. At the family level, the abundance of three taxa was remarkably increased in NW, one in OW, and one in HCs. At the genus level, five taxa were enriched in OW, eight in NW, and two in HCs. The relative abundance of the genera Megamonas was positively associated with BMI, while Eggerthella was negatively correlated with BMI. Functional prediction analysis revealed the metabolism of cofactors and vitamins and amino acid were highly enriched in OW compared to HCs. In addition, microbial functions involved in "lipid metabolism" were depleted while the "fructose and mannose metabolism" was enriched in OW compared to NW group.

CONCLUSIONS: Specific bacterial taxa involved in pathways regulating the lipid, energy, and amino acid metabolisms may underlie the weight concerns in depressed BD patients. Potential targeting gut microbial therapy is provided for overweight/obesity patients with BD, which still need further studies in the future.}, } @article {pmid36603232, year = {2022}, author = {Yu, W and Zhao, F and Ren, Z and Jin, D and Yang, X and Zhang, X}, title = {Mining attention distribution paradigm: Discover gaze patterns and their association rules behind the visual image.}, journal = {Computer methods and programs in biomedicine}, volume = {230}, number = {}, pages = {107330}, doi = {10.1016/j.cmpb.2022.107330}, pmid = {36603232}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Attention allocation reflects the way of humans filtering and organizing the information. On one hand, different task scenarios seriously affect human's rule of attention distribution, on the other hand, visual attention reflecting the cognitive and psychological process. Most of the previous studies on visual attention allocation are based on cognitive models, predicted models, or statistical analysis of eye movement data or visual images, however, these methods are inadequate to provide an inside view of gaze behavior to reveal the attention distribution pattern within scenario context. Moreover, they seldom study the association rules of these patterns. Therefore, we adopted the big data mining approach to discover the paradigm of visual attention distribution.

METHODS: We applied the data mining method to extract the gaze patterns to discover the regularities of attention distribution behavior within the scenario context. The proposed method consists of three components, tasks scenario segmented and clustered, gaze pattern mining, and association rule of frequent pattern mining.

RESULTS: The proposed approach is tested on the operation platform. The complex operation task is simultaneously segmented and clustered with the TICC-based method and evaluated by the BCI index. The operator's eye movement frequent patterns and their association rule are discovered. The results demonstrate that our method can associate the eye-tracking data with the task-oriented scene data.

DISCUSSION: The proposed method provides the benefits of being able to explicitly express and quantitatively analyze people's visual attention patterns. The proposed method can not only be applied in the field of aerospace medicine and aviation psychology, but also can likely be applied to computer-aided diagnosis and follow-up tool for neurological disease and cognitive impairment related disease, such as ADHD (Attention Deficit Hyperactivity Disorder), neglect syndrome, social attention differences in ASD (Autism spectrum disorder).}, } @article {pmid36601593, year = {2022}, author = {Schalk, G and Worrell, S and Mivalt, F and Belsten, A and Kim, I and Morris, JM and Hermes, D and Klassen, BT and Staff, NP and Messina, S and Kaufmann, T and Rickert, J and Brunner, P and Worrell, GA and Miller, KJ}, title = {Toward a fully implantable ecosystem for adaptive neuromodulation in humans: Preliminary experience with the CorTec BrainInterchange device in a canine model.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {932782}, pmid = {36601593}, issn = {1662-4548}, abstract = {This article describes initial work toward an ecosystem for adaptive neuromodulation in humans by documenting the experience of implanting CorTec's BrainInterchange (BIC) device in a beagle canine and using the BCI2000 environment to interact with the BIC device. It begins with laying out the substantial opportunity presented by a useful, easy-to-use, and widely available hardware/software ecosystem in the current landscape of the field of adaptive neuromodulation, and then describes experience with implantation, software integration, and post-surgical validation of recording of brain signals and implant parameters. Initial experience suggests that the hardware capabilities of the BIC device are fully supported by BCI2000, and that the BIC/BCI2000 device can record and process brain signals during free behavior. With further development and validation, the BIC/BCI2000 ecosystem could become an important tool for research into new adaptive neuromodulation protocols in humans.}, } @article {pmid36601085, year = {2022}, author = {Yan, L and Hou, Z and Fu, W and Yu, Y and Cui, R and Miao, Z and Lou, X and Ma, N}, title = {Association of periprocedural perfusion non-improvement with recurrent stroke after endovascular treatment for Intracranial Atherosclerotic Stenosis.}, journal = {Therapeutic advances in neurological disorders}, volume = {15}, number = {}, pages = {17562864221143178}, pmid = {36601085}, issn = {1756-2856}, abstract = {BACKGROUND: Predictors of recurrent stroke after endovascular treatment for symptomatic intracranial atherosclerotic stenosis (ICAS) remain uncertain.

OBJECTIVES: Among baseline characteristics, lesion features, and cerebral perfusion changes, we try to explore which factors are associated with the risk of recurrent stroke in symptomatic ICAS after endovascular treatment.

DESIGN: Consecutive patients with symptomatic ICAS of 70-99% receiving endovascular treatment were enrolled. All patients underwent whole-brain computer tomography perfusion (CTP) within 3 days before and 3 days after the endovascular treatment. Baseline characteristics, lesion features, and cerebral perfusion changes were collected.

METHODS: Cerebral perfusion changes were evaluated with RAPID software and calculated as preprocedural cerebral blood flow (CBF) < 30%, time to maximum of the residue function (Tmax) > 6 s, and Tmax > 4 s volumes minus postprocedural. Cerebral perfusion changes were divided into periprocedural perfusion improvement (>0 ml) and non-improvement (⩽ 0 ml). Recurrent stroke within 180 days was collected. The Cox proportional hazards analysis analyses were performed to evaluate factors associated with recurrent stroke.

RESULTS: From March 2021 to December 2021, 107 patients with symptomatic ICAS were enrolled. Of the 107 enrolled patients, 30 (28.0%) patients underwent balloon angioplasty alone and 77 patients (72.0%) underwent stenting. The perioperative complications occurred in three patients. Among CBF < 30%, Tmax > 6 s, and Tmax > 4 s volumes, Tmax > 4 s volume was available to evaluate cerebral perfusion changes. Periprocedural perfusion improvement was found in 77 patients (72.0%) and non-improvement in 30 patients (28.0%). Nine patients (8.4%) suffered from recurrent stroke in 180-day follow-up. In Cox proportional hazards analysis adjusted for age and sex, perfusion non-improvement was associated with recurrent stroke [hazards ratio (HR): 4.472; 95% CI: 1.069-18.718; p = 0.040].

CONCLUSION: In patients with symptomatic ICAS treated with endovascular treatment, recurrent stroke may be related to periprocedural cerebral perfusion non-improvement.

REGISTRATION: http://www.chictr.org.cn. Unique identifier: ChiCTR2100052925.}, } @article {pmid36600620, year = {2023}, author = {Zhang, Y and Tao, S and Coid, J and Wei, W and Wang, Q and Yue, W and Yan, H and Tan, L and Chen, Q and Yang, G and Lu, T and Wang, L and Zhang, F and Yang, J and Li, K and Lv, L and Tan, Q and Zhang, H and Ma, X and Yang, F and Lingjiang, and Wang, C and Zhao, L and Deng, W and Guo, W and Ma, X and Zhang, D and Li, }, title = {The Role of Total White Blood Cell Count in Antipsychotic Treatment for Patients with Schizophrenia.}, journal = {Current neuropharmacology}, volume = {}, number = {}, pages = {}, doi = {10.2174/1570159X21666230104090046}, pmid = {36600620}, issn = {1875-6190}, abstract = {BACKGROUND: Total white blood cell count (TWBCc), an index of chronic and low-grade inflammation, is associated with clinical symptoms and metabolic alterations in patients with schizophrenia. The effect of antipsychotics on TWBCc, predictive values of TWBCc for drug response, and role of metabolic alterations require further study.

METHODS: Patients with schizophrenia were randomized to monotherapy with risperidone, olanzapine,quetiapine, aripiprazole, ziprasidone, perphenazine or haloperidol in a 6-week pharmacological trial. We repeatedly measured clinical symptoms, TWBCc, and metabolic measures (body mass index, blood pressure, waist circumference, fasting blood lipids and glucose). We used mixed-effect linear regression models to test whether TWBCc can predict drug response. Mediation analysis to investigate metabolic alteration effects on drug response.

RESULTS: At baseline, TWBCc was higher among patients previously medicated. After treatment with risperidone, olanzapine, quetiapine, perphenazine, and haloperidol, TWBCc decreased significantly (p<0.05). Lower baseline TWBCc predicted greater reductions in Positive and Negative Syndrome Scale (PANSS) total and negative scores over time (p<0.05). We found significant mediation of TWBCc for effects of waist circumference, fasting low-density lipoprotein cholesterol, and glucose on reductions in PANSS total scores and PANSS negative subscale scores (p<0.05).

CONCLUSION: TWBCc is affected by certain antipsychotics among patients with schizophrenia, with decreases observed following short-term, but increases following long-term treatment. TWBCc is predictive of drug response, with lower TWBCc predicting better responses to antipsychotics. It also mediates the effects of certain metabolic measures on improvement of negative symptoms. This.}, } @article {pmid36600612, year = {2023}, author = {Kodama, M and Iwama, S and Morishige, M and Ushiba, J}, title = {Thirty-minute motor imagery exercise aided by EEG sensorimotor rhythm neurofeedback enhances morphing of sensorimotor cortices: a double-blind sham-controlled study.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhac525}, pmid = {36600612}, issn = {1460-2199}, abstract = {Neurofeedback training using electroencephalogram (EEG)-based brain-computer interfaces (BCIs) combined with mental rehearsals of motor behavior has demonstrated successful self-regulation of motor cortical excitability. However, it remains unclear whether the acquisition of skills to voluntarily control neural excitability is accompanied by structural plasticity boosted by neurofeedback. Here, we sought short-term changes in cortical structures induced by 30 min of BCI-based neurofeedback training, which aimed at the regulation of sensorimotor rhythm (SMR) in scalp EEG. When participants performed kinesthetic motor imagery of right finger movement with online feedback of either event-related desynchronisation (ERD) of SMR magnitude from the contralateral sensorimotor cortex (SM1) or those from other participants (i.e. placebo), the learning rate of SMR-ERD control was significantly different. Although overlapped structural changes in gray matter volumes were found in both groups, significant differences revealed by group-by-group comparison were spatially different; whereas the veritable neurofeedback group exhibited sensorimotor area-specific changes, the placebo exhibited spatially distributed changes. The white matter change indicated a significant decrease in the corpus callosum in the verum group. Furthermore, the learning rate of SMR regulation was correlated with the volume changes in the ipsilateral SM1, suggesting the involvement of interhemispheric motor control circuitries in BCI control tasks.}, } @article {pmid36598169, year = {2023}, author = {Sinha, S and Hashim, H and Finazzi-Agrò, E and Dmochowski, RR and Iacovelli, V}, title = {The bladder contractility and bladder outlet obstruction indices in children. Results of a global Delphi consensus study.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25129}, pmid = {36598169}, issn = {1520-6777}, abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of Bladder Contractility Index (BCI), Bladder Outlet Obstruction Index (BOOI), and the related evidence. This manuscript deals with children and follows previous manuscripts reporting on adult men and women.

METHODS: Eighteen experts were invited to answer the two-round survey including three foundation questions and four survey questions. Consensus was defined as ≥75% agreement. The ordinal scale (0-10) in Round 1 was classified into "strongly agree," "agree," "neutral," "disagree," and "strongly disagree" for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in children.

RESULTS: Eleven experts participated in the survey with 100% completion. Consensus was not noted with regard to any of the questions. There was a general trend toward disagreement with the utility of the BCI and BOOI in children. Systematic search yielded one publication pertaining the value of the indices in predicting long-term outcome in boys treated for posterior urethral valves.

CONCLUSIONS: This global Delphi survey of experts showed a general disinclination to use numerical indices for bladder contractility and bladder outflow obstruction in children. There is very little data on the use of the BCI and BOOI indices in children. The establishment of urodynamic indices in children might help refine the treatment of functional urological disorders in children.}, } @article {pmid36595316, year = {2022}, author = {Yasemin, M and Cruz, A and Nunes, UJ and Pires, G}, title = {Single trial detection of error-related potentials in brain-machine interfaces: A survey and comparison of methods.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acabe9}, pmid = {36595316}, issn = {1741-2552}, abstract = {OBJECTIVE: Error-related Potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain-computer interfaces (BCIs), increase the naturalness of human-machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use. The literature presents ErrP detection accuracies quite variable across studies, which raises the question of whether this variability depends more on classification pipelines or on the quality of elicited ErrPs (mostly directly related to the underlying paradigms).

APPROACH: With this purpose, 11 datasets have been used to compare several classification pipelines which were selected according to the studies that reported online performance above 75%. We also analyze the effects of different steps of the pipelines, such as resampling, window selection, augmentation, feature extraction, and classification.

MAIN RESULTS: From our analysis, we have found that shrinkage-regularized Linear Discriminant Analysis (sLDA) is the most robust method for classification, and for feature extraction, using Fisher Criterion Beamformer (FCB) spatial features and overlapped window averages result in better classification performance. The overall experimental results suggest that classification accuracy is highly dependent on user tasks in BCI experiments and on signal quality (in terms of ErrP morphology, signal-to-noise ratio (SNR), and discrimination).

SIGNIFICANCE: This study contributes to the BCI research field by responding to the need for a guideline that can direct researchers in designing ErrP-based BCI tasks by accelerating the design steps.}, } @article {pmid36594734, year = {2023}, author = {Huang, Z and Wu, J and Zhao, Y and Zhang, D and Tong, L and Gao, F and Liu, C and Chen, F}, title = {Starch-based shape memory sponge for rapid hemostasis in penetrating wounds.}, journal = {Journal of materials chemistry. B}, volume = {}, number = {}, pages = {}, doi = {10.1039/d2tb02364d}, pmid = {36594734}, issn = {2050-7518}, abstract = {Death caused by excessive blood loss has always been a global concern. Timely control of bleeding in incompressible penetrated wounds remains a great challenge. Here, we developed a shape memory sponge (SQG) based on modified starch and gelatin (Gel) to control the hemorrhage of penetrating wounds. The porous structure of SQG greatly enhanced the absorption of blood, and the adhesion of erythrocytes and platelets. The water absorption rate of SQG reached 1178.72 ± 12.18% in 10 s. SQG quickly recovered its shape in water (∼3 s) and exhibited high mechanical strength (∼38 kPa), acting as a physically packed barrier to facilitate hemostasis. Furthermore, the positively charged sponges were conducive to activating platelets and promoting the release of coagulation factors. SQG sponges possessed the lowest blood coagulation index (BCI) of 21.32 ± 0.19%, and presented good biocompatibility and obvious inhibitory effect on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Moreover, SQG sponges controlled complete bleeding in 69 ± 20 s and a bleeding loss of 334 ± 138 mg was observed, nearly 50% lower than that of gelatin sponge in rabbit liver penetrating wounds. Overall, SQG possesses a combination of potent shape recovery, rapid hemostasis, and excellent antibacterial and degradation ability, enabling promising applications for hemostasis in non-compressible penetrating wounds.}, } @article {pmid36591913, year = {2023}, author = {Wang, W and Li, B}, title = {A novel model based on a 1D-ResCNN and transfer learning for processing EEG attenuation.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-14}, doi = {10.1080/10255842.2022.2162339}, pmid = {36591913}, issn = {1476-8259}, abstract = {EEG signals are valuable signals in clinical medicine, brain research, and the study of neurological illnesses. However, EEG signal attenuation may occur at any time from signal generation through BCI device acquisition due to defects in the brain-computer interface (BCI) devices, restrictions in the dynamic network, and individual variations across the subjects. The attenuation of EEG data will alter the data distribution and lead to information fuzziness, substantially influencing subsequent EEG research. A model based on one-dimensional residual convolutional neural networks (1D-ResCNN) and transfer learning is proposed in this article to reduce the negative impacts of EEG attenuation. An end-to-end manner maps an attenuated EEG signal to a normal EEG signal. The structure employs a multi-level residual connection structure with varying weight coefficients, transferring characteristics from the bottom to the top of the convolutional neural network, enhancing feature learning. In addition, we initialize the subsequent denoising model using the transfer learning method. The combination of these two networks can well solve the attenuation problem of EEG signals. Experiments are carried out using the EEG-denoisenet data set. According to the findings, the model can yield a clear waveform with a decent SNR and RRMSE value.}, } @article {pmid36590466, year = {2022}, author = {Tonin, L and Perdikis, S and Kuzu, TD and Pardo, J and Orset, B and Lee, K and Aach, M and Schildhauer, TA and Martínez-Olivera, R and Millán, JDR}, title = {Learning to control a BMI-driven wheelchair for people with severe tetraplegia.}, journal = {iScience}, volume = {25}, number = {12}, pages = {105418}, pmid = {36590466}, issn = {2589-0042}, abstract = {Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.}, } @article {pmid36589747, year = {2022}, author = {Benito-León, M and Gil-Redondo, JC and Perez-Sen, R and Delicado, EG and Ortega, F and Gomez-Villafuertes, R}, title = {BCI, an inhibitor of the DUSP1 and DUSP6 dual specificity phosphatases, enhances P2X7 receptor expression in neuroblastoma cells.}, journal = {Frontiers in cell and developmental biology}, volume = {10}, number = {}, pages = {1049566}, pmid = {36589747}, issn = {2296-634X}, abstract = {P2X7 receptor (P2RX7) is expressed strongly by most human cancers, including neuroblastoma, where high levels of P2RX7 are correlated with a poor prognosis for patients. Tonic activation of P2X7 receptor favors cell metabolism and angiogenesis, thereby promoting cancer cell proliferation, immunosuppression, and metastasis. Although understanding the mechanisms that control P2X7 receptor levels in neuroblastoma cells could be biologically and clinically relevant, the intracellular signaling pathways involved in this regulation remain poorly understood. Here we show that (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI), an allosteric inhibitor of dual specificity phosphatases (DUSP) 1 and 6, enhances the expression of P2X7 receptor in N2a neuroblastoma cells. We found that exposure to BCI induces the phosphorylation of mitogen-activated protein kinases p38 and JNK, while it prevents the phosphorylation of ERK1/2. BCI enhanced dual specificity phosphatase 1 expression, whereas it induced a decrease in the dual specificity phosphatase 6 transcripts, suggesting that BCI-dependent inhibition of dual specificity phosphatase 1 may be responsible for the increase in p38 and JNK phosphorylation. The weaker ERK phosphorylation induced by BCI was reversed by p38 inhibition, indicating that this MAPK is involved in the regulatory loop that dampens ERK activity. The PP2A phosphatase appears to be implicated in the p38-dependent dephosphorylation of ERK1/2. In addition, the PTEN phosphatase inhibition also prevented ERK1/2 dephosphorylation, probably through p38 downregulation. By contrast, inhibition of the p53 nuclear factor decreased ERK phosphorylation, probably enhancing the activity of p38. Finally, the inhibition of either p38 or Sp1-dependent transcription halved the increase in P2X7 receptor expression induced by BCI. Moreover, the combined inhibition of both p38 and Sp1 completely prevented the effect exerted by BCI. Together, our results indicate that dual specificity phosphatase 1 acts as a novel negative regulator of P2X7 receptor expression in neuroblastoma cells due to the downregulation of the p38 pathway.}, } @article {pmid36589278, year = {2022}, author = {Shi, B and Chen, X and Yue, Z and Zeng, F and Yin, S and Wang, B and Wang, J}, title = {Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram classification.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1004301}, pmid = {36589278}, issn = {1662-5188}, abstract = {BACKGROUND: Effectively decoding electroencephalogram (EEG) pattern for specific mental tasks is a crucial topic in the development of brain-computer interface (BCI). Extracting common spatial pattern (CSP) features from motor imagery EEG signals is often highly dependent on the selection of frequency band and time interval. Therefore, optimizing frequency band and time interval would contribute to effective feature extraction and accurate EEG decoding.

OBJECTIVE: This study proposes an approach based on an improved novel global harmony search (INGHS) to optimize frequency-time parameters for effective CSP feature extraction.

METHODS: The INGHS algorithm is applied to find the optimal frequency band and temporal interval. The linear discriminant analysis and support vector machine are used for EEG pattern decoding. Extensive experimental studies are conducted on three EEG datasets to assess the effectiveness of our proposed method.

RESULTS: The average test accuracy obtained by the time-frequency parameters selected by the proposed INGHS method is slightly better than artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Furthermore, the INGHS algorithm is superior to PSO and ABC in running time.

CONCLUSION: These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.}, } @article {pmid36588886, year = {2022}, author = {Xu, G and Hao, F and Zhao, W and Qiu, J and Zhao, P and Zhang, Q}, title = {The influential factors and non-pharmacological interventions of cognitive impairment in children with ischemic stroke.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1072388}, pmid = {36588886}, issn = {1664-2295}, abstract = {BACKGROUND: The prevalence of pediatric ischemic stroke rose by 35% between 1990 and 2013. Affected patients can experience the gradual onset of cognitive impairment in the form of impaired language, memory, intelligence, attention, and processing speed, which affect 20-50% of these patients. Only few evidence-based treatments are available due to significant heterogeneity in age, pathological characteristics, and the combined epilepsy status of the affected children.

METHODS: We searched the literature published by Web of Science, Scopus, and PubMed, which researched non-pharmacological rehabilitation interventions for cognitive impairment following pediatric ischemic stroke. The search period is from the establishment of the database to January 2022.

RESULTS: The incidence of such impairment is influenced by patient age, pathological characteristics, combined epilepsy status, and environmental factors. Non-pharmacological treatments for cognitive impairment that have been explored to date mainly include exercise training, psychological intervention, neuromodulation strategies, computer-assisted cognitive training, brain-computer interfaces (BCI), virtual reality, music therapy, and acupuncture. In childhood stroke, the only interventions that can be retrieved are psychological intervention and neuromodulation strategies.

CONCLUSION: However, evidence regarding the efficacy of these interventions is relatively weak. In future studies, the active application of a variety of interventions to improve pediatric cognitive function will be necessary, and neuroimaging and electrophysiological measurement techniques will be of great value in this context. Larger multi-center prospective longitudinal studies are also required to offer more accurate evidence-based guidance for the treatment of patients with pediatric stroke.}, } @article {pmid36590862, year = {2019}, author = {Sharpee, TO and Berkowitz, JA}, title = {Linking neural responses to behavior with information-preserving population vectors.}, journal = {Current opinion in behavioral sciences}, volume = {29}, number = {}, pages = {37-44}, pmid = {36590862}, issn = {2352-1546}, abstract = {All systems for processing signals, both artificial and within animals, must obey fundamental statistical laws for how information can be processed. We discuss here recent results using information theory that provide a blueprint for building circuits where signals can be read-out without information loss. Many properties that are necessary to build information-preserving circuits are actually observed in real neurons, at least approximately. One such property is the use of logistic nonlinearity for relating inputs to neural response probability. Such nonlinearities are common in neural and intracellular networks. With this nonlinearity type, there is a linear combination of neural responses that is guaranteed to preserve Shannon information contained in the response of a neural population, no matter how many neurons it contains. This read-out measure is related to a classic quantity known as the population vector that has been quite successful in relating neural responses to animal behavior in a wide variety of cases. Nevertheless, the population vector did not withstand the scrutiny of detailed information-theoretical analyses that showed that it discards substantial amounts of information contained in the responses of a neural population. We discuss recent theoretical results showing how to modify the population vector expression to make it 'information-preserving', and what is necessary in terms of neural circuit organization to allow for lossless information transfer. Implementing these strategies within artificial systems is likely to increase their efficiency, especially for brain-machine interfaces.}, } @article {pmid36587114, year = {2023}, author = {Pan, W and Huang, X and Yu, Z and Ding, Q and Xia, L and Hua, J and Gu, B and Xiong, Q and Yu, H and Wang, J and Xu, Z and Zeng, L and Bai, G and Liu, H}, title = {Netrin-3 Suppresses Diabetic Neuropathic Pain by Gating the Intra-epidermal Sprouting of Sensory Axons.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36587114}, issn = {1995-8218}, abstract = {Diabetic neuropathic pain (DNP) is the most common disabling complication of diabetes. Emerging evidence has linked the pathogenesis of DNP to the aberrant sprouting of sensory axons into the epidermal area; however, the underlying molecular events remain poorly understood. Here we found that an axon guidance molecule, Netrin-3 (Ntn-3), was expressed in the sensory neurons of mouse dorsal root ganglia (DRGs), and downregulation of Ntn-3 expression was highly correlated with the severity of DNP in a diabetic mouse model. Genetic ablation of Ntn-3 increased the intra-epidermal sprouting of sensory axons and worsened the DNP in diabetic mice. In contrast, the elevation of Ntn-3 levels in DRGs significantly inhibited the intra-epidermal axon sprouting and alleviated DNP in diabetic mice. In conclusion, our studies identified Ntn-3 as an important regulator of DNP pathogenesis by gating the aberrant sprouting of sensory axons, indicating that Ntn-3 is a potential druggable target for DNP treatment.}, } @article {pmid36586179, year = {2022}, author = {Havaei, P and Zekri, M and Mahmoudzadeh, E and Rabbani, H}, title = {An efficient deep learning framework for P300 evoked related potential detection in EEG signal.}, journal = {Computer methods and programs in biomedicine}, volume = {229}, number = {}, pages = {107324}, doi = {10.1016/j.cmpb.2022.107324}, pmid = {36586179}, issn = {1872-7565}, abstract = {BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can exhibit their characteristics to reveal an effective solution in the detection of P300 evoked related potential (ERP). By applying a drastic number of convolutional layers, the majority of deep networks elicit sufficient properties for the output determination, leading to gigantic and time-consuming structures. In this paper, we propose a novel deep learning framework by the combination of tuned GT, and modified HOG with the CNN as "TGT-MHOG-CNN" for detection of P300 ERP in EEG signal.

METHOD: In the proposed method, GT is tuned based on triangular function for EEG signals, and then spectrograms including time-frequency information are captured. The function's parameters are justified to differentiate the signals with the P300 component. Furthermore, HOG is modified (MHOG) for the 2-D EEG signal, and consequently, gradients patterns are extracted for the target potentials. MHOG is potent in distinguishing the positive peak in the general waveform; however, GT unravels time-frequency information, which is ignored in the gradient histogram. These outputs of GT and MHOG do not share the same nature in the images nor overlap. Therefore, more extensive information is reached without redundancy or excessive information by fusing them. Combining GT and MHOG provides different patterns which benefit CNN for more precise detection. Consequently, TGT-MHOG-CNN ends in a more straightforward structure than other networks, and therefore, the whole performance is acceptable with faster rates and very high accuracy.

RESULTS: BCI Competition II and III datasets are used to evaluate the performance of the proposed method. These datasets include a complete record for P300 ERP with BCI2000 using a paradigm, and it has numerous noises, including power and muscle-based noises. The objective is to predict the correct character in each provided character selection epochs. Compared to state-of-the-art methods, simulation results indicate striking abilities of the proposed framework for P300 ERP detection. Our best record reached the P300 ERP classification rates of over 98.7% accuracy and 98.7% precision for BCI Competition II and 99% accuracy and 100% precision for BCI Competition III datasets, with superiority in execution time for the mentioned datasets.}, } @article {pmid36586146, year = {2022}, author = {Borda, E and Medagoda, DI and Airaghi Leccardi, MJI and Zollinger, EG and Ghezzi, D}, title = {Conformable neural interface based on off-stoichiometry thiol-ene-epoxy thermosets.}, journal = {Biomaterials}, volume = {293}, number = {}, pages = {121979}, doi = {10.1016/j.biomaterials.2022.121979}, pmid = {36586146}, issn = {1878-5905}, abstract = {Off-stoichiometry thiol-ene-epoxy (OSTE+) thermosets show low permeability to gases and little absorption of dissolved molecules, allow direct low-temperature dry bonding without surface treatments, have a low Young's modulus, and can be manufactured via UV polymerisation. For these reasons, OSTE+ thermosets have recently gained attention for the rapid prototyping of microfluidic chips. Moreover, their compatibility with standard clean-room processes and outstanding mechanical properties make OSTE+ an excellent candidate as a novel material for neural implants. Here we exploit OSTE+ to manufacture a conformable multilayer micro-electrocorticography array with 16 platinum electrodes coated with platinum black. The mechanical properties allow conformability to curved surfaces such as the brain. The low permeability and strong adhesion between layers improve the stability of the device. Acute experiments in mice show the multimodal capacity of the array to record and stimulate the neural tissue by smoothly conforming to the mouse cortex. Devices are not cytotoxic, and immunohistochemistry stainings reveal only modest foreign body reaction after two and six weeks of chronic implantation. This work introduces OSTE+ as a promising material for implantable neural interfaces.}, } @article {pmid36583387, year = {2022}, author = {Sato, A and Nakatani, S}, title = {Independent bilateral-eye stimulation for gaze pattern recognition based on steady-state pupil light reflex.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/acab31}, pmid = {36583387}, issn = {1741-2552}, abstract = {Objective:recently, pupil oscillations synchronized with steady visual stimuli were used as input for an interface. The proposed system, inspired by a brain-computer interface based on steady-state visual evoked potentials, does not require contact with the participant. However, the pupil oscillation mechanism limits the stimulus frequency to 2.5 Hz or less, making it hard to enhance the information transfer rate (ITR).Approach:here, we compared multiple conditions for stimulation to increase the ITR of the pupil vibration-based interface, which were called monocular-single, monocular-superposed, and binocular-independent conditions. The binocular-independent condition stimulates each eye at different frequencies respectively and mixes them by using the visual stereoscopic perception of users. The monocular-superposed condition stimulates both eyes by a mixed signal of two different frequencies. We selected the shape of the stimulation signal, evaluated the amount of spectral leakage in the monocular-superposed and binocular-independent conditions, and compared the power spectrum density at the stimulation frequency. Moreover, 5, 10, and 15 patterns of stimuli were classified in each condition.Main results:a square wave, which causes an efficient pupil response, was used as the stimulus. Spectral leakage at the beat frequency was higher in the monocular-superposed condition than in the binocular-independent one. The power spectral density of stimulus frequencies was greatest in the monocular-single condition. Finally, we could classify the 15-stimulus pattern, with ITRs of 14.4 (binocular-independent, using five frequencies), 14.5 (monocular-superimposed, using five frequencies), and 23.7 bits min[-1](monocular-single, using 15 frequencies). There were no significant differences for the binocular-independent and monocular-superposed conditions.Significance:this paper shows a way to increase the number of stimuli that can be simultaneously displayed without decreasing ITR, even when only a small number of frequencies are available. This could lead to the provision of an interface based on pupil oscillation to a wider range of users.}, } @article {pmid36583011, year = {2022}, author = {Phunruangsakao, C and Achanccaray, D and Izumi, SI and Hayashibe, M}, title = {Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1032724}, pmid = {36583011}, issn = {1662-5161}, abstract = {INTRODUCTION: Emerging deep learning approaches to decode motor imagery (MI) tasks have significantly boosted the performance of brain-computer interfaces. Although recent studies have produced satisfactory results in decoding MI tasks of different body parts, the classification of such tasks within the same limb remains challenging due to the activation of overlapping brain regions. A single deep learning model may be insufficient to effectively learn discriminative features among tasks.

METHODS: The present study proposes a framework to enhance the decoding of multiple hand-MI tasks from the same limb using a multi-branch convolutional neural network. The CNN framework utilizes feature extractors from established deep learning models, as well as contrastive representation learning, to derive meaningful feature representations for classification.

RESULTS: The experimental results suggest that the proposed method outperforms several state-of-the-art methods by obtaining a classification accuracy of 62.98% with six MI classes and 76.15 % with four MI classes on the Tohoku University MI-BCI and BCI Competition IV datasets IIa, respectively.

DISCUSSION: Despite requiring heavy data augmentation and multiple optimization steps, resulting in a relatively long training time, this scheme is still suitable for online use. However, the trade-of between the number of base learners, training time, prediction time, and system performance should be carefully considered.}, } @article {pmid36582164, year = {2022}, author = {Kotov, SV and Slyunkova, EV and Borisova, VA and Isakova, EV}, title = {[Effectiveness of brain-computer interfaces and cognitive training using computer technologies in restoring cognitive functions in patients after stroke].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {122}, number = {12. Vyp. 2}, pages = {67-75}, doi = {10.17116/jnevro202212212267}, pmid = {36582164}, issn = {1997-7298}, abstract = {OBJECTIVE: To study the effectiveness of brain-computer interfaces (BCI) and cognitive training using computer technologies in restoring cognitive functions in poststroke patients.

MATERIAL AND METHODS: Thirty-four stroke patients (mean age 59.3±10.8 years) with stroke duration of 5.1±4.7 months, were included. To assess the effectiveness of treatment, patients before and after treatment were tested using memorization of words according to the method of Luria A.R. «10 words», the Montreal Cognitive Assessment Scale (MoCA), the Clock Drawing Test (CDT). All patients received standard rehabilitation therapy (exercise therapy, physiotherapy, sessions with a speech therapist-neuropsychologist). Patients of the first group additionally received training on the «Neurochat» complex, patients of the second group - on the «Exokist-2» complex, patients of the third group - cognitive training according to standard programs using computer technology and visual material.

RESULTS: Patients of the three groups showed a significant improvement in the total MoCA score: in the 1[st] and 2[nd] groups - p<0.01, in the 3[rd] group - p<0.05. According to CDT, there was a significant change in the 2[nd] group (p=0.018). The Luria method «10 words» revealed an improvement in memory in all groups (p<0.01, p<0.05), being more pronounced in the 1[st] and 2[nd] groups.

CONCLUSION: The effectiveness of BCI in restoring cognitive functions in patients after a stroke in comparison with cognitive training without BCI has been demonstrated. However, there are reasons to believe that various BCIs have a specific effect on cognitive functions and have their own target group.}, } @article {pmid36582163, year = {2022}, author = {Borisova, VA and Isakova, EV and Kotov, SV}, title = {[Possibilities of the brain-computer interface in the correction of post-stroke cognitive impairments].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {122}, number = {12. Vyp. 2}, pages = {60-66}, doi = {10.17116/jnevro202212212260}, pmid = {36582163}, issn = {1997-7298}, abstract = {In recent years, brain-computer interfaces have been widely used in neurorehabilitation, and an extensive database of results from clinical studies conducted around the world has been accumulated, demonstrating their effectiveness in restoring motor function after a stroke. Currently, their use in post-stroke cognitive impairment is expanding. This article discusses the potential and prospects for using brain-computer interfaces for the treatment of cognitive disorders, reviews the experience of using it, presents the results of clinical studies in stroke patients, evaluates the possibilities of using this technology, describes the prospects, new directions of work on studying its effects.}, } @article {pmid36579369, year = {2022}, author = {Goueytes, D and Lassagne, H and Shulz, DE and Ego-Stengel, V and Estebanez, L}, title = {Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/acab87}, pmid = {36579369}, issn = {1741-2552}, abstract = {Objective.Distributed microstimulations at the cortical surface can efficiently deliver feedback to a subject during the manipulation of a prosthesis through a brain-machine interface (BMI). Such feedback can convey vast amounts of information to the prosthesis user and may be key to obtain an accurate control and embodiment of the prosthesis. However, so far little is known of the physiological constraints on the decoding of such patterns. Here, we aimed to test a rotary optogenetic feedback that was designed to encode efficiently the 360° movements of the robotic actuators used in prosthetics. We sought to assess its use by mice that controlled a prosthesis joint through a closed-loop BMI.Approach.We tested the ability of mice to optimize the trajectory of a virtual prosthesis joint in order to solve a rewarded reaching task. They could control the speed of the joint by modulating the activity of individual neurons in the primary motor cortex. During the task, the patterned optogenetic stimulation projected on the primary somatosensory cortex continuously delivered information to the mouse about the position of the joint.Main results.We showed that mice are able to exploit the continuous, rotating cortical feedback in the active behaving context of the task. Mice achieved better control than in the absence of feedback by detecting reward opportunities more often, and also by moving the joint faster towards the reward angular zone, and by maintaining it longer in the reward zone. Mice controlling acceleration rather than speed of the joint failed to improve motor control.Significance.These findings suggest that in the context of a closed-loop BMI, distributed cortical feedback with optimized shapes and topology can be exploited to control movement. Our study has direct applications on the closed-loop control of rotary joints that are frequently encountered in robotic prostheses.}, } @article {pmid36578777, year = {2022}, author = {Alwasiti, H and Yusoff, MZ}, title = {Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation.}, journal = {IEEE open journal of engineering in medicine and biology}, volume = {3}, number = {}, pages = {171-177}, pmid = {36578777}, issn = {2644-1276}, abstract = {Goal: Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without combining multiple subjects training datasets, which has a detrimental effect on the classification performance due to the inter-individual variability among subjects. Methods: A customized Convolutional Neural Network with mixup augmentation was trained with [Formula: see text]120 EEG trials for only one subject per model. Results: Modified ResNet18 and DenseNet121 models with mixup augmentation achieved 0.920 (95% Confidence Interval: 0.908, 0.933) and 0.933 (95% Confidence Interval: 0.922, 0.945) classification accuracy, respectively. Conclusions: We show that the designed classifiers resulted in a higher classification performance in comparison to other DL classifiers of previous studies on the same dataset, despite the limited training dataset used in this work.}, } @article {pmid36577882, year = {2022}, author = {Cao, K and Hu, Y and Gao, Z}, title = {Sense to Tune: Engaging Microglia with Dynamic Neuronal Activity.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36577882}, issn = {1995-8218}, } @article {pmid36577144, year = {2022}, author = {Duan, X and Xie, S and Lv, Y and Xie, X and Obermayer, K and Yan, H}, title = {A transfer learning-based feedback training motivates the performance of SMR-BCI.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acaee7}, pmid = {36577144}, issn = {1741-2552}, abstract = {OBJECTIVE: Feedback training is a practical approach to brain-computer interface (BCI) end-users learning to modulate their sensorimotor rhythms (SMR). BCI self-regulation learning has been shown to be influenced by subjective psychological factors, such as motivation. However, few studies have taken into account the users' self-motivation as additional guidance for the cognitive process involved in BCI learning. In this study we tested a transfer learning (TL) feedback method designed to increase self-motivation by providing information about past performance.

APPROACH: EEG signals from the previous runs were affine transformed and displayed as points on the screen, along with the newly recorded EEG signals in the current run, giving the subjects a context for self-motivation. Subjects were asked to separate the feedback points for the current run under the display of the separability of prior training. We conducted a between-subject feedback training experiment, in which 24 healthy SMR-BCI naive subjects were trained to imagine left- and right-hand movements. The participants were provided with either TL feedback or typical cursorbar (CB) feedback (control condition), for three sessions on separate days.

MAIN RESULTS: The behavioral results showed an increased challenge and stable mastery confidence, suggesting that subjects' motivation grew as the feedback training went on. The EEG results showed favorable overall training effects with TL feedback in terms of the class distinctiveness and EEG discriminancy. Performance was 28.5% higher in the third session than in the first. 41.7% of the subjects were learners including not only low-performance subjects, but also good-performance subjects who might be affected by the ceiling effect. Subjects were able to control BCI with TL feedback with a higher performance of 60.5% during the last session compared to CB feedback.

