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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 22 Feb 2024 at 01:39 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: (bci OR (brain-computer OR brain-machine OR mind-machine OR neural-control interface) NOT 26799652[PMID] ) NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)

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RevDate: 2024-02-15

Deng H, Li M, Zuo H, et al (2024)

Personalized motor imagery prediction model based on individual difference of ERP.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Motor imagery-based brain-computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference.

APPROACH: A novel paradigm named action observation-based multi-delayed matching posture task (AO-multi-DMPT) evokes ERP during a DMPT phase by retrieving picture stimuli and videos, and generates MI EEG through action observation and autonomous imagery in an AO-MI phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual's suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods.

MAIN RESULTS: The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual's ERP amplitude and ERD intensity are the largest, which helps to improve the accuracy by 14.25%.

SIGNIFICANCE: The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual's MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.

RevDate: 2024-02-19
CmpDate: 2024-02-19

Gao J, Chen H, Fang M, et al (2024)

Original speech and its echo are segregated and separately processed in the human brain.

PLoS biology, 22(2):e3002498.

Speech recognition crucially relies on slow temporal modulations (<16 Hz) in speech. Recent studies, however, have demonstrated that the long-delay echoes, which are common during online conferencing, can eliminate crucial temporal modulations in speech but do not affect speech intelligibility. Here, we investigated the underlying neural mechanisms. MEG experiments demonstrated that cortical activity can effectively track the temporal modulations eliminated by an echo, which cannot be fully explained by basic neural adaptation mechanisms. Furthermore, cortical responses to echoic speech can be better explained by a model that segregates speech from its echo than by a model that encodes echoic speech as a whole. The speech segregation effect was observed even when attention was diverted but would disappear when segregation cues, i.e., speech fine structure, were removed. These results strongly suggested that, through mechanisms such as stream segregation, the auditory system can build an echo-insensitive representation of speech envelope, which can support reliable speech recognition.

RevDate: 2024-02-17
CmpDate: 2024-02-16

Chai X, Cao T, He Q, et al (2024)

Brain-computer interface digital prescription for neurological disorders.

CNS neuroscience & therapeutics, 30(2):e14615.

Neurological and psychiatric diseases can lead to motor, language, emotional disorder, and cognitive, hearing or visual impairment By decoding the intention of the brain in real time, the Brain-computer interface (BCI) can first assist in the diagnosis of diseases, and can also compensate for its damaged function by directly interacting with the environment; In addition, provide output signals in various forms, such as actual motion, tactile or visual feedback, to assist in rehabilitation training; Further intervention in brain disorders is achieved by close-looped neural modulation. In this article, we envision the future BCI digital prescription system for patients with different functional disorders and discuss the key contents in the prescription the brain signals, coding and decoding protocols and interaction paradigms, and assistive technology. Then, we discuss the details that need to be specially included in the digital prescription for different intervention technologies. The third part summarizes previous examples of intervention, focusing on how to select appropriate interaction paradigms for patients with different functional impairments. For the last part, we discussed the indicators and influencing factors in evaluating the therapeutic effect of BCI as intervention.

RevDate: 2024-02-17

Zhang R, Guo H, Xu Z, et al (2024)

MGFKD: A semi-supervised multi-source domain adaptation algorithm for cross-subject EEG emotion recognition.

Brain research bulletin, 208:110901 pii:S0361-9230(24)00034-0 [Epub ahead of print].

Currently, most models rarely consider the negative transfer problem in the research field of cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples of target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists of three modules: 1) GFK common feature extractor: projects the feature distribution of source and target subjects to the Grassmann manifold space, and obtains the latent common features of the two feature distributions through GFK method. 2) Source domain selector: obtains pseudo-labels of the target subject through weak classifier, finds "golden source subjects" by using few known labels of target subjects. 3) Label corrector: uses a dynamic distribution balance strategy to correct the pseudo-labels of the target subject. We conducted comparison experiments on the SEED and SEED-IV datasets, and the results show that MGFKD outperforms unsupervised and semi-supervised domain adaptation algorithms, achieving an average accuracy of 87.51±7.68% and 68.79±8.25% on the SEED and SEED-IV datasets with only one labeled sample per video for target subject. Especially when the number of source domains is set as 6 and the number of known labels is set as 5, the accuracy increase to 90.20±7.57% and 69.99±7.38%, respectively. The above results prove that our proposed algorithm can efficiently and quickly improve the cross-subject EEG emotion classification performance. Since it only need a small number of labeled samples of new subjects, making it has strong application value in future EEG-based emotion recognition applications.

RevDate: 2024-02-14

Zheng Z, Ye L, Xiong W, et al (2024)

Prevalence and genomic characterization of the Bacillus cereus group strains contamination in food products in Southern China.

The Science of the total environment pii:S0048-9697(24)01042-8 [Epub ahead of print].

The Bacillus cereus group, as one of the important opportunistic foodborne pathogens, is considered a risk to public health due to foodborne diseases and an important cause of economic losses to food industries. This study aimed to gain essential information on the prevalence, phenotype, and genotype of B. cereus group strains isolated from various food products in China. A total of 890 strains of B. cereus group bacteria from 1181 food samples from 2020 to 2023 were identified using the standardized detection method. These strains were found to be prevalent in various food types, with the highest contamination rates observed in cereal flour (55.8 %) and wheat/rice noodles (45.7 %). The tested strains exhibited high resistance rates against penicillin (98.5 %) and ampicillin (98.9 %). Strains isolated from cereal flour had the highest rate of meropenem resistance (7.8 %), while strains from sausages were most resistant to vancomycin (16.8 %). A total of 234 out of the 891 B. cereus group strains were randomly selected for WGS analysis, 18.4 % of which displayed multidrug resistance. The species identification by WGS analysis revealed the presence of 10 distinct species within the B. cereus group, with B. cereus species being the most prevalent. The highest level of species diversity was observed in sausages. Notably, B. anthracis strains lacking the anthrax toxin genes were detected in flour-based food products and sausages. A total of 20 antibiotic resistance genes have been identified, with β-lactam resistance genes (bla1, bla2, BcI, BcII, and blaTEM-116) being the most common. The B. tropicus strains exhibit the highest average number of virulence genes (23.4). The diarrheal virulence genes nheABC, hblACD, and cytK were found in numerous strains. Only 4 of the 234 (1.7 %) sequenced strains contain the ces gene cluster linked to emetic symptoms. These data offer valuable insights for public health policymakers on addressing foodborne B. cereus group infections and ensuring food safety.

RevDate: 2024-02-15

Dong R, Zhang X, Li H, et al (2023)

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study.

Frontiers in neuroscience, 17:1305850.

INTRODUCTION: Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG.

METHODS: Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia.

RESULTS: Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention.

DISCUSSION: The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.

RevDate: 2024-02-15

Xie X, Chen L, Qin S, et al (2024)

Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.

Frontiers in neurorobotics, 18:1343249.

INTRODUCTION: As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities.

METHODS: This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones. However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To address this issue, this study proposes an attention-based bidirectional feature pyramid temporal convolutional network model for the classification task of MI-EEG. The model incorporates a multi-head self-attention mechanism to weigh significant features in the MI-EEG signals. It also utilizes a temporal convolution network (TCN) to separate high-level temporal features. The signals are enhanced using the sliding-window technique, and channel and time-domain information of the MI-EEG signals is extracted through convolution.

RESULTS: Additionally, a bidirectional feature pyramid structure is employed to implement attention mechanisms across different scales and multiple frequency bands of the MI-EEG signals. The performance of our model is evaluated on the BCI Competition IV-2a dataset and the BCI Competition IV-2b dataset, and the results showed that our model outperformed the state-of-the-art baseline model, with an accuracy of 87.5 and 86.3% for the subject-dependent, respectively.

DISCUSSION: In conclusion, the BFATCNet model offers a novel approach for EEG-based motor imagery classification in BCIs, effectively capturing relevant features through attention mechanisms and temporal convolutional networks. Its superior performance on the BCI Competition IV-2a and IV-2b datasets highlights its potential for real-world applications. However, its performance on other datasets may vary, necessitating further research on data augmentation techniques and integration with multiple modalities to enhance interpretability and generalization. Additionally, reducing computational complexity for real-time applications is an important area for future work.

RevDate: 2024-02-17

Gharibshahian M, Alizadeh M, Kamalabadi Farahani M, et al (2024)

Fabrication of Rosuvastatin-Incorporated Polycaprolactone -Gelatin Scaffold for Bone Repair: A Preliminary In Vitro Study.

Cell journal, 26(1):70-80.

OBJECTIVE: Rosuvastatin (RSV) is a hydrophilic, effective statin with a long half-life that stimulates bone regeneration. The present study aims to develop a new scaffold and controlled release system for RSV with favourable properties for bone tissue engineering (BTE).

MATERIALS AND METHODS: In this experimental study, high porous polycaprolactone (PCL)-gelatin scaffolds that contained different concentrations of RSV (0 mg/10 ml, 0.1 mg/10 ml, 0.5 mg/10 ml, 2.5 mg/10 ml, 12.5 mg/10 ml, and 62.5 mg/10 ml) were fabricated by the thermally-induced phase separation (TIPS) method. Mechanical and biological properties of the scaffolds were evaluated by Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), compressive strength, porosity, MTT, alkaline phosphatase (ALP) activity, water contact angle, degradation rate, pH alteration, blood clotting index (BCI), and hemocompatibility.

RESULTS: SEM analysis confirmed that the porous structure of the scaffolds contained interconnected pores. FTIR results showed that the RSV structure was maintained during the scaffold's fabrication. RSV (up to 62.5 mg/10 ml) increased compressive strength (16.342 ± 1.79 MPa), wettability (70.2), and degradation rate of the scaffolds. Scaffolds that contained 2.5 mg/10 ml RSV had the best effect on the human umbilical cord mesenchymal stem cell (HUC-MSCs) survival, hemocompatibility, and BCI. As a sustained release system, only 31.68 ± 0.1% of RSV was released from the PCL-Gelatin-2.5 mg/10 ml RSV scaffold over 30 days. In addition, the results of ALP activity showed that RSV increased the osteogenic differentiation potential of the scaffolds.

CONCLUSION: PCL-Gelatin-2.5 mg/10 ml RSV scaffolds have favorable mechanical, physical, and osteogenic properties for bone tissue and provide a favorable release system for RSV. They can mentioned as a a promising strategy for bone regeneration that should be further assessed in animals and clinical studies.

RevDate: 2024-02-17
CmpDate: 2024-02-15

Gu M, Pei W, Gao X, et al (2024)

An open dataset for human SSVEPs in the frequency range of 1-60 Hz.

Scientific data, 11(1):196.

A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system relies on the photic driving response to effectively elicit characteristic electroencephalogram (EEG) signals. However, traditional visual stimuli mainly adopt high-contrast black-and-white flickering stimulations, which are easy to cause visual fatigue. This paper presents an SSVEP dataset acquired at a wide frequency range from 1 to 60 Hz with an interval of 1 Hz using flickering stimuli under two different modulation depths. This dataset contains 64-channel EEG data from 30 healthy subjects when they fixated on a single flickering stimulus. The stimulus was rendered on an LCD display with a refresh rate of 240 Hz. Initially, the dataset was rigorously validated through comprehensive data analysis to investigate SSVEP responses and user experiences. Subsequently, BCI performance was evaluated through offline simulations of frequency-coded and phase-coded BCI paradigms. This dataset provides comprehensive and high-quality data for studying and developing SSVEP-based BCI systems.

RevDate: 2024-02-20
CmpDate: 2024-02-20

Wu X, Li G, Gao X, et al (2024)

Channel Selection for Stereo- Electroencephalography (SEEG)-Based Invasive Brain-Computer Interfaces Using Deep Learning Methods.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 32:800-811.

Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness while maintaining decoding performance. However, such endeavour is rarely reported for invasive BCIs, especially those using deep learning methods. Two deep learning-based methods, referred to as Gumbel and STG, were proposed in this paper. They were evaluated using the Stereo-electroencephalography (SEEG) signals, and compared with three other methods, including manual selection, mutual information-based method (MI), and all channels (all channels without selection). The task is to classify the SEEG signals into five movements using channels selected by each method. When 10 channels were selected, the mean classification accuracies using Gumbel, STG (referred to as STG-10), manual selection, and MI selection were 65%, 60%, 60%, and 47%, respectively, whilst the accuracy was 59% using all channels (no selection). In addition, an investigation of the selected channels showed that Gumbel and STG have successfully identified the pre-central and post-central areas, which are closely related to motor control. Both Gumbel and STG successfully selected the informative channels in SEEG recordings while maintaining decoding accuracy. This study enables future high-throughput BCIs using deep learning methods, to identify useful channels and reduce computing and wireless transmission pressure.

RevDate: 2024-02-12

Wang H, Wang Q, Cui L, et al (2024)

A molecularly defined amygdala-independent tetra-synaptic forebrain-to-hindbrain pathway for odor-driven innate fear and anxiety.

Nature neuroscience [Epub ahead of print].

Fear-related disorders (for example, phobias and anxiety) cause a substantial public health problem. To date, studies of the neural basis of fear have mostly focused on the amygdala. Here we identify a molecularly defined amygdala-independent tetra-synaptic pathway for olfaction-evoked innate fear and anxiety in male mice. This pathway starts with inputs from the olfactory bulb mitral and tufted cells to pyramidal neurons in the dorsal peduncular cortex that in turn connect to cholecystokinin-expressing (Cck[+]) neurons in the superior part of lateral parabrachial nucleus, which project to tachykinin 1-expressing (Tac1[+]) neurons in the parasubthalamic nucleus. Notably, the identified pathway is specifically involved in odor-driven innate fear. Selective activation of this pathway induces innate fear, while its inhibition suppresses odor-driven innate fear. In addition, the pathway is both necessary and sufficient for stress-induced anxiety-like behaviors. These findings reveal a forebrain-to-hindbrain neural substrate for sensory-triggered fear and anxiety that bypasses the amygdala.

RevDate: 2024-02-14

Iwama S, J Ushiba (2024)

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

OBJECTIVE: Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data.

METHODS: First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using a large-scale dataset of scalp EEG during motor imagery-based BCI use and independent dataset for generalizability confirmation (N = 147). Next, we characterized the relationship between IAF and responsive frequency band of sensorimotor rhythm, which exhibits prominent event-related desynchronization (SMR-ERD) while attempting unilateral and movement.

RESULTS: The proposed sequential Bayesian estimation algorithm (Rapid-IAF) determined IAF from less than 26-second resting EEG data among 95% of participants, indicating a clear advance over the conventional methods, which uses 2-15 minutes of data in previous literatures. We confirmed that the determined IAF corresponded to the frequency of SMR, which exhibits the most prominent event-related desynchronization during BCI use (individual SMR-ERD frequency, ISF). Moreover, intraclass correlation revealed that the estimated IAF was more stable than ISF across sessions, suggesting its reliability and utility for robust BCI use without intermittent recalibration. Conclusion In summary, our method rapidly and reliably determined IAF compared to the conventional method using the spectral power change based on task-related response. The method can be utilized to quick BCI initialization.

SIGNIFICANCE: The demonstration of rapid, task-free parametrization of individual variability of neural responses would be of importance for future BCI systems including neural communication via a cursor, an avatar or robots, and closed-loop neurofeedback training.

RevDate: 2024-02-12

Bartlett JMS, Xu K, Wong J, et al (2024)

Validation of the prognostic performance of Breast Cancer Index (BCI) in hormone receptor-positive (HR+) postmenopausal breast cancer patients in the TEAM trial.

Clinical cancer research : an official journal of the American Association for Cancer Research pii:734236 [Epub ahead of print].

PURPOSE: Early-stage HR+ breast cancer patients face a prolonged risk of recurrence even after adjuvant endocrine therapy. The Breast Cancer Index (BCI) is significantly prognostic for overall (0-10 years) and late (5-10 years) distant recurrence risk (DR) in N0 and N1 patients. Here, BCI prognostic performance was evaluated in HR+ postmenopausal women from the TEAM trial.

EXPERIMENTAL DESIGN: 3544 patients were included in the analysis (N=1519 N0, N=2025 N+). BCI risk groups were calculated using pre-specified cut-points. Kaplan-Meier analyses and log-rank tests were used to assess the prognostic significance of BCI risk groups based on DR. Hazard ratios (HR) and confidence intervals (CI) were calculated using Cox models with and without clinical covariates.

RESULTS: For overall 10-year DR, BCI was significantly prognostic in N0 (N=1196) and N1 (N=1234) patients who did not receive prior chemotherapy (p<0.001). In patients who were DR-free for 5 years, 10-year late DR rates for low- and high-risk groups were 5.4% and 9.3% (N0 cohort, N=1285) and 4.8% and 12.2% (N1 cohort, N=1625) with multivariate HRs of 2.25 (95% CI: 1.30-3.88; p=0.004) and 2.67 (95% CI: 1.53-4.63; p=<0.001), respectively. Late DR performance was substantially improved using previously optimized cut-points, identifying BCI low-risk groups with even lower 10-year late DR rates of 3.8% and 2.7% in N0 and N1 patients, respectively.

CONCLUSIONS: The TEAM trial represents the largest prognostic validation study for BCI to date and provides a more representative assessment of late DR risk to guide individualized treatment decision-making for HR+ early-stage breast cancer patients.

RevDate: 2024-02-16

Schippers A, Vansteensel MJ, Freudenburg ZV, et al (2024)

Don't put words in my mouth: Speech perception can generate False Positive activation of a speech BCI.

medRxiv : the preprint server for health sciences.

Recent studies have demonstrated that speech can be decoded from brain activity and used for brain-computer interface (BCI)-based communication. It is however also known that the area often used as a signal source for speech decoding BCIs, the sensorimotor cortex (SMC), is also engaged when people perceive speech, thus making speech perception a potential source of false positive activation of the BCI. The current study investigated if and how speech perception may interfere with reliable speech BCI control. We recorded high-density electrocorticography (HD-ECoG) data from five subjects while they performed a speech perception and speech production task and trained a support-vector machine (SVM) on the produced speech data. Our results show that decoders that are highly reliable at detecting self-produced speech from brain signals also generate false positives during the perception of speech. We conclude that speech perception interferes with reliable BCI control, and that efforts to limit the occurrence of false positives during daily-life BCI use should be implemented in BCI design to increase the likelihood of successful adaptation by end users.

RevDate: 2024-02-11

Li J, She Q, Meng M, et al (2024)

Three-stage transfer learning for motor imagery EEG recognition.

Medical & biological engineering & computing [Epub ahead of print].

Motor imagery (MI) paradigms have been widely used in neural rehabilitation and drowsiness state assessment. The progress in brain-computer interface (BCI) technology has emphasized the importance of accurately and efficiently detecting motor imagery intentions from electroencephalogram (EEG). Despite the recent breakthroughs made in developing EEG-based algorithms for decoding MI, the accuracy and efficiency of these models remain limited by technical challenges posed by cross-subject heterogeneity in EEG data processing and the scarcity of EEG data for training. Inspired by the optimal transport theory, this study aims to develop a novel three-stage transfer learning (TSTL) method, which uses the existing labeled data from a source domain to improve classification performance on an unlabeled target domain. Notably, the proposed method comprises three components, namely, the Riemannian tangent space mapping (RTSM), source domain transformer (SDT), and optimal subspace mapping (OSM). The RTSM maps a symmetric positive definite matrix from the Riemannian space to the tangent space to minimize the marginal probability distribution drift. The SDT transforms the source domain to a target domain by finding the optimal transport mapping matrix to reduce the joint probability distribution differences. The OSM finally maps the transformed source domain and original target domain to the same subspace to further mitigate the distribution discrepancy. The performance of the proposed method was validated on two public BCI datasets, and the average accuracy of the algorithm on two datasets was 72.24% and 69.29%. Our results demonstrated the improved performance of EEG-based MI detection in comparison with state-of-the-art algorithms.

RevDate: 2024-02-17
CmpDate: 2024-02-17

Demarest P, Rustamov N, Swift J, et al (2024)

A novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study.

Scientific reports, 14(1):3433.

Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4-7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity (1.29 ± 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 ± 1.10 MAD p = 0.03, q = 0.05) scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.

RevDate: 2024-02-10

Wan C, Pei M, Shi K, et al (2024)

Toward a Brain-Neuromorphics Interface.

Advanced materials (Deerfield Beach, Fla.) [Epub ahead of print].

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape our interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead. This article is protected by copyright. All rights reserved.

RevDate: 2024-02-14
CmpDate: 2024-02-14

Kocejko T, Matuszkiewicz N, Durawa P, et al (2024)

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery.

Sensors (Basel, Switzerland), 24(3):.

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

RevDate: 2024-02-14
CmpDate: 2024-02-14

Cao B, Niu H, Hao J, et al (2024)

Spatial Visual Imagery (SVI)-Based Electroencephalograph Discrimination for Natural CAD Manipulation.

Sensors (Basel, Switzerland), 24(3):.

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain-computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.

RevDate: 2024-02-16

Zhang LA, Li P, EM Callaway (2024)

High-Resolution Laminar Identification in Macaque Primary Visual Cortex Using Neuropixels Probes.

bioRxiv : the preprint server for biology.

Laminar electrode arrays allow simultaneous recording of activity of many cortical neurons and assignment to correct layers using current source density (CSD) analyses. Electrode arrays with 100-micron contact spacing can estimate borders between layer 4 versus superficial or deep layers, but in macaque primary visual cortex (V1) there are far more layers, such as 4A which is only 50-100 microns thick. Neuropixels electrode arrays have 20-micron spacing, and thus could potentially discern thinner layers and more precisely identify laminar borders. Here we show that CSD signals lack the spatial resolution required to take advantage of high density Neuropixels arrays and describe the development of approaches based on higher resolution electrical signals and analyses, including spike waveforms and spatial spread, unit density, high-frequency action potential (AP) power spectrum, temporal power change, and coherence spectrum, that afford far higher resolution of laminar distinctions, including the ability to precisely detect the borders of even the thinnest layers of V1.

RevDate: 2024-02-12

Jin F, Yang L, Yang L, et al (2024)

Dynamics Learning Rate Bias in Pigeons: Insights from Reinforcement Learning and Neural Correlates.

Animals : an open access journal from MDPI, 14(3):.

Research in reinforcement learning indicates that animals respond differently to positive and negative reward prediction errors, which can be calculated by assuming learning rate bias. Many studies have shown that humans and other animals have learning rate bias during learning, but it is unclear whether and how the bias changes throughout the entire learning process. Here, we recorded the behavior data and the local field potentials (LFPs) in the striatum of five pigeons performing a probabilistic learning task. Reinforcement learning models with and without learning rate biases were used to dynamically fit the pigeons' choice behavior and estimate the option values. Furthemore, the correlation between the striatal LFPs power and the model-estimated option values was explored. We found that the pigeons' learning rate bias shifted from negative to positive during the learning process, and the striatal Gamma (31 to 80 Hz) power correlated with the option values modulated by dynamic learning rate bias. In conclusion, our results support the hypothesis that pigeons employ a dynamic learning strategy in the learning process from both behavioral and neural aspects, providing valuable insights into reinforcement learning mechanisms of non-human animals.

RevDate: 2024-02-12

Zhu JY, Zhang ZH, Liu G, et al (2024)

Enhanced Hippocampus-Nidopallium Caudolaterale Interaction in Visual-Spatial Associative Learning of Pigeons.

Animals : an open access journal from MDPI, 14(3):.

Learning the spatial location associated with visual cues in the environment is crucial for survival. This ability is supported by a distributed interactive network. However, it is not fully understood how the most important task-related brain areas in birds, the hippocampus (Hp) and the nidopallium caudolaterale (NCL), interact in visual-spatial associative learning. To investigate the mechanisms of such coordination, synchrony and causal analysis were applied to the local field potentials of the Hp and NCL of pigeons while performing a visual-spatial associative learning task. The results showed that, over the course of learning, theta-band (4-12 Hz) oscillations in the Hp and NCL became strongly synchronized before the pigeons entered the critical choice platform for turning, with the information flowing preferentially from the Hp to the NCL. The learning process was primarily associated with the increased Hp-NCL interaction of theta rhythm. Meanwhile, the enhanced theta-band Hp-NCL interaction predicted the correct choice, supporting the pigeons' use of visual cues to guide navigation. These findings provide insight into the dynamics of Hp-NCL interaction during visual-spatial associative learning, serving to reveal the mechanisms of Hp and NCL coordination during the encoding and retrieval of visual-spatial associative memory.

RevDate: 2024-02-12

Yang L, Chen X, Yang L, et al (2024)

Phase-Amplitude Coupling between Theta Rhythm and High-Frequency Oscillations in the Hippocampus of Pigeons during Navigation.

Animals : an open access journal from MDPI, 14(3):.

Navigation is a complex task in which the hippocampus (Hp), which plays an important role, may be involved in interactions between different frequency bands. However, little is known whether this cross-frequency interaction exists in the Hp of birds during navigation. Therefore, we examined the electrophysiological characteristics of hippocampal cross-frequency interactions of domestic pigeons (Columba livia domestica) during navigation. Two goal-directed navigation tasks with different locomotor modes were designed, and the local field potentials (LFPs) were recorded for analysis. We found that the amplitudes of high-frequency oscillations in Hp were dynamically modulated by the phase of co-occurring theta-band oscillations both during ground-based maze and outdoor flight navigation. The high-frequency amplitude sub-frequency bands modulated by the hippocampal theta phase were different at different tasks, and this process was independent of the navigation path and goal. These results suggest that phase-amplitude coupling (PAC) in the avian Hp may be more associated with the ongoing cognitive demands of navigational processes. Our findings contribute to the understanding of potential mechanisms of hippocampal PAC on multi-frequency informational interactions in avian navigation and provide valuable insights into cross-species evolution.

RevDate: 2024-02-12

Yang L, Jin F, Yang L, et al (2024)

The Hippocampus in Pigeons Contributes to the Model-Based Valuation and the Relationship between Temporal Context States.

Animals : an open access journal from MDPI, 14(3):.

Model-based decision-making guides organism behavior by the representation of the relationships between different states. Previous studies have shown that the mammalian hippocampus (Hp) plays a key role in learning the structure of relationships among experiences. However, the hippocampal neural mechanisms of birds for model-based learning have rarely been reported. Here, we trained six pigeons to perform a two-step task and explore whether their Hp contributes to model-based learning. Behavioral performance and hippocampal multi-channel local field potentials (LFPs) were recorded during the task. We estimated the subjective values using a reinforcement learning model dynamically fitted to the pigeon's choice of behavior. The results show that the model-based learner can capture the behavioral choices of pigeons well throughout the learning process. Neural analysis indicated that high-frequency (12-100 Hz) power in Hp represented the temporal context states. Moreover, dynamic correlation and decoding results provided further support for the high-frequency dependence of model-based valuations. In addition, we observed a significant increase in hippocampal neural similarity at the low-frequency band (1-12 Hz) for common temporal context states after learning. Overall, our findings suggest that pigeons use model-based inferences to learn multi-step tasks, and multiple LFP frequency bands collaboratively contribute to model-based learning. Specifically, the high-frequency (12-100 Hz) oscillations represent model-based valuations, while the low-frequency (1-12 Hz) neural similarity is influenced by the relationship between temporal context states. These results contribute to our understanding of the neural mechanisms underlying model-based learning and broaden the scope of hippocampal contributions to avian behavior.

RevDate: 2024-02-09

Wang J, Luo Y, Ye F, et al (2024)

Structures and ion transport mechanisms of plant high-affinity potassium transporters.

Molecular plant pii:S1674-2052(24)00007-8 [Epub ahead of print].

Plant high-affinity K[+] transporters (HKTs) mediate Na[+] and K[+] uptake, maintain Na[+]/K[+] homeostasis, and therefore play crucial roles in plant salt tolerance. In this study, we present cryoelectron microscopy structures of HKTs from two classes, class I HKT1;1 from Arabidopsis thaliana (AtHKT1;1) and class II HKT2;1 from Triticum aestivum (TaHKT2;1), in both Na[+]- and K[+]-bound states at 2.6- to 3.0-Å resolutions. Both AtHKT1;1 and TaHKT2;1 function as homodimers. Each HKT subunit consists of four tandem domain units (D1-D4) with a repeated K[+]-channel-like M-P-M topology. In each subunit, D1-D4 assemble into an ion conduction pore with a pseudo-four-fold symmetry. Although both TaHKT2;1 and AtHKT1;1 have only one putative Na[+] ion bound in the selectivity filter with a similar coordination pattern, the two HKTs display different K[+] binding modes in the filter. TaHKT2;1 has three K[+] ions bound in the selectivity filter, but AtHKT1;1 has only two K[+] ions bound in the filter, which has a narrowed external entrance due to the presence of a Ser residue in the first filter motif. These structures, along with computational, mutational, and electrophysiological analyses, enable us to pinpoint key residues that are critical for the ion selectivity of HKTs. The findings provide new insights into the ion selectivity and ion transport mechanisms of plant HKTs and improve our understanding about how HKTs mediate plant salt tolerance and enhance crop growth.

RevDate: 2024-02-13

Hu S, Zhang Z, Zhang X, et al (2024)

ξ- π: a nonparametric model for neural power spectra decomposition.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ- π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ- π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ- π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ- π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ- π approached the clinical findings in peak discovery. Overall, ξ- π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ- π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

RevDate: 2024-02-09

Luo H, Li C, Wang S, et al (2024)

Switchable Adhesive Based on Shape Memory Polymer with Micropillars of Different Heights for Laser-Driven Noncontact Transfer Printing.

ACS applied materials & interfaces [Epub ahead of print].

Switchable adhesive is essential to develop transfer printing, which is an advanced heterogeneous material integration technique for developing electronic systems. Designing a switchable adhesive with strong adhesion strength that can also be easily eliminated to enable noncontact transfer printing still remains a challenge. Here, we report a simple yet robust design of switchable adhesive based on a thermally responsive shape memory polymer with micropillars of different heights. The adhesive takes advantage of the shape-fixing property of shape memory polymer to provide strong adhesion for a reliable pick-up and the various levels of shape recovery of micropillars under laser heating to eliminate the adhesion for robust printing in a noncontact way. Systematic experimental and numerical studies reveal the adhesion switch mechanism and provide insights into the design of switchable adhesives. This switchable adhesive design provides a good solution to develop laser-driven noncontact transfer printing with the capability of eliminating the influence of receivers on the performance of transfer printing. Demonstrations of transfer printing of silicon wafers, microscale Si platelets, and micro light emitting diode (μ-LED) chips onto various challenging nonadhesive receivers (e.g., sandpaper, stainless steel bead, leaf, or glass) to form desired two-dimensional or three-dimensional layouts illustrate its great potential in deterministic assembly.

RevDate: 2024-02-11

Xia J, Zhang F, Zhang L, et al (2024)

Magnetically Compatible Brain Electrode Arrays Based on Single-Walled Carbon Nanotubes for Long-Term Implantation.

Nanomaterials (Basel, Switzerland), 14(3):.

Advancements in brain-machine interfaces and neurological treatments urgently require the development of improved brain electrodes applied for long-term implantation, where traditional and polymer options face challenges like size, tissue damage, and signal quality. Carbon nanotubes are emerging as a promising alternative, combining excellent electronic properties and biocompatibility, which ensure better neuron coupling and stable signal acquisition. In this study, a new flexible brain electrode array based on 99.99% purity of single-walled carbon nanotubes (SWCNTs) was developed, which has 30 um × 40 um size, about 5.1 kΩ impedance, and 14.01 dB signal-to-noise ratio (SNR). The long-term implantation experiment in vivo in mice shows the proposed brain electrode can maintain stable LFP signal acquisition over 12 weeks while still achieving an SNR of 3.52 dB. The histological analysis results show that SWCNT-based brain electrodes induced minimal tissue damage and showed significantly reduced glial cell responses compared to platinum wire electrodes. Long-term stability comes from SWCNT's biocompatibility and chemical inertness, the electrode's flexible and fine structure. Furthermore, the new brain electrode array can function effectively during 7-Tesla magnetic resonance imaging, enabling the collection of local field potential and even epileptic discharges during the magnetic scan. This study provides a comprehensive study of carbon nanotubes as invasive brain electrodes, providing a new path to address the challenge of long-term brain electrode implantation.

RevDate: 2024-02-10

Cao L (2024)

A spatial-attentional mechanism underlies action-related distortions of time judgment.

eLife, 12: pii:91825.

Temporal binding has been understood as an illusion in timing judgment. When an action triggers an outcome (e.g. a sound) after a brief delay, the action is reported to occur later than if the outcome does not occur, and the outcome is reported to occur earlier than a similar outcome not caused by an action. We show here that an attention mechanism underlies the seeming illusion of timing judgment. In one method, participants watch a rotating clock hand and report event times by noting the clock hand position when the event occurs. We find that visual spatial attention is critically involved in shaping event time reports made in this way. This occurs because action and outcome events result in shifts of attention around the clock rim, thereby biasing the perceived location of the clock hand. Using a probe detection task to measure attention, we show a difference in the distribution of visual spatial attention between a single-event condition (sound only or action only) and a two-event agency condition (action plus sound). Participants accordingly report the timing of the same event (the sound or the action) differently in the two conditions: spatial attentional shifts masquerading as temporal binding. Furthermore, computational modeling based on the attention measure can reproduce the temporal binding effect. Studies that use time judgment as an implicit marker of voluntary agency should first discount the artefactual changes in event timing reports that actually reflect differences in spatial attention. The study also has important implications for related results in mental chronometry obtained with the clock-like method since Wundt, as attention may well be a critical confounding factor in the interpretation of these studies.

RevDate: 2024-02-08

Saithna A (2024)

Bioinductive Collagen Implants Reduce Rotator Cuff Retear, Yet Cost-Effectiveness and Improvement in Clinical Outcomes Are Unclear.

The estimated healthcare costs of failed arthroscopic RCRs performed in the United Statesrepresent a huge economic healthcare burden of greater than $400 million per two-year period. Unfortunately, retear rates do not appear to have improved significantly since the1980s, despite advances in surgical technology and the biomechanics of repair. The failure of these advances to translate into improved clinical results suggests that the limiting step inreducing retear rates is biology rather than the biomechanics of repair. Bioinductive collagen implants (BCIs) are an emerging and potentially useful option for biological augmentation. Recent meta-analysis of pre-clinical and clinical studies demonstrates that biological augmentation significantly lowers the risk of retear. Retrieval studies from human rotator cuff repair subjects who underwent treatment with BCI demonstrate cellular incorporation, tissue formation, and maturation providing a logical basis for reduction in retear rates as well as small increases in tendon thickness at the footprint. Although BCIs show potential as a possible game-changing solution for reducing failure rates of rotator cuff repair, concerns remain regarding cost effectiveness analyses and demonstration of functional outcome improvement.

RevDate: 2024-02-08

Ren S, Wang K, Jia X, et al (2024)

Fibrous MXene Synapse-Based Biomimetic Tactile Nervous System for Multimodal Perception and Memory.

Small (Weinheim an der Bergstrasse, Germany) [Epub ahead of print].