SIGNIFICANCE: The present study demonstrated that the proposed TL feedback method boosted psychological engagement through the self-motivated context, and further allowed subjects to modulate SMR effectively. The proposed TL feedback method also provided an alternative to typical CB feedback.}, } @article {pmid36576451, year = {2022}, author = {Rinoldi, C and Ziai, Y and Zargarian, SS and Nakielski, P and Zembrzycki, K and Haghighat Bayan, MA and Zakrzewska, AB and Fiorelli, R and Lanzi, M and Kostrzewska-Księżyk, A and Czajkowski, R and Kublik, E and Kaczmarek, L and Pierini, F}, title = {In Vivo Chronic Brain Cortex Signal Recording Based on a Soft Conductive Hydrogel Biointerface.}, journal = {ACS applied materials & interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.2c17025}, pmid = {36576451}, issn = {1944-8252}, abstract = {In neuroscience, the acquisition of neural signals from the brain cortex is crucial to analyze brain processes, detect neurological disorders, and offer therapeutic brain-computer interfaces. The design of neural interfaces conformable to the brain tissue is one of today's major challenges since the insufficient biocompatibility of those systems provokes a fibrotic encapsulation response, leading to an inaccurate signal recording and tissue damage precluding long-term/permanent implants. The design and production of a novel soft neural biointerface made of polyacrylamide hydrogels loaded with plasmonic silver nanocubes are reported herein. Hydrogels are surrounded by a silicon-based template as a supporting element for guaranteeing an intimate neural-hydrogel contact while making possible stable recordings from specific sites in the brain cortex. The nanostructured hydrogels show superior electroconductivity while mimicking the mechanical characteristics of the brain tissue. Furthermore, in vitro biological tests performed by culturing neural progenitor cells demonstrate the biocompatibility of hydrogels along with neuronal differentiation. In vivo chronic neuroinflammation tests on a mouse model show no adverse immune response toward the nanostructured hydrogel-based neural interface. Additionally, electrocorticography acquisitions indicate that the proposed platform permits long-term efficient recordings of neural signals, revealing the suitability of the system as a chronic neural biointerface.}, } @article {pmid36575664, year = {2022}, author = {Guo, Z and Wang, F and Wang, L and Tu, K and Jiang, C and Xi, Y and Hong, W and Xu, Q and Wang, X and Yang, B and Sun, B and Lin, Z and Liu, J}, title = {A flexible neural implant with ultrathin substrate for low-invasive brain-computer interface applications.}, journal = {Microsystems & nanoengineering}, volume = {8}, number = {}, pages = {133}, pmid = {36575664}, issn = {2055-7434}, abstract = {Implantable brain-computer interface (BCI) devices are an effective tool to decipher fundamental brain mechanisms and treat neural diseases. However, traditional neural implants with rigid or bulky cross-sections cause trauma and decrease the quality of the neuronal signal. Here, we propose a MEMS-fabricated flexible interface device for BCI applications. The microdevice with a thin film substrate can be readily reduced to submicron scale for low-invasive implantation. An elaborate silicon shuttle with an improved structure is designed to reliably implant the flexible device into brain tissue. The flexible substrate is temporarily bonded to the silicon shuttle by polyethylene glycol. On the flexible substrate, eight electrodes with different diameters are distributed evenly for local field potential and neural spike recording, both of which are modified by Pt-black to enhance the charge storage capacity and reduce the impedance. The mechanical and electrochemical characteristics of this interface were investigated in vitro. In vivo, the small cross-section of the device promises reduced trauma, and the neuronal signals can still be recorded one month after implantation, demonstrating the promise of this kind of flexible BCI device as a low-invasive tool for brain-computer communication.}, } @article {pmid36575091, year = {2022}, author = {Wang, H and Wang, S and Qiu, Z and Zhang, Q and Xu, S}, title = {[Design and preliminary application of outdoor flying pigeon-robot].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {6}, pages = {1209-1217}, doi = {10.7507/1001-5515.202207077}, pmid = {36575091}, issn = {1001-5515}, abstract = {Control at beyond-visual ranges is of great significance to animal-robots with wide range motion capability. For pigeon-robots, such control can be done by the way of onboard preprogram, but not constitute a closed-loop yet. This study designed a new control system for pigeon-robots, which integrated the function of trajectory monitoring to that of brain stimulation. It achieved the closed-loop control in turning or circling by estimating pigeons' flight state instantaneously and the corresponding logical regulation. The stimulation targets located at the formation reticularis medialis mesencephali (FRM) in the left and right brain, for the purposes of left- and right-turn control, respectively. The stimulus was characterized by the waveform mimicking the nerve cell membrane potential, and was activated intermittently. The wearable control unit weighted 11.8 g totally. The results showed a 90% success rate by the closed-loop control in pigeon-robots. It was convenient to obtain the wing shape during flight maneuver, by equipping a pigeon-robot with a vivo camera. It was also feasible to regulate the evolution of pigeon flocks by the pigeon-robots at different hierarchical level. All of these lay the groundwork for the application of pigeon-robots in scientific researches.}, } @article {pmid36575087, year = {2022}, author = {Pan, L and Ding, Y and Wang, S and Song, A}, title = {[Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {6}, pages = {1173-1180}, doi = {10.7507/1001-5515.202112023}, pmid = {36575087}, issn = {1001-5515}, abstract = {Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.}, } @article {pmid36575075, year = {2022}, author = {Song, H and Xu, S and Liu, G and Liu, J and Xiong, P}, title = {[Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {6}, pages = {1074-1081}, doi = {10.7507/1001-5515.202111060}, pmid = {36575075}, issn = {1001-5515}, abstract = {The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.}, } @article {pmid36575074, year = {2022}, author = {Hu, Y and Liu, Y and Cheng, C and Geng, C and Dai, B and Peng, B and Zhu, J and Dai, Y}, title = {[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {6}, pages = {1065-1073}, doi = {10.7507/1001-5515.202206052}, pmid = {36575074}, issn = {1001-5515}, abstract = {The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.}, } @article {pmid36572173, year = {2022}, author = {Zhu, X and Zhou, H and Geng, F and Wang, J and Xu, H and Hu, Y}, title = {Functional connectivity between basal forebrain and superficial amygdala negatively correlates with social fearfulness.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2022.12.020}, pmid = {36572173}, issn = {1873-7544}, abstract = {Social anxiety is characterized by an intense fear of evaluation from others and/or withdrawal from social situations. Extreme social anxiety can lead to social anxiety disorder. There remains an urgent need to investigate the neural substrates of subclinical social anxiety for early diagnosis and intervention to reduce the risk to develop social anxiety disorder. Twenty-nine young adults were recruited (10 males/19 females; mean age (SD) = 20.34 (2.29)). Trait-like social anxiety was assessed by Liebowitz Social Anxiety Scale. Functional magnetic resonance imaging was used with an emotional face-matching paradigm to probe brain activation in response to emotional stimuli including angry, fearful, and happy faces, with shape-matching as a control condition. Behavioral results showed positive correlations between Liebowitz Social Anxiety Scale scores and the reaction time in both angry and fearful conditions. The activation of superficial amygdala and the deactivation of basal forebrain in response to angry condition showed positive correlations with the level of social anxiety. In addition, the resting-state functional connectivity between these two regions was negatively correlated with the level of social anxiety. These results may help to understand the individual difference and corresponding neural underpinnings of social anxiety in the subclinical population, and might provide some insight to develop strategies for early diagnosis and interventions of social anxiety to reduce the risk of deterioration from subclinical to clinical level of social anxiety.}, } @article {pmid36569472, year = {2022}, author = {Lee Friesen, C and Lawrence, M and Ingram, TGJ and Boe, SG}, title = {Home-based portable fNIRS-derived cortical laterality correlates with impairment and function in chronic stroke.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1023246}, pmid = {36569472}, issn = {1662-5161}, abstract = {INTRODUCTION: Improved understanding of the relationship between post-stroke rehabilitation interventions and functional motor outcomes could result in improvements in the efficacy of post-stroke physical rehabilitation. The laterality of motor cortex activity (M1-LAT) during paretic upper-extremity movement has been documented as a useful biomarker of post-stroke motor recovery. However, the expensive, labor intensive, and laboratory-based equipment required to take measurements of M1-LAT limit its potential clinical utility in improving post-stroke physical rehabilitation. The present study tested the ability of a mobile functional near-infrared spectroscopy (fNIRS) system (designed to enable independent measurement by stroke survivors) to measure cerebral hemodynamics at the motor cortex in the homes of chronic stroke survivors.

METHODS: Eleven chronic stroke survivors, ranging widely in their level of upper-extremity motor deficit, used their stroke-affected upper-extremity to perform a simple unilateral movement protocol in their homes while a wireless prototype fNIRS headband took measurements at the motor cortex. Measures of participants' upper-extremity impairment and function were taken.

RESULTS: Participants demonstrated either a typically lateralized response, with an increase in contralateral relative oxyhemoglobin (ΔHbO), or response showing a bilateral pattern of increase in ΔHbO during the motor task. During the simple unilateral task, M1-LAT correlated significantly with measures of both upper-extremity impairment and function, indicating that participants with more severe motor deficits had more a more atypical (i.e., bilateral) pattern of lateralization.

DISCUSSION: These results indicate it is feasible to gain M1-LAT measures from stroke survivors in their homes using fNIRS. These findings represent a preliminary step toward the goals of using ergonomic functional neuroimaging to improve post-stroke rehabilitative care, via the capture of neural biomarkers of post-stroke motor recovery, and/or via use as part of an accessible rehabilitation brain-computer-interface.}, } @article {pmid36560172, year = {2022}, author = {Kartsch, VJ and Kumaravel, VP and Benatti, S and Vallortigara, G and Benini, L and Farella, E and Buiatti, M}, title = {Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {24}, pages = {}, pmid = {36560172}, issn = {1424-8220}, support = {842243/ERC_/European Research Council/International ; }, mesh = {Electroencephalography/methods ; Evoked Potentials, Visual ; Canonical Correlation Analysis ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; *Wearable Electronic Devices ; Algorithms ; }, abstract = {Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed.}, } @article {pmid36567362, year = {2022}, author = {Sorkhi, M and Jahed-Motlagh, MR and Minaei-Bidgoli, B and Daliri, MR}, title = {Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {22334}, pmid = {36567362}, issn = {2045-2322}, abstract = {Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems.}, } @article {pmid36565984, year = {2022}, author = {West, TO and Duchet, B and Farmer, SF and Friston, KJ and Cagnan, H}, title = {When do Bursts Matter in the Primary Motor Cortex? Investigating Changes in the Intermittencies of Beta Rhythms Associated With Movement States.}, journal = {Progress in neurobiology}, volume = {}, number = {}, pages = {102397}, doi = {10.1016/j.pneurobio.2022.102397}, pmid = {36565984}, issn = {1873-5118}, abstract = {Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms regulating them are unknown. Here, we present evidence from electrocorticography recordings made from the motor cortex to show that the statistics of bursts, such as duration or amplitude, in beta frequency (14-30Hz) rhythms significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for temporal organization of activity. Finally, we show that temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.}, } @article {pmid36563409, year = {2022}, author = {Liang, Q and Shen, Z and Sun, X and Yu, D and Liu, K and Mugo, SM and Chen, W and Wang, D and Zhang, Q}, title = {Electron Conductive and Transparent Hydrogels for Recording Brain Neural Signals and Neuromodulation.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2211159}, doi = {10.1002/adma.202211159}, pmid = {36563409}, issn = {1521-4095}, abstract = {Recording brain neural signals and optogenetic neuromodulations open frontiers in decoding brain neural information and neurodegenerative diseases therapeutics. Conventional implantable probes suffer from modulus mismatch with biological tissues and an irreconcilable tradeoff between transparency and electron conductivity. Herein, a strategy was proposed to address these tradeoffs, which generates conductive and transparent hydrogels with polypyrrole-decorated microgels as crosslinkers. The optical transparency of the electrodes can be attributed to the special structures that allow light waves to bypass the microgel particles and minimize their interaction. Demonstrated by probing the hippocampus of rat brains, the biomimetic electrode shows a prolonged capacity for simultaneous optogenetic neuromodulation and recording of brain neural signals. More importantly, an intriguing brain-machine interaction was realized, which involved signal input to the brain, brain neural signal generation, and controlling limb behaviors. This breakthrough work represents a significant scientific advancement toward decoding brain neural information and in neurodegenerative disease therapy. This article is protected by copyright. All rights reserved.}, } @article {pmid36560369, year = {2022}, author = {Akram, F and Alwakeel, A and Alwakeel, M and Hijji, M and Masud, U}, title = {A Symbols Based BCI Paradigm for Intelligent Home Control Using P300 Event-Related Potentials.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {24}, pages = {}, doi = {10.3390/s222410000}, pmid = {36560369}, issn = {1424-8220}, abstract = {Brain-Computer Interface (BCI) is a technique that allows the disabled to interact with a computer directly from their brain. P300 Event-Related Potentials (ERP) of the brain have widely been used in several applications of the BCIs such as character spelling, word typing, wheelchair control for the disabled, neurorehabilitation, and smart home control. Most of the work done for smart home control relies on an image flashing paradigm where six images are flashed randomly, and the users can select one of the images to control an object of interest. The shortcoming of such a scheme is that the users have only six commands available in a smart home to control. This article presents a symbol-based P300-BCI paradigm for controlling home appliances. The proposed paradigm comprises of a 12-symbols, from which users can choose one to represent their desired command in a smart home. The proposed paradigm allows users to control multiple home appliances from signals generated by the brain. The proposed paradigm also allows the users to make phone calls in a smart home environment. We put our smart home control system to the test with ten healthy volunteers, and the findings show that the proposed system can effectively operate home appliances through BCI. Using the random forest classifier, our participants had an average accuracy of 92.25 percent in controlling the home devices. As compared to the previous studies on the smart home control BCIs, the proposed paradigm gives the users more degree of freedom, and the users are not only able to control several home appliances but also have an option to dial a phone number and make a call inside the smart home. The proposed symbols-based smart home paradigm, along with the option of making a phone call, can effectively be used for controlling home through signals of the brain, as demonstrated by the results.}, } @article {pmid36560158, year = {2022}, author = {Saichoo, T and Boonbrahm, P and Punsawad, Y}, title = {Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {24}, pages = {}, doi = {10.3390/s22249788}, pmid = {36560158}, issn = {1424-8220}, abstract = {The research on the electroencephalography (EEG)-based brain-computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities.}, } @article {pmid36557761, year = {2022}, author = {Kiel, A and Creutz, I and Rückert, C and Kaltschmidt, BP and Hütten, A and Niehaus, K and Busche, T and Kaltschmidt, B and Kaltschmidt, C}, title = {Genome-Based Analysis of Virulence Factors and Biofilm Formation in Novel P. aeruginosa Strains Isolated from Household Appliances.}, journal = {Microorganisms}, volume = {10}, number = {12}, pages = {}, doi = {10.3390/microorganisms10122508}, pmid = {36557761}, issn = {2076-2607}, abstract = {In household washing machines, opportunistic pathogens such as Pseudomonas aeruginosa are present, which represent the household as a possible reservoir for clinical pathogens. Here, four novel P. aeruginosa strains, isolated from different sites of household appliances, were investigated regarding their biofilm formation. Only two isolates showed strong surface-adhered biofilm formation. In consequence of these phenotypic differences, we performed whole genome sequencing using Oxford Nanopore Technology together with Illumina MiSeq. Whole genome data were screened for the prevalence of 285 virulence- and biofilm-associated genes as well as for prophages. Linking biofilm phenotypes and parallelly appearing gene compositions, we assume a relevancy of the las quorum sensing system and the phage-encoded bacteriophage control infection gene bci, which was found on integrated phi297 DNA in all biofilm-forming isolates. Additionally, only the isolates revealing strong biofilm formation harbored the ϕCTX-like prophage Dobby, implicating a role of this prophage on biofilm formation. Investigations on clinically relevant pathogens within household appliances emphasize their adaptability to harsh environments, with high concentrations of detergents, providing greater insights into pathogenicity and underlying mechanisms. This in turn opens the possibility to map and characterize potentially relevant strains even before they appear as pathogens in society.}, } @article {pmid36553544, year = {2022}, author = {Jardillier, R and Koca, D and Chatelain, F and Guyon, L}, title = {Optimal microRNA Sequencing Depth to Predict Cancer Patient Survival with Random Forest and Cox Models.}, journal = {Genes}, volume = {13}, number = {12}, pages = {}, doi = {10.3390/genes13122275}, pmid = {36553544}, issn = {2073-4425}, abstract = {(1) Background: tumor profiling enables patient survival prediction. The two essential parameters to be calibrated when designing a study based on tumor profiles from a cohort are the sequencing depth of RNA-seq technology and the number of patients. This calibration is carried out under cost constraints, and a compromise has to be found. In the context of survival data, the goal of this work is to benchmark the impact of the number of patients and of the sequencing depth of miRNA-seq and mRNA-seq on the predictive capabilities for both the Cox model with elastic net penalty and random survival forest. (2) Results: we first show that the Cox model and random survival forest provide comparable prediction capabilities, with significant differences for some cancers. Second, we demonstrate that miRNA and/or mRNA data improve prediction over clinical data alone. mRNA-seq data leads to slightly better prediction than miRNA-seq, with the notable exception of lung adenocarcinoma for which the tumor miRNA profile shows higher predictive power. Third, we demonstrate that the sequencing depth of RNA-seq data can be reduced for most of the investigated cancers without degrading the prediction abilities, allowing the creation of independent validation sets at a lower cost. Finally, we show that the number of patients in the training dataset can be reduced for the Cox model and random survival forest, allowing the use of different models on different patient subgroups.}, } @article {pmid36552139, year = {2022}, author = {Qiu, P and Dai, J and Wang, T and Li, H and Ma, C and Xi, X}, title = {Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation.}, journal = {Brain sciences}, volume = {12}, number = {12}, pages = {}, doi = {10.3390/brainsci12121680}, pmid = {36552139}, issn = {2076-3425}, abstract = {Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.}, } @article {pmid36551135, year = {2022}, author = {Luo, J and Xue, N and Chen, J}, title = {A Review: Research Progress of Neural Probes for Brain Research and Brain-Computer Interface.}, journal = {Biosensors}, volume = {12}, number = {12}, pages = {}, doi = {10.3390/bios12121167}, pmid = {36551135}, issn = {2079-6374}, abstract = {Neural probes, as an invasive physiological tool at the mesoscopic scale, can decipher the code of brain connections and communications from the cellular or even molecular level, and realize information fusion between the human body and external machines. In addition to traditional electrodes, two new types of neural probes have been developed in recent years: optoprobes based on optogenetics and magnetrodes that record neural magnetic signals. In this review, we give a comprehensive overview of these three kinds of neural probes. We firstly discuss the development of microelectrodes and strategies for their flexibility, which is mainly represented by the selection of flexible substrates and new electrode materials. Subsequently, the concept of optogenetics is introduced, followed by the review of several novel structures of optoprobes, which are divided into multifunctional optoprobes integrated with microfluidic channels, artifact-free optoprobes, three-dimensional drivable optoprobes, and flexible optoprobes. At last, we introduce the fundamental perspectives of magnetoresistive (MR) sensors and then review the research progress of magnetrodes based on it.}, } @article {pmid36551100, year = {2022}, author = {Said, RR and Heyat, MBB and Song, K and Tian, C and Wu, Z}, title = {A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials.}, journal = {Biosensors}, volume = {12}, number = {12}, pages = {}, doi = {10.3390/bios12121134}, pmid = {36551100}, issn = {2079-6374}, abstract = {To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.}, } @article {pmid36550974, year = {2022}, author = {Orban, M and Elsamanty, M and Guo, K and Zhang, S and Yang, H}, title = {A Review of Brain Activity and EEG-Based Brain-Computer Interfaces for Rehabilitation Application.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {9}, number = {12}, pages = {}, doi = {10.3390/bioengineering9120768}, pmid = {36550974}, issn = {2306-5354}, abstract = {Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.}, } @article {pmid36550932, year = {2022}, author = {Abdullah, and Faye, I and Islam, MR}, title = {EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {9}, number = {12}, pages = {}, doi = {10.3390/bioengineering9120726}, pmid = {36550932}, issn = {2306-5354}, abstract = {Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain's motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10-30% of total channels, provided excellent performance compared to other existing studies.}, } @article {pmid36550229, year = {2022}, author = {Latheef, S}, title = {Brain to Brain Interfaces (BBIs) in future military operations; blurring the boundaries of individual responsibility.}, journal = {Monash bioethics review}, volume = {}, number = {}, pages = {}, pmid = {36550229}, issn = {1836-6716}, abstract = {Developments in neurotechnology took a leap forward with the demonstration of the first Brain to Brain Interface (BBI). BBIs enable direct communication between two brains via a Brain Computer Interface (BCI) and bypasses the peripheral nervous system. This discovery promises new possibilities for future battlefield technology. As battlefield technology evolves, it is more likely to place greater demands on future soldiers. Future soldiers are more likely to process large amounts of data derived from an extensive networks of humans and machines. This raises several ethical and philosophical concerns. This paper will look at BBI technology in current stages of research, future BBI applications in the military and how the potential use of BBIs in military operations challenges the way we understand the concept of responsibility. In this paper, I propose that an individual connected to a BBI ought not to be held fully responsible for her actions. The justification for this proposition is based on three key points such as an individual connected to a BBI does not have the ability to act freely, has a diminished sense of self-agency and may not be able to demonstrate authenticity of the thoughts and memories generated when connected to the interface.}, } @article {pmid36548997, year = {2022}, author = {Nagarajan, A and Robinson, N and Guan, C}, title = {Relevance based channel selection in motor imagery brain-computer interface.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acae07}, pmid = {36548997}, issn = {1741-2552}, abstract = {OBJECTIVE: Channel selection in electroencephalogram (EEG)-based brain-computer interface (BCI) has been extensively studied for over two decades, with the goal to select optimal subject-specific channels that can enhance the overall decoding efficacy of BCI. With the emergence of deep learning (DL) based BCI models, there arises a need for fresh perspectives and novel techniques to conduct channel selection. In this regard, subject-independent channel selection is relevant, since DL models trained using cross-subject data offer superior performance, and the impact of inherent inter-subject variability of EEG characteristics on subject-independent DL training is not yet fully understood.

APPROACH: Here, we propose a novel methodology for implementing subject-independent channel selection in DL based motor imagery (MI)-BCI, using layer-wise relevance propagation (LRP) and neural network pruning. Experiments were conducted using Deep ConvNet and 62-channel MI data from Korea University (KU) EEG dataset.

MAIN RESULTS: Using our proposed methodology, we achieved a 61% reduction in the number of channels without any significant drop (p=0.09) in subject-independent classification accuracy, due to the selection of highly relevant channels by LRP. LRP relevance based channel selections provide significantly better accuracies compared to conventional weight based selections while using less than 40% of the total number of channels, with differences in accuracies ranging from 5.96% to 1.72%. The performance of the adapted sparse-LRP model using only 16% of the total number of channels is similar to that of the adapted baseline model (p=0.13). Furthermore, the accuracy of the adapted sparse-LRP model using only 35% of the total number of channels exceeded that of the adapted baseline model by 0.53% (p=0.81). Analyses of channels chosen by LRP confirm the neurophysiological plausibility of selection, and emphasize the influence of motor, parietal, and occipital channels in MI-EEG classification.

SIGNIFICANCE: The proposed method addresses a traditional issue in EEG-BCI decoding, while being relevant and applicable to the latest developments in the field of BCI. We believe that our work brings forth an interesting and important application of model interpretability as a problem-solving technique.}, } @article {pmid36548189, year = {2022}, author = {Tsiamalou, A and Dardiotis, E and Paterakis, K and Fotakopoulos, G and Liampas, I and Sgantzos, M and Siokas, V and Brotis, AG}, title = {EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review.}, journal = {Neurology international}, volume = {14}, number = {4}, pages = {1046-1061}, doi = {10.3390/neurolint14040084}, pmid = {36548189}, issn = {2035-8385}, abstract = {BACKGROUND: There is increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community.

METHODS: We performed an electronic search in Scopus, looking for studies reporting on rehabilitation in patients with neurological disabilities. We used the most influential papers to outline the knowledge base and carried out a word co-occurrence analysis to identify the research hotspots. We also used depicted collaboration networks between universities, authors, and countries after analyzing the cocitations. The results were presented in summary tables, plots, and maps. Finally, a content review based on the top-20 most cited articles completed our study.

RESULTS: Our current bibliometric study was based on 874 records from 420 sources. There was vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by J. Daly and J.R. Wolpaw (708 citations). The US, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of functional magnetic imaging to EEG-based brain-machine interface, motor imagery, and deep learning.

CONCLUSIONS: EEG constitutes the most significant input in brain-computer interfaces (BCIs) and can be successfully used in the neurorehabilitation of patients with stroke symptoms, amyotrophic lateral sclerosis, and traumatic brain and spinal injuries. EEG-based BCI facilitates the training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG-filtering algorithms.}, } @article {pmid36547803, year = {2022}, author = {Zhou, F and Zheng, J and Xu, H}, title = {Lighting up Oxytocin Neurons to Nurture the Brain.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36547803}, issn = {1995-8218}, } @article {pmid36545350, year = {2022}, author = {Galiotta, V and Quattrociocchi, I and D'Ippolito, M and Schettini, F and Aricò, P and Sdoia, S and Formisano, R and Cincotti, F and Mattia, D and Riccio, A}, title = {EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1040816}, pmid = {36545350}, issn = {1662-5161}, abstract = {BACKGROUND: Disorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs).

OBJECTIVES: The aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI.

METHODS: The search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient.

RESULTS: Among the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients.

CONCLUSION: Despite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.}, } @article {pmid36543809, year = {2022}, author = {Shen, T and Yue, Y and Ba, F and He, T and Tang, X and Hu, X and Pu, J and Huang, C and Lv, W and Zhang, B and Lai, HY}, title = {Diffusion along perivascular spaces as marker for impairment of glymphatic system in Parkinson's disease.}, journal = {NPJ Parkinson's disease}, volume = {8}, number = {1}, pages = {174}, pmid = {36543809}, issn = {2373-8057}, abstract = {The brain glymphatic system is involved in the clearance of misfolding α-synuclein, the impaired glymphatic system may contribute to the progression of Parkinson's disease (PD). We aimed to analyze the diffusion tensor image along the perivascular space (DTI-ALPS) and perivascular space (PVS) burden to reveal the relationship between the glymphatic system and PD. A cross-sectional study using a 7 T MRI of 76 PD patients and 48 controls was performed to evaluate the brain's glymphatic system. The DTI-ALPS and PVS burden in basal ganglia were calculated. Correlation analyses were conducted between DTI-ALPS, PVS burden and clinical features. We detected lower DTI-ALPS in the PD subgroup relative to controls, and the differences were more pronounced in patients with Hoehn & Yahr stage greater than two. The decreased DTI-ALPS was only evident in the left hemisphere in patients in the early stage but involved both hemispheres in more advanced PD patients. Decreased DTI-ALPS were also correlated with longer disease duration, higher Unified Parkinson's Disease Rating Scale motor score (UPDRS III) and UPDRS total scores, as well as higher levodopa equivalent daily dose. Moreover, the decreased DTI-ALPS correlated with increased PVS burden, and both indexes correlated with PD disease severity. This study demonstrated decreased DTI-ALPS in PD, which might initiate from the left hemisphere and progressively involve right hemisphere with the disease progression. Decreased DTI-ALPS index correlated with increased PVS burden, indicating that both metrics could provide supporting evidence of an impaired glymphatic system. MRI evaluation of the PVS burden and diffusion along PVS are potential imaging biomarkers for PD for disease progression.}, } @article {pmid36542992, year = {2022}, author = {Wang, X and Sun, X and Ma, C and Zhang, Y and Kong, L and Huang, Z and Hu, Y and Wan, H and Wang, P}, title = {Multifunctional AuNPs@HRP@FeMOF immune scaffold with a fully automated saliva analyzer for oral cancer screening.}, journal = {Biosensors & bioelectronics}, volume = {222}, number = {}, pages = {114910}, doi = {10.1016/j.bios.2022.114910}, pmid = {36542992}, issn = {1873-4235}, abstract = {Delayed diagnosis of cancer-causing death is a worldwide concern. General diagnosis methods are invasive, time-consuming, and operation complicated, which are not suitable for preliminary screening. To address these challenges, the sensing platform based on immune scaffold and fully automated saliva analyzer (FASA) was proposed for oral cancer screening for the first time by non-invasive detection of Cyfra21-1 in saliva. Through one-step synthesis method with unique covalent and electrostatic adsorption strategy, AuNPs@HRP@FeMOF immune scaffold features multiple functions including antibody carrier, catalytic activity, and signal amplification. Highly integrated FASA with the immune scaffold provides automatic testing to avoid false-positive results and reduce pretreatment time without any user intervention. Compared with the commercial analyzer, FASA has comparable performance for Cyfra21-1 detection with a detection range of 3.1-50.0 ng/mL and R[2] of 0.971, and superior features in full automation, high integration, time saving and low cost. Oral cancer patients could be distinguished accurately by the platform with an excellent correlation (R[2] of 0.904) and average RSD (5.578%) without sample dilution. The proposed platform provides an effective and promising tool for cancer screening in point-of-care applications, which can be further extended for biomarker detection in universal body fluids, disease screening, prognosis review and homecare monitoring.}, } @article {pmid36541542, year = {2022}, author = {Fang, T and Wang, J and Mu, W and Song, Z and Zhang, X and Zhan, G and Wang, P and Bin, J and Niu, L and Zhang, L and Kang, X}, title = {Noninvasive neuroimaging and spatial filter transform enable ultra low delay motor imagery EEG decoding.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca82d}, pmid = {36541542}, issn = {1741-2552}, abstract = {Objective.The brain-computer interface (BCI) system based on sensorimotor rhythm can convert the human spirit into instructions for machine control, and it is a new human-computer interaction system with broad applications. However, the spatial resolution of scalp electroencephalogram (EEG) is limited due to the presence of volume conduction effects. Therefore, it is very meaningful to explore intracranial activities in a noninvasive way and improve the spatial resolution of EEG. Meanwhile, low-delay decoding is an essential factor for the development of a real-time BCI system.Approach.In this paper, EEG conduction is modeled by using public head anatomical templates, and cortical EEG is obtained using dynamic parameter statistical mapping. To solve the problem of a large amount of computation caused by the increase in the number of channels, the filter bank common spatial pattern method is used to obtain a spatial filter kernel, which reduces the computational cost of feature extraction to a linear level. And the feature classification and selection of important features are completed using a neural network containing band-spatial-time domain self-attention mechanisms.Main results.The results show that the method proposed in this paper achieves high accuracy for the four types of motor imagery EEG classification tasks, with fairly low latency and high physiological interpretability.Significance.The proposed decoding framework facilitates the realization of low-latency human-computer interaction systems.}, } @article {pmid36541535, year = {2022}, author = {Pires, G and Cruz, A and Jesus, D and Yasemin, M and Nunes, UJ and Sousa, T and Castelo-Branco, M}, title = {A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca798}, pmid = {36541535}, issn = {1741-2552}, abstract = {Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.}, } @article {pmid36541532, year = {2022}, author = {Wang, X and Chen, HT and Lin, CT}, title = {Error-related potential-based shared autonomy via deep recurrent reinforcement learning.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca4fb}, pmid = {36541532}, issn = {1741-2552}, abstract = {Objective.Error-related potential (ErrP)-based brain-computer interfaces (BCIs) have received a considerable amount of attention in the human-robot interaction community. In contrast to traditional BCI, which requires continuous and explicit commands from an operator, ErrP-based BCI leverages the ErrP, which is evoked when an operator observes unexpected behaviours from the robot counterpart. This paper proposes a novel shared autonomy model for ErrP-based human-robot interaction.Approach.We incorporate ErrP information provided by a BCI as useful observations for an agent and formulate the shared autonomy problem as a partially observable Markov decision process. A recurrent neural network-based actor-critic model is used to address the uncertainty in the ErrP signal. We evaluate the proposed framework in a simulated human-in-the-loop robot navigation task with both simulated users and real users.Main results.The results show that the proposed ErrP-based shared autonomy model enables an autonomous robot to complete navigation tasks more efficiently. In a simulation with 70% ErrP accuracy, agents completed the task 14.1% faster than in the no ErrP condition, while with real users, agents completed the navigation task 14.9% faster.Significance.The evaluation results confirmed that the shared autonomy via deep recurrent reinforcement learning is an effective way to deal with uncertain human feedback in a complex human-robot interaction task.}, } @article {pmid36538406, year = {2022}, author = {Kostenko, EV and Petrova, LV and Pogonchenkova, IV and Neprintseva, NV and Shurupova, ST and Kopasheva, VD and Rylsky, AV}, title = {[Innovative technologies and multimodal correction in medical rehabilitation of motor and neuropsychological disturbances due to stroke].}, journal = {Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury}, volume = {99}, number = {6}, pages = {67-78}, doi = {10.17116/kurort20229906167}, pmid = {36538406}, issn = {0042-8787}, abstract = {The article presents an overview of innovative technologies based on the methods of sensorimotor retraining of the patient using various types of biofeedback (BFB) as the most promising in the medical rehabilitation (MR) of patients with cerebral stroke (CS). The works of a high level of evidence (RCTs, national and international clinical guidelines, meta-analyses, systematic reviews) of the Medline, Pubmed, PubMed Cochrane Library databases are analyzed, ClinicalTrials.gov. It is emphasized that training with multisensory effects on visual, auditory, vestibular and kinesthetic analyzers have a beneficial effect on cognitive-motor training and retraining, neuropsychological status of the patient and increase the level of motivation to achieve success in the rehabilitation process. The synergy of multimodal effects of digital technologies, BFB, virtual reality, and the brain-computer interface will expand the capabilities and improve the efficiency of MR of after stroke-patients.}, } @article {pmid36317171, year = {2022}, author = {Imambocus, BN and Formozov, A and Zhou, F and Soba, P}, title = {A two-choice assay for noxious light avoidance with temporal distribution analysis in Drosophila melanogaster larvae.}, journal = {STAR protocols}, volume = {3}, number = {4}, pages = {101787}, pmid = {36317171}, issn = {2666-1667}, support = {P40 OD018537/OD/NIH HHS/United States ; }, mesh = {Animals ; *Drosophila melanogaster ; Larva ; *Drosophila ; Biological Assay ; }, abstract = {Two-choice assays allow assessing of different behaviors including light avoidance in Drosophila larvae. Typically, the readout is limited to a preference index at a specific end point. We provide a detailed protocol to set up light avoidance assays and map the temporal distribution of larvae based on analysis of larval intensities. We describe the assay setup and implementation of scripts for analysis, which can be easily adapted to other two-choice assays and different model organisms. For complete details on the use and execution of this protocol, please refer to Imambocus et al. (2022).}, } @article {pmid36536134, year = {2022}, author = {Hu, Y and Cao, K and Wang, F and Wu, W and Mai, W and Qiu, L and Luo, Y and Ge, WP and Sun, B and Shi, L and Zhu, J and Zhang, J and Wu, Z and Xie, Y and Duan, S and Gao, Z}, title = {Dual roles of hexokinase 2 in shaping microglial function by gating glycolytic flux and mitochondrial activity.}, journal = {Nature metabolism}, volume = {}, number = {}, pages = {}, pmid = {36536134}, issn = {2522-5812}, abstract = {Microglia continuously survey the brain parenchyma and actively shift status following stimulation. These processes demand a unique bioenergetic programme; however, little is known about the metabolic determinants in microglia. By mining large datasets and generating transgenic tools, here we show that hexokinase 2 (HK2), the most active isozyme associated with mitochondrial membrane, is selectively expressed in microglia in the brain. Genetic ablation of HK2 reduced microglial glycolytic flux and energy production, suppressed microglial repopulation, and attenuated microglial surveillance and damage-triggered migration in male mice. HK2 elevation is prominent in immune-challenged or disease-associated microglia. In ischaemic stroke models, however, HK2 deletion promoted neuroinflammation and potentiated cerebral damages. The enhanced inflammatory responses after HK2 ablation in microglia are associated with aberrant mitochondrial function and reactive oxygen species accumulation. Our study demonstrates that HK2 gates both glycolytic flux and mitochondrial activity to shape microglial functions, changes of which contribute to metabolic abnormalities and maladaptive inflammation in brain diseases.}, } @article {pmid36535036, year = {2022}, author = {Guney, OB and Ozkan, H}, title = {Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acacca}, pmid = {36535036}, issn = {1741-2552}, abstract = {OBJECTIVE: Steady-state visually evoked potentials (SSVEPs), measured with EEG (electroencephalogram), yield decent information transfer rates (ITR) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a highly novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training.

APPROACH: We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identifier DNN first, which is then fine-tuned to each participant. We transfer this ensemble of fine-tuned DNNs to the new user instance, determine the k most representative DNNs according to the participants' statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions.