Biomimetic tactile nervous system (BTNS) inspired by organisms has motivated extensive attention in wearable fields due to its biological similarity, low power consumption, and perception-memory integration. Though many works about planar-shape BTNS are developed, few researches could be found in the field of fibrous BTNS (FBTNS) which is superior in terms of strong flexibility, weavability, and high-density integration. Herein, a FBTNS with multimodal sensibility and memory is proposed, by fusing the fibrous poly lactic acid (PLA)/Ag/MXene/Pt artificial synapse and MXene/EMIMBF4 ionic conductive elastomer. The proposed FBTNS can successfully perceive external stimuli and generate synaptic responses. It also exhibits a short response time (23 ms) and low set power consumption (17 nW). Additionally, the proposed device demonstrates outstanding synaptic plasticity under both mechanical and electrical stimuli, which can simulate the memory function. Simultaneously, the fibrous devices are embedded into textiles to construct tactile arrays, by which biomimetic tactile perception and temporary memory functions are successfully implemented. This work demonstrates the as-prepared FBTNS can generate biomimetic synaptic signals to serve as artificial feeling signals, it is thought that it could offer a fabric electronic unit integrating with perception and memory for Human-Computer interaction, and has great potential to build lightweight and comfortable Brain-Computer interfaces.

RevDate: 2024-02-08

Ning M, Duwadi S, Yücel MA, et al (2024)

fNIRS Dataset During Complex Scene Analysis.

bioRxiv : the preprint server for biology pii:2024.01.23.576715.

When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location. The ability to decode the attended spatial location would facilitate brain computer interfaces for complex scene analysis (CSA). Here, we investigated capability of functional near-infrared spectroscopy (fNIRS) to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. We targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. To date, fNIRS has not been applied to decode auditory and visual-spatial attention during CSA, and thus, no such dataset exists yet. This report provides an open-access fNIRS dataset that can be used to develop, test, and compare machine learning algorithms for classifying attended locations based on the fNIRS signals on a single trial basis.

RevDate: 2024-02-07

Zhao J, Sun L, Sun Z, et al (2024)

MSEF-Net: Multi-scale edge fusion network for lumbosacral plexus segmentation with MR image.

Artificial intelligence in medicine, 148:102771.

Nerve damage of spine areas is a common cause of disability and paralysis. The lumbosacral plexus segmentation from magnetic resonance imaging (MRI) scans plays an important role in many computer-aided diagnoses and surgery of spinal nerve lesions. Due to the complex structure and low contrast of the lumbosacral plexus, it is difficult to delineate the regions of edges accurately. To address this issue, we propose a Multi-Scale Edge Fusion Network (MSEF-Net) to fully enhance the edge feature in the encoder and adaptively fuse multi-scale features in the decoder. Specifically, to highlight the edge structure feature, we propose an edge feature fusion module (EFFM) by combining the Sobel operator edge detection and the edge-guided attention module (EAM), respectively. To adaptively fuse the multi-scale feature map in the decoder, we introduce an adaptive multi-scale fusion module (AMSF). Our proposed MSEF-Net method was evaluated on the collected spinal MRI dataset with 89 patients (a total of 2848 MR images). Experimental results demonstrate that our MSEF-Net is effective for lumbosacral plexus segmentation with MR images, when compared with several state-of-the-art segmentation methods.

RevDate: 2024-02-07

Wang X, Li B, Lin Y, et al (2024)

Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Many subject-dependent methods were proposed for electroencephalogram (EEG) classification in rapid serial visual presentation (RSVP) task, which required a large amount of data from new subject and were time-consuming to calibrate system. Cross-subject classification can realize calibration reduction or zero calibration. However, cross-subject classification in RSVP task is still a challenge.

APPROACH: This study proposed a multi-source domain adaptation based tempo-spatial convolution (MDA-TSC) network for cross-subject RSVP classification. The proposed network consisted of three modules. First, the common feature extraction with multi-scale tempo-spatial convolution was constructed to extract domain-invariant features across all subjects, which could improve generalization of the network. Second, the multi-branch domain-specific feature extraction and alignment was conducted to extract and align domain-specific feature distributions of source and target domains in pairs, which could consider feature distribution differences among source domains. Third, the domain-specific classifier was exploited to optimize the network through loss functions and obtain prediction for the target domain.

MAIN RESULTS: The proposed network was evaluated on the benchmark RSVP dataset, and the cross-subject classification results showed that the proposed MDA-TSC network outperformed the reference methods. Moreover, the effectiveness of the MDA-TSC network was verified through both ablation studies and visualization.

SIGNIFICANCE: The proposed network could effectively improve cross-subject classification performance in RSVP task, and was helpful to reduce system calibration time.

RevDate: 2024-02-09
CmpDate: 2024-02-09

Cheng X, Wang S, Guo B, et al (2024)

How self-disclosure of negative experiences shapes prosociality?.

Social cognitive and affective neuroscience, 19(1):.

People frequently share their negative experiences and feelings with others. Little is known, however, about the social outcomes of sharing negative experiences and the underlying neural mechanisms. We addressed this dearth of knowledge by leveraging functional near-infrared spectroscopy (fNIRS) hyperscanning: while dyad participants took turns to share their own (self-disclosure group) or a stranger's (non-disclosure group) negative and neutral experiences, their respective brain activity was recorded simultaneously by fNIRS. We observed that sharing negative (relative to neutral) experiences enhanced greater mutual prosociality, emotional empathy and interpersonal neural synchronization (INS) at the left superior frontal cortex in the self-disclosure group compared to the non-disclosure group. Importantly, mediation analyses further revealed that in the self-disclosure (but not non-disclosure) group, the increased emotional empathy and INS elicited by sharing negative experiences relative to sharing neutral experiences promoted the enhanced prosociality through increasing interpersonal liking. These results indicate that self-disclosure of negative experiences can promote prosocial behaviors via social dynamics (defined as social affective and cognitive factors, including empathy and liking) and shared neural responses. Our findings suggest that when people express negative sentiments, they incline to follow up with positive actions.

RevDate: 2024-02-07

Zhang F, Wu H, Y Guo (2024)

Semi-supervised multi-source transfer learning for cross-subject EEG motor imagery classification.

Medical & biological engineering & computing [Epub ahead of print].

Electroencephalogram (EEG) motor imagery (MI) classification refers to the use of EEG signals to identify and classify subjects' motor imagery activities; this task has received increasing attention with the development of brain-computer interfaces (BCIs). However, the collection of EEG data is usually time-consuming and labor-intensive, which makes it difficult to obtain sufficient labeled data from the new subject to train a new model. Moreover, the EEG signals of different individuals exhibit significant differences, leading to a significant drop in the performance of a model trained on the existing subjects when directly classifying EEG signals acquired from new subjects. Therefore, it is crucial to make full use of the EEG data of the existing subjects and the unlabeled EEG data of the new target subject to improve the MI classification performance achieved for the target subject. This research study proposes a semi-supervised multi-source transfer (SSMT) learning model to address the above problems; the model learns informative and domain-invariant representations to address cross-subject MI-EEG classification tasks. In particular, a dynamic transferred weighting schema is presented to obtain the final predictions by integrating the weighted features derived from multi-source domains. The average accuracies achieved on two publicly available EEG datasets reach 83.57[Formula: see text] and 85.09[Formula: see text], respectively, validating the effectiveness of the SSMT process. The SSMT process reveals the importance of informative and domain-invariant representations in MI classification tasks, as they make full use of the domain-invariant information acquired from each subject.

RevDate: 2024-02-07

O'Keeffe AB, Merla A, K Ashkan (2024)

Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on?.

British journal of neurosurgery [Epub ahead of print].

Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.

RevDate: 2024-02-07

Casas Gómez DM, AAA Braidot (2024)

Mirror Box as a tool for training users to achieve motor imagery.

Journal of neurophysiology [Epub ahead of print].

To evaluate Mirror Visual Feedback (MVF) as a training tool for brain-computer interface (BCI) users. Because about 20%-30% of subjects need more training to operate a BCI system that uses motor imagery. Electroencephalograms (EEGs) were recorded from 18 healthy subjects, using event-related desynchronization (ERD) to observe the responses during the movement or movement intention of the hand for the conditions of Control, Imagination, and the MVF with the mirror box. Two groups of subjects were formed, Group 1: control, imagination, and MVF. Group 2: control, MVF, and imagination. There were significant differences in imagination conditions between groups using MVF before or after imagination (Right-hand p= 0.0403. Left-hand p=0.00939). The illusion of movement through MVF is not possible in all subjects, but even in those cases, we found an increase in imagination when the subject used the MVF previously. The increase in the r2s of imagination in the right and left hands suggests cross-learning. The increase in motor imagery recorded with EEG after MVF suggests that the mirror box made it easier to imagine movements. Our results provide evidence that the MVF could be used as a training tool to improve motor imagery.

RevDate: 2024-02-10
CmpDate: 2024-02-08

Zhou Y, Lu W, Yang Q, et al (2024)

[Preparation and Performance Study of a Novel Antibacterial Hemostatic Chitosan Sponge].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 55(1):190-197.

OBJECTIVE: To create a novel chitosan antibacterial hemostatic sponge (NCAHS) and to evaluate its material and biological properties.

METHODS: Chitosan, a polysaccharide, was used as the sponge substrate and different proportions of sodium tripolyphosphate (STPP), glycerol, and phenol sulfonyl ethylamine were added to prepare the sponges through the freeze-drying method. The whole-blood coagulation index (BCI) was used as the screening criterion to determine the optimal concentrations of chitosan and the other additives and the hemostatic sponges were prepared accordingly. Zein/calcium carbonate (Zein/CaCO3) composite microspheres loaded with ciprofloxacin hydrochloride were prepared and added to the hemostatic sponges to obtain NCAHS. Scanning electron microscope was used to observe the microscopic morphology and porosity of the NCAHS. The water absorption rate, in vitro antibacterial susceptibility rate against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), in vitro coagulation performance, and hemocompatibility of NCAHS were examined. The coagulation performance of NCAHS was evaluated by using rabbit liver injury and rabbit auricular artery hemorrhageear models and commercial hemostatic sponge (CHS) was used as a control. The in vivo biocompatibility, including such aspects as cytotoxicity, skin irritation in animals, and acute in vivo toxicity, of the NCAHS extracts was examined by using as a reference the national standards for biological evaluation of medical devices.

RESULTS: The NCAHS prepared with 1.5% chitosan (W/V), 0.01% STPP (W/V), 0% glycerol (V/V), 0.15% phenol-sulfonyl-ethylamine (V/V), Zein and CaCO3 at the mixing ratio of 5∶1 (W/W), Zein at the final mass concentration of 2.5 g/L, and ethanol at the final concentration of 17.5% (V/V) were fine and homogeneous, possessing a honeycomb-like porous structure with a pore size of about 200 μm. The NCAHS thus prepared had the lowest BCI value. The water absorption ([2362.16±201.15] % vs. [1102.56±91.79]%) and in vitro coagulation performance (31.338% vs. 1.591%) of NCAHS were significantly better than those of CHS (P<0.01). Tests with the in vivo auricular artery hemorrhage model ([36.00±13.42] s vs. [80.00±17.32] s) and rabbit liver bleeding model ([30.00±0] s vs. [70.00±17.32] s) showed that the hemostasis time of NCAHS was significantly shorter than that of CHS (P<0.01). NCAHS had significant inhibitory ability against S. aureus and E. coli. In addition, NCAHS showed good in vitro and in vivo biocompatibility.

CONCLUSION: NCAHS is a composite sponge that shows excellent antimicrobial properties, hemostatic effect, and biocompatibility. Therefore, its extensive application in clinical settings is warranted.

RevDate: 2024-02-06

Talkhan H, Stewart D, McIntosh T, et al (2024)

Exploring determinants of antimicrobial prescribing behaviour using the Theoretical Domains Framework.