MAIN RESULTS: The proposed method significantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2 - 1.0] seconds on two large-scale benchmark and BETA datasets, and achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility: https://github.com/osmanberke/Ensemble-of-DNNs Significance: Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-specific training.}, } @article {pmid36535004, year = {2022}, author = {Fodil, Y and Haddab, S and Kachenoura, A and Karfoul, A}, title = {A novel ANN adaptive Riemannian-based kernel classification for Motor Imagery.}, journal = {Biomedical physics & engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/acaca2}, pmid = {36535004}, issn = {2057-1976}, abstract = {More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify these matrices. This paper presents a novel Artificial Neural Network approach based on an Adaptive Riemannian Kernel, named ARK-ANN, to classify Electroencephalographic (EEG) motor imaging signals in the context of Brain Computer Interface (BCI). A multilayer perceptron is used to classify the covariance matrices of Motor Imagery (MI) signals employing an adaptive optimization of the testing set. The contribution of a geodesic filter is also assessed for the ANN and the original method which use an SVM classifier. The results demonstrate that the ARK-ANN performs better than the other methods and the geodesic filter gives slightly better results in the ARK-SVM, considered here as the reference method, in the case of inter-subject classification (accuracy of 87.4% and 86 % for ARK-ANN and ARK-SVM, respectively). Regarding the cross-subject classification, the proposed method gives an accuracy of 77.3% and increases the precision by 8.2% in comparison to the SVM based method.}, } @article {pmid36532389, year = {2022}, author = {Andrews, A}, title = {Mind Power: Thought-controlled Augmented Reality for Basic Science Education.}, journal = {Medical science educator}, volume = {32}, number = {6}, pages = {1571-1573}, pmid = {36532389}, issn = {2156-8650}, abstract = {The integration of augmented reality (AR) and brain-computer interface (BCI) technologies holds a tremendous potential to improve learning, communication, and teamwork in basic science education. The current study presents a novel interface technology solution to enable AR-BCI interoperability and allow learners to control digital objects in AR using neural commands.}, } @article {pmid36531919, year = {2022}, author = {Lyu, J and Maýe, A and Görner, M and Ruppel, P and Engel, AK and Zhang, J}, title = {Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control.}, journal = {Frontiers in neurorobotics}, volume = {16}, number = {}, pages = {1068274}, pmid = {36531919}, issn = {1662-5218}, abstract = {In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.}, } @article {pmid36530202, year = {2022}, author = {Bleuzé, A and Mattout, J and Congedo, M}, title = {Tangent space alignment: Transfer learning for Brain-Computer Interface.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1049985}, pmid = {36530202}, issn = {1662-5161}, abstract = {Statistical variability of electroencephalography (EEG) between subjects and between sessions is a common problem faced in the field of Brain-Computer Interface (BCI). Such variability prevents the usage of pre-trained machine learning models and requires the use of a calibration for every new session. This paper presents a new transfer learning (TL) method that deals with this variability. This method aims to reduce calibration time and even improve accuracy of BCI systems by aligning EEG data from one subject to the other in the tangent space of the positive definite matrices Riemannian manifold. We tested the method on 18 BCI databases comprising a total of 349 subjects pertaining to three BCI paradigms, namely, event related potentials (ERP), motor imagery (MI), and steady state visually evoked potentials (SSVEP). We employ a support vector classifier for feature classification. The results demonstrate a significant improvement of classification accuracy, as compared to a classical training-test pipeline, in the case of the ERP paradigm, whereas for both the MI and SSVEP paradigm no deterioration of performance is observed. A global 2.7% accuracy improvement is obtained compared to a previously published Riemannian method, Riemannian Procrustes Analysis (RPA). Interestingly, tangent space alignment has an intrinsic ability to deal with transfer learning for sets of data that have different number of channels, naturally applying to inter-dataset transfer learning.}, } @article {pmid36529022, year = {2022}, author = {Li, H and Zhang, D and Xie, J}, title = {MI-DABAN: A dual-attention-based adversarial network for motor imagery classification.}, journal = {Computers in biology and medicine}, volume = {152}, number = {}, pages = {106420}, doi = {10.1016/j.compbiomed.2022.106420}, pmid = {36529022}, issn = {1879-0534}, abstract = {The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) is widely used because of its convenience and safety. However, due to the distributional disparity between EEG signals, data from other subjects cannot be used directly to train a subject-specific classifier. For efficient use of the labeled data, domain transfer learning and adversarial learning are gradually applied to BCI classification tasks. While these methods improve classification performance, they only align globally and ignore task-specific class boundaries, which may lead to the blurring of features near the classification boundaries. Simultaneously, they employ fully shared generators to extract features, resulting in the loss of domain-specific information and the destruction of performance. To address these issues, we propose a novel dual-attention-based adversarial network for motor imagery classification (MI-DABAN). Our framework leverages multiple subjects' knowledge to improve a single subject's motor imagery classification performance by cleverly using a novel adversarial learning method and two unshared attention blocks. Specifically, without introducing additional domain discriminators, we iteratively maximize and minimize the output difference between the two classifiers to implement adversarial learning to ensure accurate domain alignment. Among them, maximization is used to identify easily confused samples near the decision boundary, and minimization is used to align the source and target domain distributions. Moreover, for the shallow features from source and target domains, we use two non-shared attention blocks to preserve domain-specific information, which can prevent the negative transfer of domain information and further improve the classification performance on test data. We conduct extensive experiments on two publicly available EEG datasets, namely BCI Competition IV Datasets 2a and 2b. The experiment results demonstrate our method's effectiveness and superiority.}, } @article {pmid36528312, year = {2022}, author = {Kim, H and Kim, JS and Chung, CK}, title = {Identification of cerebral cortices processing acceleration, velocity, and position during directional reaching movement with deep neural network and explainable AI.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119783}, doi = {10.1016/j.neuroimage.2022.119783}, pmid = {36528312}, issn = {1095-9572}, abstract = {Cerebral cortical representation of motor kinematics is crucial for understanding human motor behavior, potentially extending to efficient control of the brain-computer interface. Numerous single-neuron studies have found the existence of a relationship between neuronal activity and motor kinematics such as acceleration, velocity, and position. Despite differences between kinematic characteristics, it is hard to distinguish neural representations of these kinematic characteristics with macroscopic functional images such as electroencephalography (EEG) and magnetoencephalography (MEG). The reason might be because cortical signals are not sensitive enough to segregate kinematic characteristics due to their limited spatial and temporal resolution. Considering different roles of each cortical area in producing movement, there might be a specific cortical representation depending on characteristics of acceleration, velocity, and position. Recently, neural network modeling has been actively pursued in the field of decoding. We hypothesized that neural features of each kinematic parameter could be identified with a high-performing model for decoding with an explainable AI method. Time-series deep neural network (DNN) models were used to measure the relationship between cortical activity and motor kinematics during reaching movement. With DNN models, kinematic parameters of reaching movement in a 3D space were decoded based on cortical source activity obtained from MEG data. An explainable artificial intelligence (AI) method was then adopted to extract the map of cortical areas, which strongly contributed to decoding each kinematics from DNN models. We found that there existed differed as well as shared cortical areas for decoding each kinematic attribute. Shared areas included bilateral supramarginal gyri and superior parietal lobules known to be related to the goal of movement and sensory integration. On the other hand, dominant areas for each kinematic parameter (the contralateral motor cortex for acceleration, the contralateral parieto-frontal network for velocity, and bilateral visuomotor areas for position) were mutually exclusive. Regarding the visuomotor reaching movement, the motor cortex was found to control the muscle force, the parieto-frontal network encoded reaching movement from sensory information, and visuomotor areas computed limb and gaze coordination in the action space. To the best of our knowledge, this is the first study to discriminate kinematic cortical areas using DNN models and explainable AI.}, } @article {pmid36527133, year = {2022}, author = {Sgroi, DC and Treuner, K and Zhang, Y and Piper, T and Salunga, R and Ahmed, I and Doos, L and Thornber, S and Taylor, KJ and Brachtel, E and Pirrie, S and Schnabel, CA and Rea, D and Bartlett, JMS}, title = {Correlative studies of the Breast Cancer Index (HOXB13/IL17BR) and ER, PR, AR, AR/ER ratio and Ki67 for prediction of extended endocrine therapy benefit: a Trans-aTTom study.}, journal = {Breast cancer research : BCR}, volume = {24}, number = {1}, pages = {90}, pmid = {36527133}, issn = {1465-542X}, abstract = {BACKGROUND: Multiple clinical trials demonstrate consistent but modest benefit of adjuvant extended endocrine therapy (EET) in HR + breast cancer patients. Predictive biomarkers to identify patients that benefit from EET are critical to balance modest reductions in risk against potential side effects of EET. This study compares the performance of the Breast Cancer Index, BCI (HOXB13/IL17BR, H/I), with expression of estrogen (ER), progesterone (PR), and androgen receptors (AR), and Ki67, for prediction of EET benefit.

METHODS: Node-positive (N+) patients from the Trans-aTTom study with available tissue specimen and BCI results (N = 789) were included. Expression of ER, PR, AR, and Ki67 was assessed by quantitative immunohistochemistry. BCI (H/I) gene expression analysis was conducted by quantitative RT-PCR. Statistical significance of the treatment by biomarker interaction was evaluated by likelihood ratio tests based on multivariate Cox proportional models, adjusting for age, tumor size, grade, and HER2 status. Pearson's correlation coefficients were calculated to evaluate correlations between BCI (H/I) versus ER, PR, AR, Ki67 and AR/ER ratio.

RESULTS: EET benefit, measured by the difference in risk of recurrence between patients treated with tamoxifen for 10 versus 5 years, is significantly associated with increasing values of BCI (H/I) (interaction P = 0.01). In contrast, expression of ER (P = 0.83), PR (P = 0.66), AR (P = 0.78), Ki67 (P = 0.87) and AR/ER ratio (P = 0.84) exhibited no significant relationship with EET benefit. BCI (H/I) showed a very weak negative correlation with ER (r = - 0.18), PR (r = - 0.25), and AR (r = - 0.14) expression, but no correlation with either Ki67 (r = 0.04) or AR/ER ratio (r = 0.02).

CONCLUSION: These findings are consistent with the growing body of evidence that BCI (H/I) is significantly predictive of response to EET and outcome. Results from this direct comparison demonstrate that expression of ER, PR, AR, Ki67 or AR/ER ratio are not predictive of benefit from EET. BCI (H/I) is the only clinically validated biomarker that predicts EET benefit.}, } @article {pmid36525745, year = {2022}, author = {Kern, K and Vukelić, M and Guggenberger, R and Gharabaghi, A}, title = {Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients.}, journal = {NeuroImage. Clinical}, volume = {37}, number = {}, pages = {103289}, doi = {10.1016/j.nicl.2022.103289}, pmid = {36525745}, issn = {2213-1582}, abstract = {Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.}, } @article {pmid36524791, year = {2022}, author = {Sinha, S and Dmochowski, RR and Hashim, H and Finazzi-Agrò, E and Iacovelli, V}, title = {The bladder contractility and bladder outlet obstruction indices in adult women. Results of a global Delphi consensus study.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25114}, pmid = {36524791}, issn = {1520-6777}, abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of the bladder contractility index (BCI), bladder outlet obstruction index (BOOI), and the related evidence. This manuscript deals with adult women and follows a previous manuscript reporting on adult men.

METHODS: Twenty-nine experts were invited to answer the two-round survey including three foundation questions and 12 survey questions. Consensus was defined as ≥75% agreement. The ordinal scale (0-10) in round 1 was classified into "strongly agree," "agree," "neutral," "disagree," and "strongly disagree" for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in women.

RESULTS: Eighteen experts participated in the survey with 100% completion. Consensus was noted with regard to 2 of 12 questions, both in the negative. The experts had a consensus that BOOI was neither accurate nor useful and a similar negative trend was noted with regard to BCI. However, there was support, short of consensus, for the utility on an index of bladder contractility and bladder outflow obstruction. Systematic search yielded eight publications pertaining to stress urinary incontinence (n = 6), pelvic organ prolapse (n = 1), and intra-sphincteric botulinum toxin (n = 1).

CONCLUSIONS: Experts had significant concerns with regard to the use of the male BCI and BOOI in adult women despite a general recognition of the need for numerical indices of contractility and obstruction. Systematic search showed a striking lack of evidence in this regard.}, } @article {pmid36523756, year = {2022}, author = {Proverbio, AM and Tacchini, M and Jiang, K}, title = {Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems.}, journal = {Frontiers in behavioral neuroscience}, volume = {16}, number = {}, pages = {1025870}, pmid = {36523756}, issn = {1662-5153}, abstract = {OBJECTIVE: A majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity.

METHODS: A total of 30 participants were shown 280 color pictures (adult, infant, and animal faces; human bodies; written words; checkerboards; and objects) and 120 auditory files (speech, music, and affective vocalizations). This paradigm did not involve target selection to avoid artifactual waves linked to decision-making and response preparation (e.g., P300 and motor potentials), masking the neural signature of semantic representation. Overall, 12,000 ERP waveforms × 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected.

RESULTS: Clear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel to the literature. Results are discussed from the view of current knowledge of ERP functional properties and with respect to machine learning classification methods previously applied to similar data.

CONCLUSION: The data showed a high level of accuracy (p ≤ 0.01) in the discriminating the perceptual categories eliciting the various electrical potentials by statistical analyses. Therefore, the ERP markers identified in this study could be significant tools for optimizing BCI systems [pattern recognition or artificial intelligence (AI) algorithms] applied to EEG/ERP signals.}, } @article {pmid36523527, year = {2022}, author = {Kophamel, S and Ward, LC and Konovalov, DA and Mendez, D and Ariel, E and Cassidy, N and Bell, I and Balastegui Martínez, MT and Munns, SL}, title = {Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning.}, journal = {Ecology and evolution}, volume = {12}, number = {12}, pages = {e9610}, pmid = {36523527}, issn = {2045-7758}, abstract = {Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass - (-0.03 [intercept] - 0.29 * length[2]/resistance at 50 kHz + 1.07 * body mass - 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%-0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.}, } @article {pmid36523445, year = {2022}, author = {Ferracuti, F and Freddi, A and Iarlori, S and Monteriù, A and Omer, KIM and Porcaro, C}, title = {A human-in-the-loop approach for enhancing mobile robot navigation in presence of obstacles not detected by the sensory set.}, journal = {Frontiers in robotics and AI}, volume = {9}, number = {}, pages = {909971}, pmid = {36523445}, issn = {2296-9144}, abstract = {Human-in-the-loop approaches can greatly enhance the human-robot interaction by making the user an active part of the control loop, who can provide a feedback to the robot in order to augment its capabilities. Such feedback becomes even more important in all those situations where safety is of utmost concern, such as in assistive robotics. This study aims to realize a human-in-the-loop approach, where the human can provide a feedback to a specific robot, namely, a smart wheelchair, to augment its artificial sensory set, extending and improving its capabilities to detect and avoid obstacles. The feedback is provided by both a keyboard and a brain-computer interface: with this scope, the work has also included a protocol design phase to elicit and evoke human brain event-related potentials. The whole architecture has been validated within a simulated robotic environment, with electroencephalography signals acquired from different test subjects.}, } @article {pmid36522455, year = {2022}, author = {Iwama, S and Yanagisawa, T and Hirose, R and Ushiba, J}, title = {Beta rhythmicity in human motor cortex reflects neural population coupling that modulates subsequent finger coordination stability.}, journal = {Communications biology}, volume = {5}, number = {1}, pages = {1375}, pmid = {36522455}, issn = {2399-3642}, mesh = {Humans ; *Motor Cortex/physiology ; Movement/physiology ; Transcranial Magnetic Stimulation/methods ; Electroencephalography ; Periodicity ; }, abstract = {Human behavior is not performed completely as desired, but is influenced by the inherent rhythmicity of the brain. Here we show that anti-phase bimanual coordination stability is regulated by the dynamics of pre-movement neural oscillations in bi-hemispheric primary motor cortices (M1) and supplementary motor area (SMA). In experiment 1, pre-movement bi-hemispheric M1 phase synchrony in beta-band (M1-M1 phase synchrony) was online estimated from 129-channel scalp electroencephalograms. Anti-phase bimanual tapping preceded by lower M1-M1 phase synchrony exhibited significantly longer duration than tapping preceded by higher M1-M1 phase synchrony. Further, the inter-individual variability of duration was explained by the interaction of pre-movement activities within the motor network; lower M1-M1 phase synchrony and spectral power at SMA were associated with longer duration. The necessity of cortical interaction for anti-phase maintenance was revealed by sham-controlled repetitive transcranial magnetic stimulation over SMA in another experiment. Our results demonstrate that pre-movement cortical oscillatory coupling within the motor network unknowingly influences bimanual coordination performance in humans after consolidation, suggesting the feasibility of augmenting human motor ability by covertly monitoring preparatory neural dynamics.}, } @article {pmid36515725, year = {2022}, author = {Kruppa, C and Benner, S and Brinkemper, A and Aach, M and Reimertz, C and Schildhauer, TA}, title = {[New technologies and robotics].}, journal = {Unfallchirurgie (Heidelberg, Germany)}, volume = {}, number = {}, pages = {}, pmid = {36515725}, issn = {2731-703X}, abstract = {The development of increasingly more complex computer and electromotor technologies enables the increasing use and expansion of robot-assisted systems in trauma surgery rehabilitation; however, the currently available devices are rarely comprehensively applied but are often used within pilot projects and studies. Different technological approaches, such as exoskeletal systems, functional electrical stimulation, soft robotics, neurorobotics and brain-machine interfaces are used and combined to read and process the communication between, e.g., residual musculature or brain waves, to transfer them to the executing device and to enable the desired execution.Currently, the greatest amount of evidence exists for the use of exoskeletal systems with different modes of action in the context of gait and stance rehabilitation in paraplegic patients; however, their use also plays a role in the rehabilitation of fractures close to the hip joint and endoprosthetic care. So-called single joint systems are also being tested in the rehabilitation of functionally impaired extremities, e.g., after knee prosthesis implantation. At this point, however, the current data situation is still too limited to be able to make a clear statement about the use of these technologies in the trauma surgery "core business" of rehabilitation after fractures and other joint injuries.For rehabilitation after limb amputation, in addition to the further development of myoelectric prostheses, the current development of "sentient" prostheses is of great interest. The use of 3D printing also plays a role in the production of individualized devices.Due to the current progress of artificial intelligence in all fields, ground-breaking further developments and widespread application possibilities in the rehabilitation of trauma patients are to be expected.}, } @article {pmid36509440, year = {2023}, author = {Shapiro, SB and Llerena, PA and Mowery, TM and Miele, EA and Wackym, PA}, title = {Subtemporalis Muscle Middle Cranial Fossa Bone-Island Craniotomy Technique for Placement of an Active Transcutaneous Bone-Conduction Implant.}, journal = {Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology}, volume = {44}, number = {1}, pages = {54-60}, pmid = {36509440}, issn = {1537-4505}, mesh = {Humans ; *Hearing Loss, Mixed Conductive-Sensorineural/surgery ; Bone Conduction/physiology ; Cranial Fossa, Middle/surgery ; *Hearing Aids ; Muscles ; Hearing Loss, Conductive/surgery ; Treatment Outcome ; *Speech Perception ; }, abstract = {OBJECTIVE: Placement of an active transcutaneous bone-conduction implant (BCI) requires drilling of a precise bone bed to accommodate the device and allow for fixation points to make appropriate contact with bone, which can be difficult even when lifts are used. We describe a subtemporalis muscle middle cranial fossa bone-island craniotomy technique that simplifies the procedure and obviates the need for lifts in securing the device.

STUDY DESIGN: Prospective case series.

SETTING: Tertiary academic medical center.

PATIENTS: Seventeen patients underwent surgery for placement of 18 transcutaneous BCIs, 14 for conductive or mixed hearing loss, and 4 for single-sided deafness.

INTERVENTIONS: Surgical placement of a transcutaneous BCI with a bone-island craniotomy technique.

MAIN OUTCOME MEASURES: Functional gain in air-conduction thresholds, aided air-bone gap, frequency of need for lifts, and minor and major complications.

RESULTS: For the conductive or mixed hearing loss cohort, with the transcutaneous BCI in place, there was a highly statistically significant mean functional gain of 35.4 dB hearing level (HL) (range, 16.7-50.25 dB HL; standard deviation, 12.4 dB HL) compared with the unaided condition (p < 0.0001; 95% confidence interval, 36.6-51.6 dB HL). Lifts were not needed in any case. There was one minor complication requiring a second procedure in a patient who had previously received radiation and no major complications. There was no device loss or failure.

CONCLUSIONS: A subtemporalis muscle middle cranial fossa bone-island craniotomy technique eliminates the need for lifts and is a safe and effective method for placement of a transcutaneous BCI.}, } @article {pmid36507325, year = {2022}, author = {Du, Y and Huang, J and Huang, X and Shi, K and Zhou, N}, title = {Dual attentive fusion for EEG-based brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1044631}, pmid = {36507325}, issn = {1662-4548}, abstract = {The classification based on Electroencephalogram (EEG) is a challenging task in the brain-computer interface (BCI) field due to data with a low signal-to-noise ratio. Most current deep learning based studies in this challenge focus on designing a desired convolutional neural network (CNN) to learn and classify the raw EEG signals. However, only CNN itself may not capture the highly discriminative patterns of EEG due to a lack of exploration of attentive spatial and temporal dynamics. To improve information utilization, this study proposes a Dual Attentive Fusion Model (DAFM) for the EEG-based BCI. DAFM is employed to capture the spatial and temporal information by modeling the interdependencies between the features from the EEG signals. To our best knowledge, our method is the first to fuse spatial and temporal dimensions in an interactive attention module. This module improves the expression ability of the extracted features. Extensive experiments implemented on four publicly available datasets demonstrate that our method outperforms state-of-the-art methods. Meanwhile, this work also indicates the effectiveness of Dual Attentive Fusion Module.}, } @article {pmid36507305, year = {2022}, author = {La Fisca, L and Vandenbulcke, V and Wauthia, E and Miceli, A and Simoes Loureiro, I and Ris, L and Lefebvre, L and Gosselin, B and Pernet, CR}, title = {Biases in BCI experiments: Do we really need to balance stimulus properties across categories?.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {900571}, pmid = {36507305}, issn = {1662-5188}, abstract = {Brain Computer Interfaces (BCIs) consist of an interaction between humans and computers with a specific mean of communication, such as voice, gestures, or even brain signals that are usually recorded by an Electroencephalogram (EEG). To ensure an optimal interaction, the BCI algorithm typically involves the classification of the input signals into predefined task-specific categories. However, a recurrent problem is that the classifier can easily be biased by uncontrolled experimental conditions, namely covariates, that are unbalanced across the categories. This issue led to the current solution of forcing the balance of these covariates across the different categories which is time consuming and drastically decreases the dataset diversity. The purpose of this research is to evaluate the need for this forced balance in BCI experiments involving EEG data. A typical design of neural BCIs involves repeated experimental trials using visual stimuli to trigger the so-called Event-Related Potential (ERP). The classifier is expected to learn spatio-temporal patterns specific to categories rather than patterns related to uncontrolled stimulus properties, such as psycho-linguistic variables (e.g., phoneme number, familiarity, and age of acquisition) and image properties (e.g., contrast, compactness, and homogeneity). The challenges are then to know how biased the decision is, which features affect the classification the most, which part of the signal is impacted, and what is the probability to perform neural categorization per se. To address these problems, this research has two main objectives: (1) modeling and quantifying the covariate effects to identify spatio-temporal regions of the EEG allowing maximal classification performance while minimizing the biasing effect, and (2) evaluating the need to balance the covariates across categories when studying brain mechanisms. To solve the modeling problem, we propose using a linear parametric analysis applied to some observable and commonly studied covariates to them. The biasing effect is quantified by comparing the regions highly influenced by the covariates with the regions of high categorical contrast, i.e., parts of the ERP allowing a reliable classification. The need to balance the stimulus's inner properties across categories is evaluated by assessing the separability between category-related and covariate-related evoked responses. The procedure is applied to a visual priming experiment where the images represent items belonging to living or non-living entities. The observed covariates are the commonly controlled psycho-linguistic variables and some visual features of the images. As a result, we identified that the category of the stimulus mostly affects the late evoked response. The covariates, when not modeled, have a biasing effect on the classification, essentially in the early evoked response. This effect increases with the diversity of the dataset and the complexity of the algorithm used. As the effects of both psycho-linguistic variables and image features appear outside of the spatio-temporal regions of significant categorical contrast, the proper selection of the region of interest makes the classification reliable. Having proved that the covariate effects can be separated from the categorical effect, our framework can be further used to isolate the category-dependent evoked response from the rest of the EEG to study neural processes involved when seeing living vs. non-living entities.}, } @article {pmid36507057, year = {2022}, author = {Wang, Y and Liu, S and Wang, H and Zhao, Y and Zhang, XD}, title = {Neuron devices: emerging prospects in neural interfaces and recognition.}, journal = {Microsystems & nanoengineering}, volume = {8}, number = {}, pages = {128}, pmid = {36507057}, issn = {2055-7434}, abstract = {Neuron interface devices can be used to explore the relationships between neuron firing and synaptic transmission, as well as to diagnose and treat neurological disorders, such as epilepsy and Alzheimer's disease. It is crucial to exploit neuron devices with high sensitivity, high biocompatibility, multifunctional integration and high-speed data processing. During the past decades, researchers have made significant progress in neural electrodes, artificial sensory neuron devices, and neuromorphic optic neuron devices. The main part of the review is divided into two sections, providing an overview of recently developed neuron interface devices for recording electrophysiological signals, as well as applications in neuromodulation, simulating the human sensory system, and achieving memory and recognition. We mainly discussed the development, characteristics, functional mechanisms, and applications of neuron devices and elucidated several key points for clinical translation. The present review highlights the advances in neuron devices on brain-computer interfaces and neuroscience research.}, } @article {pmid36504642, year = {2022}, author = {Ren, Z and Han, X and Wang, B}, title = {The performance evaluation of the state-of-the-art EEG-based seizure prediction models.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1016224}, pmid = {36504642}, issn = {1664-2295}, abstract = {The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.}, } @article {pmid36502931, year = {2023}, author = {Ojeda, A and Wagner, M and Maric, V and Ramanathan, D and Mishra, J}, title = {EEG source derived salience network coupling supports real-world attention switching.}, journal = {Neuropsychologia}, volume = {178}, number = {}, pages = {108445}, doi = {10.1016/j.neuropsychologia.2022.108445}, pmid = {36502931}, issn = {1873-3514}, abstract = {While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.}, } @article {pmid36502205, year = {2022}, author = {Fernández-Rodríguez, Á and Darves-Bornoz, A and Velasco-Álvarez, F and Ron-Angevin, R}, title = {Effect of Stimulus Size in a Visual ERP-Based BCI under RSVP.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {23}, pages = {}, pmid = {36502205}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials ; Eye Movements ; Electroencephalography/methods ; }, abstract = {Rapid serial visual presentation (RSVP) is currently one of the most suitable paradigms for use with a visual brain-computer interface based on event-related potentials (ERP-BCI) by patients with a lack of ocular motility. However, gaze-independent paradigms have not been studied as closely as gaze-dependent ones, and variables such as the sizes of the stimuli presented have not yet been explored under RSVP. Hence, the aim of the present work is to assess whether stimulus size has an impact on ERP-BCI performance under the RSVP paradigm. Twelve participants tested the ERP-BCI under RSVP using three different stimulus sizes: small (0.1 × 0.1 cm), medium (1.9 × 1.8 cm), and large (20.05 × 19.9 cm) at 60 cm. The results showed significant differences in accuracy between the conditions; the larger the stimulus, the better the accuracy obtained. It was also shown that these differences were not due to incorrect perception of the stimuli since there was no effect from the size in a perceptual discrimination task. The present work therefore shows that stimulus size has an impact on the performance of an ERP-BCI under RSVP. This finding should be considered by future ERP-BCI proposals aimed at users who need gaze-independent systems.}, } @article {pmid36501860, year = {2022}, author = {Gannouni, S and Belwafi, K and Alangari, N and AboAlsamh, H and Belghith, A}, title = {Classification Strategies for P300-Based BCI-Spellers Adopting the Row Column Paradigm.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {23}, pages = {}, pmid = {36501860}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Event-Related Potentials, P300 ; Electroencephalography ; Signal Processing, Computer-Assisted ; User-Computer Interface ; }, abstract = {Acknowledging the importance of the ability to communicate with other people, the researcher community has developed a series of BCI-spellers, with the goal of regaining communication and interaction capabilities with the environment for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, we believe that the development of efficient signal processing algorithms and strategies will go a long way towards achieving novel assistive technologies using new human-computer interfaces. In this paper, we present various classification strategies that would be adopted by P300 spellers adopting the row/column paradigm. The presented strategies have obtained high accuracy rates compared with existent similar research works.}, } @article {pmid36501753, year = {2022}, author = {Jochumsen, M and Hougaard, BI and Kristensen, MS and Knoche, H}, title = {Implementing Performance Accommodation Mechanisms in Online BCI for Stroke Rehabilitation: A Study on Perceived Control and Frustration.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {23}, pages = {}, pmid = {36501753}, issn = {1424-8220}, mesh = {Humans ; *Stroke Rehabilitation ; Electroencephalography ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Feedback ; *Stroke ; }, abstract = {Brain-computer interfaces (BCIs) are successfully used for stroke rehabilitation, but the training is repetitive and patients can lose the motivation to train. Moreover, controlling the BCI may be difficult, which causes frustration and leads to even worse control. Patients might not adhere to the regimen due to frustration and lack of motivation/engagement. The aim of this study was to implement three performance accommodation mechanisms (PAMs) in an online motor imagery-based BCI to aid people and evaluate their perceived control and frustration. Nineteen healthy participants controlled a fishing game with a BCI in four conditions: (1) no help, (2) augmented success (augmented successful BCI-attempt), (3) mitigated failure (turn unsuccessful BCI-attempt into neutral output), and (4) override input (turn unsuccessful BCI-attempt into successful output). Each condition was followed-up and assessed with Likert-scale questionnaires and a post-experiment interview. Perceived control and frustration were best predicted by the amount of positive feedback the participant received. PAM-help increased perceived control for poor BCI-users but decreased it for good BCI-users. The input override PAM frustrated the users the most, and they differed in how they wanted to be helped. By using PAMs, developers have more freedom to create engaging stroke rehabilitation games.}, } @article {pmid36495049, year = {2022}, author = {Sorinas, J and Troyano, JCF and Ferrández, JM and Fernandez, E}, title = {Unraveling the Development of an Algorithm for Recognizing Primary Emotions Through Electroencephalography.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2250057}, doi = {10.1142/S0129065722500575}, pmid = {36495049}, issn = {1793-6462}, abstract = {The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective brain-computer interface (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. Twelve seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-based emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent (SD) approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Moreover, new insights regarding subject-independent (SI) approximation have been discussed, although the results were not conclusive.}, } @article {pmid36494390, year = {2022}, author = {Rouanne, V and Costecalde, T and Benabid, AL and Aksenova, T}, title = {Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {21316}, pmid = {36494390}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; Task Performance and Analysis ; Electrocorticography ; Brain ; Computer Simulation ; Electroencephalography ; }, abstract = {Brain-computer interfaces (BCIs) translate brain signals into commands to external effectors, and mainly target severely disabled users. The usability of BCIs may be improved by reducing their major constraints, such as the necessity for special training sessions to initially calibrate and later keep up to date the neural signal decoders. In this study, we show that it is possible to train and update BCI decoders during free use of motor BCIs. In addition to the neural signal decoder controlling effectors (control decoder), one more classifier is proposed to detect neural correlates of BCI motor task performances (MTP). MTP decoders reveal whether the actions performed by BCI effectors matched the user's intentions. The combined outputs of MTP and control decoders allow forming training datasets to update the control decoder online and in real time during free use of BCIs. The usability of the proposed auto-adaptive BCI (aaBCI) is demonstrated for two principle BCIs paradigms: with discrete outputs (4 classes BCI, virtual 4-limb exoskeleton), and with continuous outputs (cursor 2D control). The proof of concept was performed in an online simulation study using an ECoG dataset collected from a tetraplegic during a BCI clinical trial. The control decoder reached a multiclass area under the ROC curve of 0.7404 using aaBCI, compared to a chance level of 0.5173 and to 0.8187 for supervised training for the multiclass BCI, and a cosine similarity of 0.1211 using aaBCI, compared to a chance level of 0.0036 and to 0.2002 for supervised training for the continuous BCI.}, } @article {pmid36483633, year = {2022}, author = {de Seta, V and Toppi, J and Colamarino, E and Molle, R and Castellani, F and Cincotti, F and Mattia, D and Pichiorri, F}, title = {Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1016862}, pmid = {36483633}, issn = {1662-5161}, abstract = {Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.}, } @article {pmid36481698, year = {2022}, author = {Chinchani, AM and Paliwal, S and Ganesh, S and Chandrasekhar, V and Yu, BM and Sridharan, D}, title = {Tracking momentary fluctuations in human attention with a cognitive brain-machine interface.}, journal = {Communications biology}, volume = {5}, number = {1}, pages = {1346}, pmid = {36481698}, issn = {2399-3642}, mesh = {Humans ; *Brain-Computer Interfaces ; Cognition ; }, abstract = {Selective attention produces systematic effects on neural states. It is unclear whether, conversely, momentary fluctuations in neural states have behavioral significance for attention. We investigated this question in the human brain with a cognitive brain-machine interface (cBMI) for tracking electrophysiological steady-state visually evoked potentials (SSVEPs) in real-time. Discrimination accuracy (d') was significantly higher when target stimuli were triggered at high, versus low, SSVEP power states. Target and distractor SSVEP power was uncorrelated across the hemifields, and target d' was unaffected by distractor SSVEP power states. Next, we trained participants on an auditory neurofeedback paradigm to generate biased, cross-hemispheric competitive interactions between target and distractor SSVEPs. The strongest behavioral effects emerged when competitive SSVEP dynamics unfolded at a timescale corresponding to the deployment of endogenous attention. In sum, SSVEP power dynamics provide a reliable readout of attentional state, a result with critical implications for tracking and training human attention.}, } @article {pmid36481619, year = {2022}, author = {Yuan, TF and Ng, CH and Hu, S}, title = {Addressing the mental health of children in quarantine with COVID-19 during the Omicron variant era.}, journal = {Asian journal of psychiatry}, volume = {80}, number = {}, pages = {103371}, pmid = {36481619}, issn = {1876-2026}, } @article {pmid36478044, year = {2023}, author = {Zhang, Z and Chen, Y and Zheng, L and Du, J and Wei, S and Zhu, X and Xiong, JW}, title = {A DUSP6 inhibitor suppresses inflammatory cardiac remodeling and improves heart function after myocardial infarction.}, journal = {Disease models & mechanisms}, volume = {16}, number = {5}, pages = {}, doi = {10.1242/dmm.049662}, pmid = {36478044}, issn = {1754-8411}, mesh = {Animals ; Rats ; Dual Specificity Phosphatase 6 ; Fibrosis ; *Myocardial Infarction/complications/drug therapy ; Ventricular Remodeling ; }, abstract = {Acute myocardial infarction (MI) results in loss of cardiomyocytes and abnormal cardiac remodeling with severe inflammation and fibrosis. However, how cardiac repair can be achieved by timely resolution of inflammation and cardiac fibrosis remains incompletely understood. Our previous findings have shown that dual-specificity phosphatase 6 (DUSP6) is a regeneration repressor from zebrafish to rats. In this study, we found that intravenous administration of the DUSP6 inhibitor (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI) improved heart function and reduced cardiac fibrosis in MI rats. Mechanistic analysis revealed that BCI attenuated macrophage inflammation through NF-κB and p38 signaling, independent of DUSP6 inhibition, leading to the downregulation of various cytokines and chemokines. In addition, BCI suppressed differentiation-related signaling pathways and decreased bone-marrow cell differentiation into macrophages through inhibiting DUSP6. Furthermore, intramyocardial injection of poly (D, L-lactic-co-glycolic acid)-loaded BCI after MI had a notable effect on cardiac repair. In summary, BCI improves heart function and reduces abnormal cardiac remodeling by inhibiting macrophage formation and inflammation post-MI, thus providing a promising pro-drug candidate for the treatment of MI and related heart diseases. This article has an associated First Person interview with the first author of the paper.}, } @article {pmid36476879, year = {2022}, author = {Cao, W and Li, JH and Lin, S and Xia, QQ and Du, YL and Yang, Q and Ye, YZ and Zeng, LH and Li, XY and Xu, J and Luo, JH}, title = {NMDA receptor hypofunction underlies deficits in parvalbumin interneurons and social behavior in neuroligin 3 R451C knockin mice.}, journal = {Cell reports}, volume = {41}, number = {10}, pages = {111771}, doi = {10.1016/j.celrep.2022.111771}, pmid = {36476879}, issn = {2211-1247}, mesh = {Animals ; Mice ; *Autism Spectrum Disorder ; *Parvalbumins ; Receptors, N-Methyl-D-Aspartate ; Social Behavior ; }, abstract = {Neuroligins (NLs), a family of postsynaptic cell-adhesion molecules, have been associated with autism spectrum disorder. We have reported that dysfunction of the medial prefrontal cortex (mPFC) leads to social deficits in an NL3 R451C knockin (KI) mouse model of autism. However, the underlying molecular mechanism remains unclear. Here, we find that N-methyl-D-aspartate receptor (NMDAR) function and parvalbumin-positive (PV+) interneuron number and expression are reduced in the mPFC of the KI mice. Selective knockdown of NMDAR subunit GluN1 in the mPFC PV+ interneuron decreases its intrinsic excitability. Restoring NMDAR function by its partial agonist D-cycloserine rescues the PV+ interneuron dysfunction and social deficits in the KI mice. Interestingly, early D-cycloserine administration at adolescence prevents adult KI mice from social deficits. Together, our results suggest that NMDAR hypofunction and the resultant PV+ interneuron dysfunction in the mPFC may constitute a central node in the pathogenesis of social deficits in the KI mice.}, } @article {pmid36476748, year = {2022}, author = {Asahina, T and Shimba, K and Kotani, K and Jimbo, Y}, title = {Improving the accuracy of decoding monkey brain-machine interface data by estimating the state of unobserved cell assemblies.}, journal = {Journal of neuroscience methods}, volume = {385}, number = {}, pages = {109764}, doi = {10.1016/j.jneumeth.2022.109764}, pmid = {36476748}, issn = {1872-678X}, abstract = {BACKGROUND: The brain-machine interface is a technology that has been used for improving the quality of life of individuals with physical disabilities and also healthy individuals. It is important to improve the methods used for decoding the brain-machine interface data as the accuracy and speed of movements achieved using the existing technology are not comparable to the normal body.

Decoding of brain-machine interface data using the proposed method resulted in improved decoding accuracy compared to the existing method.

CONCLUSIONS: The results demonstrated the usefulness of cell assembly state estimation method for decoding the brain-machine interface data.

NEW METHOD: We incorporated a novel method of estimating cell assembly states using spike trains with the existing decoding method that used only firing rate data. Synaptic connectivity pattern was used as feature values in addition to firing rate. Publicly available monkey brain-machine interface datasets were used in the study.

RESULTS: As long as the decoding was successful, the root mean square error of the proposed method was significantly smaller than the existing method. Artificial neural netowork-based decoding method resulted in more stable decoding, and also improved the decoding accuracy due to incorporation of synaptic connectivity pattern.}, } @article {pmid36471144, year = {2022}, author = {Bex, A and Mathon, B}, title = {Advances, technological innovations, and future prospects in stereotactic brain biopsies.}, journal = {Neurosurgical review}, volume = {46}, number = {1}, pages = {5}, pmid = {36471144}, issn = {1437-2320}, mesh = {Humans ; *Brain Neoplasms/diagnosis/surgery/pathology ; Inventions ; Stereotaxic Techniques ; Biopsy/methods ; Brain/surgery/pathology ; }, abstract = {Stereotactic brain biopsy is one of the most frequently performed brain surgeries. This review aimed to expose the latest cutting-edge and updated technologies and innovations available to neurosurgeons to safely perform stereotactic brain biopsy by minimizing the risks of complications and ensuring that the procedure is successful, leading to a histological diagnosis. We also examined methods for improving preoperative, intraoperative, and postoperative workflows. We performed a comprehensive state-of-the-art literature review. Intraoperative histology, fluorescence, and imaging techniques appear as smart tools to improve the diagnostic yield of biopsy. Constant innovations such as optical methods and augmented reality are also being made to increase patient safety. Robotics and integrated imaging techniques provide an enhanced intraoperative workflow. Patients' management algorithms based on early discharge after biopsy optimize the patient's personal experience and make the most efficient possible use of the available hospital resources. Many new trends are emerging, constantly improving patient care and safety, as well as surgical workflow. A parameter that must be considered is the cost-effectiveness of these devices and the possibility of using them on a daily basis. The decision to implement a new instrument in the surgical workflow should also be dependent on the number of procedures per year, the existing stereotactic equipment, and the experience of each center. Research on patients' postbiopsy management is another mandatory approach to enhance the safety profile of stereotactic brain biopsy and patient satisfaction, as well as to reduce healthcare costs.}, } @article {pmid36471022, year = {2022}, author = {Chen, W and Wu, J and Wei, R and Wu, S and Xia, C and Wang, D and Liu, D and Zheng, L and Zou, T and Li, R and Qi, X and Zhang, X}, title = {Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study.}, journal = {Insights into imaging}, volume = {13}, number = {1}, pages = {184}, pmid = {36471022}, issn = {1869-4101}, abstract = {OBJECTIVE: This study aimed to develop a deep learning (DL) model to improve the diagnostic performance of EIC and ASPECTS in acute ischemic stroke (AIS).

METHODS: Acute ischemic stroke patients were retrospectively enrolled from 5 hospitals. We proposed a deep learning model to simultaneously segment the infarct and estimate ASPECTS automatically using baseline CT. The model performance of segmentation and ASPECTS scoring was evaluated using dice similarity coefficient (DSC) and ROC, respectively. Four raters participated in the multi-reader and multicenter (MRMC) experiment to fulfill the region-based ASPECTS reading under the assistance of the model or not. At last, sensitivity, specificity, interpretation time and interrater agreement were used to evaluate the raters' reading performance.

RESULTS: In total, 1391 patients were enrolled for model development and 85 patients for external validation with onset to CT scanning time of 176.4 ± 93.6 min and NIHSS of 5 (IQR 2-10). The model achieved a DSC of 0.600 and 0.762 and an AUC of 0.876 (CI 0.846-0.907) and 0.729 (CI 0.679-0.779), in the internal and external validation set, respectively. The assistance of the DL model improved the raters' average sensitivities and specificities from 0.254 (CI 0.22-0.26) and 0.896 (CI 0.884-0.907), to 0.333 (CI 0.301-0.345) and 0.915 (CI 0.904-0.926), respectively. The average interpretation time of the raters was reduced from 219.0 to 175.7 s (p = 0.035). Meanwhile, the interrater agreement increased from 0.741 to 0.980.

CONCLUSIONS: With the assistance of our proposed DL model, radiologists got better performance in the detection of AIS lesions on NCCT.}, } @article {pmid36470437, year = {2022}, author = {Wang, H and Xia, H and Xu, Z and Natsuki, T and Ni, QQ}, title = {Effect of surface structure on the antithrombogenicity performance of poly(-caprolactone)-cellulose acetate small-diameter tubular scaffolds.}, journal = {International journal of biological macromolecules}, volume = {226}, number = {}, pages = {132-142}, doi = {10.1016/j.ijbiomac.2022.11.315}, pmid = {36470437}, issn = {1879-0003}, abstract = {Small-diameter artificial blood vessels have always faced the problem of thrombosis. In this research, three types of poly(-caprolactone)-cellulose acetate (PCL-CA) composite nanofiber membranes were prepared by various collectors to make into a tubular scaffold with a 4.5-mm diameter. The collector consisted of two sizes of stainless steel wire mesh large-mesh (LM) and small-mesh (SM), respectively. There is also a random flat (RF) that acts as the third type collector. The nanofiber membrane's surface structure mimicked the collectors' surface morphology, they named LM, SM and RF scaffolds. The water contact angles of RF and LM scaffolds are 126.5° and 105.5°, and the distinct square-groove construction greatly improves the contact angle of LM. The tubular scaffolds' radial mechanical property test demonstrated that the large-mesh (LM) tubular scaffold enhanced the strain and tensile strength; the tensile strength and strain are 30 % and 148 % higher than that of the random-flat (RF) tubular scaffold, respectively. The suture retention strength value of the LM tubular scaffold was 103 % higher than that of the RF tubular scaffold. The cytotoxicity and antithrombogenicity performance were also evaluated, the LM tubular scaffold has 88 % cell viability, and the 5-min blood coagulation index (BCI) value was 89 %, which is much higher than other tubular scaffolds. The findings indicate that changing the tubular scaffold's surface morphology cannot only enhance the mechanical and hydrophilic properties but also increase cell survival and antithrombogenicity performance. Thus, the development of a small-diameter artificial blood vessel will be a big step toward solving the problem on thrombosis. Furthermore, artificial blood vessel is expected to be a candidate material for biomedical applications.}, } @article {pmid36468060, year = {2022}, author = {Carino-Escobar, RI and Rodríguez-García, ME and Ramirez-Nava, AG and Quinzaños-Fresnedo, J and Ortega-Robles, E and Arias-Carrion, O and Valdés-Cristerna, R and Cantillo-Negrete, J}, title = {A case report: Upper limb recovery from stroke related to SARS-CoV-2 infection during an intervention with a brain-computer interface.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1010328}, pmid = {36468060}, issn = {1664-2295}, abstract = {COVID-19 may increase the risk of acute ischemic stroke that can cause a loss of upper limb function, even in patients with low risk factors. However, only individual cases have been reported assessing different degrees of hospitalization outcomes. Therefore, outpatient recovery profiles during rehabilitation interventions are needed to better understand neuroplasticity mechanisms required for upper limb motor recovery. Here, we report the progression of physiological and clinical outcomes during upper limb rehabilitation of a 41-year-old patient, without any stroke risk factors, which presented a stroke on the same day as being diagnosed with COVID-19. The patient, who presented hemiparesis with incomplete motor recovery after conventional treatment, participated in a clinical trial consisting of an experimental brain-computer interface (BCI) therapy focused on upper limb rehabilitation during the chronic stage of stroke. Clinical and physiological features were measured throughout the intervention, including the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), the Modified Ashworth Scale (MAS), corticospinal excitability using transcranial magnetic stimulation, cortical activity with electroencephalography, and upper limb strength. After the intervention, the patient gained 8 points and 24 points of FMA-UE and ARAT, respectively, along with a reduction of one point of MAS. In addition, grip and pinch strength doubled. Corticospinal excitability of the affected hemisphere increased while it decreased in the unaffected hemisphere. Moreover, cortical activity became more pronounced in the affected hemisphere during movement intention of the paralyzed hand. Recovery was higher compared to that reported in other BCI interventions in stroke and was due to a reengagement of the primary motor cortex of the affected hemisphere during hand motor control. This suggests that patients with stroke related to COVID-19 may benefit from a BCI intervention and highlights the possibility of a significant recovery in these patients, even in the chronic stage of stroke.}, } @article {pmid36466619, year = {2022}, author = {Mussi, MG and Adams, KD}, title = {EEG hybrid brain-computer interfaces: A scoping review applying an existing hybrid-BCI taxonomy and considerations for pediatric applications.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1007136}, pmid = {36466619}, issn = {1662-5161}, abstract = {Most hybrid brain-computer interfaces (hBCI) aim at improving the performance of single-input BCI. Many combinations are possible to configure an hBCI, such as using multiple brain input signals, different stimuli or more than one input system. Multiple studies have been done since 2010 where such interfaces have been tested and analyzed. Results and conclusions are promising but little has been discussed as to what is the best approach for the pediatric population, should they use hBCI as an assistive technology. Children might face greater challenges when using BCI and might benefit from less complex interfaces. Hence, in this scoping review we included 42 papers that developed hBCI systems for the purpose of control of assistive devices or communication software, and we analyzed them through the lenses of potential use in clinical settings and for children. We extracted taxonomic categories proposed in previous studies to describe the types of interfaces that have been developed. We also proposed interface characteristics that could be observed in different hBCI, such as type of target, number of targets and number of steps before selection. Then, we discussed how each of the extracted characteristics could influence the overall complexity of the system and what might be the best options for applications for children. Effectiveness and efficiency were also collected and included in the analysis. We concluded that the least complex hBCI interfaces might involve having a brain inputs and an external input, with a sequential role of operation, and visual stimuli. Those interfaces might also use a minimal number of targets of the strobic type, with one or two steps before the final selection. We hope this review can be used as a guideline for future hBCI developments and as an incentive to the design of interfaces that can also serve children who have motor impairments.}, } @article {pmid36463881, year = {2022}, author = {Senathirajah, Y and Solomonides, AE}, title = {Best Papers in Human Factors and Sociotechnical Development.}, journal = {Yearbook of medical informatics}, volume = {31}, number = {1}, pages = {221-225}, pmid = {36463881}, issn = {2364-0502}, mesh = {Humans ; *COVID-19 ; *Medical Informatics ; Electronic Health Records ; MEDLINE ; *Social Media ; }, abstract = {OBJECTIVES: To select the best papers that made original and high impact contributions in human factors and organizational issues in biomedical informatics in 2021.

METHODS: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2021 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 3,206 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams.

RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces and mobile health. This year three papers were clearly outstanding and help advance in the field. They provide examples of examining novel and important topics such as the nature of human-machine interaction behavior and norms, use of social-media based design for an electronic health record, and emerging topics such as brain-computer interfaces. thematic development of electronic health records and usability techniques, and condition-focused patient facing tools. Those concerning the Corona Virus Disease 2019 (COVID-19) were included as part of that section.

CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.}, } @article {pmid36463200, year = {2022}, author = {Yan, JJ and Ding, XJ and He, T and Chen, AX and Zhang, W and Yu, ZX and Cheng, XY and Wei, CY and Hu, QD and Liu, XY and Zhang, YL and He, M and Xie, ZY and Zha, X and Xu, C and Cao, P and Li, H and Xu, XH}, title = {A circuit from the ventral subiculum to anterior hypothalamic nucleus GABAergic neurons essential for anxiety-like behavioral avoidance.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {7464}, pmid = {36463200}, issn = {2041-1723}, mesh = {Male ; Animals ; Mice ; *GABAergic Neurons ; *Anterior Hypothalamic Nucleus ; Anxiety Disorders ; Anxiety ; Hippocampus ; }, abstract = {Behavioral observations suggest a connection between anxiety and predator defense, but the underlying neural mechanisms remain unclear. Here we examine the role of the anterior hypothalamic nucleus (AHN), a node in the predator defense network, in anxiety-like behaviors. By in vivo recordings in male mice, we find that activity of AHN GABAergic (AHN[Vgat+]) neurons shows individually stable increases when animals approach unfamiliar objects in an open field (OF) or when they explore the open-arm of an elevated plus-maze (EPM). Moreover, object-evoked AHN activity overlap with predator cue responses and correlate with the object and open-arm avoidance. Crucially, exploration-triggered optogenetic inhibition of AHN[Vgat+] neurons reduces object and open-arm avoidance. Furthermore, retrograde viral tracing identifies the ventral subiculum (vSub) of the hippocampal formation as a significant input to AHN[Vgat+] neurons in driving avoidance behaviors in anxiogenic situations. Thus, convergent activation of AHN[Vgat+] neurons serves as a shared mechanism between anxiety and predator defense to promote behavioral avoidance.}, } @article {pmid36460220, year = {2022}, author = {Pan, H and Fu, Y and Li, Z and Wen, F and Hu, J and Wu, B}, title = {Images Reconstruction from Functional Magnetic Resonance Imaging Patterns Based on the Improved Deep Generative Multiview Model.}, journal = {Neuroscience}, volume = {509}, number = {}, pages = {103-112}, doi = {10.1016/j.neuroscience.2022.11.021}, pmid = {36460220}, issn = {1873-7544}, abstract = {Reconstructing visual stimulus images from the brain activity signals is an important research task in the field of brain decoding. Many methods of reconstructing visual stimulus images mainly focus on how to use deep learning to classify the brain activities measured by functional magnetic resonance imaging or identify visual stimulus images. Accurate reconstruction of visual stimulus images by using deep learning still remains challenging. This paper proposes an improved deep generative multiview model to further promote the accuracy of reconstructing visual stimulus images. Firstly, an encoder based on residual-in-residual dense blocks is designed to fit the deep and multiview visual features of human natural state, and extract the features of visual stimulus images. Secondly, the structure of original decoder is extended to a deeper network in the deep generative multiview model, which makes the features obtained by each deconvolution layer more distinguishable. Finally, we configure the parameters of the optimizer and compare the performance of various optimizers under different parameter values, and then the one with the best performance is chosen and adopted to the whole model. The performance evaluations conducted on two publicly available datasets demonstrate that the improved model has more accurate reconstruction effectiveness than the original deep generative multiview model.}, } @article {pmid36456595, year = {2022}, author = {Dimova-Edeleva, V and Ehrlich, SK and Cheng, G}, title = {Brain computer interface to distinguish between self and other related errors in human agent collaboration.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {20764}, pmid = {36456595}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Support Vector Machine ; Movement ; Acclimatization ; }, abstract = {When a human and machine collaborate on a shared task, ambiguous events might occur that could be perceived as an error by the human partner. In such events, spontaneous error-related potentials (ErrPs) are evoked in the human brain. Knowing whom the human perceived as responsible for the error would help a machine in co-adaptation and shared control paradigms to better adapt to human preferences. Therefore, we ask whether self- and agent-related errors evoke different ErrPs. Eleven subjects participated in an electroencephalography human-agent collaboration experiment with a collaborative trajectory-following task on two collaboration levels, where movement errors occurred as trajectory deviations. Independently of the collaboration level, we observed a higher amplitude of the responses on the midline central Cz electrode for self-related errors compared to observed errors made by the agent. On average, Support Vector Machines classified self- and agent-related errors with 72.64% accuracy using subject-specific features. These results demonstrate that ErrPs can tell if a person relates an error to themselves or an external autonomous agent during collaboration. Thus, the collaborative machine will receive more informed feedback for the error attribution that allows appropriate error identification, a possibility for correction, and avoidance in future actions.}, } @article {pmid36456558, year = {2022}, author = {Tian, X and Chen, Y and Majka, P and Szczupak, D and Perl, YS and Yen, CC and Tong, C and Feng, F and Jiang, H and Glen, D and Deco, G and Rosa, MGP and Silva, AC and Liang, Z and Liu, C}, title = {An integrated resource for functional and structural connectivity of the marmoset brain.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {7416}, pmid = {36456558}, issn = {2041-1723}, mesh = {Animals ; *Callithrix ; *Brain/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; Computer Simulation ; }, abstract = {Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations, (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflects individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.}, } @article {pmid36455079, year = {2022}, author = {Bian, R and Wu, H and Liu, B and Wu, D}, title = {Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-based BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3225878}, pmid = {36455079}, issn = {1558-0210}, abstract = {Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers.}, } @article {pmid36452175, year = {2022}, author = {Weisinger, B and Pandey, DP and Saver, JL and Hochberg, A and Bitton, A and Doniger, GM and Lifshitz, A and Vardi, O and Shohami, E and Segal, Y and Reznik Balter, S and Djemal Kay, Y and Alter, A and Prasad, A and Bornstein, NM}, title = {Frequency-tuned electromagnetic field therapy improves post-stroke motor function: A pilot randomized controlled trial.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1004677}, pmid = {36452175}, issn = {1664-2295}, abstract = {BACKGROUND AND PURPOSE: Impaired upper extremity (UE) motor function is a common disability after ischemic stroke. Exposure to extremely low frequency and low intensity electromagnetic fields (ELF-EMF) in a frequency-specific manner (Electromagnetic Network Targeting Field therapy; ENTF therapy) is a non-invasive method available to a wide range of patients that may enhance neuroplasticity, potentially facilitating motor recovery. This study seeks to quantify the benefit of the ENTF therapy on UE motor function in a subacute ischemic stroke population.

METHODS: In a randomized, sham-controlled, double-blind trial, ischemic stroke patients in the subacute phase with moderately to severely impaired UE function were randomly allocated to active or sham treatment with a novel, non-invasive, brain computer interface-based, extremely low frequency and low intensity ENTF therapy (1-100 Hz, < 1 G). Participants received 40 min of active ENTF or sham treatment 5 days/week for 8 weeks; ~three out of the five treatments were accompanied by 10 min of concurrent physical/occupational therapy. Primary efficacy outcome was improvement on the Fugl-Meyer Assessment - Upper Extremity (FMA-UE) from baseline to end of treatment (8 weeks).

RESULTS: In the per protocol set (13 ENTF and 8 sham participants), mean age was 54.7 years (±15.0), 19% were female, baseline FMA-UE score was 23.7 (±11.0), and median time from stroke onset to first stimulation was 11 days (interquartile range (IQR) 8-15). Greater improvement on the FMA-UE from baseline to week 4 was seen with ENTF compared to sham stimulation, 23.2 ± 14.1 vs. 9.6 ± 9.0, p = 0.007; baseline to week 8 improvement was 31.5 ± 10.7 vs. 23.1 ± 14.1. Similar favorable effects at week 8 were observed for other UE and global disability assessments, including the Action Research Arm Test (Pinch, 13.4 ± 5.6 vs. 5.3 ± 6.5, p = 0.008), Box and Blocks Test (affected hand, 22.5 ± 12.4 vs. 8.5 ± 8.6, p < 0.0001), and modified Rankin Scale (-2.5 ± 0.7 vs. -1.3 ± 0.7, p = 0.0005). No treatment-related adverse events were reported.

CONCLUSIONS: ENTF stimulation in subacute ischemic stroke patients was associated with improved UE motor function and reduced overall disability, and results support its safe use in the indicated population. These results should be confirmed in larger multicenter studies.

CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT04039178, identifier: NCT04039178.}, } @article {pmid36450968, year = {2022}, author = {Vansteensel, MJ and Klein, E and van Thiel, G and Gaytant, M and Simmons, Z and Wolpaw, JR and Vaughan, TM}, title = {Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.}, journal = {Journal of neurology}, volume = {}, number = {}, pages = {}, pmid = {36450968}, issn = {1432-1459}, abstract = {Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain-computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.}, } @article {pmid36450871, year = {2022}, author = {Kaushik, P and Moye, A and Vugt, MV and Roy, PP}, title = {Decoding the cognitive states of attention and distraction in a real-life setting using EEG.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {20649}, pmid = {36450871}, issn = {2045-2322}, mesh = {Humans ; Animals ; Electroencephalography ; *Brain-Computer Interfaces ; *Automobile Driving ; *Gastropoda ; Attention ; Cognition ; }, abstract = {Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.}, } @article {pmid36447267, year = {2022}, author = {Li, Y and Qu, T and Li, D and Jing, J and Deng, Q and Wan, X}, title = {Human herpesvirus 7 encephalitis in an immunocompetent adult and a literature review.}, journal = {Virology journal}, volume = {19}, number = {1}, pages = {200}, pmid = {36447267}, issn = {1743-422X}, mesh = {Adult ; Child ; Humans ; *Herpesvirus 7, Human/genetics ; *Encephalitis, Herpes Simplex ; *Roseolovirus Infections/complications/diagnosis ; Electroencephalography ; High-Throughput Nucleotide Sequencing ; }, abstract = {BACKGROUND: Human herpesvirus 7 (HHV-7) is a common virus that infects children early and is accompanied by lifelong latency in cells, which is easy to reactivate in immunodeficient adults, but the underlying pathological mechanism is uncertain in immunocompetent adults without peculiar past medical history. Even though the clinical manifestation of the encephalitis caused by HHV-7 is uncommon in immunocompetent adults, the HHV-7 infection should not be neglected for encephalitis for unknown reasons.

CASE PRESENTATION: We reported here a case of HHV-7 encephalitis with epileptic seizures. While the brain computer tomography was standard, electroencephalography displayed slow waves in the temporal and bilateral frontal areas, then HHV-7 DNA was detected in the metagenomic next-generation sequencing of cerebrospinal fluid. Fortunately, the patient recovered after treatment and was discharged 2 months later. We also collected the related cases and explored a better way to illuminate the underlying mechanism.

CONCLUSION: The case indicates clinicians should memorize HHV-7 as an unusual etiology of encephalitis to make an early diagnosis and therapy.}, } @article {pmid36446933, year = {2022}, author = {Yu, XD and Zhu, Y and Sun, QX and Deng, F and Wan, J and Zheng, D and Gong, W and Xie, SZ and Shen, CJ and Fu, JY and Huang, H and Lai, HY and Jin, J and Li, Y and Li, XM}, title = {Distinct serotonergic pathways to the amygdala underlie separate behavioral features of anxiety.}, journal = {Nature neuroscience}, volume = {25}, number = {12}, pages = {1651-1663}, pmid = {36446933}, issn = {1546-1726}, mesh = {Animals ; Mice ; Amygdala ; Anxiety ; *Anxiety Disorders ; *Basolateral Nuclear Complex ; Receptors, GABA-B ; *Serotonin ; }, abstract = {Anxiety-like behaviors in mice include social avoidance and avoidance of bright spaces. Whether these features are distinctly regulated is unclear. We demonstrate that in mice, social and anxiogenic stimuli, respectively, increase and decrease serotonin (5-HT) levels in basal amygdala (BA). In dorsal raphe nucleus (DRN), 5-HT∩vGluT3 neurons projecting to BA parvalbumin (DRN[5-HT∩vGluT3]-BA[PV]) and pyramidal (DRN[5-HT∩vGluT3]-BA[Pyr]) neurons have distinct intrinsic properties and gene expression and respond to anxiogenic and social stimuli, respectively. Activation of DRN[5-HT∩vGluT3]→BA[PV] inhibits 5-HT release via GABAB receptors on serotonergic terminals in BA, inducing social avoidance and avoidance of bright spaces. Activation of DRN[5-HT∩vGluT3]→BA neurons inhibits two subsets of BA[Pyr] neurons via 5-HT1A receptors (HTR1A) and 5-HT1B receptors (HTR1B). Pharmacological inhibition of HTR1A and HTR1B in BA induces avoidance of bright spaces and social avoidance, respectively. These findings highlight the functional significance of heterogenic inputs from DRN to BA subpopulations in the regulation of separate anxiety-related behaviors.}, } @article {pmid36446797, year = {2022}, author = {Nicolelis, MAL and Alho, EJL and Donati, ARC and Yonamine, S and Aratanha, MA and Bao, G and Campos, DSF and Almeida, S and Fischer, D and Shokur, S}, title = {Training with noninvasive brain-machine interface, tactile feedback, and locomotion to enhance neurological recovery in individuals with complete paraplegia: a randomized pilot study.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {20545}, pmid = {36446797}, issn = {2045-2322}, mesh = {Adult ; Male ; Humans ; Feedback ; *Brain-Computer Interfaces ; Pilot Projects ; Brazil ; Paraplegia ; Locomotion ; *Spinal Cord Injuries/therapy ; }, abstract = {In recent years, our group and others have reported multiple cases of consistent neurological recovery in people with spinal cord injury (SCI) following a protocol that integrates locomotion training with brain machine interfaces (BMI). The primary objective of this pilot study was to compare the neurological outcomes (motor, tactile, nociception, proprioception, and vibration) in both an intensive assisted locomotion training (LOC) and a neurorehabilitation protocol integrating assisted locomotion with a noninvasive brain-machine interface (L + BMI), virtual reality, and tactile feedback. We also investigated whether individuals with chronic-complete SCI could learn to perform leg motor imagery. We ran a parallel two-arm randomized pilot study; the experiments took place in São Paulo, Brazil. Eight adults sensorimotor-complete (AIS A) (all male) with chronic (> 6 months) traumatic spinal SCI participated in the protocol that was organized in two blocks of 14 weeks of training and an 8-week follow-up. The participants were allocated to either the LOC group (n = 4) or L + BMI group (n = 4) using block randomization (blinded outcome assessment). We show three important results: (i) locomotion training alone can induce some level of neurological recovery in sensorimotor-complete SCI, and (ii) the recovery rate is enhanced when such locomotion training is associated with BMI and tactile feedback (∆Mean Lower Extremity Motor score improvement for LOC = + 2.5, L + B = + 3.5; ∆Pinprick score: LOC = + 3.75, L + B = + 4.75 and ∆Tactile score LOC = + 4.75, L + B = + 9.5). (iii) Furthermore, we report that the BMI classifier accuracy was significantly above the chance level for all participants in L + B group. Our study shows potential for sensory and motor improvement in individuals with chronic complete SCI following a protocol with BMIs and locomotion therapy. We report no dropouts nor adverse events in both subgroups participating in the study, opening the possibility for a more definitive clinical trial with a larger cohort of people with SCI.Trial registration: http://www.ensaiosclinicos.gov.br/ identifier RBR-2pb8gq.}, } @article {pmid36444397, year = {2022}, author = {Heubel-Moenen, FCJI and Ansems, LEM and Verhezen, PWM and Wetzels, RJH and van Oerle, RGM and Straat, RJMHE and Megy, K and Downes, K and Henskens, YMC and Beckers, EAM and Joore, MA}, title = {Effectiveness and costs of a stepwise versus an all-in-one approach to diagnose mild bleeding disorders.}, journal = {British journal of haematology}, volume = {}, number = {}, pages = {}, doi = {10.1111/bjh.18570}, pmid = {36444397}, issn = {1365-2141}, abstract = {The diagnostic work-up of patients referred to the haematologist for bleeding evaluation is performed in a stepwise way: bleeding history and results of screening laboratory tests guide further diagnostic evaluation. This can be ineffective, time-consuming and burdensome for patients. To improve this strategy, the initial laboratory investigation can be extended. In a model-based approach, effectiveness and costs of a conventional stepwise versus a newly proposed all-in-one diagnostic approach for bleeding evaluation were evaluated and compared, using data from an observational patient cohort study, including adult patients referred for bleeding evaluation. In the all-in-one approach, specialized platelet function tests, coagulation factors, and fibrinolysis tests were included in the initial investigation. Final diagnosis, hospital resource use and costs and patient burden were compared. A total of 150 patients were included. Compared to the stepwise approach, in the all-in-one approach, 19 additional patients reached a diagnosis and patient burden was lower, but total costs per patient were higher [€359, 95% bootstrapped confidence interval (BCI) 283-518, p = 0.001]. For bleeding evaluation of patients referred to the haematologist, an all-in-one diagnostic approach has a higher diagnostic yield and reduces patient burden, at a higher cost. This raises the question what costs justify the diagnosis of a bleeding disorder and a less burdensome diagnostic strategy.}, } @article {pmid36441876, year = {2022}, author = {Strypsteen, T and BertrandSenior Member, A}, title = {Bandwidth-efficient distributed neural network architectures with application to neuro-sensor networks.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2022.3225019}, pmid = {36441876}, issn = {2168-2208}, abstract = {In this paper, we describe a conceptual design methodology to design distributed neural network architectures that can perform efficient inference within sensor networks with communication bandwidth constraints. The different sensor channels are distributed across multiple sensor devices, which have to exchange data over bandwidth-limited communication channels to solve a classification task. Our design methodology starts from a user-defined centralized neural network and transforms it into a distributed architecture in which the channels are distributed over different nodes. The distributed network consists of two parallel branches, whose outputs are fused at the fusion center. The first branch collects classification results from local, node-specific classifiers while the second branch compresses each node's signal and then reconstructs the multi-channel time series for classification at the fusion center. We further improve bandwidth gains by dynamically activating the compression path when the local classifications do not suffice. We validate this method on a motor execution task in an emulated EEG sensor network and analyze the resulting bandwidth-accuracy trade-offs. Our experiments show that the proposed framework enables up to a factor 20 in bandwidth reduction and factor 9 in power reduction with minimal loss (up to 2%) in classification accuracy compared to the centralized baseline on the demonstrated task. The proposed method offers a way to smoothly transform a centralized architecture to a distributed, bandwidth-efficient network amenable for low-power sensor networks. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.}, } @article {pmid36441469, year = {2022}, author = {Wood, CR and Xi, Y and Yang, WJ and Wang, H}, title = {Insight into Neuroethical Considerations of the Newly Emerging Technologies and Techniques of the Global Brain Initiatives.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36441469}, issn = {1995-8218}, } @article {pmid36440598, year = {2022}, author = {Kong, X and Shu, X and Wang, J and Liu, D and Ni, Y and Zhao, W and Wang, L and Gao, Z and Chen, J and Yang, B and Guo, X and Wang, Z}, title = {Fine-tuning of mTOR signaling by the UBE4B-KLHL22 E3 ubiquitin ligase cascade in brain development.}, journal = {Development (Cambridge, England)}, volume = {}, number = {}, pages = {}, doi = {10.1242/dev.201286}, pmid = {36440598}, issn = {1477-9129}, abstract = {Spatiotemporal regulation of the mechanistic target of rapamycin (mTOR) pathway is pivotal for establishment of brain architecture. Dysregulation of mTOR signaling is associated with a variety of neurodevelopmental disorders (NDDs). Here, we discover that the UBE4B-KLHL22 E3 ubiquitin ligase cascade regulates mTOR activity in neurodevelopment. In a mouse model with UBE4B conditionally deleted in the nervous system, animals display severe growth defects, spontaneous seizures, and premature death. Loss of UBE4B in the brains of mutant mice results in depletion of neural precursor cells (NPCs) and impairment of neurogenesis. Mechanistically, UBE4B polyubiquitinates and degrades KLHL22, an E3 ligase previously shown to degrade the GATOR1 component DEPDC5. Deletion of UBE4B causes upregulation of KLHL22 and hyperactivation of mTOR, leading to defective proliferation and differentiation of NPCs. Suppression of KLHL22 expression reverses the elevated activity of mTOR caused by acute local deletion of UBE4B. Prenatal treatment with the mTOR inhibitor rapamycin rescues neurogenesis defects in Ube4b mutant mice. Taken together, these findings demonstrate that UBE4B and KLHL22 are essential for maintenance and differentiation of the precursor pool through fine-tuning of mTOR activity.}, } @article {pmid36438642, year = {2022}, author = {Sisti, HM and Beebe, A and Bishop, M and Gabrielsson, E}, title = {A brief review of motor imagery and bimanual coordination.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1037410}, pmid = {36438642}, issn = {1662-5161}, abstract = {Motor imagery is increasingly being used in clinical settings, such as in neurorehabilitation and brain computer interface (BCI). In stroke, patients lose upper limb function and must re-learn bimanual coordination skills necessary for the activities of daily living. Physiotherapists integrate motor imagery with physical rehabilitation to accelerate recovery. In BCIs, users are often asked to imagine a movement, often with sparse instructions. The EEG pattern that coincides with this cognitive task is captured, then used to execute an external command, such as operating a neuroprosthetic device. As such, BCIs are dependent on the efficient and reliable interpretation of motor imagery. While motor imagery improves patient outcome and informs BCI research, the cognitive and neurophysiological mechanisms which underlie it are not clear. Certain types of motor imagery techniques are more effective than others. For instance, focusing on kinesthetic cues and adopting a first-person perspective are more effective than focusing on visual cues and adopting a third-person perspective. As motor imagery becomes more dominant in neurorehabilitation and BCIs, it is important to elucidate what makes these techniques effective. The purpose of this review is to examine the research to date that focuses on both motor imagery and bimanual coordination. An assessment of current research on these two themes may serve as a useful platform for scientists and clinicians seeking to use motor imagery to help improve bimanual coordination, either through augmenting physical therapy or developing more effective BCIs.}, } @article {pmid36437049, year = {2022}, author = {Robinson, DA and Foster, ME and Bennett, CH and Bhandarkar, A and Webster, ER and Celebi, A and Celebi, N and Fuller, EJ and Stavila, V and Spataru, CD and Ashby, DS and Marinella, MJ and Krishnakumar, R and Allendorf, MD and Talin, AA}, title = {Tunable Intervalence Charge Transfer in Ruthenium Prussian Blue Analogue Enables Stable and Efficient Biocompatible Artificial Synapses.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2207595}, doi = {10.1002/adma.202207595}, pmid = {36437049}, issn = {1521-4095}, abstract = {Emerging concepts for neuromorphic computing, bioelectronics, and brain-computer interfacing inspire new research avenues aimed at understanding the relationship between oxidation state and conductivity in unexplored materials. This report expands the materials playground for neuromorphic devices to include a mixed valence inorganic 3D coordination framework, a ruthenium Prussian blue analogue (RuPBA), for flexible and biocompatible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. The electrochemically tunable degree of mixed valency and electronic coupling between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory (DFT) computations and application of electron transfer theory to in-situ spectroscopy of intervalence charge transfer. Retention of programmed states is improved by nearly two orders of magnitude compared to extensively studied organic polymers, thus reducing the frequency, complexity and energy costs associated with error correction schemes. This report demonstrates dopamine-mediated plasticity of RuPBA synapses and biocompatibility of RuPBA with neuronal cells, evoking prospective application for brain-computer interfacing. This article is protected by copyright. All rights reserved.}, } @article {pmid36433362, year = {2022}, author = {Wang, H and Zhu, C and Jin, W and Tang, J and Wu, Z and Chen, K and Hong, H}, title = {A Linear-Power-Regulated Wireless Power Transfer Method for Decreasing the Heat Dissipation of Fully Implantable Microsystems.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {22}, pages = {}, pmid = {36433362}, issn = {1424-8220}, mesh = {*Wireless Technology ; *Hot Temperature ; Prostheses and Implants ; Electric Impedance ; Body Temperature Regulation ; }, abstract = {Magnetic coupling resonance wireless power transfer can efficiently provide energy to intracranial implants under safety constraints, and is the main way to power fully implantable brain-computer interface systems. However, the existing maximum efficiency tracking wireless power transfer system is aimed at optimizing the overall system efficiency, but the efficiency of the secondary side is not optimized. Moreover, the parameters of the transmitter and the receiver change nonlinearly in the power control process, and the efficiency tracking mainly depends on wireless communication. The heat dissipation caused by the unoptimized receiver efficiency and the wireless communication delay in power control will inevitably affect neural activity and even cause damage, thus affecting the results of neuroscience research. Here, a linear-power-regulated wireless power transfer method is proposed to realize the linear change of the received power regulation and optimize the receiver efficiency, and a miniaturized linear-power-regulated wireless power transfer system is developed. With the received power control, the efficiency of the receiver is increased to more than 80%, which can significantly reduce the heating of fully implantable microsystems. The linear change of the received power regulation makes the reflected impedance in the transmitter change linearly, which will help to reduce the dependence on wireless communication and improve biological safety in received power control applications.}, } @article {pmid36429765, year = {2022}, author = {Chang, D and Xiang, Y and Zhao, J and Qian, Y and Li, F}, title = {Exploration of Brain-Computer Interaction for Supporting Children's Attention Training: A Multimodal Design Based on Attention Network and Gamification Design.}, journal = {International journal of environmental research and public health}, volume = {19}, number = {22}, pages = {}, pmid = {36429765}, issn = {1660-4601}, mesh = {Child ; Humans ; *Brain-Computer Interfaces ; Gamification ; Brain ; Cognition ; Computers ; }, abstract = {Recent developments in brain-computer interface (BCI) technology have shown great potential in terms of estimating users' mental state and supporting children's attention training. However, existing training tasks are relatively simple and lack a reliable task-generation process. Moreover, the training experience has not been deeply studied, and the empirical validation of the training effect is still insufficient. This study thusly proposed a BCI training system for children's attention improvement. In particular, to achieve a systematic training process, the attention network was referred to generate the training games for alerting, orienting and executive attentions, and to improve the training experience and adherence, the gamification design theory was introduced to derive attractive training tasks. A preliminary experiment was conducted to set and modify the training parameters. Subsequently, a series of contrasting user experiments were organized to examine the impact of BCI training. To test the training effect of the proposed system, a hypothesis-testing approach was adopted. The results revealed that the proposed BCI gamification attention training system can significantly improve the participants' attention behaviors and concentration ability. Moreover, an immersive, inspiring and smooth training process can be created, and a pleasant user experience can be achieved. Generally, this work is promising in terms of providing a valuable reference for related practices, especially for how to generate BCI attention training tasks using attention networks and how to improve training adherence by integrating multimodal gamification elements.}, } @article {pmid36428885, year = {2022}, author = {Shovon, MSH and Islam, MJ and Nabil, MNAK and Molla, MM and Jony, AI and Mridha, MF}, title = {Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {12}, number = {11}, pages = {}, pmid = {36428885}, issn = {2075-4418}, abstract = {Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called 'HE-HER2Net' has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.}, } @article {pmid36428289, year = {2022}, author = {Si, C and Qin, H and Chuanzhuang, Y and Wei, T and Lin, X}, title = {Study of event-related potentials by withdrawal friction on the fingertip.}, journal = {Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)}, volume = {}, number = {}, pages = {e13232}, doi = {10.1111/srt.13232}, pmid = {36428289}, issn = {1600-0846}, abstract = {OBJECTIVES: The lack of understanding about the brain's reaction processes in perceiving touch and separation between skin and object surfaces is a barrier to the development of existing brain-computer interface technologies and virtual haptics. These technologies are limited in their ability to advance. It leaves prosthesis users with a limited amount of tactile information that they can feel. This study aims to determine whether distinct surface aspects of various items trigger different reactions from the brain when friction is removed from the surface.

METHODS: When friction is suddenly removed from the surface of an item, a technique called event-related potential, (ERP) is used to study the features of people's EEGs. It is done after the subject has actively explored the object's surface. A 64-channels EEG collecting system was utilized to acquire EEG data from the individuals. [Corrections added on 5 December 2022, after first online publication: The preceding sentence has been updated.] The event-related potentials for friction removal were generated using the Oddball paradigm, and the samples consisted of sandpaper with three distinct degrees of roughness. We utilized a total of 20 participants, 10 of whom were male, and 10 of whom were female, with a mean age of 21 years.

RESULTS: It was discovered that the P3 component of event-related potentials, which is essential for cognition, was noticeably absent in the friction withdrawal response for various roughnesses. It was the case regardless of whether the surface was smooth or rough. Moreover, there was no statistically significant difference between the P1 andP2 components, which suggests that the brain could not recognize the surface properties of objects with varying roughness as the friction withdrawal was being performed.

CONCLUSIONS: It has been demonstrated that tactile recognition does not occur after friction withdrawal. The findings of this paper could have significant repercussions for future research involving the study of haptic perception and brain-computer interaction in prosthetic hands. It is a step toward future research on the mechanisms underlying human tactile perception, so think of it as preparation.}, } @article {pmid36427669, year = {2023}, author = {Deng, X and Wang, Z and Liu, K and Xiang, X}, title = {A GAN model encoded by CapsEEGNet for visual EEG encoding and image reproduction.}, journal = {Journal of neuroscience methods}, volume = {384}, number = {}, pages = {109747}, doi = {10.1016/j.jneumeth.2022.109747}, pmid = {36427669}, issn = {1872-678X}, abstract = {In last few decades, reading the human mind is an innovative topic in scientific research. Recent studies in neuroscience indicate that it is possible to decode the signals of the human brain based on the neuroimaging data. The work in this paper explores the possibility of building an end-to-end BCI system to learn and visualize the brain thoughts evoked by the stimulating images. To achieve this goal, it designs an experiment to collect the EEG signals evoked by randomly presented images. Based on these data, this work analyzes and compares the classification abilities by several improved methods, including the Transformer, CapsNet and the ensemble strategies. After obtaining the optimal method to be the encoder, this paper proposes a distribution-to-distribution mapping network to transform an encoded latent feature vector into a prior image feature vector. To visualize the brain thoughts, a pretrained IC-GAN model is used to receive these image feature vectors and generate images. Extensive experiments are carried out and the results show that the proposed method can effectively deal with the small sample data original from the less electrode channels. By examining the generated images coming from the EEG signals, it verifies that the proposed model is capable of reproducing the images seen by human eyes to some extent.}, } @article {pmid36426541, year = {2022}, author = {Colucci, A and Vermehren, M and Cavallo, A and Angerhöfer, C and Peekhaus, N and Zollo, L and Kim, WS and Paik, NJ and Soekadar, SR}, title = {Brain-Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not?.}, journal = {Neurorehabilitation and neural repair}, volume = {36}, number = {12}, pages = {747-756}, pmid = {36426541}, issn = {1552-6844}, mesh = {Humans ; *Exoskeleton Device ; *Brain-Computer Interfaces ; *Neurological Rehabilitation ; Brain ; *Robotics ; }, abstract = {The development of brain-computer interface-controlled exoskeletons promises new treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting brain/neural activity into control signals of wearable actuators, brain/neural exoskeletons (B/NEs) enable the execution of movements despite impaired motor function. Beyond the use as assistive devices, it was shown that-upon repeated use over several weeks-B/NEs can trigger motor recovery, even in chronic paralysis. Recent development of lightweight robotic actuators, comfortable and portable real-world brain recordings, as well as reliable brain/neural control strategies have paved the way for B/NEs to enter clinical care. Although B/NEs are now technically ready for broader clinical use, their promotion will critically depend on early adopters, for example, research-oriented physiotherapists or clinicians who are open for innovation. Data collected by early adopters will further elucidate the underlying mechanisms of B/NE-triggered motor recovery and play a key role in increasing efficacy of personalized treatment strategies. Moreover, early adopters will provide indispensable feedback to the manufacturers necessary to further improve robustness, applicability, and adoption of B/NEs into existing therapy plans.}, } @article {pmid36423320, year = {2022}, author = {Chen, YF and Fu, R and Wu, J and Song, J and Ma, R and Jiang, YC and Zhang, M}, title = {Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2022.3224506}, pmid = {36423320}, issn = {2168-2208}, abstract = {While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p<0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks.}, } @article {pmid36422533, year = {2022}, author = {Diao, X and Luo, D and Wang, D and Lai, J and Li, Q and Zhang, P and Huang, H and Wu, L and Lu, S and Hu, S}, title = {Lurasidone versus Quetiapine for Cognitive Impairments in Young Patients with Bipolar Depression: A Randomized, Controlled Study.}, journal = {Pharmaceuticals (Basel, Switzerland)}, volume = {15}, number = {11}, pages = {}, pmid = {36422533}, issn = {1424-8247}, abstract = {The clinical efficacy of lurasidone and quetiapine, two commonly prescribed atypical antipsychotics for bipolar depression, has been inadequately studied in young patients. In this randomized and controlled study, we aimed to compare the effects of these two drugs on cognitive function, emotional status, and metabolic profiles in children and adolescents with bipolar depression. We recruited young participants (aged 10-17 years old) with a DSM-5 diagnosis of bipolar disorder during a depressive episode, who were then randomly assigned to two groups and treated with flexible doses of lurasidone (60 to 120 mg/day) or quetiapine (300 to 600 mg/day) for consecutive 8 weeks, respectively. All the participants were clinically evaluated on cognitive function using the THINC-it instrument at baseline and week 8, and emotional status was assessed at baseline and the end of week 2, 4, and 8. Additionally, the changes in weight and serum metabolic profiles (triglyceride, cholesterol, and fasting blood glucose) during the trial were also analyzed. In results, a total of 71 patients were randomly assigned to the lurasidone group (n = 35) or the quetiapine group (n = 36), of which 31 patients completed the whole treatment course. After an 8-week follow-up, participants in the lurasidone group showed better performance in the Symbol Check Reaction and Accuracy Tests, when compared to those in the quetiapine group. No inter-group difference was observed in the depression scores, response rate, or remission rate throughout the trial. In addition, there was no significant difference in serum metabolic profiles between the lurasidone group and the quetiapine group, including triglyceride level, cholesterol level, and fasting blood glucose level. However, the quetiapine group presented a more apparent change in body weight than the lurasidone group. In conclusion, the present study provided preliminary evidence that quetiapine and lurasidone had an equivalent anti-depressive effect, and lurasidone appeared to be superior to quetiapine in improving the cognitive function of young patients with bipolar depression.}, } @article {pmid36421880, year = {2022}, author = {Li, J and Huang, B and Wang, F and Xie, Q and Xu, C and Huang, H and Pan, J}, title = {A Potential Prognosis Indicator Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness.}, journal = {Brain sciences}, volume = {12}, number = {11}, pages = {}, pmid = {36421880}, issn = {2076-3425}, abstract = {For patients with disorders of consciousness, such as unresponsive wakefulness syndrome (UWS) patients and minimally conscious state (MCS) patients, their long treatment cycle and high cost commonly put a heavy burden on the patient's family and society. Therefore, it is vital to accurately diagnose and predict consciousness recovery for such patients. In this paper, we explored the role of the P300 signal based on an audiovisual BCI in the classification and prognosis prediction of patients with disorders of consciousness. This experiment included 18 patients: 10 UWS patients and 8 MCS- patients. At the three-month follow-up, we defined patients with an improved prognosis (from UWS to MCS-, from UWS to MCS+, or from MCS- to MCS+) as "improved patients" and those who stayed in UWS/MCS as "not improved patients". First, we compared and analyzed different types of patients, and the results showed that the P300 detection accuracy rate of "improved" patients was significantly higher than that of "not improved" patients. Furthermore, the P300 detection accuracy of traumatic brain injury (TBI) patients was significantly higher than that of non-traumatic brain injury (NTBI, including acquired brain injury and cerebrovascular disease) patients. We also found that there was a positive linear correlation between P300 detection accuracy and CRS-R score, and patients with higher P300 detection accuracy were likely to achieve higher CRS-R scores. In addition, we found that the patients with higher P300 detection accuracies tend to have better prognosis in this audiovisual BCI. These findings indicate that the detection accuracy of P300 is significantly correlated with the level of consciousness, etiology, and prognosis of patients. P300 can be used to represent the preservation level of consciousness in clinical neurophysiology and predict the possibility of recovery in patients with disorders of consciousness.}, } @article {pmid36421877, year = {2022}, author = {Zavala Hernández, JG and Barbosa-Santillán, LI}, title = {Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units.}, journal = {Brain sciences}, volume = {12}, number = {11}, pages = {}, pmid = {36421877}, issn = {2076-3425}, abstract = {The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain-computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.}, } @article {pmid36419166, year = {2022}, author = {Jervis-Rademeyer, H and Ong, K and Djuric, A and Munce, S and Musselman, KE and Marquez-Chin, C}, title = {Therapists' perspectives on using brain-computer interface-triggered functional electrical stimulation therapy for individuals living with upper extremity paralysis: a qualitative case series study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {19}, number = {1}, pages = {127}, pmid = {36419166}, issn = {1743-0003}, support = {//CIHR/Canada ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Paralysis ; Qualitative Research ; Upper Extremity ; }, abstract = {BACKGROUND: Brain computer interface-triggered functional electrical stimulation therapy (BCI-FEST) has shown promise as a therapy to improve upper extremity function for individuals who have had a stroke or spinal cord injury. The next step is to determine whether BCI-FEST could be used clinically as part of broader therapy practice. To do this, we need to understand therapists' opinions on using the BCI-FEST and what limitations potentially exist. Therefore, we conducted a qualitative exploratory study to understand the perspectives of therapists on their experiences delivering BCI-FEST and the feasibility of large-scale clinical implementation.