Research in social & administrative pharmacy : RSAP pii:S1551-7411(23)00514-4 [Epub ahead of print].

BACKGROUND: Few theoretically-based, qualitative studies have explored determinants of antimicrobial prescribing behaviour in hospitals. Understanding these can promote successful development and implementation of behaviour change interventions (BCIs).

OBJECTIVE: (s): To use the Theoretical Domains Framework (TDF) to explore determinants of clinicians' antimicrobial prescribing behaviour, identifying barriers (i.e., impediments) and facilitators to appropriate antimicrobial practice.

METHODS: Semi-structured interviews with purposively-sampled doctors and pharmacists with a wide range of specialties and expertise in Hamad Medical Corporation hospitals in Qatar. Interviews based on previous quantitative research and the TDF were audio-recorded, transcribed and independently analysed by two researchers using the TDF as an initial coding framework.

RESULTS: Data saturation was achieved after interviewing eight doctors and eight pharmacists. Inter-related determinants of antimicrobial prescribing behaviour linked to ten TDF domains were identified as barriers and facilitators that may contribute to inappropriate or appropriate antimicrobial prescribing. The main barriers identified were around hospital guidelines and electronic system deficiencies (environmental context and resources); knowledge gaps relating to guidelines and appropriate prescribing (knowledge); restricted roles/responsibilities of microbiologists and pharmacists (professional role and identity); challenging antimicrobial prescribing decisions (memory, attention and decision processes); and professional hierarchies and poor multidisciplinary teamworking (social influences). Key facilitators included guidelines compliance (goals and intentions), and participants' beliefs about the consequences of appropriate or inappropriate prescribing. Further education and training, and some changes to guidelines including their accessibility were also considered essential.

CONCLUSIONS: Antimicrobial prescribing behaviour in hospitals is a complex process influenced by a broad range of determinants including specific barriers and facilitators. The in-depth understanding of this complexity provided by this work may support the development of an effective BCI to promote appropriate antimicrobial stewardship.

RevDate: 2024-02-06

Damasio A, H Damasio (2024)

Homeostatic Feelings and the Emergence of Consciousness.

Journal of cognitive neuroscience pii:119429 [Epub ahead of print].

In this article, we summarize our views on the problem of consciousness and outline the current version of a novel hypothesis for how conscious minds can be generated in mammalian organisms. We propose that a mind can be considered conscious when three processes are in place: the first is a continuous generation of interoceptive feelings, which results in experiencing of the organism's internal operations; the second is the equally continuous production of images, generated according to the organism's sensory perspective relative to its surround; the third combines feeling/experience and perspective resulting in a process of subjectivity relative to the image contents. We also propose a biological basis for these three components: the peripheral and central physiology of interoception and exteroception help explain the implementation of the first two components, whereas the third depends on central nervous system integration, at multiple levels, from spinal cord, brainstem, and diencephalic nuclei, to selected regions of the mesial cerebral cortices.

RevDate: 2024-02-07

Kueper N, Chari K, Bütefür J, et al (2024)

EEG and EMG dataset for the detection of errors introduced by an active orthosis device.

Frontiers in human neuroscience, 18:1304311.

RevDate: 2024-02-07
CmpDate: 2024-02-07

Lee HG, Jung IH, Park BS, et al (2024)

Altered Metabolic Phenotypes and Hypothalamic Neuronal Activity Triggered by Sodium-Glucose Cotransporter 2 Inhibition (Diabetes Metab J 2023;47:784-95).

Diabetes & metabolism journal, 48(1):159-160.

RevDate: 2024-02-09
CmpDate: 2024-02-07

Tanaka T (2024)

Evaluating the Bayesian causal inference model of intentional binding through computational modeling.

Scientific reports, 14(1):2979.

Intentional binding refers to the subjective compression of the time interval between an action and its consequence. While intentional binding has been widely used as a proxy for the sense of agency, its underlying mechanism has been largely veiled. Bayesian causal inference (BCI) has gained attention as a potential explanation, but currently lacks sufficient empirical support. Thus, this study implemented various computational models to describe the possible mechanisms of intentional binding, fitted them to individual observed data, and quantitatively evaluated their performance. The BCI models successfully isolated the parameters that potentially contributed to intentional binding (i.e., causal belief and temporal prediction) and generally better explained an observer's time estimation than traditional models such as maximum likelihood estimation. The estimated parameter values suggested that the time compression resulted from an expectation that the actions would immediately cause sensory outcomes. Furthermore, I investigated the algorithm that realized this BCI and found probability-matching to be a plausible candidate; people might heuristically reconstruct event timing depending on causal uncertainty rather than optimally integrating causal and temporal posteriors. The evidence demonstrated the utility of computational modeling to investigate how humans infer the causal and temporal structures of events and individual differences in that process.

RevDate: 2024-02-07
CmpDate: 2024-02-07

Zhang X, Wang X, Li Y, et al (2024)

Characterization of Retinal VIP-Amacrine Cell Development During the Critical Period.

Cellular and molecular neurobiology, 44(1):19.

Retinal vasoactive intestinal peptide amacrine cells (VIP-ACs) play an important role in various retinal light-mediated pathological processes related to different developmental ocular diseases and even mental disorders. It is important to characterize the developmental changes in VIP-ACs to further elucidate their mechanisms of circuit function. We bred VIP-Cre mice with Ai14 and Ai32 to specifically label retinal VIP-ACs. The VIP-AC soma and spine density generally increased, from postnatal day (P)0 to P35, reaching adult levels at P14 and P28, respectively. The VIP-AC soma density curve was different with the VIP-AC spine density curve. The total retinal VIP content reached a high level plateau at P14 but was decreased in adults. From P14 to P16, the resting membrane potential (RMP) became more negative, and the input resistance decreased. Cell membrane capacitance (MC) showed three peaks at P7, P12 and P16. The RMP and MC reached a stable level similar to the adult level at P18, whereas input resistance reached a stable level at P21. The percentage of sustained voltage-dependent potassium currents peaked at P16 and remained stable thereafter. The spontaneous excitatory postsynaptic current and spontaneous inhibitory postsynaptic current frequencies and amplitudes, as well as charge transfer, peaked at P12 to P16; however, there were also secondary peaks at different time points. In conclusion, we found that the second, third and fourth weeks after birth were important periods of VIP-AC development. Many developmental changes occurred around eye opening. The development of soma, dendrite and electrophysiological properties showed uneven dynamics of progression. Cell differentiation may contribute to soma development whereas the changes of different ion channels may play important role for spine development.

RevDate: 2024-02-05

Chugh N, S Aggarwal (2024)

Spatial Decoding for Gaze Independent Brain-Computer Interface Based on Covert Visual Attention Shift Using Electroencephalography.

Clinical EEG and neuroscience [Epub ahead of print].

The gaze-independent brain-computer interface (BCI) device is used to re-establish interaction for individuals who have abnormal eye movement. It may be possible to control the BCI by shifting your attention spatially. However, spatial attention is rarely employed to increase the effectiveness of target detection and is typically used to provide a simple "yes" or "no" response to the target recognition inquiry. To improve the effectiveness of detecting target, it is crucial to take advantage of the possible advantages of spatial attention. N2-posterior-contralateral (N2pc) component reflects correlates of visual spatial attention and is used to determine target position. In this study, a long-short-term memory (LSTM) network is used to answer "yes/no" questions by decoding covert spatial attention based on N2pc characteristics using EEG signals. The proposed LSTM-based model's average decoding accuracy is 92.79%. The target detection efficiency was successfully increased by about 4% when compared to conventional machine learning algorithms. The proposed model is tested on the independent dataset to validate its performance. The results of this work show that N2pc characteristics can be employed in gaze-independent BCIs for tracking covert attention shifts, which may help persons with poor eye mobility to connect with their environment.

RevDate: 2024-02-07

Wang J, Du X, Yao S, et al (2024)

Mesoscale organization of ventral and dorsal visual pathways in macaque monkey revealed by 7T fMRI.

Progress in neurobiology, 234:102584 pii:S0301-0082(24)00020-0 [Epub ahead of print].

In human and nonhuman primate brains, columnar (mesoscale) organization has been demonstrated to underlie both lower and higher order aspects of visual information processing. Previous studies have focused on identifying functional preferences of mesoscale domains in specific areas; but there has been little understanding of how mesoscale domains may cooperatively respond to single visual stimuli across dorsal and ventral pathways. Here, we have developed ultrahigh-field 7 T fMRI methods to enable simultaneous mapping, in individual macaque monkeys, of response in both dorsal and ventral pathways to single simple color and motion stimuli. We provide the first evidence that anatomical V2 cytochrome oxidase-stained stripes are well aligned with fMRI maps of V2 stripes, settling a long-standing controversy. In the ventral pathway, a systematic array of paired color and luminance processing domains across V4 was revealed, suggesting a novel organization for surface information processing. In the dorsal pathway, in addition to high quality motion direction maps of MT, MST and V3A, alternating color and motion direction domains in V3 are revealed. As well, submillimeter motion domains were observed in peripheral LIPd and LIPv. In sum, our study provides a novel global snapshot of how mesoscale networks in the ventral and dorsal visual pathways form the organizational basis of visual objection recognition and vision for action.

RevDate: 2024-02-03

Pérez-Velasco S, Marcos-Martínez D, Santamaría-Vázquez E, et al (2024)

Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values.

Computer methods and programs in biomedicine, 246:108048 pii:S0169-2607(24)00044-0 [Epub ahead of print].

BACKGROUND AND OBJECTIVE: Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain not well understood. Most MI-BCIs use the sensorimotor rhythms elicited in the primary motor cortex (M1) and somatosensory cortex (S1), which consist of an event-related desynchronization followed by an event-related synchronization. Consequently, this has resulted in systems that only record signals around M1 and S1. However, MI could involve a more complex network including sensory, association, and motor areas. In this study, we hypothesize that the superior accuracies achieved by new deep learning (DL) models applied to MI decoding rely on focusing on a broader MI activation of the brain. Parallel to the success of DL, the field of explainable artificial intelligence (XAI) has seen continuous development to provide explanations for DL networks success. The goal of this study is to use XAI in combination with DL to extract information about MI brain activation patterns from non-invasive electroencephalography (EEG) signals.

METHODS: We applied an adaptation of Shapley additive explanations (SHAP) to EEGSym, a state-of-the-art DL network with exceptional transfer learning capabilities for inter-subject MI classification. We obtained the SHAP values from two public databases comprising 171 users generating left and right hand MI instances with and without real-time feedback.

RESULTS: We found that EEGSym based most of its prediction on the signal of the frontal electrodes, i.e. F7 and F8, and on the first 1500 ms of the analyzed imagination period. We also found that MI involves a broad network not only based on M1 and S1, but also on the prefrontal cortex (PFC) and the posterior parietal cortex (PPC). We further applied this knowledge to select a 8-electrode configuration that reached inter-subject accuracies of 86.5% ± 10.6% on the Physionet dataset and 88.7% ± 7.0% on the Carnegie Mellon University's dataset.

CONCLUSION: Our results demonstrate the potential of combining DL and SHAP-based XAI to unravel the brain network involved in producing MI. Furthermore, SHAP values can optimize the requirements for out-of-laboratory BCI applications involving real users.

RevDate: 2024-02-02

Gao J, Lin C, Zhang C, et al (2024)

Exploring the Function of (+)-Naltrexone Precursors: Their Activity as TLR4 Antagonists and Potential in Treating Morphine Addiction.

Journal of medicinal chemistry [Epub ahead of print].

Disruptions in the toll-like receptor 4 (TLR4) signaling pathway are linked to chronic inflammation, neuropathic pain, and drug addiction. (+)-Naltrexone, an opioid-derived TLR4 antagonist with a (+)-isomer configuration, does not interact with classical opioid receptors and has moderate blood-brain barrier permeability. Herein, we developed a concise 10-step synthesis for (+)-naltrexone and explored its precursors, (+)-14-hydroxycodeinone (1) and (+)-14-hydroxymorphinone (3). These precursors exhibited TLR4 antagonistic activities 100 times stronger than (+)-naltrexone, particularly inhibiting the TLR4-TRIF pathway. In vivo studies showed that these precursors effectively reduced behavioral effects of morphine, like sensitization and conditioned place preference by suppressing microglial activation and TNF-α expression in the medial prefrontal cortex and ventral tegmental area. Additionally, 3 displayed a longer half-life and higher oral bioavailability than 1. Overall, this research optimized (+)-naltrexone synthesis and identified its precursors as potent TLR4 antagonists, offering potential treatments for morphine addiction.

RevDate: 2024-02-02

Chen H, Wang D, Xu M, et al (2024)

CRE-TSCAE: A Novel Classification Model Based on Stacked Convolutional Autoencoder for Dual-target RSVP-BCI Tasks.

IEEE transactions on bio-medical engineering, PP: [Epub ahead of print].

OBJECTIVE: The RSVP (Rapid Serial Visual Presentation) paradigm facilitates target identification in a rapid picture stream, which is applied extensively in military target surveillance and police monitoring. Most researchers concentrate on the single target RSVP-BCI whereas the study of dual-target is scarcely conducted, limiting RSVP application considerably.

METHODS: This paper proposed a novel classification model named Common Representation Extraction-Targeted Stacked Convolutional Autoencoder (CRE-TSCAE) to detect two targets with one nontarget in RSVP tasks. CRE generated a common representation for each target class to reduce variability from different trials of the same class and distinguish the difference between two targets better. TSCAE aimed to control uncertainty in the training process while requiring less target training data. The model learned a compact and discriminative feature through the training from several learning tasks so as to distinguish each class effectively.

RESULTS: It was validated on the World Robot Contest 2021 and 2022 ERP datasets. Experimental results showed that CRE-TSCAE outperformed the state-of-the-art RSVP decoding algorithms and the Average ACC was 71.25%, improving 6.5% at least over the rest.

CONCLUSION: It demonstrated that CRE-TSCAE showed a strong ability to extract discriminative latent features in detecting the differences among two targets with nontarget, which guaranteed increased classification accuracy.

SIGNIFICANCE: CRE-TSCAE provided an innovative and effective classification model for dual-target RSVP-BCI tasks and some insights into the neurophysiological distinction between different targets.

RevDate: 2024-02-02

Ma D, Hu M, Yang X, et al (2024)

Structural basis for sugar perception by Drosophila gustatory receptors.

Science (New York, N.Y.) [Epub ahead of print].

Insects rely on a family of seven-transmembrane proteins called gustatory receptors (GRs) to encode different taste modalities such as sweet and bitter. Here we report structures of Drosophila sweet taste receptors GR43a and GR64a in the apo and sugar-bound states. Both GRs form tetrameric sugar-gated cation channels, composed of one central pore domain (PD) and four peripheral ligand-binding domains (LBDs). Whereas GR43a is specifically activated by the monosaccharide fructose that binds to a narrow pocket in LBD, disaccharides sucrose and maltose selectively activate GR64a by binding to a larger and flatter pocket in LBD. Sugar binding to LBD induces local conformational changes, which are subsequently transferred to PD to cause channel opening. Our studies have revealed structural basis for sugar recognition and activation of GRs.

RevDate: 2024-02-03

Gerdle B, Dahlqvist Leinhard O, Lund E, et al (2024)

Pain and the biochemistry of fibromyalgia: patterns of peripheral cytokines and chemokines contribute to the differentiation between fibromyalgia and controls and are associated with pain, fat infiltration and content.

Frontiers in pain research (Lausanne, Switzerland), 5:1288024.

OBJECTIVES: This explorative study analyses interrelationships between peripheral compounds in saliva, plasma, and muscles together with body composition variables in healthy subjects and in fibromyalgia patients (FM). There is a need to better understand the extent cytokines and chemokines are associated with body composition and which cytokines and chemokines differentiate FM from healthy controls.

METHODS: Here, 32 female FM patients and 30 age-matched female healthy controls underwent a clinical examination that included blood sample, saliva samples, and pain threshold tests. In addition, the subjects completed a health questionnaire. From these blood and saliva samples, a panel of 68 mainly cytokines and chemokines were determined. Microdialysis of trapezius and erector spinae muscles, phosphorus-31 magnetic resonance spectroscopy of erector spinae muscle, and whole-body magnetic resonance imaging for determination of body composition (BC)-i.e., muscle volume, fat content and infiltration-were also performed.

RESULTS: After standardizing BC measurements to remove the confounding effect of Body Mass Index, fat infiltration and content are generally increased, and fat-free muscle volume is decreased in FM. Mainly saliva proteins differentiated FM from controls. When including all investigated compounds and BC variables, fat infiltration and content variables were most important, followed by muscle compounds and cytokines and chemokines from saliva and plasma. Various plasma proteins correlated positively with pain intensity in FM and negatively with pain thresholds in all subjects taken together. A mix of increased plasma cytokines and chemokines correlated with an index covering fat infiltration and content in different tissues. When muscle compounds were included in the analysis, several of these were identified as the most important regressors, although many plasma and saliva proteins remained significant.

DISCUSSION: Peripheral factors were important for group differentiation between FM and controls. In saliva (but not plasma), cytokines and chemokines were significantly associated with group membership as saliva compounds were increased in FM. The importance of peripheral factors for group differentiation increased when muscle compounds and body composition variables were also included. Plasma proteins were important for pain intensity and sensitivity. Cytokines and chemokines mainly from plasma were also significantly and positively associated with a fat infiltration and content index.

CONCLUSION: Our findings of associations between cytokines and chemokines and fat infiltration and content in different tissues confirm that inflammation and immune factors are secreted from adipose tissue. FM is clearly characterized by complex interactions between peripheral tissues and the peripheral and central nervous systems, including nociceptive, immune, and neuroendocrine processes.

RevDate: 2024-02-02

Simon A, Bech S, Loquet G, et al (2024)

Cortical linear encoding and decoding of sounds: Similarities and differences between naturalistic speech and music listening.

The European journal of neuroscience [Epub ahead of print].

Linear models are becoming increasingly popular to investigate brain activity in response to continuous and naturalistic stimuli. In the context of auditory perception, these predictive models can be 'encoding', when stimulus features are used to reconstruct brain activity, or 'decoding' when neural features are used to reconstruct the audio stimuli. These linear models are a central component of some brain-computer interfaces that can be integrated into hearing assistive devices (e.g., hearing aids). Such advanced neurotechnologies have been widely investigated when listening to speech stimuli but rarely when listening to music. Recent attempts at neural tracking of music show that the reconstruction performances are reduced compared with speech decoding. The present study investigates the performance of stimuli reconstruction and electroencephalogram prediction (decoding and encoding models) based on the cortical entrainment of temporal variations of the audio stimuli for both music and speech listening. Three hypotheses that may explain differences between speech and music stimuli reconstruction were tested to assess the importance of the speech-specific acoustic and linguistic factors. While the results obtained with encoding models suggest different underlying cortical processing between speech and music listening, no differences were found in terms of reconstruction of the stimuli or the cortical data. The results suggest that envelope-based linear modelling can be used to study both speech and music listening, despite the differences in the underlying cortical mechanisms.

RevDate: 2024-02-05
CmpDate: 2024-02-05

Zhang Y, Quan Z, Lou F, et al (2024)

A proton birdcage coil integrated with interchangeable single loops for multi-nuclear MRI/MRS.

Journal of Zhejiang University. Science. B, 25(2):168-180.

Energy metabolism is fundamental for life. It encompasses the utilization of carbohydrates, lipids, and proteins for internal processes, while aberrant energy metabolism is implicated in many diseases. In the present study, using three-dimensional (3D) printing from polycarbonate via fused deposition modeling, we propose a multi-nuclear radiofrequency (RF) coil design with integrated [1]H birdcage and interchangeable X-nuclei ([2]H, [13]C, [23]Na, and [31]P) single-loop coils for magnetic resonance imaging (MRI)/magnetic resonance spectroscopy (MRS). The single-loop coil for each nucleus attaches to an arc bracket that slides unrestrictedly along the birdcage coil inner surface, enabling convenient switching among various nuclei and animal handling. Compared to a commercial [1]H birdcage coil, the proposed [1]H birdcage coil exhibited superior signal-excitation homogeneity and imaging signal-to-noise ratio (SNR). For X-nuclei study, prominent peaks in spectroscopy for phantom solutions showed excellent SNR, and the static and dynamic peaks of in vivo spectroscopy validated the efficacy of the coil design in structural imaging and energy metabolism detection simultaneously.

RevDate: 2024-02-06
CmpDate: 2024-02-06

Chen Y, Xu X, Ding K, et al (2024)

TRIM25 promotes glioblastoma cell growth and invasion via regulation of the PRMT1/c-MYC pathway by targeting the splicing factor NONO.

Journal of experimental & clinical cancer research : CR, 43(1):39.

BACKGROUND: Ubiquitination plays an important role in proliferating and invasive characteristic of glioblastoma (GBM), similar to many other cancers. Tripartite motif 25 (TRIM25) is a member of the TRIM family of proteins, which are involved in tumorigenesis through substrate ubiquitination.

METHODS: Difference in TRIM25 expression levels between nonneoplastic brain tissue samples and primary glioma samples was demonstrated using publicly available glioblastoma database, immunohistochemistry, and western blotting. TRIM25 knockdown GBM cell lines (LN229 and U251) and patient derived GBM stem-like cells (GSCs) GBM#021 were used to investigate the function of TRIM25 in vivo and in vitro. Co-immunoprecipitation (Co-IP) and mass spectrometry analysis were performed to identify NONO as a protein that interacts with TRIM25. The molecular mechanisms underlying the promotion of GBM development by TRIM25 through NONO were investigated by RNA-seq and validated by qRT-PCR and western blotting.

RESULTS: We observed upregulation of TRIM25 in GBM, correlating with enhanced glioblastoma cell growth and invasion, both in vitro and in vivo. Subsequently, we screened a panel of proteins interacting with TRIM25; mass spectrometry and co-immunoprecipitation revealed that NONO was a potential substrate of TRIM25. TRIM25 knockdown reduced the K63-linked ubiquitination of NONO, thereby suppressing the splicing function of NONO. Dysfunctional NONO resulted in the retention of the second intron in the pre-mRNA of PRMT1, inhibiting the activation of the PRMT1/c-MYC pathway.

CONCLUSIONS: Our study demonstrates that TRIM25 promotes glioblastoma cell growth and invasion by regulating the PRMT1/c-MYC pathway through mediation of the splicing factor NONO. Targeting the E3 ligase activity of TRIM25 or the complex interactions between TRIM25 and NONO may prove beneficial in the treatment of GBM.

RevDate: 2024-02-03

Zhang J, Guo H, Wang L, et al (2024)

Cediranib enhances the transcription of MHC-I by upregulating IRF-1.

Biochemical pharmacology, 221:116036 pii:S0006-2952(24)00019-4 [Epub ahead of print].

Diminished or lost Major Histocompatibility Complex class I (MHC-I) expression is frequently observed in tumors, which obstructs the immune recognition of tumor cells by cytotoxic T cells. Restoring MHC-I expression by promoting its transcription and improving protein stability have been promising strategies for reestablishing anti-tumor immune responses. Here, through cell-based screening models, we found that cediranib significantly upregulated MHC-I expression in tumor cells. This finding was confirmed in various non-small cell lung cancer (NSCLC) cell lines and primary patient-derived lung cancer cells. Furthermore, we discovered cediranib achieved MHC-I upregulation through transcriptional regulation. interferon regulatory factor 1 (IRF-1) was required for cediranib induced MHC-I transcription and the absence of IRF-1 eliminated this effect. Continuing our research, we found cediranib triggered STAT1 phosphorylation and promoted IRF-1 transcription subsequently, thus enhancing downstream MHC-I transcription. In vivo study, we further confirmed that cediranib increased MHC-I expression, enhanced CD8[+] T cell infiltration, and improved the efficacy of anti-PD-L1 therapy. Collectively, our study demonstrated that cediranib could elevate MHC-I expression and enhance responsiveness to immune therapy, thereby providing a theoretical foundation for its potential clinical trials in combination with immunotherapy.