METHODS: Semi-structured interviews were conducted with physical therapists (PTs) and occupational therapists (OTs) who have delivered BCI-FEST. Interview questions were developed using the COM-B (Capability, Opportunity, Motivation-Behaviour) model of behaviour change. COM-B components were used to inform deductive content analysis while other subthemes were detected using an inductive approach.

RESULTS: We interviewed PTs (n = 3) and OTs (n = 3), with 360 combined hours of experience delivering BCI-FEST. Components and subcomponents of the COM-B determined deductively included: (1) Capability (physical, psychological), (2) Opportunity (physical, social), and (3) Motivation (automatic, reflective). Under each deductive subcomponent, one to two inductive subthemes were identified (n = 8). Capability and Motivation were perceived as strengths, and therefore supported therapists' decisions to use BCI-FEST. Under Opportunity, for both subcomponents (physical, social), therapists recognized the need for more support to clinically implement BCI-FEST.

CONCLUSIONS: We identified facilitating and limiting factors to BCI-FEST delivery in a clinical setting according to clinicians. These factors implied that education, training, a support network or mentors, and restructuring the physical environment (e.g., scheduling) should be targeted as interventions. The results of this study may help to inform future development of new technologies and interventions.}, } @article {pmid36418525, year = {2022}, author = {Baudry, AS and Vanlemmens, L and Congard, A and Untas, A and Segura-Djezzar, C and Lefeuvre-Plesse, C and Coussy, F and Guiu, S and Frenel, JS and Sauterey, B and Yakimova, S and Christophe, V}, title = {Emotional processes in partners' quality of life at various stages of breast cancer pathway: a longitudinal study.}, journal = {Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation}, volume = {}, number = {}, pages = {}, pmid = {36418525}, issn = {1573-2649}, abstract = {INTRODUCTION: Several studies have shown that emotional competence (EC) impacts cancer adjustment via anxiety and depression symptoms. The objective was to test this model for the quality of life (QoL) of partners: first, the direct effect of partners' EC on their QoL, anxiety and depression symptoms after cancer diagnosis (T1), after chemotherapy (T2) and after radiotherapy (T3); Second, the indirect effects of partners' EC at T1 on their QoL at T2 and T3 through anxiety and depression symptoms.

METHODS: 192 partners of women with breast cancer completed a questionnaire at T1, T2 and T3 to assess their EC (PEC), anxiety and depression symptoms (HADS) and QoL (Partner-YW-BCI). Partial correlations and regression analyses were performed to test direct and indirect effects of EC on issues.

RESULTS: EC at T1 predicted fewer anxiety and depression symptoms at each time and all dimensions of QoL, except for career management and financial difficulties. EC showed different significant indirect effects (i.e. via anxiety or depression symptoms) on all sub-dimensions of QoL, except for financial difficulties, according to the step of care pathway (T2 and T3). Anxiety and depression played a different role in the psychological processes that influence QoL.

CONCLUSION: Findings confirm the importance of taking emotional processes into account in the adjustment of partners, especially regarding their QoL and the support they may provide to patients. It, thus, seems important to integrate EC in future health models and psychosocial interventions focused on partners or caregivers.}, } @article {pmid36408731, year = {2022}, author = {Kucewicz, MT and Worrell, GA and Axmacher, N}, title = {Direct electrical brain stimulation of human memory: lessons learnt and future perspectives.}, journal = {Brain : a journal of neurology}, volume = {}, number = {}, pages = {}, doi = {10.1093/brain/awac435}, pmid = {36408731}, issn = {1460-2156}, abstract = {Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of successful attempts at improving performance in a range of memory tasks, the optimal approaches and parameters for memory enhancement have yet to be determined. On a more fundamental level, it remains elusive how delivering electrical current in a given brain area leads to enhanced memory processing. Starting from the local and distal physiological effects on neural populations, the mechanisms of enhanced memory encoding, maintenance, consolidation, or recall in response to direct electrical stimulation are only now being unraveled. With the advent of innovative neurotechnologies for concurrent recording and stimulation intracranially in the human brain, it becomes possible to study both acute and chronic effects of stimulation on memory performance and the underlying neural activities. In this review, we summarize the effects of various invasive stimulation approaches for modulating memory functions. We first outline the challenges that were faced in the initial studies of memory enhancement and the lessons learned. Electrophysiological biomarkers are then reviewed as more objective measures of the stimulation effects than behavioral outcomes. Finally, we classify the various stimulation approaches into continuous and phasic modulation with open or closed loop for responsive stimulation based on analysis of the recorded neural activities. Although the potential advantage of closed-loop responsive stimulation over the classic open-loop approaches is inconclusive, we foresee the emerging results from ongoing longitudinal studies and clinical trials to shed light on both the mechanisms and optimal strategies for improving declarative memory. Adaptive stimulation based on the biomarker analysis over extended periods of time is proposed as a future direction for obtaining lasting effects on memory functions. Chronic tracking and modulation of neural activities intracranially through adaptive stimulation opens tantalizing new avenues to continually monitor and treat memory and cognitive deficits in a range of brain disorders. Brain co-processors created with machine-learning tools and wireless bi-directional connectivity to seamlessly integrate implanted devices with smartphones and cloud computing are poised to enable real-time automated analysis of large data volumes and adaptively tune electrical stimulation based on electrophysiological biomarkers of behavioral states. Next generation implantable devices for high-density recording and stimulation of electrophysiological activities, and technologies for distributed brain-computer interfaces are presented as selected future perspectives for modulating human memory and associated mental processes.}, } @article {pmid36408095, year = {2022}, author = {Hu, J and Zou, J and Wan, Y and Yao, Q and Dong, P and Li, G and Wu, X and Zhang, L and Liang, D and Zeng, Q and Huang, G}, title = {Rehabilitation of motor function after stroke: A bibliometric analysis of global research from 2004 to 2022.}, journal = {Frontiers in aging neuroscience}, volume = {14}, number = {}, pages = {1024163}, pmid = {36408095}, issn = {1663-4365}, abstract = {BACKGROUND AND AIMS: The mortality rate of stroke has been increasing worldwide. Poststroke somatic dysfunctions are common. Motor function rehabilitation of patients with such somatic dysfunctions enhances the quality of life and has long been the primary practice to achieve functional recovery. In this regard, we aimed to delineate the new trends and frontiers in stroke motor function rehabilitation literature published from 2004 to 2022 using a bibliometric software.

METHODS: All documents related to stroke rehabilitation and published from 2004 to 2022 were retrieved from the Web of Science Core Collection. Publication output, research categories, countries/institutions, authors/cocited authors, journals/cocited journals, cocited references, and keywords were assessed using VOSviewer v.1.6.15.0 and CiteSpace version 5.8. The cocitation map was plotted according to the analysis results to intuitively observe the research hotspots.

RESULTS: Overall, 3,302 articles were retrieved from 78 countries or regions and 564 institutions. Over time, the publication outputs increased annually. In terms of national contribution, the United States published the most papers, followed by China, Japan, South Korea, and Canada. Yeungnam University had the most articles among all institutions, followed by Emory University, Fudan University, and National Taiwan University. Jang Sung Ho and Wolf S.L. were the most productive (56 published articles) and influential (cited 1,121 times) authors, respectively. "Effect of constraint-induced movement therapy on upper extremity function 3-9 months after stroke: the Extremity Constraint Induced Therapy Evaluation randomized clinical trial" was the most frequently cited reference. Analysis of keywords showed that upper limbs, Fugl-Meyer assessment, electromyography, virtual reality, telerehabilitation, exoskeleton, and brain-computer interface were the research development trends and focus areas for this topic.

CONCLUSION: Publications regarding motor function rehabilitation following stroke are likely to continuously increase. Research on virtual reality, telemedicine, electroacupuncture, the brain-computer interface, and rehabilitation robots has attracted increasing attention, with these topics becoming the hotspots of present research and the trends of future research.}, } @article {pmid36408074, year = {2022}, author = {Cui, Z and Li, Y and Huang, S and Wu, X and Fu, X and Liu, F and Wan, X and Wang, X and Zhang, Y and Qiu, H and Chen, F and Yang, P and Zhu, S and Li, J and Chen, W}, title = {BCI system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study.}, journal = {Cognitive neurodynamics}, volume = {16}, number = {6}, pages = {1283-1301}, pmid = {36408074}, issn = {1871-4080}, abstract = {UNLABELLED: In the recent years, the increasing applications of brain-computer interface (BCI) in rehabilitation programs have enhanced the chances of functional recovery for patients with neurological disorders. We presented and validated a BCI system with a lower-limb robot for short-term training of patients with spinal cord injury (SCI). The cores of this system included: (1) electroencephalogram (EEG) features related to motor intention reported through experiments and used to drive the robot; (2) a decision tree to determine the training mode provided for patients with different degrees of injuries. Seven SCI patients (one American Spinal Injury Association Impairment Scale (AIS) A, three AIS B, and three AIS C) participated in the short-term training with this system. All patients could learn to use the system rapidly and maintained a high intensity during the training program. The strength of the lower limb key muscles of the patients was improved. Four AIS A/B patients were elevated to AIS C. The cumulative results indicate that clinical application of the BCI system with lower-limb robot is feasible and safe, and has potentially positive effects on SCI patients.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09801-6.}, } @article {pmid36405787, year = {2022}, author = {Jaipriya, D and Sriharipriya, KC}, title = {A comparative analysis of masking empirical mode decomposition and a neural network with feed-forward and back propagation along with masking empirical mode decomposition to improve the classification performance for a reliable brain-computer interface.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1010770}, pmid = {36405787}, issn = {1662-5188}, abstract = {In general, extraction and classification are used in various fields like image processing, pattern recognition, signal processing, and so on. Extracting effective characteristics from raw electroencephalogram (EEG) signals is a crucial role of the brain-computer interface for motor imagery. Recently, there has been a great deal of focus on motor imagery in the EEG signals since they encode a person's intent to do an action. Researchers have been using MI signals to assist paralyzed people and even move them on their own with certain equipment, like wheelchairs. As a result, proper decoding is an important step required for the interconnection of the brain and the computer. EEG decoding is a challenging process because of poor SNR, complexity, and other reasons. However, choosing an appropriate method to extract the features to improve the performance of motor imagery recognition is still a research hotspot. To extract the features of the EEG signal in the classification task, this paper proposes a Masking Empirical Mode Decomposition (MEMD) based Feed Forward Back Propagation Neural Network (MEMD-FFBPNN). The dataset consists of EEG signals which are first normalized using the minimax method and given as input to the MEMD to extract the features and then given to the FFBPNN to classify the tasks. The accuracy of the proposed method MEMD-FFBPNN has been measured using the confusion matrix, mean square error and which has been recorded up to 99.9%. Thus, the proposed method gives better accuracy than the other conventional methods.}, } @article {pmid36403238, year = {2022}, author = {Zhang, F and Zhang, L and Xia, J and Zhao, W and Dong, S and Ye, Z and Pan, G and Luo, J and Zhang, S}, title = {Multimodal Electrocorticogram Active Electrode Array Based on Zinc Oxide-Thin Film Transistors.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2204467}, doi = {10.1002/advs.202204467}, pmid = {36403238}, issn = {2198-3844}, abstract = {Active electrocorticogram (ECoG) electrodes can amplify weak electrophysiological signals and improve anti-interference ability; however, traditional active electrodes are opaque and cannot realize photoelectric collaborative observation. In this study, an active and fully transparent ECoG array based on zinc oxide thin-film transistors (ZnO TFTs) is developed as a local neural signal amplifier for electrophysiological monitoring. The transparency of the proposed ECoG array is up to 85%, which is superior to that of the previously reported active electrode arrays. Various electrical characterizations have demonstrated its ability to record electrophysiological signals with a higher signal-to-noise ratio of 19.9 dB compared to the Au grid (13.2 dB). The high transparency of the ZnO-TFT electrode array allows the concurrent collection of high-quality electrophysiological signals (32.2 dB) under direct optical stimulation of the optogenetic mice brain. The ECoG array can also work under 7-Tesla magnetic resonance imaging to record local brain signals without affecting brain tissue imaging. As the most transparent active ECoG array to date, it provides a powerful multimodal tool for brain observation, including recording brain activity under synchronized optical modulation and 7-Tesla magnetic resonance imaging.}, } @article {pmid36403143, year = {2022}, author = {Klein, E and Kinsella, M and Stevens, I and Fried-Oken, M}, title = {Ethical issues raised by incorporating personalized language models into brain-computer interface communication technologies: a qualitative study of individuals with neurological disease.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-11}, doi = {10.1080/17483107.2022.2146217}, pmid = {36403143}, issn = {1748-3115}, abstract = {PURPOSE: To examine the views of individuals with neurodegenerative diseases about ethical issues related to incorporating personalized language models into brain-computer interface (BCI) communication technologies.

METHODS: Fifteen semi-structured interviews and 51 online free response surveys were completed with individuals diagnosed with neurodegenerative disease that could lead to loss of speech and motor skills. Each participant responded to questions after six hypothetical ethics vignettes were presented that address the possibility of building language models with personal words and phrases in BCI communication technologies. Data were analyzed with consensus coding, using modified grounded theory.

RESULTS: Four themes were identified. (1) The experience of a neurodegenerative disease shapes preferences for personalized language models. (2) An individual's identity will be affected by the ability to personalize the language model. (3) The motivation for personalization is tied to how relationships can be helped or harmed. (4) Privacy is important to people who may need BCI communication technologies. Responses suggest that the inclusion of personal lexica raises ethical issues. Stakeholders want their values to be considered during development of BCI communication technologies.

CONCLUSIONS: With the rapid development of BCI communication technologies, it is critical to incorporate feedback from individuals regarding their ethical concerns about the storage and use of personalized language models. Stakeholder values and preferences about disability, privacy, identity and relationships should drive design, innovation and implementation.IMPLICATIONS FOR REHABILITATIONIndividuals with neurodegenerative diseases are important stakeholders to consider in development of natural language processing within brain-computer interface (BCI) communication technologies.The incorporation of personalized language models raises issues related to disability, identity, relationships, and privacy.People who may one day rely on BCI communication technologies care not just about usability of communication technology but about technology that supports their values and priorities.Qualitative ethics-focused research is a valuable tool for exploring stakeholder perspectives on new capabilities of BCI communication technologies, such as the storage and use of personalized language models.}, } @article {pmid36400152, year = {2022}, author = {Xi, C and Li, A and Lai, J and Huang, X and Zhang, P and Yan, S and Jiao, M and Huang, H and Hu, S}, title = {Brain-gut microbiota multimodal predictive model in patients with bipolar depression.}, journal = {Journal of affective disorders}, volume = {323}, number = {}, pages = {140-152}, doi = {10.1016/j.jad.2022.11.026}, pmid = {36400152}, issn = {1573-2517}, abstract = {BACKGROUND: The "microbiota-gut-brain axis" which bridges the brain and gut microbiota is involved in the pathological mechanisms of bipolar disorder (BD), but rare is known about the exact association patterns and the potential for clinical diagnosis and treatment outcome prediction.

METHODS: At baseline, fecal samples and resting-state MRI data were collected from 103 BD depression patients and 39 healthy controls (HCs) for metagenomic sequencing and network-based functional connectivity (FC), grey matter volume (GMV) analyses. All patients then received 4-weeks quetiapine treatment and were further classified as responders and non-responders. Based on pre-treatment datasets, the correlation networks were established between gut microbiota and neuroimaging measures and the multimodal kernal combination support vector machine (SVM) classifiers were constructed to distinguish BD patients from HCs, and quetiapine responders from non-responders.

RESULTS: The multi-modal pre-treatment characteristics of quetiapine responders, were closer to the HCs compared to non-responders. And the correlation network analyses found the substantial correlations existed in HC between the Anaerotruncus_ unclassified，Porphyromonas_asaccharolytica，Actinomyces_graevenitzii et al. and the functional connectomes involved default mode network (DMN),somatomotor (SM), visual, limbic and basal ganglia networks were disrupted in BD. Moreover, in terms of the multimodal classifier, it reached optimized area under curve (AUC-ROC) at 0.9517 when classified BD from HC, and also acquired 0.8292 discriminating quetiapine responders from non-responders, which consistently better than even using the best unique modality.

LIMITATIONS: Lack post-treatment and external validation datasets; size of HCs is modest.

CONCLUSIONS: Multi-modalities of combining pre-treatment gut microbiota with neuroimaging endophenotypes might be a superior approach for accurate diagnosis and quetiapine efficacy prediction in BD.}, } @article {pmid36398685, year = {2022}, author = {Bhuvaneshwari, M and Grace Mary Kanaga, E and George, ST}, title = {Classification of SSVEP-EEG signals using CNN and Red Fox Optimization for BCI applications.}, journal = {Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine}, volume = {}, number = {}, pages = {9544119221135714}, doi = {10.1177/09544119221135714}, pmid = {36398685}, issn = {2041-3033}, abstract = {Classification of electroencephalography (EEG) signals associated with Steady-state visually evoked potential (SSVEP) is prominent because of its potential in restoring the communication and controlling capability of paralytic people. However, SSVEP signals classification is a challenging task for researchers because of its low signal-to-noise ratio, non-stationary and high dimensional properties. A proficient technique has to be evolved to classify the SSVEP-based EEG data. In recent times, convolutional neural network (CNN) has reached a quantum leap in EEG signal classification. Therefore, the proposed system employs CNN to classify the SSVEP-based EEG signals. Though CNN has proved its proficiency in handling EEG signal classification problems, the calibration of hyperparameters is required to enhance the performance of the model. The calibration of a hyperparameter is a time-consuming task, hence proposed an automated hyperparameter optimization technique using the Red Fox Optimization Algorithm (RFO). The effectiveness of the algorithm is evaluated by comparing it with the performance of Harris Hawk Optimization (HHO), Flower Pollination Algorithm (FPA), Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) based hyperparameter optimized CNN applied to the SSVEP based EEG signals multiclass dataset. The experimental results infer that the proposed algorithm can achieve a testing accuracy of 88.91% which is higher than other comparative algorithms like HHO, FPA, GWO and WOA. The above-mentioned values clearly show that the proposed algorithm achieved competitive performance when compared to the other reported algorithm.}, } @article {pmid36398508, year = {2022}, author = {Rainey, S and Dague, KO and Crisp, R}, title = {Brain-State Transitions, Responsibility, and Personal Identity.}, journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees}, volume = {31}, number = {4}, pages = {453-463}, doi = {10.1017/S096318012100092X}, pmid = {36398508}, issn = {1469-2147}, mesh = {Humans ; *Self Concept ; *Brain-Computer Interfaces ; Morals ; Brain ; }, abstract = {This article examines the emerging possibility of "brain-state transitioning," in which one brain state is prompted through manipulating the dynamics of the active brain. The technique, still in its infancy, is intended to provide the basis for novel treatments for brain-based disorders. Although a detailed literature exists covering topics around brain-machine interfaces, where targets of brain-based activity include artificial limbs, hardware, and software, there is less concentration on the brain itself as a target for instrumental intervention. This article examines some of the science behind brain-state transitioning, before extending beyond current possibilities in order to explore philosophical and ethical questions about how transitions could be seen to impact on assessment of responsibility and personal identity. It concludes with some thoughts on how best to pursue this nascent approach while accounting for the philosophical and ethical issues.}, } @article {pmid36398434, year = {2022}, author = {Zhang, J and Wang, L and Xue, Y and Lei, IM and Chen, X and Zhang, P and Cai, C and Liang, X and Lu, Y and Liu, J}, title = {Engineering Electrodes with Robust Conducting Hydrogel Coating for Neural Recording and Modulation.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2209324}, doi = {10.1002/adma.202209324}, pmid = {36398434}, issn = {1521-4095}, abstract = {Coating conventional metallic electrodes with conducting polymers has enabled the essential characteristics required for bioelectronics, such as biocompatibility, electrical conductivity, mechanical compliance, and the capacity for structural and chemical functionalization of the bioelectrodes. However, the fragile interface between the conducting polymer and the electrode in wet physiological environment greatly limits their utility and reliability. Here, a general yet reliable strategy to seamlessly interface conventional electrodes with conducting hydrogel coatings is established, featuring tissue-like modulus, highly-desirable electrochemical properties, robust interface, and long-term reliability. Numerical modeling reveals the role of toughening mechanism, synergy of covalent anchorage of long-chain polymers, and chemical cross-linking, in improving the long-term robustness of the interface. Through in vivo implantation in freely-moving mouse models, it is shown that stable electrophysiological recording can be achieved, while the conducting hydrogel-electrode interface remains robust during the long-term low-voltage electrical stimulation. This simple yet versatile design strategy addresses the long-standing technical challenges in functional bioelectrode engineering, and opens up new avenues for the next-generation diagnostic brain-machine interfaces.}, } @article {pmid36395140, year = {2022}, author = {Faes, A and Camarrone, F and Hulle, MMV}, title = {Single Finger Trajectory Prediction From Intracranial Brain Activity Using Block-Term Tensor Regression With Fast and Automatic Component Extraction.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2022.3216589}, pmid = {36395140}, issn = {2162-2388}, abstract = {Multiway-or tensor-based decoding techniques for brain-computer interfaces (BCIs) are believed to better account for the multilinear structure of brain signals than conventional vector-or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding so that conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our block-term tensor regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression). BTTR accounts for the underlying (hidden) data structure in a fully automatic and computationally efficient manner, leading to a significant performance gain over conventional vector-or matrix-based techniques in a challenging real-world application. As a challenging real-world application, we apply BTTR to accurately predict single finger movement trajectories from intracranial recordings in human subjects. We compare the obtained performance with that of the state-of-the-art.}, } @article {pmid36394044, year = {2022}, author = {Insausti-Delgado, A and López-Larraz, E and Nishimura, Y and Ziemann, U and Ramos-Murguialday, A}, title = {Non-invasive brain-spine interface: Continuous control of trans-spinal magnetic stimulation using EEG.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {10}, number = {}, pages = {975037}, pmid = {36394044}, issn = {2296-4185}, abstract = {Brain-controlled neuromodulation has emerged as a promising tool to promote functional recovery in patients with motor disorders. Brain-machine interfaces exploit this neuromodulatory strategy and could be used for restoring voluntary control of lower limbs. In this work, we propose a non-invasive brain-spine interface (BSI) that processes electroencephalographic (EEG) activity to volitionally control trans-spinal magnetic stimulation (ts-MS), as an approach for lower-limb neurorehabilitation. This novel platform allows to contingently connect motor cortical activation during leg motor imagery with the activation of leg muscles via ts-MS. We tested this closed-loop system in 10 healthy participants using different stimulation conditions. This BSI efficiently removed stimulation artifacts from EEG regardless of ts-MS intensity used, allowing continuous monitoring of cortical activity and real-time closed-loop control of ts-MS. Our BSI induced afferent and efferent evoked responses, being this activation ts-MS intensity-dependent. We demonstrated the feasibility, safety and usability of this non-invasive BSI. The presented system represents a novel non-invasive means of brain-controlled neuromodulation and opens the door towards its integration as a therapeutic tool for lower-limb rehabilitation.}, } @article {pmid36389253, year = {2022}, author = {Li, P and Su, J and Belkacem, AN and Cheng, L and Chen, C}, title = {Corrigendum: Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1024150}, doi = {10.3389/fnins.2022.1024150}, pmid = {36389253}, issn = {1662-4548}, abstract = {[This corrects the article DOI: 10.3389/fnins.2022.971039.].}, } @article {pmid36389231, year = {2022}, author = {Hou, X and Zhao, J and Zhang, H}, title = {Reconstruction of perceived face images from brain activities based on multi-attribute constraints.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1015752}, pmid = {36389231}, issn = {1662-4548}, abstract = {Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an important application in the field of brain-computer interfaces. Existing methods do not fully consider the multiple facial attributes represented in face images, and their different activity patterns at multiple brain regions are often ignored, which causes the reconstruction performance very poor. In the current study, we propose an algorithmic framework that efficiently combines multiple face-selective brain regions for precise multi-attribute perceived face reconstruction. Our framework consists of three modules: a multi-task deep learning network (MTDLN), which is developed to simultaneously extract the multi-dimensional face features attributed to facial expression, identity and gender from one single face image, a set of linear regressions (LR), which is built to map the relationship between the multi-dimensional face features and the brain signals from multiple brain regions, and a multi-conditional generative adversarial network (mcGAN), which is used to generate the perceived face images constrained by the predicted multi-dimensional face features. We conduct extensive fMRI experiments to evaluate the reconstruction performance of our framework both subjectively and objectively. The results show that, compared with the traditional methods, our proposed framework better characterizes the multi-attribute face features in a face image, better predicts the face features from brain signals, and achieves better reconstruction performance of both seen and unseen face images in both visual effects and quantitative assessment. Moreover, besides the state-of-the-art intra-subject reconstruction performance, our proposed framework can also realize inter-subject face reconstruction to a certain extent.}, } @article {pmid36388974, year = {2022}, author = {Keogh, C and FitzGerald, JJ}, title = {Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics.}, journal = {iScience}, volume = {25}, number = {11}, pages = {105428}, pmid = {36388974}, issn = {2589-0042}, abstract = {The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals.}, } @article {pmid36387766, year = {2022}, author = {Bak, S and Yeu, M and Jeong, J}, title = {Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy.}, journal = {Computational intelligence and neuroscience}, volume = {2022}, number = {}, pages = {1034983}, pmid = {36387766}, issn = {1687-5273}, mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; Support Vector Machine ; Prefrontal Cortex ; Brain ; }, abstract = {It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.}, } @article {pmid36387584, year = {2022}, author = {Cui, X and Wu, Y and Wu, J and You, Z and Xiahou, J and Ouyang, M}, title = {A review: Music-emotion recognition and analysis based on EEG signals.}, journal = {Frontiers in neuroinformatics}, volume = {16}, number = {}, pages = {997282}, pmid = {36387584}, issn = {1662-5196}, abstract = {Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.}, } @article {pmid36381629, year = {2022}, author = {Lee, SH and Thunemann, M and Lee, K and Cleary, DR and Tonsfeldt, KJ and Oh, H and Azzazy, F and Tchoe, Y and Bourhis, AM and Hossain, L and Ro, YG and Tanaka, A and Kılıç, K and Devor, A and Dayeh, SA}, title = {Scalable Thousand Channel Penetrating Microneedle Arrays on Flex for Multimodal and Large Area Coverage BrainMachine Interfaces.}, journal = {Advanced functional materials}, volume = {32}, number = {25}, pages = {}, pmid = {36381629}, issn = {1616-301X}, support = {F32 MH120886/MH/NIMH NIH HHS/United States ; R01 DA050159/DA/NIDA NIH HHS/United States ; UG3 NS123723/NS/NINDS NIH HHS/United States ; R01 NS123655/NS/NINDS NIH HHS/United States ; DP2 EB029757/EB/NIBIB NIH HHS/United States ; R01 MH111359/MH/NIMH NIH HHS/United States ; }, abstract = {The Utah array powers cutting-edge projects for restoration of neurological function, such as BrainGate, but the underlying electrode technology has itself advanced little in the last three decades. Here, advanced dual-side lithographic microfabrication processes is exploited to demonstrate a 1024-channel penetrating silicon microneedle array (SiMNA) that is scalable in its recording capabilities and cortical coverage and is suitable for clinical translation. The SiMNA is the first penetrating microneedle array with a flexible backing that affords compliancy to brain movements. In addition, the SiMNA is optically transparent permitting simultaneous optical and electrophysiological interrogation of neuronal activity. The SiMNA is used to demonstrate reliable recordings of spontaneous and evoked field potentials and of single unit activity in chronically implanted mice for up to 196 days in response to optogenetic and to whisker air-puff stimuli. Significantly, the 1024-channel SiMNA establishes detailed spatiotemporal mapping of broadband brain activity in rats. This novel scalable and biocompatible SiMNA with its multimodal capability and sensitivity to broadband brain activity will accelerate the progress in fundamental neurophysiological investigations and establishes a new milestone for penetrating and large area coverage microelectrode arrays for brain-machine interfaces.}, } @article {pmid36379711, year = {2022}, author = {Heusser, MR and Bourrelly, C and Gandhi, NJ}, title = {Decoding the Time Course of Spatial Information from Spiking and Local Field Potential Activities in the Superior Colliculus.}, journal = {eNeuro}, volume = {9}, number = {6}, pages = {}, pmid = {36379711}, issn = {2373-2822}, mesh = {Animals ; *Superior Colliculi/physiology ; Macaca mulatta ; *Saccades ; Eye Movements ; Neurons/physiology ; }, abstract = {Place code representation is ubiquitous in circuits that encode spatial parameters. For visually guided eye movements, neurons in many brain regions emit spikes when a stimulus is presented in their receptive fields and/or when a movement is directed into their movement fields. Crucially, individual neurons respond for a broad range of directions or eccentricities away from the optimal vector, making it difficult to decode the stimulus location or the saccade vector from each cell's activity. We investigated whether it is possible to decode the spatial parameter with a population-level analysis, even when the optimal vectors are similar across neurons. Spiking activity and local field potentials (LFPs) in the superior colliculus (SC) were recorded with a laminar probe as monkeys performed a delayed saccade task to one of eight targets radially equidistant in direction. A classifier was applied offline to decode the spatial configuration as the trial progresses from sensation to action. For spiking activity, decoding performance across all eight directions was highest during the visual and motor epochs and lower but well above chance during the delay period. Classification performance followed a similar pattern for LFP activity too, except the performance during the delay period was limited mostly to the preferred direction. Increasing the number of neurons in the population consistently increased classifier performance for both modalities. Overall, this study demonstrates the power of population activity for decoding spatial information not possible from individual neurons.}, } @article {pmid36376487, year = {2022}, author = {Jia, Y and Xu, S and Han, G and Wang, B and Wang, Z and Lan, C and Zhao, P and Gao, M and Zhang, Y and Jiang, W and Qiu, B and Liu, R and Hsu, YC and Sun, Y and Liu, C and Liu, Y and Bai, R}, title = {Transmembrane water-efflux rate measured by magnetic resonance imaging as a biomarker of the expression of aquaporin-4 in gliomas.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {36376487}, issn = {2157-846X}, abstract = {The water-selective channel protein aquaporin-4 (AQP4) contributes to the migration and proliferation of gliomas, and to their resistance to therapy. Here we show, in glioma cell cultures, in subcutaneous and orthotopic gliomas in rats, and in glioma tumours in patients, that transmembrane water-efflux rate is a sensitive biomarker of AQP4 expression and can be measured via conventional dynamic-contrast-enhanced magnetic resonance imaging. Water-efflux rates correlated with stages of glioma proliferation as well as with changes in the heterogeneity of intra-tumoural and inter-tumoural AQP4 in rodent and human gliomas following treatment with temozolomide and with the AQP4 inhibitor TGN020. Regions with low water-efflux rates contained higher fractions of stem-like slow-cycling cells and therapy-resistant cells, suggesting that maps of water-efflux rates could be used to identify gliomas that are resistant to therapies.}, } @article {pmid36376067, year = {2022}, author = {Iwama, S and Zhang, Y and Ushiba, J}, title = {De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex.}, journal = {eNeuro}, volume = {9}, number = {6}, pages = {}, pmid = {36376067}, issn = {2373-2822}, mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; *Cerebral Cortex ; Brain/physiology ; Algorithms ; Computers ; }, abstract = {Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for sensorimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance improvement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found systematic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed decision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural responses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.}, } @article {pmid36373709, year = {2022}, author = {Lyu, C and Yu, C and Sun, G and Zhao, Y and Cai, R and Sun, H and Wang, X and Jia, G and Fan, L and Chen, X and Zhou, L and Shen, Y and Gao, L and Li, X}, title = {Deconstruction of Vermal Cerebellum in Ramp Locomotion in Mice.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2203665}, doi = {10.1002/advs.202203665}, pmid = {36373709}, issn = {2198-3844}, abstract = {The cerebellum is involved in encoding balance, posture, speed, and gravity during locomotion. However, most studies are carried out on flat surfaces, and little is known about cerebellar activity during free ambulation on slopes. Here, it has been imaged the neuronal activity of cerebellar molecular interneurons (MLIs) and Purkinje cells (PCs) using a miniaturized microscope while a mouse is walking on a slope. It has been found that the neuronal activity of vermal MLIs specifically enhanced during uphill and downhill locomotion. In addition, a subset of MLIs is activated during entire uphill or downhill positions on the slope and is modulated by the slope inclines. In contrast, PCs showed counter-balanced neuronal activity to MLIs, which reduced activity at the ramp peak. So, PCs may represent the ramp environment at the population level. In addition, chemogenetic inactivation of lobule V of the vermis impaired uphill locomotion. These results revealed a novel micro-circuit in the vermal cerebellum that regulates ambulatory behavior in 3D terrains.}, } @article {pmid36371498, year = {2022}, author = {Willsey, MS and Nason-Tomaszewski, SR and Ensel, SR and Temmar, H and Mender, MJ and Costello, JT and Patil, PG and Chestek, CA}, title = {Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {6899}, pmid = {36371498}, issn = {2041-1723}, mesh = {Animals ; Male ; *Brain-Computer Interfaces ; Macaca mulatta ; Neural Networks, Computer ; Movement ; Algorithms ; }, abstract = {Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention-trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.}, } @article {pmid36368035, year = {2022}, author = {Rommel, C and Paillard, J and Moreau, T and Gramfort, A}, title = {Data augmentation for learning predictive models on EEG: a systematic comparison.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca220}, pmid = {36368035}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Sleep Stages ; Sleep ; }, abstract = {Objective.The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods. This work proposes to better validate the existing data augmentation approaches through a unified and exhaustive analysis.Approach.We compare quantitatively 13 different augmentations with two different predictive tasks, datasets and models, using three different types of experiments.Main results.We demonstrate that employing the adequate data augmentations can bring up to 45% accuracy improvements in low data regimes compared to the same model trained without any augmentation. Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task.Significance.Our results highlight the best data augmentations to consider for sleep stage classification and motor imagery brain-computer interfaces. More broadly, it demonstrates that EEG classification tasks benefit from adequate data augmentation.}, } @article {pmid36366726, year = {2022}, author = {Wu, L and Ren, K and Chen, G and Wang, H and Li, H and Xu, L}, title = {Hemostatic effect and safety evaluation of the absorbable macroporous polysaccharides composite hemostatic material prepared by a green fabrication approach.}, journal = {Journal of biomaterials applications}, volume = {}, number = {}, pages = {8853282221139026}, doi = {10.1177/08853282221139026}, pmid = {36366726}, issn = {1530-8022}, abstract = {Carboxymethyl chitosan is widely used in the medical field such as wound healing and other medical fields. We previously fabricated the absorbable macroporous polysaccharides composite hemostatics (AMPCs) mainly composed of carboxymethyl chitosan which possess excellent hemostatic effect. To further elucidate the impact of CMCTs on the hemostatic effect and biosafety of AMPCs, carboxymethyl chitosan with different properties were used to prepare AMPCs. By comparing the physical and chemical properties, AMPCs performed high water absorption ability, especially Group 1 (swelling ratio reached 5792%), which facilitated the rapid formation of blood clots. It was confirmed by blood clotting index (BCI) and blood coagulation tests in vitro that Group 1 showed a slightly higher coagulation capacity than groups 2 and 3, which may be due to the positive charge on the surface of the cations in the salts attaches to the negative charge on the surface of the red blood cells, an electrostatic neutralization reaction occurs. The biosafety was a preliminary evaluation by implanted AMPCs into the back of Sprague-Dawley rats and the tissue was harvested after feeding for 28 days. The AMPCs exhibited good biosafety for whole blood and major organs during the degradation in vivo: during the degradation of AMPCs, excluding changes in some serum indicators, no tissue necrosis or inflammatory cell infiltration was observed in these organs, either by gross observation or histological analysis. These findings demonstrate that expecting to develop a highly functional and safe hemostatic agent based on Group 1 for rapid hemostasis applications in emergencies.}, } @article {pmid36366225, year = {2022}, author = {Hu, H and Pu, Z and Li, H and Liu, Z and Wang, P}, title = {Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {21}, pages = {}, pmid = {36366225}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Imagination ; Signal Processing, Computer-Assisted ; Brain ; Electroencephalography/methods ; Algorithms ; }, abstract = {The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.}, } @article {pmid36366001, year = {2022}, author = {Chuang, CC and Lee, CC and So, EC and Yeng, CH and Chen, YJ}, title = {Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {21}, pages = {}, pmid = {36366001}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Amyotrophic Lateral Sclerosis ; Neural Networks, Computer ; Electroencephalography/methods ; Photic Stimulation ; Algorithms ; }, abstract = {Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain-computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.}, } @article {pmid36365948, year = {2022}, author = {Emsawas, T and Morita, T and Kimura, T and Fukui, KI and Numao, M}, title = {Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {21}, pages = {}, pmid = {36365948}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Emotions ; Palliative Care ; }, abstract = {Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain-computer interface tasks to extract feature maps and classify the target output. However, the EEG analysis remains challenging since it requires consideration of various architectural design components that influence the representational ability of extracted features. This study proposes an EEG-based emotion classification model called the multi-kernel temporal and spatial convolution network (MultiT-S ConvNet). The multi-scale kernel is used in the model to learn various time resolutions, and separable convolutions are applied to find related spatial patterns. In addition, we enhanced both the temporal and spatial filters with a lightweight gating mechanism. To validate the performance and classification accuracy of MultiT-S ConvNet, we conduct subject-dependent and subject-independent experiments on EEG-based emotion datasets: DEAP and SEED. Compared with existing methods, MultiT-S ConvNet outperforms with higher accuracy results and a few trainable parameters. Moreover, the proposed multi-scale module in temporal filtering enables extracting a wide range of EEG representations, covering short- to long-wavelength components. This module could be further implemented in any model of EEG-based convolution networks, and its ability potentially improves the model's learning capacity.}, } @article {pmid36365824, year = {2022}, author = {Khare, SK and Gaikwad, N and Bokde, ND}, title = {An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {21}, pages = {}, pmid = {36365824}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; Wavelet Analysis ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Algorithms ; Support Vector Machine ; Signal Processing, Computer-Assisted ; }, abstract = {Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.}, } @article {pmid36364633, year = {2022}, author = {Frenzel, J and Kupferer, A and Zink, M and Mayr, SG}, title = {Laminin Adsorption and Adhesion of Neurons and Glial Cells on Carbon Implanted Titania Nanotube Scaffolds for Neural Implant Applications.}, journal = {Nanomaterials (Basel, Switzerland)}, volume = {12}, number = {21}, pages = {}, pmid = {36364633}, issn = {2079-4991}, abstract = {Interfacing neurons persistently to conductive matter constitutes one of the key challenges when designing brain-machine interfaces such as neuroelectrodes or retinal implants. Novel materials approaches that prevent occurrence of loss of long-term adhesion, rejection reactions, and glial scarring are highly desirable. Ion doped titania nanotube scaffolds are a promising material to fulfill all these requirements while revealing sufficient electrical conductivity, and are scrutinized in the present study regarding their neuron-material interface. Adsorption of laminin, an essential extracellular matrix protein of the brain, is comprehensively analyzed. The implantation-dependent decline in laminin adsorption is revealed by employing surface characteristics such as nanotube diameter, ζ-potential, and surface free energy. Moreover, the viability of U87-MG glial cells and SH-SY5Y neurons after one and four days are investigated, as well as the material's cytotoxicity. The higher conductivity related to carbon implantation does not affect the viability of neurons, although it impedes glial cell proliferation. This gives rise to novel titania nanotube based implant materials with long-term stability, and could reduce undesirable glial scarring.}, } @article {pmid36359646, year = {2022}, author = {Wang, K and Tian, F and Xu, M and Zhang, S and Xu, L and Ming, D}, title = {Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface.}, journal = {Entropy (Basel, Switzerland)}, volume = {24}, number = {11}, pages = {}, pmid = {36359646}, issn = {1099-4300}, abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel-Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.}, } @article {pmid36359454, year = {2022}, author = {Syrov, N and Yakovlev, L and Nikolaeva, V and Kaplan, A and Lebedev, M}, title = {Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {12}, number = {11}, pages = {}, pmid = {36359454}, issn = {2075-4418}, abstract = {Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation.}, } @article {pmid36358440, year = {2022}, author = {Adhia, DB and Mani, R and Turner, PR and Vanneste, S and De Ridder, D}, title = {Infraslow Neurofeedback Training Alters Effective Connectivity in Individuals with Chronic Low Back Pain: A Secondary Analysis of a Pilot Randomized Placebo-Controlled Study.}, journal = {Brain sciences}, volume = {12}, number = {11}, pages = {}, pmid = {36358440}, issn = {2076-3425}, abstract = {This study explored the effect of electroencephalographic infraslow neurofeedback (EEG ISF-NF) training on effective connectivity and tested whether such effective connectivity changes are correlated with changes in pain and disability in people with chronic low back pain. This involved secondary analysis of a pilot double-blinded randomised placebo-controlled study. Participants (n = 60) were randomised to receive ISF-NF targeting either the pregenual anterior cingulate cortex (pgACC), dorsal anterior cingulate and somatosensory cortex (dACC + S1), ratio of pgACC*2/dACC + S1, or Sham-NF. Resting-state EEG and clinical outcomes were assessed at baseline, immediately after intervention, and at one-week and one-month follow-up. Kruskal-Wallis tests demonstrated significant between-group differences in effective connectivity from pgACC to S1L at one-month follow up and marginal significant changes from S1L to pgACC at one-week and one-month follow up. Mann-Whitney U tests demonstrated significant increases in effective connectivity in the ISF-NF up-training pgACC group when compared to the Sham-NF group (pgACC to S1L at one-month (p = 0.013), and S1L to pgACC at one-week (p = 0.008) and one-month follow up (p = 0.016)). Correlational analyses demonstrated a significant negative correlation (ρ = -0.630, p = 0.038) between effective connectivity changes from pgACC to S1L and changes in pain severity at one-month follow-up. The ISF-NF training pgACC can reduce pain via influencing effective connectivity between pgACC and S1L.}, } @article {pmid36358428, year = {2022}, author = {Cao, L and Wu, H and Chen, S and Dong, Y and Zhu, C and Jia, J and Fan, C}, title = {A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.}, journal = {Brain sciences}, volume = {12}, number = {11}, pages = {}, pmid = {36358428}, issn = {2076-3425}, abstract = {Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.}, } @article {pmid36356391, year = {2022}, author = {Wan, Z and Yang, R and Huang, M and Alsaadi, FE and Sheikh, MM and Wang, Z}, title = {Segment alignment based cross-subject motor imagery classification under fading data.}, journal = {Computers in biology and medicine}, volume = {151}, number = {Pt A}, pages = {106267}, doi = {10.1016/j.compbiomed.2022.106267}, pmid = {36356391}, issn = {1879-0534}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Imagination ; Algorithms ; }, abstract = {Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.}, } @article {pmid36356309, year = {2022}, author = {Petrosyan, A and Voskoboinikov, A and Sukhinin, D and Makarova, A and Skalnaya, A and Arkhipova, N and Sinkin, M and Ossadtchi, A}, title = {Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca1e1}, pmid = {36356309}, issn = {1741-2552}, mesh = {Humans ; *Electrocorticography/methods ; Speech/physiology ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Electrodes ; }, abstract = {Objective. Speech decoding, one of the most intriguing brain-computer interface applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes.Approach. We collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single stereotactic electroencephalographic (sEEG) shaft or an electrocorticographic (ECoG) stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation.Mainresults. We achieved on average 55% accuracy using only six channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only eight channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature.Significance. We showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.}, } @article {pmid36355738, year = {2022}, author = {Chen, R and Xu, G and Jia, Y and Zhou, C and Wang, Z and Pei, J and Han, C and Wang, Y and Zhang, S}, title = {Enhancement of time-frequency energy for the classification of motor imagery electroencephalogram based on an improved FitzHugh-Nagumo neuron system.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3219450}, pmid = {36355738}, issn = {1558-0210}, abstract = {Brain-computer interface (BCI) based on motor imagery (MI) electroencephalogram (EEG) has become an essential way for rehabilitation, because of the activation and interaction of motor neurons between the brain and rehabilitation devices in recent years. However, due to the discrepancies between individuals, the frequency ranges can be different even for the same rhythm component of EEG recordings, which brings difficulties to the extraction of features for MI classification. Typical algorithms for MI classification such as common spatial patterns (CSP) require multi-channel analysis and lack frequency information. With the development of BCI, the single-channel BCI system has become indispensable for simplicity of use. However, the currently available single-channel detection methods have low classification accuracy. To address this issue, two novel frameworks based on an improved two-dimensional nonlinear FitzHugh-Nagumo (FHN) neuron system are proposed to extract features of the single-channel MI. To evaluate the effectiveness of the proposed methods, this research utilized an open-access database (BCI competition IV dataset 2a), an offline database, and a 10-fold cross-validation procedure. Experimental results showed that the improved nonlinear FHN system can transfer the energy of noise into MI, thereby effectively enhancing the time-frequency energy. Compared with the traditional methods, the proposed methods can achieve higher classification accuracy and robustness.}, } @article {pmid36354027, year = {2022}, author = {Johnson, KA and Worbe, Y and Foote, KD and Butson, CR and Gunduz, A and Okun, MS}, title = {Tourette syndrome: clinical features, pathophysiology, and treatment.}, journal = {The Lancet. Neurology}, volume = {}, number = {}, pages = {}, doi = {10.1016/S1474-4422(22)00303-9}, pmid = {36354027}, issn = {1474-4465}, abstract = {Tourette syndrome is a chronic neurodevelopmental disorder characterised by motor and phonic tics that can substantially diminish the quality of life of affected individuals. Evaluating and treating Tourette syndrome is complex, in part due to the heterogeneity of symptoms and comorbidities between individuals. The underlying pathophysiology of Tourette syndrome is not fully understood, but recent research in the past 5 years has brought new insights into the genetic variations and the alterations in neurophysiology and brain networks contributing to its pathogenesis. Treatment options for Tourette syndrome are expanding with novel pharmacological therapies and increased use of deep brain stimulation for patients with symptoms that are refractory to pharmacological or behavioural treatments. Potential predictors of patient responses to therapies for Tourette syndrome, such as specific networks modulated during deep brain stimulation, can guide clinical decisions. Multicentre data sharing initiatives have enabled several advances in our understanding of the genetics and pathophysiology of Tourette syndrome and will be crucial for future large-scale research and in refining effective treatments.}, } @article {pmid36351832, year = {2022}, author = {Fischer, L and Mojica Soto-Albors, R and Tang, VD and Bicknell, B and Grienberger, C and Francioni, V and Naud, R and Palmer, LM and Takahashi, N}, title = {Dendritic Mechanisms for In Vivo Neural Computations and Behavior.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {42}, number = {45}, pages = {8460-8467}, pmid = {36351832}, issn = {1529-2401}, mesh = {*Dendrites/physiology ; *Pyramidal Cells/physiology ; Neurons/physiology ; Hippocampus ; Learning ; Models, Neurological ; Action Potentials/physiology ; }, abstract = {Dendrites receive the vast majority of a single neuron's inputs, and coordinate the transformation of these signals into neuronal output. Ex vivo and theoretical evidence has shown that dendrites possess powerful processing capabilities, yet little is known about how these mechanisms are engaged in the intact brain or how they influence circuit dynamics. New experimental and computational technologies have led to a surge in interest to unravel and harness their computational potential. This review highlights recent and emerging work that combines established and cutting-edge technologies to identify the role of dendrites in brain function. We discuss active dendritic mediation of sensory perception and learning in neocortical and hippocampal pyramidal neurons. Complementing these physiological findings, we present theoretical work that provides new insights into the underlying computations of single neurons and networks by using biologically plausible implementations of dendritic processes. Finally, we present a novel brain-computer interface task, which assays somatodendritic coupling to study the mechanisms of biological credit assignment. Together, these findings present exciting progress in understanding how dendrites are critical for in vivo learning and behavior, and highlight how subcellular processes can contribute to our understanding of both biological and artificial neural computation.}, } @article {pmid36351413, year = {2022}, author = {McNamara, CG and Rothwell, M and Sharott, A}, title = {Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium.}, journal = {Cell reports}, volume = {41}, number = {6}, pages = {111616}, doi = {10.1016/j.celrep.2022.111616}, pmid = {36351413}, issn = {2211-1247}, support = {MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MC_UU_00003/6/MRC_/Medical Research Council/United Kingdom ; 209120/Z/17/Z/WT_/Wellcome Trust/United Kingdom ; }, mesh = {Animals ; Rats ; *Deep Brain Stimulation ; *Parkinson Disease/therapy ; Basal Ganglia/physiology ; Neurons/physiology ; Brain ; }, abstract = {Closed-loop interaction has the potential to regulate ongoing brain activity by continuously binding an external stimulation to specific dynamics of a neural circuit. Achieving interactive modulation requires a stable brain-machine feedback loop. Here, we demonstrate that it is possible to maintain oscillatory brain activity in a desired state by delivering stimulation accurately aligned with the timing of each cycle. We develop a fast algorithm that responds on a cycle-by-cycle basis to stimulate basal ganglia nuclei at predetermined phases of successive cortical beta cycles in parkinsonian rats. Using this approach, an equilibrium emerges between the modified brain signal and feedback-dependent stimulation pattern, leading to sustained amplification or suppression of the oscillation depending on the phase targeted. Beta amplification slows movement speed by biasing the animal's mode of locomotion. Together, these findings show that highly responsive, phase-dependent stimulation can achieve a stable brain-machine interaction that leads to robust modulation of ongoing behavior.}, } @article {pmid36350872, year = {2022}, author = {Zhang, M and Wu, J and Song, J and Fu, R and Ma, R and Jiang, YC and Chen, YF}, title = {Decoding Coordinated Directions of Bimanual Movements from EEG Signals.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3220884}, pmid = {36350872}, issn = {1558-0210}, abstract = {Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35%. The binary classification accuracies achieved 80.24 ± 6.25, 82.62 ± 7.82, and 86.28 ± 5.50% for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50%, 75.67 ± 7.18%, and 77.79 ± 5.65%, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.}, } @article {pmid36350815, year = {2022}, author = {Sattler, S and Pietralla, D}, title = {Public attitudes towards neurotechnology: Findings from two experiments concerning Brain Stimulation Devices (BSDs) and Brain-Computer Interfaces (BCIs).}, journal = {PloS one}, volume = {17}, number = {11}, pages = {e0275454}, pmid = {36350815}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; Public Opinion ; Stereotaxic Techniques ; Morals ; Brain/physiology ; }, abstract = {This study contributes to the emerging literature on public perceptions of neurotechnological devices (NTDs) in their medical and non-medical applications, depending on their invasiveness, framing effects, and interindividual differences related to personal needs and values. We conducted two web-based between-subject experiments (2×2×2) using a representative, nation-wide sample of the adult population in Germany. Using vignettes describing how two NTDs, brain stimulation devices (BSDs; NExperiment 1 = 1,090) and brain-computer interfaces (BCIs; NExperiment 2 = 1,089), function, we randomly varied the purpose (treatment vs. enhancement) and invasiveness (noninvasive vs. invasive) of the NTD, and assessed framing effects (variable order of assessing moral acceptability first vs. willingness to use first). We found a moderate moral acceptance and willingness to use BSDs and BCIs. Respondents preferred treatment over enhancement purposes and noninvasive over invasive devices. We also found a framing effect and explored the role of personal characteristics as indicators of personal needs and values (e.g., stress, religiosity, and gender). Our results suggest that the future demand for BSDs or BCIs may depend on the purpose, invasiveness, and personal needs and values. These insights can inform technology developers about the public's needs and concerns, and enrich legal and ethical debates.}, } @article {pmid36349931, year = {2022}, author = {Zwerus, EL and van Deurzen, DFP and van den Bekerom, MPJ and The, B and Eygendaal, D}, title = {Distal Biceps Tendon Ruptures: Diagnostic Strategy Through Physical Examination.}, journal = {The American journal of sports medicine}, volume = {50}, number = {14}, pages = {3956-3962}, pmid = {36349931}, issn = {1552-3365}, mesh = {Humans ; Cohort Studies ; Reproducibility of Results ; *Physical Examination ; }, abstract = {BACKGROUND: Distinguishing a complete from a partial distal biceps tendon rupture is essential, as a complete rupture may require repair on short notice to restore function, whereas partial ruptures can be treated nonsurgically in most cases. Reliability of physical examination is crucial to determine the right workup and treatment in patients with a distal biceps tendon rupture.