RevDate: 2024-02-01

Pouryosef M, Abedini-Nassab R, S Mohammad Reza Akrami (2024)

A Novel Framework for Epileptic Seizure Detection using Electroencephalogram Signals based on the Bat Feature Selection Algorithm.

Neuroscience pii:S0306-4522(24)00024-1 [Epub ahead of print].

The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.

RevDate: 2024-02-01

Proverbio AM (2024)

The temporal dynamics of visual imagery and BCI: Comment on "Visual mental imagery: Evidence for a heterarchical neural architecture" by Spagna et al.

Physics of life reviews, 48:174-175 pii:S1571-0645(24)00006-X [Epub ahead of print].

RevDate: 2024-02-01

Wang Y, Seki T, Gkoupidenis P, et al (2024)

Aqueous chemimemristor based on proton-permeable graphene membranes.

Proceedings of the National Academy of Sciences of the United States of America, 121(6):e2314347121.

Memristive devices, electrical elements whose resistance depends on the history of applied electrical signals, are leading candidates for future data storage and neuromorphic computing. Memristive devices typically rely on solid-state technology, while aqueous memristive devices are crucial for biology-related applications such as next-generation brain-machine interfaces. Here, we report a simple graphene-based aqueous memristive device with long-term and tunable memory regulated by reversible voltage-induced interfacial acid-base equilibria enabled by selective proton permeation through the graphene. Surface-specific vibrational spectroscopy verifies that the memory of the graphene resistivity arises from the hysteretic proton permeation through the graphene, apparent from the reorganization of interfacial water at the graphene/water interface. The proton permeation alters the surface charge density on the CaF2 substrate of the graphene, affecting graphene's electron mobility, and giving rise to synapse-like resistivity dynamics. The results pave the way for developing experimentally straightforward and conceptually simple aqueous electrolyte-based neuromorphic iontronics using two-dimensional (2D) materials.

RevDate: 2024-02-02

Liu R, Chao Y, Ma X, et al (2024)

ERTNet: an interpretable transformer-based framework for EEG emotion recognition.

Frontiers in neuroscience, 18:1320645.

BACKGROUND: Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy.

METHODS: We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state.

RESULTS: Experiments' results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data.

DISCUSSION: Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.

RevDate: 2024-02-04

Guo H, Zhou C, Zheng M, et al (2024)

Insights into the role of derailed endocytic trafficking pathway in cancer: From the perspective of cancer hallmarks.

Pharmacological research, 201:107084 pii:S1043-6618(24)00028-8 [Epub ahead of print].

The endocytic trafficking pathway is a highly organized cellular program responsible for the regulation of membrane components and uptake of extracellular substances. Molecules internalized into the cell through endocytosis will be sorted for degradation or recycled back to membrane, which is determined by a series of sorting events. Many receptors, enzymes, and transporters on the membrane are strictly regulated by endocytic trafficking process, and thus the endocytic pathway has a profound effect on cellular homeostasis. However, the endocytic trafficking process is typically dysregulated in cancers, which leads to the aberrant retention of receptor tyrosine kinases and immunosuppressive molecules on cell membrane, the loss of adhesion protein, as well as excessive uptake of nutrients. Therefore, hijacking endocytic trafficking pathway is an important approach for tumor cells to obtain advantages of proliferation and invasion, and to evade immune attack. Here, we summarize how dysregulated endocytic trafficking process triggers tumorigenesis and progression from the perspective of several typical cancer hallmarks. The impact of endocytic trafficking pathway to cancer therapy efficacy is also discussed.

RevDate: 2024-01-31

Wang Y, Liu W, Wang Y, et al (2024)

Long-term HD-tDCS modulates dynamic changes of brain activity on patients with disorders of consciousness: A resting-state EEG study.

Computers in biology and medicine, 170:108084 pii:S0010-4825(24)00168-9 [Epub ahead of print].

OBJECTIVE: High-definition transcranial direct current stimulation (HD-tDCS) has been an effective neurostimulation method in the treatment of disorders of consciousness (DOC). However, the effects and mechanism of HD-tDCS are still unclear.

METHODS: This study recruited 8 DOC patients and applied 20-min sessions of 2 mA HD-tDCS (central anode electrode at Pz) for 14 consecutive days. We record DOC patients' EEG data and Coma Recovery Scale-Revised (CRS-R) values at four time point: baseline (T0), after 1 day's and 7,14 days' parietal HD-tDCS treatment (T1, T2, T3). Power spectral density (PSD), relative power (RP), spectral entropy and spectral exponent were calculated to evaluate the EEG dynamic changes of DOC patients during long-term parietal HD-tDCS. At last, we calculated the correlation between changes of EEG features and changes of CRS-R values.

RESULT: After 1 day's parietal HD-tDCS, DOC patients' CRS-R value had not changed (8.25 ± 1.91). HD-tDCS improved DOC patients' CRS-R value at T2 (9.75 ± 1.91, p < 0.05) and at T3 (11.38 ± 2.77, p < 0.05), compared with that at T0 (8.25 ± 1.91). As the treatment time increased, the EEG PSD decayed more slowly. Specifically, the delta frequency band RP decreased, while the alpha, beta, and gamma frequency bands RP increased. EEG oscillation characteristics changed but not significant at T1 (p > 0.05), and showed significant changes at T2 and T3 (p < 0.05). The spectral entropy continuously increased and the spectral exponent continuously decreased from T0 to T3. Specifically, the spectral entropy and spectral exponent of the parietal and occipital regions were significantly higher at T2 and T3 than that at T0 (p < 0.05). In addition, The changes in EEG features of the parietal and occipital lobes were correlated with changes in CRS-R value, especially between T2 and T0.

CONCLUSION: Long-term parietal HD-tDCS can improve the consciousness level and brain activity in DOC patients. Resting-state EEG can evaluate the dynamic changes of brain activity in DOC patients during HD-tDCS. EEG oscillation and non-oscillatory activity might be used to explain the mechanism of HD-tDCS on DOC patients.

RevDate: 2024-01-31

Yan S, Hu Y, Zhang R, et al (2024)

Multilayer network-based channel selection for motor imagery brain-computer interface.

Journal of neural engineering [Epub ahead of print].

The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands. Approach: We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient (MPC) of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine (SVM) classifier with a linear kernel is trained to accurately identify MI tasks. Main results: We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (paired t-tests, p <0.05). Significance: The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.

RevDate: 2024-01-31

Mobaien A, Boostani R, S Sanei (2024)

Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering.

Journal of neural engineering [Epub ahead of print].

The P300-based brain-computer interfaces (BCIs) typically employ a visual oddball paradigm, where different objects (linked to specific commands) are randomly and frequently intensified. Upon observing the target object, users experience a elicitation of a P300 event-related potential in their electroencephalography (EEG). However, detecting the P300 signal can be challenging due to its very low signal-to-noise ratio (SNR), often compromised by the sequence of visual evoked potentials (VEPs) generated in the occipital regions of the brain in response to periodic visual stimuli. The main objective of this study is to investigate how visual evoked potentials (VEPs) impact P300-based BCIs. Subsequently, the study aims to propose a method for EEG spatial filtering to alleviate the effect of VEPs and enhance the overall performance of these BCIs. Approach: Our approach entails analyzing recorded EEG signals from visual P300-based BCIs through temporal, spectral, and spatial analysis techniques to identify the impact of VEPs. Subsequently, we introduce a regularized version of the xDAWN algorithm, a well-established spatial filter known for enhancing single-trial P300s. This aims to simultaneously enhance P300 signals and suppress VEPs, contributing to an improved overall signal quality. Main results: Analyzing EEG signals shows that VEPs can significantly contaminate P300 signals, resulting in a decrease in the overall performance of P300-based BCIs. However, our proposed method for simultaneous enhancement of P300 and suppression of VEPs has demonstrated improved performance in P300-based BCIs. This improvement has been verified through several experiments conducted with real P300 data. Significance: This study focuses on the effects of VEPs on the performance of P300-based BCIs, a problem that has not been adequately addressed in previous studies. It opens up a new path for investigating these BCIs. Moreover, the proposed spatial filtering technique has the potential to further enhance the performance of these systems.

RevDate: 2024-01-31

John AR, Singh AK, Gramann K, et al (2024)

Prediction of cognitive conflict during unexpected robot behavior under different mental workload donditions in a physical human-robot collaboration.

Journal of neural engineering [Epub ahead of print].

Brain-Computer Interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact error awareness as it might raise safety concerns in pHRC. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. Electroencephalography (EEG) data, perceived workload, task and physical performance were recorded from twenty-four participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error. This prediction model could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

RevDate: 2024-02-01

Miziev S, Pawlak WA, N Howard (2023)

Comparative analysis of energy transfer mechanisms for neural implants.

Frontiers in neuroscience, 17:1320441.

As neural implant technologies advance rapidly, a nuanced understanding of their powering mechanisms becomes indispensable, especially given the long-term biocompatibility risks like oxidative stress and inflammation, which can be aggravated by recurrent surgeries, including battery replacements. This review delves into a comprehensive analysis, starting with biocompatibility considerations for both energy storage units and transfer methods. The review focuses on four main mechanisms for powering neural implants: Electromagnetic, Acoustic, Optical, and Direct Connection to the Body. Among these, Electromagnetic Methods include techniques such as Near-Field Communication (RF). Acoustic methods using high-frequency ultrasound offer advantages in power transmission efficiency and multi-node interrogation capabilities. Optical methods, although still in early development, show promising energy transmission efficiencies using Near-Infrared (NIR) light while avoiding electromagnetic interference. Direct connections, while efficient, pose substantial safety risks, including infection and micromotion disturbances within neural tissue. The review employs key metrics such as specific absorption rate (SAR) and energy transfer efficiency for a nuanced evaluation of these methods. It also discusses recent innovations like the Sectored-Multi Ring Ultrasonic Transducer (S-MRUT), Stentrode, and Neural Dust. Ultimately, this review aims to help researchers, clinicians, and engineers better understand the challenges of and potentially create new solutions for powering neural implants.

RevDate: 2024-02-01

Wang X, Ivanov AP, JB Edel (2024)

Biocompatible Biphasic Iontronics Enable Neuron-Like Ionic Signal Transmission.

Research (Washington, D.C.), 7:0294.

Biocompatible connections between external artificial devices and living organisms show promise for future neuroprosthetics and therapeutics. The study in Science by Zhao and colleagues introduces a cascade-heterogated biphasic gel (HBG) iontronic device, which facilitates electronic-to-multi-ionic signal transduction for abiotic-biotic interfaces. Inspired by neuron signaling, the HBG device demonstrated its biocompatibility by regulating neural activity in biological tissue, paving the way for wearable and implantable devices, including brain-computer interfaces.