PURPOSES: The primary aim of this study was to find a (combination of) test(s) that serves best to diagnose a complete rupture with certainty in the acute phase (≤1 month) without missing any complete ruptures. The secondary aims were to determine the best (combination of) test(s) to identify a chronic (>1 month) rupture of the distal biceps tendon and indicate additional imaging in case partial ruptures or tendinitis are suspected.

STUDY DESIGN: Cohort study (Diagnosis); Level of evidence, 2.

METHODS: A total of 86 patients with anterior elbow complaints or suspected distal biceps injury underwent standardized physical examination, including the Hook test, passive forearm pronation test, biceps crease interval (BCI), and biceps crease ratio. Diagnosis was confirmed intraoperatively (68 cases), by magnetic resonance imaging (13 cases), or by ultrasound (5 cases).

RESULTS: A combination of the Hook test and BCI (ie, both tests are positive) was most accurate for both acute and chronic ruptures but with a different purpose. For acute complete ruptures, sensitivity was 94% and specificity was 100%. In chronic cases, specificity was also 100%. Weakness on active supination and palpation of the tendon footprint provided excellent sensitivity of 100% for chronic complete ruptures and partial ruptures, respectively.

CONCLUSION: The combination of a positive Hook test and BCI serves best to accurately diagnose acute complete ruptures of the distal biceps tendon. Weakness on active supination and pain on palpation of the tendon footprint provide excellent sensitivity for chronic complete ruptures and partial ruptures. Using these tests in all suspected distal biceps ruptures allows a physician to refrain from imaging for a diagnostic purpose in certain cases, to limit treatment delay and thereby provide better treatment outcome, and to avoid hospital and social costs.}, } @article {pmid36349662, year = {2023}, author = {Peterson, V and Merk, T and Bush, A and Nikulin, V and Kühn, AA and Neumann, WJ and Richardson, RM}, title = {Movement decoding using spatio-spectral features of cortical and subcortical local field potentials.}, journal = {Experimental neurology}, volume = {359}, number = {}, pages = {114261}, doi = {10.1016/j.expneurol.2022.114261}, pmid = {36349662}, issn = {1090-2430}, mesh = {Humans ; *Brain-Computer Interfaces ; Movement/physiology ; Electrocorticography ; Brain/physiology ; *Parkinson Disease/therapy ; }, abstract = {The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.}, } @article {pmid36349568, year = {2023}, author = {Khademi, Z and Ebrahimi, F and Kordy, HM}, title = {A review of critical challenges in MI-BCI: From conventional to deep learning methods.}, journal = {Journal of neuroscience methods}, volume = {383}, number = {}, pages = {109736}, doi = {10.1016/j.jneumeth.2022.109736}, pmid = {36349568}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination ; }, abstract = {Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools to control, for example, wheelchair for people with disabilities, robotic control, and exoskeleton control. This success largely depends on the machine learning (ML) approaches like deep learning (DL) models. DL algorithms provide effective and powerful models to analyze compact and complex EEG data optimally for MI-BCI applications. DL models with CNN network have revolutionized computer vision through end-to-end learning from raw data. Meanwhile, RNN networks have been able to decode EEG signals by processing sequences of time series data. However, many challenges in the MI-BCI field have affected the performance of DL models. A major challenge is the individual differences in the EEG signal of different subjects. Therefore, the model must be retrained from the scratch for each new subject, which leads to computational costs. Analyzing the EEG signals is challenging due to its low signal to noise ratio and non-stationary nature. Additionally, limited size of existence datasets can lead to overfitting which can be prevented by using transfer learning (TF) approaches. The main contributions of this study are discovering major challenges in the MI-BCI field by reviewing the state of art machine learning models and then suggesting solutions to address these challenges by focusing on feature selection, feature extraction and classification methods.}, } @article {pmid36346867, year = {2022}, author = {Fu, K and Du, C and Wang, S and He, H}, title = {Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2022.3217767}, pmid = {36346867}, issn = {2162-2388}, abstract = {Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of humans; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of the human brain. In this article, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predict multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parameterized by a multi-view variational autoencoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representation learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.}, } @article {pmid36346232, year = {2022}, author = {Sake, SM and Kosch, C and Blockus, S and Haid, S and Gunesch, AP and Zhang, X and Friesland, M and Trummer, SB and Grethe, C and Kühnel, A and Rückert, J and Duprex, WP and Huang, J and Rameix-Welti, MA and Empting, M and Fischer, N and Hirsch, AKH and Schulz, TF and Pietschmann, T}, title = {Respiratory Syncytial Virus Two-Step Infection Screen Reveals Inhibitors of Early and Late Life Cycle Stages.}, journal = {Antimicrobial agents and chemotherapy}, volume = {}, number = {}, pages = {e0103222}, doi = {10.1128/aac.01032-22}, pmid = {36346232}, issn = {1098-6596}, abstract = {Human respiratory syncytial virus (hRSV) infection is a leading cause of severe respiratory tract infections. Effective, directly acting antivirals against hRSV are not available. We aimed to discover new and chemically diverse candidates to enrich the hRSV drug development pipeline. We used a two-step screen that interrogates compound efficacy after primary infection and a consecutive virus passaging. We resynthesized selected hit molecules and profiled their activities with hRSV lentiviral pseudotype cell entry, replicon, and time-of-addition assays. The breadth of antiviral activity was tested against recent RSV clinical strains and human coronavirus (hCoV-229E), and in pseudotype-based entry assays with non-RSV viruses. Screening 6,048 molecules, we identified 23 primary candidates, of which 13 preferentially scored in the first and 10 in the second rounds of infection, respectively. Two of these molecules inhibited hRSV cell entry and selected for F protein resistance within the fusion peptide. One molecule inhibited transcription/replication in hRSV replicon assays, did not select for phenotypic hRSV resistance and was active against non-hRSV viruses, including hCoV-229E. One compound, identified in the second round of infection, did not measurably inhibit hRSV cell entry or replication/transcription. It selected for two coding mutations in the G protein and was highly active in differentiated BCi-NS1.1 lung cells. In conclusion, we identified four new hRSV inhibitor candidates with different modes of action. Our findings build an interesting platform for medicinal chemistry-guided derivatization approaches followed by deeper phenotypical characterization in vitro and in vivo with the aim of developing highly potent hRSV drugs.}, } @article {pmid36343405, year = {2022}, author = {Pu, X and Yi, P and Chen, K and Ma, Z and Zhao, D and Ren, Y}, title = {EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer.}, journal = {Computers in biology and medicine}, volume = {151}, number = {Pt A}, pages = {106248}, doi = {10.1016/j.compbiomed.2022.106248}, pmid = {36343405}, issn = {1879-0534}, mesh = {*Electroencephalography/methods ; Artifacts ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Muscles ; Algorithms ; }, abstract = {Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a.k.a. noise) due to the high temporal resolution. Thus, it is crucial to remove the noise in received EEG signal. Recently, deep learning-based EEG signal denoising approaches have achieved impressive performance compared with traditional ones. It is well known that the characteristics of self-similarity (including non-local and local ones) of data (e.g., natural images and time-domain signals) are widely leveraged for denoising. However, existing deep learning-based EEG signal denoising methods ignore either the non-local self-similarity (e.g., 1-D convolutional neural network) or local one (e.g., fully connected network and recurrent neural network). To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet. Specifically, we comprehensively take into account the non-local and local self-similarity of EEG signal through the transformer module. By fusing non-local self-similarity in self-attention blocks and local self-similarity in feed forward blocks, the negative impact caused by noises and outliers can be reduced significantly. Extensive experiments show that, compared with other state-of-the-art models, EEGDnet achieves much better performance in terms of both quantitative and qualitative metrics. Specifically, EEGDnet can achieve 18% and 11% improvements in correlation coefficients when removing ocular artifacts and muscle artifacts, respectively.}, } @article {pmid36343004, year = {2022}, author = {Zhang, D and Liu, S and Zhang, J and Li, G and Suo, D and Liu, T and Luo, J and Ming, Z and Wu, J and Yan, T}, title = {Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2022.3219812}, pmid = {36343004}, issn = {2168-2208}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs.

METHODS: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated.

RESULTS: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%).

SIGNIFICANCE: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.}, } @article {pmid36340769, year = {2022}, author = {Lee, HS and Schreiner, L and Jo, SH and Sieghartsleitner, S and Jordan, M and Pretl, H and Guger, C and Park, HS}, title = {Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1009878}, pmid = {36340769}, issn = {1662-4548}, abstract = {Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.}, } @article {pmid36340754, year = {2022}, author = {Tang, C and Gao, T and Li, Y and Chen, B}, title = {EEG channel selection based on sequential backward floating search for motor imagery classification.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1045851}, pmid = {36340754}, issn = {1662-4548}, abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.}, } @article {pmid36337859, year = {2022}, author = {He, C and Du, Y and Zhao, X}, title = {A separable convolutional neural network-based fast recognition method for AR-P300.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {986928}, pmid = {36337859}, issn = {1662-5161}, abstract = {Augmented reality-based brain-computer interface (AR-BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR-SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR-P300). SepCNN achieved single extraction of AR-P300 features and improved the recognition speed. A nine-target AR-P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR-P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging.}, } @article {pmid36337857, year = {2022}, author = {Floreani, ED and Rowley, D and Kelly, D and Kinney-Lang, E and Kirton, A}, title = {On the feasibility of simple brain-computer interface systems for enabling children with severe physical disabilities to explore independent movement.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1007199}, pmid = {36337857}, issn = {1662-5161}, abstract = {INTRODUCTION: Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI.

MATERIALS AND METHODS: Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated.

RESULTS: All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 ± 12.1%, participants were more reliably able to activate (95.7 ± 11.3%) the device than hold still (62.1 ± 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 ± 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions.

DISCUSSION: BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of "effective" BCI use. Participants exhibited PM skills that would categorize them as "emerging operational learners." Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.}, } @article {pmid36337363, year = {2022}, author = {Li, L}, title = {Preface to special topic on brain-machine interface.}, journal = {National science review}, volume = {9}, number = {10}, pages = {nwac211}, doi = {10.1093/nsr/nwac211}, pmid = {36337363}, issn = {2053-714X}, } @article {pmid36337269, year = {2022}, author = {Song, X and Zeng, Y and Tong, L and Shu, J and Yang, Q and Kou, J and Sun, M and Yan, B}, title = {Corrigendum to "A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets".}, journal = {Computational intelligence and neuroscience}, volume = {2022}, number = {}, pages = {9819804}, pmid = {36337269}, issn = {1687-5273}, abstract = {[This corrects the article DOI: 10.1155/2022/4752450.].}, } @article {pmid36335198, year = {2022}, author = {Smrdel, A}, title = {Use of common spatial patterns for early detection of Parkinson's disease.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {18793}, pmid = {36335198}, issn = {2045-2322}, mesh = {Humans ; *Parkinson Disease/diagnosis ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; Early Diagnosis ; }, abstract = {One of the most common diseases that affects human brain is Parkinson's disease. Detection of Parkinson's disease (PD) poses a serious challenge. Robust methods for feature extraction allowing separation between the electroencephalograms (EEG) of healthy subjects and PD patients are required. We used the EEG records of healthy subjects and PD patients which were subject to auditory tasks. We used the common spatial patterns (CSP) and Laplacian mask as methods to allow robust selection and extraction of features. We used the derived CSP whitening matrix to determine those channels that are the most promising in the terms of differentiating between EEGs of healthy controls and of PD patients. Using the selection of features calculated using the CSP we managed to obtain the classification accuracy of 85% when classifying EEG records belonging to groups of controls or PD patients. Using the features calculated using the Laplacian operator we obtained the classification accuracy of 90%. Diagnosing the PD in early stages using EEG is possible. The CSP proved to be a promising technique to detect informative channels and to separate between the groups. Use of the combination of features calculated using the Laplacian offers good separability between the two groups.}, } @article {pmid36335102, year = {2022}, author = {Zhao, LH and Lin, J and Ji, SY and Zhou, XE and Mao, C and Shen, DD and He, X and Xiao, P and Sun, J and Melcher, K and Zhang, Y and Yu, X and Xu, HE}, title = {Structure insights into selective coupling of G protein subtypes by a class B G protein-coupled receptor.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {6670}, pmid = {36335102}, issn = {2041-1723}, mesh = {*Heterotrimeric GTP-Binding Proteins/metabolism ; Urocortins/metabolism ; }, abstract = {The ability to couple with multiple G protein subtypes, such as Gs, Gi/o, or Gq/11, by a given G protein-coupled receptor (GPCR) is critical for many physiological processes. Over the past few years, the cryo-EM structures for all 15 members of the medically important class B GPCRs, all in complex with Gs protein, have been determined. However, no structure of class B GPCRs with Gq/11 has been solved to date, limiting our understanding of the precise mechanisms of G protein coupling selectivity. Here we report the structures of corticotropin releasing factor receptor 2 (CRF2R) bound to Urocortin 1 (UCN1), coupled with different classes of heterotrimeric G proteins, G11 and Go. We compare these structures with the structure of CRF2R in complex with Gs to uncover the structural differences that determine the selective coupling of G protein subtypes by CRF2R. These results provide important insights into the structural basis for the ability of CRF2R to couple with multiple G protein subtypes.}, } @article {pmid36333482, year = {2022}, author = {Gao, Z and Pang, Z and Chen, Y and Lei, G and Zhu, S and Li, G and Shen, Y and Xu, W}, title = {Restoring After Central Nervous System Injuries: Neural Mechanisms and Translational Applications of Motor Recovery.}, journal = {Neuroscience bulletin}, volume = {38}, number = {12}, pages = {1569-1587}, pmid = {36333482}, issn = {1995-8218}, mesh = {Animals ; *Spinal Cord Injuries/therapy ; Motor Neurons/physiology ; Brain ; *Stroke ; Recovery of Function/physiology ; }, abstract = {Central nervous system (CNS) injuries, including stroke, traumatic brain injury, and spinal cord injury, are leading causes of long-term disability. It is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated limb. Previous studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote recovery. However, the ability to increase plasticity in the injured brain is restricted and difficult to improve. Therefore, over several decades, researchers have been prompted to enhance the compensation by the unaffected hemisphere. Animal experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor function. In addition, several clinical treatments have been designed to restore ipsilateral motor control, including brain stimulation, nerve transfer surgery, and brain-computer interface systems. Here, we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.}, } @article {pmid36332422, year = {2022}, author = {Zhan, Q and Wang, L and Ren, L and Huang, X}, title = {A novel heterogeneous transfer learning method based on data stitching for the sequential coding brain computer interface.}, journal = {Computers in biology and medicine}, volume = {151}, number = {Pt A}, pages = {106220}, doi = {10.1016/j.compbiomed.2022.106220}, pmid = {36332422}, issn = {1879-0534}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Machine Learning ; Imagination ; }, abstract = {OBJECTIVE: For the brain computer interface (BCI), it is necessary to collect enough electroencephalography (EEG) signals to train the classification model. When the operation dimension of BCI is large, it will bring great burden to data acquisition. Fortunately, this problem can be solved by our proposed transfer learning method.

METHOD: For the sequential coding experimental paradigm, the multi-band data stitching with label alignment and tangent space mapping (MDSLATSM) algorithm is proposed as a novel heterogeneous transfer learning method. After filtering by multi-band filtering, the artificial signals can be obtained by data stitching from the source domain, which build a bridge between the source domain and target domain. To make the distribution of two domains closer, their covariance matrices are aligned by label alignment. After mapping to the tangent space, the features are extracted from the Riemannian manifold. Finally, the classification results are obtained with feature selection and classification.

RESULTS: Our data set includes the EEG signals from 16 subjects. For the heterogeneous transfer learning of cross-label, the average classification accuracy is 78.28%. MDSLATSM is also tested for cross-subject, and the average classification accuracy is 64.01%, which is better than existing methods.

SIGNIFICANCE: Combining multi-band filtering, data stitching, label alignment and tangent space mapping, a novel heterogeneous transfer learning method can be achieved with superior performance, which promotes the practical application of the BCI systems.}, } @article {pmid36331650, year = {2022}, author = {Zhang, S and Wu, L and Yu, S and Shi, E and Qiang, N and Gao, H and Zhao, J and Zhao, S}, title = {An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2022.3214225}, pmid = {36331650}, issn = {2162-2388}, abstract = {Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.}, } @article {pmid36331633, year = {2022}, author = {Wang, R and Liu, Y and Shi, J and Peng, B and Fei, W and Bi, L}, title = {Sound Target Detection under Noisy Environment Using Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3219595}, pmid = {36331633}, issn = {1558-0210}, abstract = {As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications.}, } @article {pmid36329083, year = {2022}, author = {Mencel, J and Marusiak, J and Jaskólska, A and Kamiński, Ł and Kurzyński, M and Wołczowski, A and Jaskólski, A and Kisiel-Sajewicz, K}, title = {Motor imagery training of goal-directed reaching in relation to imagery of reaching and grasping in healthy people.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {18610}, pmid = {36329083}, issn = {2045-2322}, mesh = {Humans ; Brain-Computer Interfaces ; Electroencephalography ; Goals ; *Hand Strength/physiology ; Imagination/physiology ; *Motor Skills/physiology ; Evoked Potentials/physiology ; *Imagery, Psychotherapy/education ; }, abstract = {The study aimed to determine whether four weeks of motor imagery training (MIT) of goal-directed reaching (reaching to grasp task) would affect the cortical activity during motor imagery of reaching (MIR) and grasping (MIG) in the same way. We examined cortical activity regarding event-related potentials (ERPs) in healthy young participants. Our study also evaluated the subjective vividness of the imagery. Furthermore, we aimed to determine the relationship between the subjective assessment of motor imagery (MI) ability to reach and grasp and the cortical activity during those tasks before and after training to understand the underlying neuroplasticity mechanisms. Twenty-seven volunteers participated in MIT of goal-directed reaching and two measurement sessions before and after MIT. During the sessions 128-channel electroencephalography (EEG) was recorded during MIR and MIG. Also, participants assessed the vividness of the MI tasks using a visual analog scale (VAS). The vividness of imagination improved significantly (P < .05) after MIT. A repeated measures ANOVA showed that the task (MIR/MIG) and the location of electrodes had a significant effect on the ERP's amplitude (P < .05). The interaction between the task, location, and session (before/after MIT) also had a significant effect on the ERP's amplitude (P < .05). Finally, the location of electrodes and the interaction between location and session had a significant effect on the ERP's latency (P < .05). We found that MIT influenced the EEG signal associated with reaching differently than grasping. The effect was more pronounced for MIR than for MIG. Correlation analysis showed that changes in the assessed parameters due to MIT reduced the relationship between the subjective evaluation of imagining and the EEG signal. This finding means that the subjective evaluation of imagining cannot be a simple, functional insight into the bioelectrical activity of the cerebral cortex expressed by the ERPs in mental training. The changes we noted in ERPs after MIT may benefit the use of non-invasive EEG in the brain-computer interface (BCI) context.Trial registration: NCT04048083.}, } @article {pmid36327603, year = {2022}, author = {Fu, Y and Zhu, Y and Zhang, Y and Hu, S}, title = {Is AlphaFold a perfect experimental assistant of psychiatric drug discovery in precision psychiatry era?.}, journal = {Asian journal of psychiatry}, volume = {78}, number = {}, pages = {103305}, doi = {10.1016/j.ajp.2022.103305}, pmid = {36327603}, issn = {1876-2026}, mesh = {Humans ; *Psychiatry ; *Mental Disorders/drug therapy ; Drug Discovery ; }, } @article {pmid36323230, year = {2022}, author = {Al-Sheikh, U and Kang, L}, title = {Kir2.1 channel: Macrophage plasticity in tumor microenvironment.}, journal = {Cell metabolism}, volume = {34}, number = {11}, pages = {1613-1615}, doi = {10.1016/j.cmet.2022.10.009}, pmid = {36323230}, issn = {1932-7420}, mesh = {Humans ; *Tumor Microenvironment ; Macrophages/metabolism ; *Neoplasms/metabolism ; }, abstract = {Diverse ion channels have dysregulated functional expression in the tumor microenvironment (TME). In this issue of Cell Metabolism, Chen et al. reveal that high intratumoral K[+] ions restrict the plasticity of tumor-associated macrophages (TAMs). Inhibition of the Kir2.1 potassium channel induced metabolic reprogramming and repolarization of pro-tumor M2-TAMs to tumoricidal M1-like states.}, } @article {pmid36318565, year = {2022}, author = {Bian, Y and Zhao, L and Li, J and Guo, T and Fu, X and Qi, H}, title = {Improvements in classification of left and right foot motor intention using modulated steady-state somatosensory evoked potential induced by electrical stimulation and motor imagery.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3218682}, pmid = {36318565}, issn = {1558-0210}, abstract = {In recent years, motor imagery-based brain-computer interface (MI-BCI) has been applied to motor rehabilitation in patients with motor dysfunction. However, traditional MI-BCI is rarely used for foot motor intention recognition because the motor cortex regions of both feet are anatomically close to each other, and traditional event-related desynchronization (ERD) patterns for MI-BCI have insufficient spatial discrimination. This study introduced steady-state somatosensory evoked potentials (SSSEPs) by synchronous bilateral feet electrical stimulation at different frequencies, which were used as carrier signals modulated by unilateral foot motor intention. Fifteen subjects participated in MI and MI-SSSEP tasks. A Riemannian geometry classifier with a task-related component analysis (TRCA) spatial filter was proposed to demodulate the variation in SSSEP features and discriminate the left and right foot motor intentions. The feature outcomes indicated that the amplitude and phase synchronization of the SSSEPs could be well modulated by unilateral foot MI tasks under the MI-SSSEP paradigm. The classification results revealed that the modulated SSSEP features played an important role in the recognition of left-right foot discrimination. Moreover, the proposed TRCA-based method outperformed the other three methods and improved the foot average classification accuracy to 81.07±13.29%, with the highest accuracy attained at 97.00%. Compared with the traditional MI paradigm, the foot motor intention recognition accuracy of the MI-SSSEP paradigm was significantly improved, from nearly 60% to more than 80%. This work provides a practical method for left-right foot motor intention recognition and expands the application of MI-BCI in the field of lower-extremity motor function rehabilitation.}, } @article {pmid36318386, year = {2022}, author = {Gorur, K and Eraslan, B}, title = {The single-channel dry electrode SSVEP-based biometric approach: data augmentation techniques against overfitting for RNN-based deep models.}, journal = {Physical and engineering sciences in medicine}, volume = {45}, number = {4}, pages = {1219-1240}, pmid = {36318386}, issn = {2662-4737}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials/physiology ; Neural Networks, Computer ; Electrodes ; Biometry ; }, abstract = {Biometric studies based on electroencephalography (EEG) have received increasing attention because each individual has a dynamic and unique pattern. However, classic EEG-based biometrics have significant deficiencies, including noise-prone signals, gel-based electrodes, and the need for multi-training/multi-channel acquisition and high mental effort. In contrast, steady-state visually evoked potential (SSVEP)-based biometrics have the important advantages of high signal-to-noise ratio and untrained usage. Dynamic brain potential responses are a natural subconscious activity and can be elicited by flickering lights having distinct frequencies, such as cell phone flashes, without extra physical or mental effort. Few studies involving multi-channel/multi-trial SSVEP-based biometric research are available in the current literature. Moreover, there is a lack of research comparing them to the single-channel single-trial dry electrode-implemented SSVEP-based biometric approach using Recurrent Neural Networks (RNN). Furthermore, to the best of our knowledge, no prior work has proposed an SSVEP-based biometric comparison of the RNNs using data augmentation strategies against overfitting. It was observed that the biometric recognition results were promising, achieving up to 100% accuracy and > 97% sensitivity and specificity scores for 11 subjects. F-scores were also yielded as > 97% values. This single-channel SSVEP-based biometric approach using RNN deep models may offer low-cost, user-friendly, and reliable individual identification authentication, leading to significant application domains.}, } @article {pmid36317357, year = {2022}, author = {Ogino, M and Hamada, N and Mitsukura, Y}, title = {Simultaneous multiple-stimulus auditory brain-computer interface with semi-supervised learning and prior probability distribution tuning.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9edd}, pmid = {36317357}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Acoustic Stimulation/methods ; Evoked Potentials ; Supervised Machine Learning ; Probability ; Electroencephalography/methods ; Event-Related Potentials, P300 ; }, abstract = {Objective.Auditory brain-computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However, existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli.Approach.For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR).Main results.The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ± 11.46% and an ITR of 2.67 ± 1.09 bits min[-1], in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits min[-1], respectively. The event-related potential analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement.Significance.Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.}, } @article {pmid36317288, year = {2022}, author = {Jia, H and Sun, Z and Duan, F and Zhang, Y and Caiafa, CF and Solé-Casals, J}, title = {Improving pre-movement pattern detection with filter bank selection.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9e75}, pmid = {36317288}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Support Vector Machine ; Movement ; Upper Extremity ; Algorithms ; Imagination ; }, abstract = {Objective. Pre-movement decoding plays an important role in detecting the onsets of actions using low-frequency electroencephalography (EEG) signals before the movement of an upper limb. In this work, a binary classification method is proposed between two different states.Approach. The proposed method, referred to as filter bank standard task-related component analysis (FBTRCA), is to incorporate filter bank selection into the standard task-related component analysis (STRCA) method. In FBTRCA, the EEG signals are first divided into multiple sub-bands which start at specific fixed frequencies and end frequencies that follow in an arithmetic sequence. The STRCA method is then applied to the EEG signals in these bands to extract CCPs. The minimum redundancy maximum relevance feature selection method is used to select essential features from these correlation patterns in all sub-bands. Finally, the selected features are classified using the binary support vector machine classifier. A convolutional neural network (CNN) is an alternative approach to select canonical correlation patterns.Main Results. Three methods were evaluated using EEG signals in the time window from 2 s before the movement onset to 1 s after the movement onset. In the binary classification between a movement state and the resting state, the FBTRCA achieved an average accuracy of 0.8968 ± 0.0847 while the accuracies of STRCA and CNN were 0.8228 ± 0.1149 and 0.8828 ± 0.0917, respectively. In the binary classification between two actions, the accuracies of STRCA, CNN, and FBTRCA were 0.6611 ± 0.1432, 0.6993 ± 0.1271, 0.7178 ± 0.1274, respectively. Feature selection using filter banks, as in FBTRCA, produces comparable results to STRCA.Significance. The proposed method provides a way to select filter banks in pre-movement decoding, and thus it improves the classification performance. The improved pre-movement decoding of single upper limb movements is expected to provide people with severe motor disabilities with a more natural, non-invasive control of their external devices.}, } @article {pmid36317255, year = {2022}, author = {Guo, Z and Chen, F}, title = {Decoding lexical tones and vowels in imagined tonal monosyllables using fNIRS signals.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9e1d}, pmid = {36317255}, issn = {1741-2552}, mesh = {Humans ; *Speech ; Language ; *Speech Perception ; Imagery, Psychotherapy ; }, abstract = {Objective.Speech is a common way of communication. Decoding verbal intent could provide a naturalistic communication way for people with severe motor disabilities. Active brain computer interaction (BCI) speller is one of the most commonly used speech BCIs. To reduce the spelling time of Chinese words, identifying vowels and tones that are embedded in imagined Chinese words is essential. Functional near-infrared spectroscopy (fNIRS) has been widely used in BCI because it is portable, non-invasive, safe, low cost, and has a relatively high spatial resolution.Approach.In this study, an active BCI speller based on fNIRS is presented by covertly rehearsing tonal monosyllables with vowels (i.e. /a/, /i/, /o/, and /u/) and four lexical tones in Mandarin Chinese (i.e. tones 1, 2, 3, and 4) for 10 s.Main results.fNIRS results showed significant differences in the right superior temporal gyrus between imagined vowels with tone 2/3/4 and those with tone 1 (i.e. more activations and stronger connections to other brain regions for imagined vowels with tones 2/3/4 than for those with tone 1). Speech-related areas for tone imagery (i.e. the right hemisphere) provided majority of information for identifying tones, while the left hemisphere had advantages in vowel identification. Having decoded both vowels and tones during the post-stimulus 15 s period, the average classification accuracies exceeded 40% and 70% in multiclass (i.e. four classes) and binary settings, respectively. To spell words more quickly, the time window size for decoding was reduced from 15 s to 2.5 s while the classification accuracies were not significantly reduced.Significance.For the first time, this work demonstrated the possibility of discriminating lexical tones and vowels in imagined tonal syllables simultaneously. In addition, the reduced time window for decoding indicated that the spelling time of Chinese words could be significantly reduced in the fNIRS-based BCIs.}, } @article {pmid36317254, year = {2022}, author = {Lee, C and Vaskov, AK and Gonzalez, MA and Vu, PP and Davis, AJ and Cederna, PS and Chestek, CA and Gates, DH}, title = {Use of regenerative peripheral nerve interfaces and intramuscular electrodes to improve prosthetic grasp selection: a case study.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9e1c}, pmid = {36317254}, issn = {1741-2552}, mesh = {Female ; Humans ; *Artificial Limbs ; Electrodes ; Electromyography/methods ; Hand/physiology ; Hand Strength ; *Muscle, Skeletal/physiology ; Peripheral Nerves/physiology ; }, abstract = {Objective.Advanced myoelectric hands enable users to select from multiple functional grasps. Current methods for controlling these hands are unintuitive and require frequent recalibration. This case study assessed the performance of tasks involving grasp selection, object interaction, and dynamic postural changes using intramuscular electrodes with regenerative peripheral nerve interfaces (RPNIs) and residual muscles.Approach.One female with unilateral transradial amputation participated in a series of experiments to compare the performance of grasp selection controllers with RPNIs and intramuscular control signals with controllers using surface electrodes. These experiments included a virtual grasp-matching task with and without a concurrent cognitive task and physical tasks with a prosthesis including standardized functional assessments and a functional assessment where the individual made a cup of coffee ('Coffee Task') that required grasp transitions.Main results.In the virtual environment, the participant was able to select between four functional grasps with higher accuracy using the RPNI controller (92.5%) compared to surface controllers (81.9%). With the concurrent cognitive task, performance of the virtual task was more consistent with RPNI controllers (reduced accuracy by 1.1%) compared to with surface controllers (4.8%). When RPNI signals were excluded from the controller with intramuscular electromyography (i.e. residual muscles only), grasp selection accuracy decreased by up to 24%. The participant completed the Coffee Task with 11.7% longer completion time with the surface controller than with the RPNI controller. She also completed the Coffee Task with 11 fewer transition errors out of a maximum of 25 total errors when using the RPNI controller compared to surface controller.Significance.The use of RPNI signals in concert with residual muscles and intramuscular electrodes can improve grasp selection accuracy in both virtual and physical environments. This approach yielded consistent performance without recalibration needs while reducing cognitive load associated with pattern recognition for myoelectric control (clinical trial registration number NCT03260400).}, } @article {pmid36315547, year = {2022}, author = {Phang, CR and Chen, CH and Cheng, YY and Chen, YJ and Ko, LW}, title = {Frontoparietal Dysconnection in Covert Bipedal Activity for Enhancing the Performance of the Motor Preparation-based Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3217298}, pmid = {36315547}, issn = {1558-0210}, abstract = {Motor-based brain-computer interfaces (BCIs) were developed from the brain signals during motor imagery (MI), motor preparation (MP), and motor execution (ME). Motor-based BCIs provide an active rehabilitation scheme for post-stroke patients. However, BCI based solely on MP was rarely investigated. Since MP is the precedence phase before MI or ME, MP-BCI could potentially detect brain commands at an earlier state. This study proposes a bipedal MP-BCI system, which is actuated by the reduction in frontoparietal connectivity strength. Three substudies, including bipedal classification, neurofeedback, and post-stroke analysis, were performed to validate the performance of our proposed model. In bipedal classification, functional connectivity was extracted by Pearson's correlation model from electroencephalogram (EEG) signals recorded while the subjects were performing MP and MI. The binary classification of MP achieved short-lived peak accuracy of 73.73(±7.99)% around 200-400 ms post-cue. The peak accuracy was found synchronized to the MP-related potential and the decrement in frontoparietal connection strength. The connection strengths of the right frontal and left parietal lobes in the alpha range were found negatively correlated to the classification accuracy. In the subjective neurofeedback study, the majority of subjects reported that motor preparation instead of the motor imagery activated the frontoparietal dysconnection. Post-stroke study also showed that patients exhibit lower frontoparietal connections compared to healthy subjects during both MP and ME phases. These findings suggest that MP reduced alpha band functional frontoparietal connectivity and the EEG signatures of left and right foot MP could be discriminated more effectively during this phase. A neurofeedback paradigm based on the frontoparietal network could also be utilized to evaluate post-stroke rehabilitation training.}, } @article {pmid36315544, year = {2022}, author = {Gao, Y and Liu, Y and She, Q and Zhang, J}, title = {Domain Adaptive Algorithm Based on Multi-manifold Embedded Distributed Alignment for Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2022.3218453}, pmid = {36315544}, issn = {2168-2208}, abstract = {The use of transfer learning in brain-computer interfaces (BCIs) has potential applications. As electroencephalogram (EEG) signals vary among different paradigms and subjects, existing EEG transfer learning algorithms mainly focus on the alignment of the original space. They may not discover hidden details owing to the low-dimensional structure of EEG. To effectively transfer data from a source to target domain, a multi-manifold embedding domain adaptive algorithm is proposed for BCI. First, we aligned the EEG covariance matrix in the Riemannian manifold and extracted the characteristics of each source domain in the tangent space to reflect the differences between different source domains. Subsequently, we mapped the extracted characteristics to the Grassmann manifold to obtain a common feature representation. In domain adaptation, the geometric and statistical attributes of EEG data were considered simultaneously, and the target domain divergence matrix was updated with pseudo-labels to maximize the inter-class distance and minimize the intra-class distance. Datasets generated via BCIs were used to verify the effectiveness of the algorithm. Under two experimental paradigms, namely single-source to single-target and multi-source to single-target, the average accuracy of the algorithm on three datasets was 73.31% and 81.02%, respectively, which is more than that of several state-of-the-art EEG cross-domain classification approaches. Our multi-manifold embedded domain adaptive method achieved satisfactory results on EEG transfer learning. The method can achieve effective EEG classification without a same subject's training set.}, } @article {pmid36315056, year = {2022}, author = {Pan, Y and Zhu, Y and Xu, C and Pan, C and Shi, Y and Zou, J and Li, Y and Hu, X and Zhou, B and Zhao, C and Gao, Q and Zhang, J and Wu, A and Chen, X and Li, J}, title = {Biomimetic Yolk-Shell Nanocatalysts for Activatable Dual-Modal-Image-Guided Triple-Augmented Chemodynamic Therapy of Cancer.}, journal = {ACS nano}, volume = {16}, number = {11}, pages = {19038-19052}, doi = {10.1021/acsnano.2c08077}, pmid = {36315056}, issn = {1936-086X}, mesh = {Humans ; Biomimetics ; Hydrogen Peroxide/metabolism ; Manganese Compounds/pharmacology ; Cell Line, Tumor ; Oxides ; *Neoplasms/diagnostic imaging/drug therapy ; Glutathione/metabolism ; Glucose Oxidase/metabolism ; *Nanoparticles ; Tumor Microenvironment ; }, abstract = {Fenton reaction-based chemodynamic therapy (CDT), which applies metal ions to convert less active hydrogen peroxide (H2O2) into more harmful hydroxyl peroxide (·OH) for tumor treatment, has attracted increasing interest recently. However, the CDT is substantially hindered by glutathione (GSH) scavenging effect on ·OH, low intracellular H2O2 level, and low reaction rate, resulting in unsatisfactory efficacy. Here, a cancer cell membrane (CM)-camouflaged Au nanorod core/mesoporous MnO2 shell yolk-shell nanocatalyst embedded with glucose oxidase (GOD) and Dox (denoted as AMGDC) is constructed for synergistic triple-augmented CDT and chemotherapy of tumor under MRI/PAI guidance. Benefiting from the homologous adhesion and immune escaping property of the cancer CM, the nanocatalysts can target tumor and gradually accumulate in tumor site. For triple-augmented CDT, first, the MnO2 shell reacts with intratumoral GSH to generate Mn[2+] and glutathione disulfide, which achieves Fenton-like ion delivery and weakening of GSH-mediated scavenging effect, leading to GSH depletion-enhanced CDT. Second, the intratumoral glucose can be oxidized to H2O2 and gluconic acid by GOD, achieving supplementary H2O2-enhanced CDT. Next, the AuNRs absorbing in NIR-II elevate the local tumor temperature upon NIR-II laser irradiation, achieving photothermal-enhanced CDT. Dox is rapidly released for adjuvant chemotherapy due to responsive degradation of MnO2 shell. Moreover, GSH-activated PAI/MRI can be used to monitor CDT process. This study provides a great paradigm for enhancing CDT-mediated antitumor efficacy.}, } @article {pmid36313812, year = {2022}, author = {Cui, Y and Xie, S and Xie, X and Zhang, X and Liu, X}, title = {Dynamic probability integration for electroencephalography-based rapid serial visual presentation performance enhancement: Application in nighttime vehicle detection.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1006361}, pmid = {36313812}, issn = {1662-5188}, abstract = {BACKGROUND: Rapid serial visual presentation (RSVP) has become a popular target detection method by decoding electroencephalography (EEG) signals, owing to its sensitivity and effectiveness. Most current research on EEG-based RSVP tasks focused on feature extraction algorithms developed to deal with the non-stationarity and low signal-to-noise ratio (SNR) of EEG signals. However, these algorithms cannot handle the problem of no event-related potentials (ERP) component or miniature ERP components caused by the attention lapses of human vision in abnormal conditions. The fusion of human-computer vision can obtain complementary information, making it a promising way to become an efficient and general way to detect objects, especially in attention lapses.

METHODS: Dynamic probability integration (DPI) was proposed in this study to fuse human vision and computer vision. A novel basic probability assignment (BPA) method was included, which can fully consider the classification capabilities of different heterogeneous information sources for targets and non-targets and constructs the detection performance model for the weight generation based on classification capabilities. Furthermore, a spatial-temporal hybrid common spatial pattern-principal component analysis (STHCP) algorithm was designed to decode EEG signals in the RSVP task. It is a simple and effective method of distinguishing target and non-target using spatial-temporal features.

RESULTS: A nighttime vehicle detection based on the RSVP task was performed to evaluate the performance of DPI and STHCP, which is one of the conditions of attention lapses because of its decrease in visual information. The average AUC of DPI was 0.912 ± 0.041 and increased by 11.5, 5.2, 3.4, and 1.7% compared with human vision, computer vision, naive Bayesian fusion, and dynamic belief fusion (DBF), respectively. A higher average balanced accuracy of 0.845 ± 0.052 was also achieved using DPI, representing that DPI has the balanced detection capacity of target and non-target. Moreover, STHCP obtained the highest AUC of 0.818 ± 0.06 compared with the other two baseline methods and increased by 15.4 and 23.4%.

CONCLUSION: Experimental results indicated that the average AUC and balanced accuracy of the proposed fusion method were higher than individual detection methods used for fusion, as well as two excellent fusion methods. It is a promising way to improve detection performance in RSVP tasks, even in abnormal conditions.}, } @article {pmid36313593, year = {2022}, author = {Ma, T and Li, Y and Huggins, JE and Zhu, J and Kang, J}, title = {Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface.}, journal = {Journal of the American Statistical Association}, volume = {117}, number = {539}, pages = {1122-1133}, pmid = {36313593}, issn = {0162-1459}, support = {R01 DA048993/DA/NIDA NIH HHS/United States ; R01 GM124061/GM/NIGMS NIH HHS/United States ; R01 MH105561/MH/NIMH NIH HHS/United States ; }, abstract = {A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of multi-channel real EEG signals under the P300 ERP-BCI design. We aim to identify relevant spatial temporal differences of the neural activity, which provides statistical evidence of P300 ERP responses and helps design individually efficient and accurate BCIs. As one key finding of our single participant analysis, there is a 90% posterior probability that the target ERPs of the channels around visual cortex reach their negative peaks around 200 milliseconds post-stimulus. Our analysis identifies five important channels (PO7, PO8, Oz, P4, Cz) for the BCI speller leading to a 100% prediction accuracy. From the analyses of nine other participants, we consistently select the identified five channels, and the selection frequencies are robust to small variations of bandpass filters and kernel hyper-parameters.}, } @article {pmid36312030, year = {2022}, author = {Lim, J and Wang, PT and Shaw, SJ and Gong, H and Armacost, M and Liu, CY and Do, AH and Heydari, P and Nenadic, Z}, title = {Artifact propagation in subdural cortical electrostimulation: Characterization and modeling.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1021097}, pmid = {36312030}, issn = {1662-4548}, abstract = {Cortical stimulation via electrocorticography (ECoG) may be an effective method for inducing artificial sensation in bi-directional brain-computer interfaces (BD-BCIs). However, strong electrical artifacts caused by electrostimulation may significantly degrade or obscure neural information. A detailed understanding of stimulation artifact propagation through relevant tissues may improve existing artifact suppression techniques or inspire the development of novel artifact mitigation strategies. Our work thus seeks to comprehensively characterize and model the propagation of artifacts in subdural ECoG stimulation. To this end, we collected and analyzed data from eloquent cortex mapping procedures of four subjects with epilepsy who were implanted with subdural ECoG electrodes. From this data, we observed that artifacts exhibited phase-locking and ratcheting characteristics in the time domain across all subjects. In the frequency domain, stimulation caused broadband power increases, as well as power bursts at the fundamental stimulation frequency and its super-harmonics. The spatial distribution of artifacts followed the potential distribution of an electric dipole with a median goodness-of-fit of R [2] = 0.80 across all subjects and stimulation channels. Artifacts as large as ±1,100 μV appeared anywhere from 4.43 to 38.34 mm from the stimulation channel. These temporal, spectral and spatial characteristics can be utilized to improve existing artifact suppression techniques, inspire new strategies for artifact mitigation, and aid in the development of novel cortical stimulation protocols. Taken together, these findings deepen our understanding of cortical electrostimulation and provide critical design specifications for future BD-BCI systems.}, } @article {pmid36310494, year = {2022}, author = {Li, M and Gong, A and Nan, W and Xu, B and Ding, P and Fu, Y}, title = {[Neurofeedback technology based on functional near infrared spectroscopy imaging and its applications].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {5}, pages = {1041-1049}, doi = {10.7507/1001-5515.202204031}, pmid = {36310494}, issn = {1001-5515}, mesh = {*Neurofeedback/methods ; Spectroscopy, Near-Infrared/methods ; Brain/diagnostic imaging ; Magnetic Resonance Imaging ; Technology ; }, abstract = {Neurofeedback (NF) technology based on electroencephalogram (EEG) data or functional magnetic resonance imaging (fMRI) has been widely studied and applied. In contrast, functional near infrared spectroscopy (fNIRS) has become a new technique in NF research in recent years. fNIRS is a neuroimaging technology based on hemodynamics, which has the advantages of low cost, good portability and high spatial resolution, and is more suitable for use in natural environments. At present, there is a lack of comprehensive review on fNIRS-NF technology (fNIRS-NF) in China. In order to provide a reference for the research of fNIRS-NF technology, this paper first describes the principle, key technologies and applications of fNIRS-NF, and focuses on the application of fNIRS-NF. Finally, the future development trend of fNIRS-NF is prospected and summarized. In conclusion, this paper summarizes fNIRS-NF technology and its application, and concludes that fNIRS-NF technology has potential practicability in neurological diseases and related fields. fNIRS can be used as a good method for NF training. This paper is expected to provide reference information for the development of fNIRS-NF technology.}, } @article {pmid36310493, year = {2022}, author = {Cao, H and Jung, TP and Chen, Y and Mei, J and Li, A and Xu, M and Ming, D}, title = {[Research advances in non-invasive brain-computer interface control strategies].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {5}, pages = {1033-1040}, doi = {10.7507/1001-5515.202205013}, pmid = {36310493}, issn = {1001-5515}, mesh = {Humans ; Electroencephalography ; *Brain-Computer Interfaces ; *Communication Aids for Disabled ; User-Computer Interface ; Brain/physiology ; }, abstract = {Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.}, } @article {pmid36306641, year = {2022}, author = {Scott, CJ and de Mestre, AM and Verheyen, KL and Arango-Sabogal, JC}, title = {Bayesian accuracy estimates and fit for purpose thresholds of cytology and culture of endometrial swab samples for detecting endometritis in mares.}, journal = {Preventive veterinary medicine}, volume = {209}, number = {}, pages = {105783}, doi = {10.1016/j.prevetmed.2022.105783}, pmid = {36306641}, issn = {1873-1716}, mesh = {Horses ; Animals ; Female ; *Endometritis/diagnosis/veterinary/microbiology ; Retrospective Studies ; Bayes Theorem ; *Horse Diseases/diagnosis/epidemiology/microbiology ; Endometrium ; }, abstract = {The overall aim of this work was to identify the potential impact of misclassification errors associated with routine screening and diagnostic testing for endometritis in mares. Using Bayesian latent class models (BLCM), specific objectives were to: 1) estimate the diagnostic accuracy of cytology and culture of endometrial swab samples to detect endometritis in mares; 2) assess the impact of different cytology thresholds on test accuracy and misclassification costs; and 3) assess the sensitivity (Se) and specificity (Sp) of a diagnostic strategy including both tests interpreted in series and parallel. Diagnostic and pre-breeding endometrial swab samples collected from 3448 mares based at breeding premises located in the South East of England between 2014 and 2020 were retrospectively analysed. Culture results were classified as positive according to three different case definitions: (A) > 90% of the growth colonies were a monoculture; (B) pathogenic or pathogenic and non-pathogenic bacteria were identified; and (C) any growth was observed. Endometrial smears were graded based on the percent of polymorphonuclear cells (PMN) per high power field (HPF). A hierarchical BLCM was fitted using the cross-tabulated results of the three culture case definitions with a cytology threshold fixed at > 0.5% PMN. Fit for purpose cytology thresholds were proposed using a misclassification cost analysis in the context of good antimicrobial stewardship and for varying endometritis prevalence estimates. Median [95% Bayesian credible intervals (BCI)] cytology Se estimates were 6.5% (2.2-11.6), 6.4% (2.2-10.8) and 6.3% (2.2-10.8) for scenario A, B and C, respectively. Median (95% BCI) cytology Sp estimates were 88.8% (83.1-94.8), 88.9% (83.9-93.8) and 88.8% (84.0-93.8) for scenarios A, B and C, respectively. Median (95% BCI) culture Se estimates were 37.5% (29.9-46.0), 42.3% (33.8-51.1) and 46.4% (35.7-55.9) for scenarios A, B and C, respectively. Median (95% BCI) culture Sp estimates were 92.8% (84.3-99.0), 91.5% (82.5-98.0) and 90.8% (80.1-97.4) for scenarios A, B and C, respectively. Regardless of the culture case definition, Se and Sp of cytology (> 0.5% PMN) was lower than previously reported for swab samples in studies using histology as the reference standard test. The misclassification cost term decreased as the cytology threshold increased for all scenarios and all prevalence contexts, suggesting that, regardless of the endometritis prevalence in the population, increasing the cytology threshold would reduce the misclassification costs associated with false positive mares contributing to good antimicrobial stewardship.}, } @article {pmid36306303, year = {2022}, author = {Lee, T and Nam, S and Hyun, DJ}, title = {Adaptive Window Method Based on FBCCA for Optimal SSVEP Recognition.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3217789}, pmid = {36306303}, issn = {1558-0210}, abstract = {In the conventional studies related to steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the window length (detection time) was typically predetermined through the offline analysis, which had limitations of practical applicability of a BCI system due to the inter-subject/trial variability of electroencephalography (EEG) signals. To address these limitations, this study aims to automatically optimize the window length for each trial based on training-free approaches and proposes a novel adaptive window method (ANCOVA-based filter-bank canonical correlation analysis, ABFCCA) for SSVEP-based BCIs. The proposed method is based on analysis of covariance (ANCOVA) which is applied after feature extraction by the conventional training-free SSVEP recognition approaches. To evaluate the performance of the proposed method, conventional fixed window and recent adaptive window methods were compared using two open-access datasets. In the Benchmark dataset, the average information transfer rate (ITR) was 146.81 bits/min, the average accuracy 93.55%, and the average window length 1.53 s. In the OpenBMI dataset, the average ITR was 119.01 bits/min, the average accuracy 83.50%, and the average window length 0.65 s. The proposed method significantly outperformed the conventional approaches with fixed window in terms of the accuracy and ITR, and is applicable to various SSVEP-based BCI paradigms based on the criterion of significance level without offline analysis to find optimal hyper-parameters. ABFCCA is enabled the practical use of various BCI systems by automatically optimizing the window length independently.}, } @article {pmid36304780, year = {2022}, author = {Valeriani, D and Santoro, F and Ienca, M}, title = {The present and future of neural interfaces.}, journal = {Frontiers in neurorobotics}, volume = {16}, number = {}, pages = {953968}, pmid = {36304780}, issn = {1662-5218}, abstract = {The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.}, } @article {pmid36303917, year = {2022}, author = {Liu, S}, title = {Applying antagonistic activation pattern to the single-trial classification of mental arithmetic.}, journal = {Heliyon}, volume = {8}, number = {10}, pages = {e11102}, pmid = {36303917}, issn = {2405-8440}, abstract = {BACKGROUND: At present, the application of fNIRS in the field of brain-computer interface (BCI) is being a hot topic. By fNIRS-BCI, the brain realizes the control of external devices. A state-of-the-art BCI system has five steps which are cerebral cortex signal acquisition, data pre-processing, feature selection and extraction, feature classification and application interface. Proper feature selection and extraction are crucial to the final fNIRS-BCI effect. This paper proposes a feature selection and extraction method for the mental arithmetic task. Specifically, we modified the antagonistic activation pattern approach and used the combination of antagonistic activation patterns to extract features for enhancement of the classification accuracy with low calculation costs.

METHODS: Experiments are conducted on an open-acquisition dataset including fNIRS signals of eight healthy subjects of mental arithmetic (MA) tasks and rest tasks. First, the signals are filtered using band-pass filters to remove noise. Second, channels are selected by prior knowledge about antagonistic activation patterns. We used cerebral blood volume (CBV) and cerebral oxygen exchange (COE) of selected each channel to build novel attributes. Finally, we proposed three groups of attributes which are CBV, COE and CBV + COE. Based on attributes generated by the proposed method, we calculated temporal statistical measures (average, variance, maximum, minimum and slope). Any two of five statistical measures were combined as feature sets.

MAIN RESULTS: With the LDA, QDA, and SVM classifiers, the proposed method obtained higher classification accuracies the basic control method. The maximum classification accuracies achieved by the proposed method are 67.45 ± 14.56% with LDA classifier, 89.73 ± 5.71% with QDA classifier, and 87.04 ± 6.88% with SVM classifier. The novel method reduced the running time by 3.75 times compared with the method incorporating all channels into the feature set. Therefore, the novel method reduces the computational costs while maintaining high classification accuracy. The results are validated by another open-access dataset including MA and rest tasks of 29 healthy subjects.}, } @article {pmid36300170, year = {2022}, author = {Zhang, Y and Liu, D and Zhang, P and Li, T and Li, Z and Gao, F}, title = {Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {938518}, pmid = {36300170}, issn = {1662-4548}, abstract = {Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.}, } @article {pmid36299440, year = {2022}, author = {Xu, Y and Yin, H and Yi, W and Huang, X and Jian, W and Wang, C and Hu, R}, title = {Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI.}, journal = {Computational intelligence and neuroscience}, volume = {2022}, number = {}, pages = {1603104}, pmid = {36299440}, issn = {1687-5273}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Algorithms ; Calibration ; Imagination ; }, abstract = {A long calibration procedure limits the use in practice for a motor imagery (MI)-based brain-computer interface (BCI) system. To tackle this problem, we consider supervised and semisupervised transfer learning. However, it is a challenge for them to cope with high intersession/subject variability in the MI electroencephalographic (EEG) signals. Based on the framework of unsupervised manifold embedded knowledge transfer (MEKT), we propose a supervised MEKT algorithm (sMEKT) and a semisupervised MEKT algorithm (ssMEKT), respectively. sMEKT only has limited labelled samples from a target subject and abundant labelled samples from multiple source subjects. Compared to sMEKT, ssMEKT adds comparably more unlabelled samples from the target subject. After performing Riemannian alignment (RA) and tangent space mapping (TSM), both sMEKT and ssMEKT execute domain adaptation to shorten the differences among subjects. During domain adaptation, to make use of the available samples, two algorithms preserve the source domain discriminability, and ssMEKT preserves the geometric structure embedded in the labelled and unlabelled target domains. Moreover, to obtain a subject-specific classifier, sMEKT minimizes the joint probability distribution shift between the labelled target and source domains, whereas ssMEKT performs the joint probability distribution shift minimization between the unlabelled target domain and all labelled domains. Experimental results on two publicly available MI datasets demonstrate that our algorithms outperform the six competing algorithms, where the sizes of labelled and unlabelled target domains are variable. Especially for the target subjects with 10 labelled samples and 270/190 unlabelled samples, ssMEKT shows 5.27% and 2.69% increase in average accuracy on the two abovementioned datasets compared to the previous best semisupervised transfer learning algorithm (RA-regularized common spatial patterns-weighted adaptation regularization, RA-RCSP-wAR), respectively. Therefore, our algorithms can effectively reduce the need of labelled samples for the target subject, which is of importance for the MI-based BCI application.}, } @article {pmid36298430, year = {2022}, author = {Ng, CR and Fiedler, P and Kuhlmann, L and Liley, D and Vasconcelos, B and Fonseca, C and Tamburro, G and Comani, S and Lui, TK and Tse, CY and Warsito, IF and Supriyanto, E and Haueisen, J}, title = {Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {20}, pages = {}, pmid = {36298430}, issn = {1424-8220}, mesh = {Humans ; Reproducibility of Results ; Electrodes ; *Electroencephalography ; *Brain-Computer Interfaces ; Electric Impedance ; }, abstract = {Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.}, } @article {pmid36298196, year = {2022}, author = {Zhang, S and Li, H and Li, L and Lu, J and Zuo, Z}, title = {A High-Capacity Steganography Algorithm Based on Adaptive Frequency Channel Attention Networks.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {20}, pages = {}, pmid = {36298196}, issn = {1424-8220}, mesh = {*Image Processing, Computer-Assisted/methods ; *Algorithms ; Neural Networks, Computer ; }, abstract = {Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.}, } @article {pmid36298064, year = {2022}, author = {Oikonomou, VP}, title = {An Adaptive Task-Related Component Analysis Method for SSVEP Recognition.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {20}, pages = {}, pmid = {36298064}, issn = {1424-8220}, mesh = {*Evoked Potentials, Visual ; Electroencephalography/methods ; Bayes Theorem ; Algorithms ; *Brain-Computer Interfaces ; Photic Stimulation ; }, abstract = {Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject's calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain-computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin.}, } @article {pmid36297922, year = {2022}, author = {Liu, N and Wang, H and Wang, S and Xu, B and Qu, L}, title = {Liquid Oxygen Compatibility and Ultra-Low-Temperature Mechanical Properties of Modified epoxy Resin Containing Phosphorus and Nitrogen.}, journal = {Polymers}, volume = {14}, number = {20}, pages = {}, pmid = {36297922}, issn = {2073-4360}, abstract = {Endowing epoxy resin (EP) with prospective liquid oxygen compatibility (LOC) as well as enhanced ultra-low-temperature mechanical properties is urgently required in order to broaden its applications in aerospace engineering. In this study, a reactive phosphorus/nitrogen-containing aromatic ethylenediamine (BSEA) was introduced as a reactive component to enhance the LOC and ultra-low-temperature mechanical properties of an EP/biscitraconimide resin (BCI) system. The resultant EP thermosets showed no sensitivity reactions in the 98J liquid oxygen impact test (LOT) when the BSEA content reached 4 wt% or 5 wt%, indicating that they were compatible with liquid oxygen. Moreover, the bending properties, fracture toughness and impact strength of BSEA-modified EP were greatly enhanced at RT and cryogenic temperatures (77 K) at an appropriate level of BSEA content. The bending strength (251.64 MPa) increased by 113.67%, the fracture toughness (2.97 MPa·m[1/2]) increased by 81.10%, and the impact strength (31.85 kJ·m[-2]) increased by 128.81% compared with that of pure EP at 77 K. All the above results demonstrate that the BSEA exhibits broad application potential in liquid oxygen tanks and in the cryogenic field.}, } @article {pmid36291203, year = {2022}, author = {Ning, Y and Wan, G and Liu, T and Zhang, S}, title = {Volitional Generation of Reproducible, Efficient Temporal Patterns.}, journal = {Brain sciences}, volume = {12}, number = {10}, pages = {}, pmid = {36291203}, issn = {2076-3425}, abstract = {One of the extraordinary characteristics of the biological brain is the low energy expense it requires to implement a variety of biological functions and intelligence as compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as a contributor for the brain to run on low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how it evolves throughout learning have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm. Two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1) by learning the BMI paradigm. Moreover, most neurons that were not directly assigned to control the BMI did not boost their excitability, and they demonstrated an overall energy-efficient manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting that a cohesive rather than dissociable processing underlies the refinement of energy-efficient temporal patterns.}, } @article {pmid36290990, year = {2022}, author = {Filho, G and Júnior, C and Spinelli, B and Damasceno, I and Fiuza, F and Morya, E}, title = {All-Polymeric Electrode Based on PEDOT:PSS for In Vivo Neural Recording.}, journal = {Biosensors}, volume = {12}, number = {10}, pages = {}, pmid = {36290990}, issn = {2079-6374}, mesh = {Animals ; Rats ; *Neurons/physiology ; Rats, Wistar ; *Polymers ; Microelectrodes ; }, abstract = {One of the significant challenges today in the brain-machine interfaces that use invasive methods is the stability of the chronic record. In recent years, polymer-based electrodes have gained notoriety for achieving mechanical strength values close to that of brain tissue, promoting a lower immune response to the implant. In this work, we fabricated fully polymeric electrodes based on PEDOT:PSS for neural recording in Wistar rats. We characterized the electrical properties and both in vitro and in vivo functionality of the electrodes. Additionally, we employed histological processing and microscopical visualization to evaluate the tecidual immune response at 7, 14, and 21 days post-implant. Electrodes with 400-micrometer channels showed a 12 dB signal-to-noise ratio. Local field potentials were characterized under two conditions: anesthetized and free-moving. There was a proliferation of microglia at the tissue-electrode interface in the early days, though there was a decrease after 14 days. Astrocytes also migrated to the interface, but there was not continuous recruitment of these cells in the tissue; there was inflammatory stability by day 21. The signal was not affected by this inflammatory action, demonstrating that fully polymeric electrodes can be an alternative means to prolong the valuable time of neural recordings.}, } @article {pmid36290910, year = {2022}, author = {Chen, W and Chen, SK and Liu, YH and Chen, YJ and Chen, CS}, title = {An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System.}, journal = {Biosensors}, volume = {12}, number = {10}, pages = {}, pmid = {36290910}, issn = {2079-6374}, mesh = {Humans ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; *Wheelchairs ; Reactive Oxygen Species ; Photic Stimulation ; Electroencephalography/methods ; Algorithms ; }, abstract = {Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain-computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human-machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study.}, } @article {pmid36289356, year = {2022}, author = {Bak, S and Jeong, Y and Yeu, M and Jeong, J}, title = {Brain-computer interface to predict impulse buying behavior using functional near-infrared spectroscopy.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {18024}, pmid = {36289356}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *COVID-19 ; Prefrontal Cortex/diagnostic imaging/physiology ; Biomarkers ; }, abstract = {As the rate of vaccination against COVID-19 is increasing, demand for overseas travel is also increasing. Despite people's preference for duty-free shopping, previous studies reported that duty-free shopping increases impulse buying behavior. There are also self-reported tools to measure their impulse buying behavior, but it has the disadvantage of relying on the human memory and perception. Therefore, we propose a Brain-Computer Interface (BCI)-based brain signal processing methodology to supplement these limitations and to reduce ambiguity and conjecture of data. To achieve this goal, we focused on the brain's prefrontal cortex (PFC) activity, which supervises human decision-making and is closely related to impulse buying behavior. The PFC activation is observed by recording signals using a functional near-infrared spectroscopy (fNIRS) while inducing impulse buying behavior in virtual computing environments. We found that impulse buying behaviors were not only higher in online duty-free shops than in online regular stores, but the fNIRS signals were also different on the two sites. We also achieved an average accuracy of 93.78% in detecting impulse buying patterns using a support vector machine. These results were identical to the people's self-reported responses. This study provides evidence as a potential biomarker for detecting impulse buying behavior with fNIRS.}, } @article {pmid36289267, year = {2022}, author = {An, KM and Shim, JH and Kwon, H and Lee, YH and Yu, KK and Kwon, M and Chun, WY and Hirosawa, T and Hasegawa, C and Iwasaki, S and Kikuchi, M and Kim, K}, title = {Detection of the 40 Hz auditory steady-state response with optically pumped magnetometers.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {17993}, pmid = {36289267}, issn = {2045-2322}, mesh = {*Helium ; *Magnetoencephalography/methods ; Brain/diagnostic imaging/physiology ; Neuroimaging ; Functional Neuroimaging ; }, abstract = {Magnetoencephalography (MEG) is a functional neuroimaging technique that noninvasively detects the brain magnetic field from neuronal activations. Conventional MEG measures brain signals using superconducting quantum interference devices (SQUIDs). SQUID-MEG requires a cryogenic environment involving a bulky non-magnetic Dewar flask and the consumption of liquid helium, which restricts the variability of the sensor array and the gap between the cortical sources and sensors. Recently, miniature optically pumped magnetometers (OPMs) have been developed and commercialized. OPMs do not require cryogenic cooling and can be placed within millimeters from the scalp. In the present study, we arranged six OPM sensors on the temporal area to detect auditory-related brain responses in a two-layer magnetically shielded room. We presented the auditory stimuli of 1 kHz pure-tone bursts with 200 ms duration and obtained the M50 and M100 components of auditory-evoked fields. We delivered the periodic stimuli with a 40 Hz repetition rate and observed the gamma-band power changes and inter-trial phase coherence of auditory steady-state responses at 40 Hz. We found that the OPM sensors have a performance comparable to that of conventional SQUID-MEG sensors, and our results suggest the feasibility of using OPM sensors for functional neuroimaging and brain-computer interface applications.}, } @article {pmid36288717, year = {2022}, author = {Wang, L and Zhan, G and Maimaitiyiming, Y and Su, Y and Lin, S and Liu, J and Su, K and Lin, J and Shen, S and He, W and Wang, F and Chen, J and Sun, S and Xue, Y and Gu, J and Chen, X and Zhang, J and Zhang, L and Wang, Q and Chang, KJ and Chiou, SH and Björklund, M and Naranmandura, H and Cheng, X and Hsu, CH}, title = {m[6]A modification confers thermal vulnerability to HPV E7 oncotranscripts via reverse regulation of its reader protein IGF2BP1 upon heat stress.}, journal = {Cell reports}, volume = {41}, number = {4}, pages = {111546}, doi = {10.1016/j.celrep.2022.111546}, pmid = {36288717}, issn = {2211-1247}, mesh = {Humans ; *Alphapapillomavirus/metabolism ; Carcinogenesis ; Heat-Shock Proteins ; Heat-Shock Response ; Papillomaviridae ; Papillomavirus E7 Proteins/genetics/metabolism ; *Papillomavirus Infections ; Proteasome Endopeptidase Complex ; RNA, Messenger/genetics/metabolism ; RNA, Viral/genetics ; Ubiquitin ; RNA-Binding Proteins ; }, abstract = {Human papillomavirus (HPV)-induced carcinogenesis critically depends on the viral early protein 7 (E7), making E7 an attractive therapeutic target. Here, we report that the E7 messenger RNA (mRNA)-containing oncotranscript complex can be selectively targeted by heat treatment. In HPV-infected cells, viral E7 mRNA is modified by N[6]-methyladenosine (m[6]A) and stabilized by IGF2BP1, a cellular m[6]A reader. Heat treatment downregulates E7 mRNA and protein by destabilizing IGF2BP1 without the involvement of canonical heat-shock proteins and reverses HPV-associated carcinogenesis in vitro and in vivo. Mechanistically, heat treatment promotes IGF2BP1 aggregation only in the presence of m[6]A-modified E7 mRNA to form distinct heat-induced m[6]A E7 mRNA-IGF2BP1 granules, which are resolved by the ubiquitin-proteasome system. Collectively, our results not only show a mutual regulation between m[6]A RNA and its reader but also provide a heat-treatment-based therapeutic strategy for HPV-associated malignancies by specifically downregulating E7 mRNA-IGF2BP1 oncogenic complex.}, } @article {pmid36288219, year = {2022}, author = {Kalafatovich, J and Lee, M and Lee, SW}, title = {Learning Spatiotemporal Graph Representations for Visual Perception using EEG Signals.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3217344}, pmid = {36288219}, issn = {1558-0210}, abstract = {Perceiving and recognizing objects enable interaction with the external environment. Recently, decoding brain signals based on brain-computer interface (BCI) that recognize the user's intentions by just looking at objects has attracted attention as a next-generation intuitive interface. However, classifying signals from different objects is very challenging, and in practice, decoding performance for visual perception is not yet high enough to be used in real environments. In this study, we aimed to classify single-trial electroencephalography signals evoked by visual stimuli into their corresponding semantic category. We proposed a two-stream convolutional neural network to increase classification performance. The model consists of a spatial stream and a temporal stream that use graph convolutional neural network and channel-wise convolutional neural network respectively. Two public datasets were used to evaluate the proposed model; (i) SU DB (a set of 72 photographs of objects belonging to 6 semantic categories) and MPI DB (8 exemplars belonging to two categories). Our results outperform state-of-the-art methods, with accuracies of 54.28 ± 7.89% for SU DB (6-class) and 84.40 ± 8.03% for MPI DB (2-class). These results could facilitate the application of intuitive BCI systems based on visual perception.}, } @article {pmid36288214, year = {2022}, author = {Jin, J and Qu, T and Xu, R and Wang, X and Cichocki, A}, title = {Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3217573}, pmid = {36288214}, issn = {1558-0210}, abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The openaccess BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p<0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI.}, } @article {pmid36287496, year = {2022}, author = {Henriques, DHN and Alves, AMH and Kuntze, MM and Garcia, LDFR and Bortoluzzi, EA and Teixeira, CDS}, title = {Effect of dental tissue thickness on the measurement of oxygen saturation by two different pulse oximeters.}, journal = {Brazilian dental journal}, volume = {33}, number = {5}, pages = {26-34}, pmid = {36287496}, issn = {1806-4760}, mesh = {Humans ; *Oxygen Saturation ; *Oximetry ; Oxygen ; Dental Enamel ; Diamond ; }, abstract = {This study aimed to evaluate the influence of different dental tissue thickness on the measurement of oxygen saturation (SpO2) levels in high (HP) and low (LP) blood perfusion by comparing the values obtained from two different pulse oximeters (POs) - BCI and Sense 10. Thirty freshly extracted human teeth had their crowns interposed between the POs and an optical simulator, which emulated the SpO2 and heart beats per minute (bpm) at HP (100% SpO2/75 bpm) and LP (86% SpO2/75 bpm) modes. Afterwards, the palatine/lingual surfaces of the dental crowns were worn with diamond drills. The reading of SpO2 was performed again using the POs alternately through the buccal surface of each dental crown. Data were analyzed by the Wilcoxon, Mann-Whitney and Kendall Tau-b tests (α=5%). The results showed significant difference at the HP and LP modes in the SpO2 readouts through the different dental thicknesses with the use of BCI, and at the LP mode with the use of Sense 10, which had a significant linear correlation (p<0.0001) and lower SpO2 readout values in relation to the increase of the dental thickness. Irrespective of tooth thickness, Sense 10 had significantly higher readout values (p<0.0001) than BCI at both perfusion modes. The interposition of different thicknesses of enamel and dentin influenced the POs measurement of SpO2, specially at the low perfusion mode. The POs were more accurate in SpO2 measurement when simulated perfusion levels were higher.}, } @article {pmid36286988, year = {2022}, author = {Zhu, L and Wang, M and Fu, P and Liu, Y and Zhang, H and Roe, AW and Xi, W}, title = {Precision 1070 nm Ultrafast Laser-Induced Photothrombosis of Depth-Targeted Vessels In Vivo.}, journal = {Small methods}, volume = {}, number = {}, pages = {e2200917}, doi = {10.1002/smtd.202200917}, pmid = {36286988}, issn = {2366-9608}, abstract = {The cerebrovasculature plays an essential role in neurovascular and homeostatic functions in health and disease conditions. Many efforts have been made for developing vascular thrombosis methods to study vascular dysfunction in vivo, while technical challenges remain, such as accuracy and depth-selectivity to target a single vessel in the cerebral cortex. Herein, this paper first demonstrates the evaluation and quantification of the feasibility and effects of Rose Bengal (RB)-induced photothrombosis with 720-1070 nm ultrafast lasers in a raster scan. A flexible and reproducible approach is then proposed to employ a 1070 nm ultrafast laser with a spiral scan for producing RB-induced occlusion, which is described as precision ultrafast laser-induced photothrombosis (PLP). Combine with two-photon microscopy imaging, this PLP displays highly precise and fast occlusion induction of various vessel types, sizes, and depths, which enhances the precision and power of the photothrombosis protocol. Overall, the PLP method provides a real-time, practical, precise, and depth-selected single-vessel photothrombosis technology in the cerebral cortex with commercially available optical equipment, which is crucial for exploring brain vascular function with high spatial-temporal resolution in the brain.}, } @article {pmid36285909, year = {2022}, author = {Cavallaro, G and Murri, A and Nelson, E and Gorrasi, R and Quaranta, N}, title = {The Impact of the COVID-19 Lockdown on Quality of Life in Adult Cochlear Implant Users: A Survey Study.}, journal = {Audiology research}, volume = {12}, number = {5}, pages = {518-526}, pmid = {36285909}, issn = {2039-4330}, abstract = {BACKGROUND: The COVID-19 pandemic rapidly spread through Europe in the first months of 2020. On the 9th of March 2020, the Italian government ordered a national lock-down. The study's objectives were: to investigate the effect of lockdown on CI users; and to detect the difference in the perception of discomfort existing between unilateral cochlear implant (UCI) users and bilateral cochlear implant (BCI) users, due to the lockdown experience.

METHODS: A 17-item, web-based, anonymous online survey was administered to 57 CI users, exploring hearing performance, emotions, practical issues, behavior, and tinnitus. Participation in the study was voluntary.

RESULTS: all CI users obtained an abnormal score in all questionnaire themes. For the emotion theme and the practical issue theme, the age range 61-90 showed a significant difference between UCI and BCI users in favor of BCI users (emotion theme: UCI mean = 3.9, BCI mean = 2.3, p = 0.0138; practical issues: UCI mean = 4, BCI mean = 3, p = 0.0031).

CONCLUSIONS: CI users experienced the lockdown negatively as regards behavior, emotions, hearing performance, and in practical issues. CI subjects with UCI in old age suffered more from the experience of lockdown than subjects with BCI in the same age, with regards to emotions and practical issues.}, } @article {pmid36285542, year = {2022}, author = {Yu, H and Ni, P and Tian, Y and Zhao, L and Li, M and Li, X and Wei, W and Wei, J and Du, X and Wang, Q and Guo, W and Deng, W and Ma, X and Coid, J and Li, T}, title = {Association of the plasma complement system with brain volume deficits in bipolar and major depressive disorders.}, journal = {Psychological medicine}, volume = {}, number = {}, pages = {1-11}, doi = {10.1017/S0033291722003282}, pmid = {36285542}, issn = {1469-8978}, abstract = {BACKGROUND: Inflammation plays a crucial role in the pathogenesis of major depressive disorder (MDD) and bipolar disorder (BD). This study aimed to examine whether the dysregulation of complement components contributes to brain structural defects in patients with mood disorders.

METHODS: A total of 52 BD patients, 35 MDD patients, and 53 controls were recruited. The human complement immunology assay was used to measure the levels of complement factors. Whole brain-based analysis was performed to investigate differences in gray matter volume (GMV) and cortical thickness (CT) among the BD, MDD, and control groups, and relationships were explored between neuroanatomical differences and levels of complement components.

RESULTS: GMV in the medial orbital frontal cortex (mOFC) and middle cingulum was lower in both patient groups than in controls, while the CT of the left precentral gyrus and left superior frontal gyrus were affected differently in the two disorders. Concentrations of C1q, C4, factor B, factor H, and properdin were higher in both patient groups than in controls, while concentrations of C3, C4 and factor H were significantly higher in BD than in MDD. Concentrations of C1q, factor H, and properdin showed a significant negative correlation with GMV in the mOFC at the voxel-wise level.

CONCLUSIONS: BD and MDD are associated with shared and different alterations in levels of complement factors and structural impairment in the brain. Structural defects in mOFC may be associated with elevated levels of certain complement factors, providing insight into the shared neuro-inflammatory pathogenesis of mood disorders.}, } @article {pmid36284139, year = {2022}, author = {Li, P and Garg, AK and Zhang, LA and Rashid, MS and Callaway, EM}, title = {Cone opponent functional domains in primary visual cortex combine signals for color appearance mechanisms.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {6344}, pmid = {36284139}, issn = {2041-1723}, mesh = {*Calcium ; *Primary Visual Cortex ; Retinal Cone Photoreceptor Cells/physiology ; Color Perception/physiology ; Retina/physiology ; Photic Stimulation/methods ; Color ; }, abstract = {Studies of color perception have led to mechanistic models of how cone-opponent signals from retinal ganglion cells are integrated to generate color appearance. But it is unknown how this hypothesized integration occurs in the brain. Here we show that cone-opponent signals transmitted from retina to primary visual cortex (V1) are integrated through highly organized circuits within V1 to implement the color opponent interactions required for color appearance. Combining intrinsic signal optical imaging (ISI) and 2-photon calcium imaging (2PCI) at single cell resolution, we demonstrate cone-opponent functional domains (COFDs) that combine L/M cone-opponent and S/L + M cone-opponent signals following the rules predicted from psychophysical studies of color perception. These give rise to an orderly organization of hue preferences of the neurons within the COFDs and the generation of hue "pinwheels". Thus, spatially organized neural circuits mediate an orderly transition from cone-opponency to color appearance that begins in V1.}, } @article {pmid36283830, year = {2022}, author = {Xing, D and Truccolo, W and Borton, DA}, title = {Emergence of distinct neural subspaces in motor cortical dynamics during volitional adjustments of ongoing locomotion.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.0746-22.2022}, pmid = {36283830}, issn = {1529-2401}, abstract = {The ability to modulate ongoing walking gait with precise, voluntary adjustments, is what allows animals to navigate complex terrains. However, how the nervous system generates the signals to precisely control the limbs while simultaneously maintaining locomotion is poorly understood. One potential strategy is to distribute the neural activity related to these two functions into distinct cortical activity co-activation subspaces so that both may be carried out simultaneously without disruptive interference. To investigate this hypothesis, we recorded the activity of primary motor cortex in male nonhuman primates during obstacle avoidance on a treadmill. We found that the same neural population was active during both basic unobstructed locomotion and volitional obstacle avoidance movements. We identified the neural modes spanning the subspace of the low-dimensional dynamics in M1 and found a subspace that consistently maintains the same cyclic activity throughout obstacle stepping, despite large changes in the movement itself. All of the variance corresponding to this large change in movement during the obstacle avoidance was confined to its own distinct subspace. Furthermore, neural decoders built for ongoing locomotion did not generalize to decoding obstacle avoidance during locomotion. Our findings suggest that separate underlying subspaces emerge during complex locomotion that coordinate ongoing locomotor-related neural dynamics with volitional gait adjustments. These findings may have important implications for the development of brain-machine interfaces.SIGNIFICANCE STATEMENT:Locomotion and precise, goal-directed movements, are two distinct movement modalities with known differing requirements of motor cortical input. Previous studies have characterized the cortical activity during obstacle avoidance while walking in rodents and felines, but to-date, no such studies have been completed in primates. Additionally, in any animal model, it is unknown how these two movements are represented in M1 low dimensional dynamics when both activities are performed at the same time, such as during obstacle avoidance. We developed a novel obstacle avoidance paradigm in freely-moving non-human primates and discovered that the rhythmic locomotion-related dynamics and the voluntary, gait-adjustment movement, separate into distinct subspaces in M1 cortical activity. Our analysis on decoding generalization may also have important implications for the development of brain-machine interfaces.}, } @article {pmid36280665, year = {2022}, author = {Ji, SY and Dong, YJ and Chen, LN and Zang, SK and Wang, J and Shen, DD and Guo, J and Qin, J and Zhang, H and Wang, WW and Shen, Q and Zhang, Y and Song, Z and Mao, C}, title = {Molecular basis for the activation of thyrotropin-releasing hormone receptor.}, journal = {Cell discovery}, volume = {8}, number = {1}, pages = {116}, pmid = {36280665}, issn = {2056-5968}, } @article {pmid36280089, year = {2022}, author = {Xue, Y and Zhu, J and Huang, X and Xu, X and Li, X and Zheng, Y and Zhu, Z and Jin, K and Ye, J and Gong, W and Si, K}, title = {A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed.}, journal = {Journal of biomedical informatics}, volume = {136}, number = {}, pages = {104233}, doi = {10.1016/j.jbi.2022.104233}, pmid = {36280089}, issn = {1532-0480}, mesh = {Humans ; *Deep Learning ; *Glaucoma/diagnosis ; Diagnostic Techniques, Ophthalmological ; Photography/methods ; Diagnosis, Computer-Assisted/methods ; }, abstract = {Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is not yet sufficient for monitoring glaucoma progression in isolation. There is an urgent need to develop more comprehensive diagnostic methods with higher accuracy. In this study, we proposed a multi- feature deep learning (MFDL) system based on intraocular pressure (IOP), color fundus photograph (CFP) and visual field (VF) to classify the glaucoma into four severity levels. We designed a three-phase framework for glaucoma severity diagnosis from coarse to fine, which contains screening, detection and classification. We trained it on 6,131 samples from 3,324 patients and tested it on independent 240 samples from 185 patients. Our results show that MFDL achieved a higher accuracy of 0.842 (95 % CI, 0.795-0.888) than the direct four classification deep learning (DFC-DL, accuracy of 0.513 [0.449-0.576]), CFP-based single-feature deep learning (CFP-DL, accuracy of 0.483 [0.420-0.547]) and VF-based single-feature deep learning (VF-DL, accuracy of 0.725 [0.668-0.782]). Its performance was statistically significantly superior to that of 8 juniors. It also outperformed 3 seniors and 1 expert, and was comparable with 2 glaucoma experts (0.842 vs 0.854, p = 0.663; 0.842 vs 0.858, p = 0.580). With the assistance of MFDL, junior ophthalmologists achieved statistically significantly higher accuracy performance, with the increased accuracy ranged from 7.50 % to 17.9 %, and that of seniors and experts were 6.30 % to 7.50 % and 5.40 % to 7.50 %. The mean diagnosis time per patient of MFDL was 5.96 s. The proposed model can potentially assist ophthalmologists in efficient and accurate glaucoma diagnosis that could aid the clinical management of glaucoma.}, } @article {pmid36278046, year = {2022}, author = {Cao, X and Zhu, L and Qi, R and Wang, X and Sun, G and Ying, Y and Chen, R and Li, X and Gao, L}, title = {Effect of a High Estrogen Level in Early Pregnancy on the Development and Behavior of Marmoset Offspring.}, journal = {ACS omega}, volume = {7}, number = {41}, pages = {36175-36183}, pmid = {36278046}, issn = {2470-1343}, abstract = {The use of assisted reproductive technology (ART) has risen steadily worldwide over the past 3 decades and helps many infertile families. However, ART treatments lead to an abnormal internal environment in the uterus, which may increase the risks of health problems for the offspring. Higher maternal estradiol (E2) is a notable feature in women who use ART treatments, and this has been suggested as a key factor for the risk of diseases in the offspring. In the current study, we have established a marmoset model with a high E2 level in early pregnancy to examine its potential risk to the development and behavior of the offspring. In comparison with the normal group, babies of the high E2 group exhibited lower average survival rates and birth weights. However, those who survived in the high E2 group demonstrated normal vocal production with rich call repertoires, normal speed during locomotion, and normal behaviors in the home cage. In contrast to the normal group, surviving babies of the high E2 group spent more time sleeping during development without signs of sleep disorders. In summary, our study revealed that high estrogen in early pregnancy may cause low survival rates and birth weights of the offspring, though the surviving infants did not show obvious behavioral deficiencies during development. The current study is a valuable and highly important non-human primate study for evaluating the safety of ART treatments. However, it is worth noting that some results did not reach the significant level, which may be due to the small sample size caused by animal shortage stemming from the COVID-19 epidemic.}, } @article {pmid36277512, year = {2022}, author = {Jamil, N and Belkacem, AN and Lakas, A}, title = {On enhancing students' cognitive abilities in online learning using brain activity and eye movements.}, journal = {Education and information technologies}, volume = {}, number = {}, pages = {1-35}, pmid = {36277512}, issn = {1360-2357}, abstract = {The COVID-19 pandemic has interrupted education institutions in over 150 nations, affecting billions of students. Many governments have forced a transition in higher education from in-person to remote learning. After this abrupt, worldwide transition away from the classroom, some question whether online education will continue to grow in acceptance in post-pandemic times. However, new technology, such as the brain-computer interface and eye-tracking, have the potential to improve the remote learning environment, which currently faces several obstacles and deficiencies. Cognitive brain computer interfaces can help us develop a better understanding of brain functions, allowing for the development of more effective learning methodologies and the enhancement of brain-based skills. We carried out a systematic literature review of research on the use of brain computer interfaces and eye-tracking to measure students' cognitive skills during online learning. We found that, because many experimental tasks depend on recorded rather than real-time video, students don't have direct and real-time interaction with their teacher. Further, we found no evidence in any of the reviewed papers for brain-to-brain synchronization during remote learning. This points to a potentially fruitful future application of brain computer interfaces in education, investigating whether the brains of student-teacher pairs who interact with the same course content have increasingly similar brain patterns.}, } @article {pmid36277476, year = {2022}, author = {Wang, G and Cerf, M}, title = {Brain-Computer Interface using neural network and temporal-spectral features.}, journal = {Frontiers in neuroinformatics}, volume = {16}, number = {}, pages = {952474}, pmid = {36277476}, issn = {1662-5196}, abstract = {Brain-Computer Interfaces (BCIs) are increasingly useful for control. Such BCIs can be used to assist individuals who lost mobility or control over their limbs, for recreational purposes such as gaming or semi-autonomous driving, or as an interface toward man-machine integration. Thus far, the performance of algorithms used for thought decoding has been limited. We show that by extracting temporal and spectral features from electroencephalography (EEG) signals and, following, using deep learning neural network to classify those features, one can significantly improve the performance of BCIs in predicting which motor action was imagined by a subject. Our movement prediction algorithm uses Sequential Backward Selection technique to jointly choose temporal and spectral features and a radial basis function neural network for the classification. The method shows an average performance increase of 3.50% compared to state-of-the-art benchmark algorithms. Using two popular public datasets our algorithm reaches 90.08% accuracy (compared to an average benchmark of 79.99%) on the first dataset and 88.74% (average benchmark: 82.01%) on the second dataset. Given the high variability within- and across-subjects in EEG-based action decoding, we suggest that using features from multiple modalities along with neural network classification protocol is likely to increase the performance of BCIs across various tasks.}, } @article {pmid36273413, year = {2022}, author = {Sinha, S and Finazzi-Agrò, E and Dmochowski, RR and Hashim, H and Iacovelli, V}, title = {The bladder contractility and bladder outlet obstruction indices in adult men: Results of a global Delphi consensus study.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25073}, pmid = {36273413}, issn = {1520-6777}, abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of Bladder Contractility Index (BCI) and Bladder Outlet Obstruction Index (BOOI) and the related evidence.

METHODS: Twenty-eight experts were invited to answer the two-round survey including three foundation questions and 15 survey questions. Consensus was defined as ≥75% agreement. The ordinal scale (0-10) in round 1 was classified into "strongly agree," "agree," "neutral," "disagree," and "strongly disagree" for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in men.

RESULTS: Nineteen experts participated in the survey with 100% completion. Consensus was noted with regard to 6 of 19 questions. Experts strongly agreed with utility of quantifying bladder contractility and bladder outflow obstruction with near unanimity regarding the latter. There was consensus that BCI and BOOI were accurate, that BCI was clinically useful, and for defining severe bladder outflow obstruction as BOOI > 80. Systematic search yielded 69 publications (BCI 45; BOOI 50). Most studies examined the indices as a continuous variable or by standard cutoffs (BCI 100, 150; BOOI 20, 40).

CONCLUSION: There is general agreement among experts on need for indices to quantify bladder contractility and bladder outflow obstruction as well as with regard to accuracy and utility of BCI and BOOI indices. Few studies have examined the discriminant power of existing cutoffs or explored new ones. This is an extraordinary knowledge gap in the field of urology.}, } @article {pmid36272285, year = {2022}, author = {A, W and Du, F and He, Y and Wu, B and Liu, F and Liu, Y and Zheng, W and Li, G and Wang, X}, title = {Graphene oxide reinforced hemostasis of gelatin sponge in noncompressible hemorrhage via synergistic effects.}, journal = {Colloids and surfaces. B, Biointerfaces}, volume = {220}, number = {}, pages = {112891}, doi = {10.1016/j.colsurfb.2022.112891}, pmid = {36272285}, issn = {1873-4367}, mesh = {Humans ; *Gelatin/pharmacology ; Hemostasis ; *Hemostatics/pharmacology ; Hemorrhage/drug therapy ; }, abstract = {Effective hemostasis for noncompressible bleeding has been a key challenge because of the deep, narrow, and irregular wounds. Swellable gelatin is an available hemostatic material but is limited by weak mechanical strength and slow liquid absorption. Herein, the design of a gelatin and graphene oxide (GO) composite sponge (GP-GO) that possesses stable cross-linked networks and excellent absorbability, is reported. The GP-GOs are constructed via the thermal radical polymerization technique, using methacrylate gelatin (Gel-MA) and poly(ethylene glycol) diacrylate (PEGDA) as the crosslinker, while GO is uniformly fixed in the network via the curing reaction to further strengthen the stability. The optimized GP-GO5 with GO addition (5 wt%) exhibits high porosity (> 90%), distinguished liquid absorption rate (106 ms), rapidly responsive swelling (422% expansion within 10 s), and stable mechanical properties. The addition of GO effectively reinforces coagulation stimulation of GP-GOs though the stimulation of platelets and the enrichment effect at the interface, significantly reducing the blood coagulation index (BCI) (< 17.5%). Hemostatic mechanism study indicated the liquid absorbability of GP-GOs is the critical foundation to trigger the subsequent physical expansion, blood cells enrichment, and coagulation stimulations. Besides, GP-GO5 exhibits excellent biosafety assessed by hemolysis and cytotoxicity. Under the synergistic effects, the biocompatible GP-GO5 showed excellent hemostatic properties in the hemostasis of severe bleeding and noncompressible wounds compared with a pure gelatin sponge (GP) and the commercial hemostatic agent Celox™. This study demonstrated a promising candidate for practical application of noncompressible wound hemostasis.}, } @article {pmid36271004, year = {2022}, author = {Zhai, X and Mao, C and Shen, Q and Zang, S and Shen, DD and Zhang, H and Chen, Z and Wang, G and Zhang, C and Zhang, Y and Liu, Z}, title = {Molecular insights into the distinct signaling duration for the peptide-induced PTH1R activation.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {6276}, pmid = {36271004}, issn = {2041-1723}, mesh = {*Receptor, Parathyroid Hormone, Type 1/genetics ; *Teriparatide/pharmacology ; Ligands ; Cryoelectron Microscopy ; Amino Acid Sequence ; Parathyroid Hormone/pharmacology ; Peptides/chemistry ; Receptors, G-Protein-Coupled ; }, abstract = {The parathyroid hormone type 1 receptor (PTH1R), a class B1 G protein-coupled receptor, plays critical roles in bone turnover and Ca[2+] homeostasis. Teriparatide (PTH) and Abaloparatide (ABL) are terms as long-acting and short-acting peptide, respectively, regarding their marked duration distinctions of the downstream signaling. However, the mechanistic details remain obscure. Here, we report the cryo-electron microscopy structures of PTH- and ABL-bound PTH1R-Gs complexes, adapting similar overall conformations yet with notable differences in the receptor ECD regions and the peptide C-terminal portions. 3D variability analysis and site-directed mutagenesis studies uncovered that PTH-bound PTH1R-Gs complexes display less motions and are more tolerant of mutations in affecting the receptor signaling than ABL-bound complexes. Furthermore, we combined the structural analysis and signaling assays to delineate the molecular basis of the differential signaling durations induced by these peptides. Our study deepens the mechanistic understanding of ligand-mediated prolonged or transient signaling.}, } @article {pmid36270622, year = {2022}, author = {Liu, Y and Luo, C and Zheng, J and Liang, J and Ding, N}, title = {Working memory asymmetrically modulates auditory and linguistic processing of speech.}, journal = {NeuroImage}, volume = {264}, number = {}, pages = {119698}, doi = {10.1016/j.neuroimage.2022.119698}, pmid = {36270622}, issn = {1095-9572}, mesh = {Humans ; *Memory, Short-Term/physiology ; Speech/physiology ; Linguistics ; *Speech Perception/physiology ; Language ; }, abstract = {Working memory load can modulate speech perception. However, since speech perception and working memory are both complex functions, it remains elusive how each component of the working memory system interacts with each speech processing stage. To investigate this issue, we concurrently measure how the working memory load modulates neural activity tracking three levels of linguistic units, i.e., syllables, phrases, and sentences, using a multiscale frequency-tagging approach. Participants engage in a sentence comprehension task and the working memory load is manipulated by asking them to memorize either auditory verbal sequences or visual patterns. It is found that verbal and visual working memory load modulate speech processing in similar manners: Higher working memory load attenuates neural activity tracking of phrases and sentences but enhances neural activity tracking of syllables. Since verbal and visual WM load similarly influence the neural responses to speech, such influences may derive from the domain-general component of WM system. More importantly, working memory load asymmetrically modulates lower-level auditory encoding and higher-level linguistic processing of speech, possibly reflecting reallocation of attention induced by mnemonic load.}, } @article {pmid36270502, year = {2022}, author = {Sosulski, J and Tangermann, M}, title = {Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9c98}, pmid = {36270502}, issn = {1741-2552}, mesh = {Humans ; Discriminant Analysis ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Algorithms ; }, abstract = {Objective.Covariance matrices of noisy multichannel electroencephalogram (EEG) time series data provide essential information for the decoding of brain signals using machine learning methods. However, small datasets and high dimensionality make it hard to estimate these matrices. In brain-computer interfaces (BCI) based on event-related potentials (ERP) and a linear discriminant analysis (LDA) classifier, the state of the art covariance estimation uses shrinkage regularization. As this is a general covariance regularization approach, we aim at improving LDA further by better exploiting the domain-specific characteristics of the EEG to regularize the covariance estimates.Approach.We propose to enforce a block-Toeplitz structure for the covariance matrix of the LDA, which implements an assumption of signal stationarity in short time windows for each channel.Main results.An offline re-analysis of data collected from 213 subjects under 13 different event-related potential BCI protocols showed a significantly increased binary classification performance of this 'ToeplitzLDA' compared to shrinkage regularized LDA (up to 6 AUC points,p < 0.001) and Riemannian classification approaches (up to 2 AUC points,p < 0.001). In an unsupervised visual speller application, this improvement would translate to a relative reduction of spelling errors by 81% on average for 25 subjects. Additionally, aside from lower memory and reduced time complexity for LDA training, ToeplitzLDA proves to be robust against drastic increases of the number of temporal features.Significance.The proposed covariance estimation allows BCI researchers to improve classification rates and reduce calibration times of BCI protocols using event-related potentials and thus support the usability of corresponding applications. Its lower computational and memory needs could make it a valuable algorithm especially for mobile BCIs.}, } @article {pmid36270467, year = {2022}, author = {Liu, S and Zhang, J and Wang, A and Wu, H and Zhao, Q and Long, J}, title = {Subject adaptation convolutional neural network for EEG-based motor imagery classification.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9c94}, pmid = {36270467}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Imagery, Psychotherapy/methods ; Algorithms ; Imagination ; }, abstract = {Objective.Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted.Approach.Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject.Main results.Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain-computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN.Significance.This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.}, } @article {pmid36270430, year = {2022}, author = {Spencer, M and Kameneva, T and Grayden, DB and Burkitt, AN and Meffin, H}, title = {Quantifying visual acuity for pre-clinical testing of visual prostheses.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ac9c95}, pmid = {36270430}, issn = {1741-2552}, abstract = {OBJECTIVE: Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity.

APPROACH: This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provides a quantified level of visual perception in the clinical Snellen and MAR scales. The magnitude of the resulting value was demonstrated using scaled standardized "C" and "E" optotypes.

MAIN RESULTS: The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different electrode pitch configurations and differently sized spreads of neural activity. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in both circumstances.

SIGNIFICANCE: Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.}, } @article {pmid36269910, year = {2022}, author = {Sakkalis, V and Krana, M and Farmaki, C and Bourazanis, C and Gaitatzis, D and Pediaditis, M}, title = {Augmented Reality Driven Steady-State Visual Evoked Potentials for Wheelchair Navigation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2960-2969}, doi = {10.1109/TNSRE.2022.3215695}, pmid = {36269910}, issn = {1558-0210}, mesh = {Humans ; Evoked Potentials, Visual ; *Augmented Reality ; *Wheelchairs ; Electroencephalography ; *Brain-Computer Interfaces ; Photic Stimulation ; }, abstract = {Medically oriented Brain Computer Interfaces (BCIs) have been proposed as a promising approach addressed to individuals suffering from severe paralysis. Steady-State Visual Evoked Potentials (SSVEPs) in particular have been proven successful in many different applications, achieving high information throughput with short or even no training. However, efficient electric wheelchair navigation combining high accuracy and comfort is still not demonstrated. In this paper, we propose the use of an SSVEP-based universal control system featuring augmented reality (AR) glasses in an attempt to increase ease of use and patient acceptability without making compromises on BCI performance. The system received positive user-feedback, reaching a mean accuracy of 90%. Merits and pitfalls of the system proposed are also addressed.}, } @article {pmid36269374, year = {2022}, author = {Margenau, EL and Wood, PB and Brown, DJ and Ryan, CW}, title = {Evaluating Mechanisms of Short-term Woodland Salamander Response to Forest Management.}, journal = {Environmental management}, volume = {}, number = {}, pages = {}, doi = {10.1007/s00267-022-01735-3}, pmid = {36269374}, issn = {1432-1009}, abstract = {Contemporary forest management often requires meeting diverse ecological objectives including maintaining ecosystem function and promoting biodiversity through timber harvesting. Wildlife are essential in this process by providing ecological services that can facilitate forest resiliency in response to timber harvesting. However, the mechanisms driving species' responses remain ambiguous. The goal of this study was to assess mechanisms influencing eastern red-backed salamander (RBS; Plethodon cinereus) response to overstory cover removal. We evaluated two mitigation strategies for the RBS in response to overstory removal. We used a before-after-control-impact design to study how (1) retaining residual trees or (2) eliminating soil compaction affected RBS surface counts and body condition index (BCI) up to two-years post-treatment. Additionally, we assessed how surface counts of RBS were influenced by overstory tree cover. Surface counts of RBS were not strongly influenced by overstory removal when tree residuals were retained. Body condition index increased in treatments where harvest residuals were retained. In treatments where soil compaction was eliminated, surface counts and BCI were inversely related. Finally, surface counts from both mitigation strategies were not strongly influenced by overstory cover. Overall, both mitigation techniques appeared to ameliorate impacts of overstory removal on RBS. These results highlight the importance of understanding mechanisms driving species' responses to forest management. To reduce the perceived negative effects of overstory removal on RBS, incorporating these mitigation measures may contribute to the viability and stability of RBS populations. Incorporating species' life history traits into management strategies could increase continuity of ecological function and integrity through harvesting.}, } @article {pmid36264857, year = {2022}, author = {Yan, Z and Yang, X and Jin, Y}, title = {Considerate motion imagination classification method using deep learning.}, journal = {PloS one}, volume = {17}, number = {10}, pages = {e0276526}, pmid = {36264857}, issn = {1932-6203}, mesh = {*Deep Learning ; Algorithms ; Imagination ; *Brain-Computer Interfaces ; Electroencephalography/methods ; }, abstract = {In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the proposed model can accurately represent the distribution of electroencephalography electrodes in non-Euclidean space and fully consider the spatial correlation between electrodes. In addition, the spatial-spectral-temporal multi-dimensional feature information was extracted from the spatial-temporal graph representation and spatial-spectral graph representation transformed from the original electroencephalography signal using the dual branch architecture. Finally, the attention mechanism and global feature aggregation module were designed and combined with graph convolution to adaptively capture the dynamic correlation intensity and effective feature of electroencephalography signals in various dimensions. A series of contrast experiments and ablation experiments on several different public brain-computer interface datasets demonstrated that the excellence of proposed method. It is worth mentioning that, the proposed model is a general framework for the classification of electroencephalography signals, which is suitable for emotion recognition, sleep staging and other fields based on electroencephalography research. Moreover, the model has the potential to be applied in the medical field of motion imagination rehabilitation in real life.}, } @article {pmid36264734, year = {2022}, author = {Feng, L and Shan, H and Zhang, Y and Zhu, Z}, title = {An Efficient Model-Compressed EEGNet Accelerator for Generalized Brain-Computer Interfaces with Near Sensor Intelligence.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2022.3215962}, pmid = {36264734}, issn = {1940-9990}, abstract = {Brain-computer interfaces (BCIs) is promising in interacting with machines through electroencephalogram (EEG) signal. The compact end-to-end neural network model for generalized BCIs, EEGNet, has been implemented in hardware to get near sensor intelligence, but without enough efficiency. To utilize EEGNet in low-power wearable device for long-term use, this paper proposes an efficient EEGNet inference accelerator. Firstly, the EEGNet model is compressed by embedded channel selection, normalization merging, and product quantization. The customized accelerator based on the compressed model is then designed. The multilayer convolutions are achieved by reusing multiplying-accumulators and processing elements (PEs) to minimize area of logic circuits, and the weights and intermediate results are quantized to minimize memory sizes. The PEs are clock-gated to save power. Experimental results in FPGA on three datasets show the good generalizing ability of the proposed design across three BCI diagrams, which only consumes 3.31% area and 1.35% power compared to the one-to-one parallel design. The speedup factors of 1.4, 3.5, and 3.7 are achieved by embedded channel selection with negligible loss of accuracy (-0.80%). The presented accelerator is also synthesized in 65nm CMOS low power (LP) process and consumes 0.23M gates, 24.4ms/inference, 0.267mJ/inference, which is 87.22% more efficient than the implementation of EEGNet in a RISC-V MCU realized in 40nm CMOS LP process in terms of area, and 20.77% more efficient in terms of energy efficiency on BCIC-IV-2a dataset.}, } @article {pmid36264427, year = {2022}, author = {Ishida, S and Matsukawa, Y and Yuba, T and Naito, Y and Matsuo, K and Majima, T and Gotoh, M}, title = {Urodynamic risk factors of asymptomatic bacteriuria in men with non-neurogenic lower urinary tract symptoms.}, journal = {World journal of urology}, volume = {40}, number = {12}, pages = {3035-3041}, pmid = {36264427}, issn = {1433-8726}, mesh = {Middle Aged ; Male ; Humans ; Aged ; Urodynamics ; Retrospective Studies ; *Bacteriuria/epidemiology/complications ; Urinary Bladder ; *Lower Urinary Tract Symptoms/epidemiology/complications ; *Urinary Bladder Neck Obstruction/complications ; Risk Factors ; }, abstract = {PURPOSE: To investigate the prevalence of asymptomatic bacteriuria (ASB) in middle-aged and older men with non-neurogenic lower urinary tract symptoms (LUTS) and clarify urodynamic factors related to the presence of ASB.

METHODS: We retrospectively reviewed the clinical data of men with LUTS who underwent urine culture examination, LUTS severity assessment, and urodynamic studies. The patients were allocated into two groups (the ASB + LUTS and LUTS-only) according to presence or absence of ASB. The patients' characteristics and urodynamic factors related to the development of ASB were assessed using univariate, binomial logistic regression, and receiver-operating characteristic (ROC) curve analyses.

RESULTS: Of 440 men, 93 (21.1%) had ASB. Parameters related to voiding functions, such as maximum flow rate, post-void residual urine volume, bladder voiding efficiency (BVE), and bladder contractility index (BCI), were significantly reduced in the ASB + LUTS group, while bladder outlet obstruction index was not different between the groups. Binomial logistic regression analysis showed that the presence of diabetes, lower BCI, and lower BVE were significantly associated with the presence of ASB. In addition, ROC analysis identified 55% as the optimal cutoff value of BVE for the presence of ASB, with a sensitivity of 84% and specificity of 83%.

CONCLUSIONS: ASB was found in > 20% of men with non-neurogenic LUTS and was associated with decreased bladder contractility and decreased BVE. BVE could predict presence of ASB with high sensitivity and specificity.}, } @article {pmid36261030, year = {2022}, author = {Song, CY and Hsieh, HL and Pesaran, B and Shanechi, MM}, title = {Modeling and inference methods for switching regime-dependent dynamical systems with multiscale neural observations.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9b94}, pmid = {36261030}, issn = {1741-2552}, mesh = {*Models, Neurological ; Algorithms ; *Brain-Computer Interfaces ; Normal Distribution ; Brain ; }, abstract = {Objective.Realizing neurotechnologies that enable long-term neural recordings across multiple spatial-temporal scales during naturalistic behaviors requires new modeling and inference methods that can simultaneously address two challenges. First, the methods should aggregate information across all activity scales from multiple recording sources such as spiking and field potentials. Second, the methods should detect changes in the regimes of behavior and/or neural dynamics during naturalistic scenarios and long-term recordings. Prior regime detection methods are developed for a single scale of activity rather than multiscale activity, and prior multiscale methods have not considered regime switching and are for stationary cases.Approach.Here, we address both challenges by developing a switching multiscale dynamical system model and the associated filtering and smoothing methods. This model describes the encoding of an unobserved brain state in multiscale spike-field activity. It also allows for regime-switching dynamics using an unobserved regime state that dictates the dynamical and encoding parameters at every time-step. We also design the associated switching multiscale inference methods that estimate both the unobserved regime and brain states from simultaneous spike-field activity.Main results.We validate the methods in both extensive numerical simulations and prefrontal spike-field data recorded in a monkey performing saccades for fluid rewards. We show