RevDate: 2024-01-30

Sartipi S, M Cetin (2024)

Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings suffer from heterogeneity across subjects and labeled data insufficiency, designing a classifier that performs the MI independently from the subject with limited labeled samples would be desirable. To overcome these limitations, we propose a novel subject-independent semi-supervised deep architecture (SSDA). The proposed SSDA consists of two parts: an unsupervised and a supervised element. The training set contains both labeled and unlabeled data samples from multiple subjects. First, the unsupervised part, known as the columnar spatiotemporal auto-encoder (CST-AE), extracts latent features from all the training samples by maximizing the similarity between the original and reconstructed data. A dimensional scaling approach is employed to reduce the dimensionality of the representations while preserving their discriminability. Second, a supervised part learns a classifier based on the labeled training samples using the latent features acquired in the unsupervised part. Moreover, we employ center loss in the supervised part to minimize the embedding space distance of each point in a class to its center. The model optimizes both parts of the network in an end-to-end fashion. The performance of the proposed SSDA is evaluated on test subjects who were not seen by the model during the training phase. To assess the performance, we use two benchmark EEG-based MI task datasets. The results demonstrate that SSDA outperforms state-of-the-art methods and that a small number of labeled training samples can be sufficient for strong classification performance.

RevDate: 2024-01-30

Lin JW, Fan ZC, Tzou SC, et al (2024)

Temporal alpha dissimilarity of ADHD brain network in comparison with CPT and CATA.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Attention deficit hyperactivity disorder (ADHD) is a chronic neurological and psychiatric disorder that affects children during their development. To find neural patterns for ADHD, and provide subjective features as decision references to assist specialist and physicians. Many studies devoted to investigate the neural dynamics of brain of resting-state or continuous performance tests (CPT) with EEG or functional magnetic resonance image (fMRI). The present study use coherence, which is one of the functional connectivity (FC) method, to analyze the neural patterns of children and adolescents (8-16 years old) under CPT and continuous auditory test of attention (CATA) task. In the meantime, electroencephalography (EEG) oscillations were recorded by a wireless brain-computer interface (BCI). 72 children were enrolled, of which 53 participants were diagnosed as ADHD and 19 presented to be typical developing (TD). The experimental results exhibited higher difference in alpha and theta bands between the TD group and the ADHD group. While the differences between the TD group and the ADHD group in all four frequency domains were greater than under CPT conditions. Statistically significant differences (p<0.05) were observed between the ADHD and TD groups in the alpha rhythm during the CATA task in the short-range of coherence. For the temporal lobe FC during the CATA task, the TD group exhibited statistically significantly FC (p<0.05) in the alpha rhythm compared to the ADHD group. These findings offering new possibilities for more techniques and diagnostic methods in finding more ADHD features. The differences in alpha and beta frequencies were more pronounced in the ADHD group during the CPT task compared to the CATA task. Additionally, the disparities in brain activity were more evident across delta, theta, alpha and beta frequency domains when the task given was a CATA as opposed to a CPT. The findings presented the underlying mechanisms of the FC differences between children and adolescents with ADHD. Moreover, these findings should extend to use machine learning approaches to assist the ADHD classification and diagnosis.

RevDate: 2024-01-31
CmpDate: 2024-01-31

Yuan T, Wang Y, Jin Y, et al (2024)

Coupling of Slack and NaV1.6 sensitizes Slack to quinidine blockade and guides anti-seizure strategy development.

eLife, 12: pii:87559.

Quinidine has been used as an anticonvulsant to treat patients with KCNT1-related epilepsy by targeting gain-of-function KCNT1 pathogenic mutant variants. However, the detailed mechanism underlying quinidine's blockade against KCNT1 (Slack) remains elusive. Here, we report a functional and physical coupling of the voltage-gated sodium channel NaV1.6 and Slack. NaV1.6 binds to and highly sensitizes Slack to quinidine blockade. Homozygous knockout of NaV1.6 reduces the sensitivity of native sodium-activated potassium currents to quinidine blockade. NaV1.6-mediated sensitization requires the involvement of NaV1.6's N- and C-termini binding to Slack's C-terminus and is enhanced by transient sodium influx through NaV1.6. Moreover, disrupting the Slack-NaV1.6 interaction by viral expression of Slack's C-terminus can protect against Slack[G269S]-induced seizures in mice. These insights about a Slack-NaV1.6 complex challenge the traditional view of 'Slack as an isolated target' for anti-epileptic drug discovery efforts and can guide the development of innovative therapeutic strategies for KCNT1-related epilepsy.

RevDate: 2024-01-31

Wu M, Ouyang R, Zhou C, et al (2023)

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.

Frontiers in neuroscience, 17:1345770.

INTRODUCTION: Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application.

METHODS: In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied.

RESULTS: The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%.

DISCUSSION: The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.

RevDate: 2024-01-31

Tai P, Ding P, Wang F, et al (2023)

Brain-computer interface paradigms and neural coding.

Frontiers in neuroscience, 17:1345961.

Brain signal patterns generated in the central nervous system of brain-computer interface (BCI) users are closely related to BCI paradigms and neural coding. In BCI systems, BCI paradigms and neural coding are critical elements for BCI research. However, so far there have been few references that clearly and systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding. Therefore, these contents are expounded and the existing main BCI paradigms and neural coding are introduced in the review. Finally, the challenges and future research directions of BCI paradigm and neural coding were discussed, including user-centered design and evaluation for BCI paradigms and neural coding, revolutionizing the traditional BCI paradigms, breaking through the existing techniques for collecting brain signals and combining BCI technology with advanced AI technology to improve brain signal decoding performance. It is expected that the review will inspire innovative research and development of the BCI paradigm and neural coding.

RevDate: 2024-01-29

Schiff ND, Diringer M, Diserens K, et al (2024)

Brain-Computer Interfaces for Communication in Patients with Disorders of Consciousness: A Gap Analysis and Scientific Roadmap.

Neurocritical care [Epub ahead of print].

BACKGROUND: We developed a gap analysis that examines the role of brain-computer interfaces (BCI) in patients with disorders of consciousness (DoC), focusing on their assessment, establishment of communication, and engagement with their environment.

METHODS: The Curing Coma Campaign convened a Coma Science work group that included 16 clinicians and neuroscientists with expertise in DoC. The work group met online biweekly and performed a gap analysis of the primary question.

RESULTS: We outline a roadmap for assessing BCI readiness in patients with DoC and for advancing the use of BCI devices in patients with DoC. Additionally, we discuss preliminary studies that inform development of BCI solutions for communication and assessment of readiness for use of BCIs in DoC study participants. Special emphasis is placed on the challenges posed by the complex pathophysiologies caused by heterogeneous brain injuries and their impact on neuronal signaling. The differences between one-way and two-way communication are specifically considered. Possible implanted and noninvasive BCI solutions for acute and chronic DoC in adult and pediatric populations are also addressed.

CONCLUSIONS: We identify clinical and technical gaps hindering the use of BCI in patients with DoC in each of these contexts and provide a roadmap for research aimed at improving communication for adults and children with DoC, spanning the clinical spectrum from intensive care unit to chronic care.

RevDate: 2024-01-29

Luo J, Cui W, Xu S, et al (2024)

A Cross-Scale Transformer and Triple-View Attention based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is a promising target detection technique by using electroencephalogram (EEG) signals. However, existing deep learning approaches seldom considered dependencies of multi-scale temporal features and discriminative multi-view spectral features simultaneously, which limits the representation learning ability of the model and undermine the EEG classification performance. In addition, recent transfer learning-based methods generally failed to obtain transferable cross-subject invariant representations and commonly ignore the individual-specific information, leading to the poor cross-subject transfer performance. In response to these limitations, we propose a cross-scale Transformer and triple-view attention based domain-rectified transfer learning (CST-TVA-DRTL) for the RSVP classification. Specially, we first develop a cross-scale Transformer (CST) to extract multi-scale temporal features and exploit the dependencies of different scales features. Then, a triple-view attention (TVA) is designed to capture spectral features from triple views of multi-channel time-frequency images. Finally, a domain-rectified transfer learning (DRTL) framework is proposed to simultaneously obtain transferable domain-invariant representations and untransferable domain-specific representations, then utilize domain-specific information to rectify domain-invariant representations to adapt to target data. Experimental results on two public RSVP datasets suggests that our CST-TVA-DRTL outperforms the state-of-the-art methods in the RSVP classification task. The source code of our model is publicly available in https://github.com/ljbuaa/CST_TVA_DRTL.

RevDate: 2024-01-30

Valencia D, Mercier PP, A Alimohammad (2024)

Efficient In Vivo Neural Signal Compression Using an Autoencoder-based Neural Network.

IEEE transactions on biomedical circuits and systems, PP: [Epub ahead of print].

Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) applications utilizing continuous local field potentials (LFPs) for cognitive decoding, the volume of neural data to be transmitted to a computer imposes relatively high data rate requirements. This is particularly true for BCIs employing high-density intracortical recordings with hundreds or thousands of electrodes. This article introduces the first autoencoder-based compression digital circuit for the efficient transmission of LFP neural signals. Various algorithmic and architectural-level optimizations are implemented to significantly reduce the computational complexity and memory requirements of the designed in vivo compression circuit. This circuit employs an autoencoder-based neural network, providing a robust signal reconstruction. The application-specific integrated circuit (ASIC) of the in vivo compression logic occupies the smallest silicon area and consumes the lowest power among the reported state-of-the-art compression ASICs. Additionally, it offers a higher compression rate and a superior signal-to-noise and distortion ratio.

RevDate: 2024-01-31

Jiao Y, Lei M, Zhu J, et al (2023)

Advances in electrode interface materials and modification technologies for brain-computer interfaces.

Biomaterials translational, 4(4):213-233.

Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of information between the human brain and external devices, but also provides a brand-new means for the diagnosis and treatment of brain-related diseases. The neural electrode interface part of brain-computer interface is an important area for electrical, optical and chemical signal transmission between brain tissue system and external electronic devices, which determines the performance of brain-computer interface. In order to solve the problems of insufficient flexibility, insufficient signal recognition ability and insufficient biocompatibility of traditional rigid electrodes, researchers have carried out extensive studies on the neuroelectrode interface in terms of materials and modification techniques. This paper introduces the biological reactions that occur in neuroelectrodes after implantation into brain tissue and the decisive role of the electrode interface for electrode function. Following this, the latest research progress on neuroelectrode materials and interface materials is reviewed from the aspects of neuroelectrode materials and modification technologies, firstly taking materials as a clue, and then focusing on the preparation process of neuroelectrode coatings and the design scheme of functionalised structures.

RevDate: 2024-01-29
CmpDate: 2024-01-29

Miao T, Symonds A, Hickman OJ, et al (2024)

Inhibition of Bromodomain Proteins Enhances Oncolytic HAdVC5 Replication and Efficacy in Pancreatic Ductal Adenocarcinoma (PDAC) Models.

International journal of molecular sciences, 25(2):.

Pancreatic ductal adenocarcinoma (PDAC) is the most aggressive type of pancreatic cancer, which rapidly develops resistance to the current standard of care. Several oncolytic Human AdenoViruses (HAdVs) have been reported to re-sensitize drug-resistant cancer cells and in combination with chemotherapeutics attenuate solid tumour growth. Obstacles preventing greater clinical success are rapid hepatic elimination and limited viral replication and spread within the tumour microenvironment. We hypothesised that higher intratumoural levels of the virus could be achieved by altering cellular epigenetic regulation. Here we report on the screening of an enriched epigenetics small molecule library and validation of six compounds that increased viral gene expression and replication. The greatest effects were observed with three epigenetic inhibitors targeting bromodomain (BRD)-containing proteins. Specifically, BRD4 inhibitors enhanced the efficacy of Ad5 wild type, Ad∆∆, and Ad-3∆-A20T in 3-dimensional co-culture models of PDAC and in vivo xenografts. RNAseq analysis demonstrated that the inhibitors increased viral E1A expression, altered expression of cell cycle regulators and inflammatory factors, and attenuated expression levels of tumour cell oncogenes such as c-Myc and Myb. The data suggest that the tumour-selective Ad∆∆ and Ad-3∆-A20T combined with epigenetic inhibitors is a novel strategy for the treatment of PDAC by eliminating both cancer and associated stromal cells to pave the way for immune cell access even after systemic delivery of the virus.

RevDate: 2024-01-30
CmpDate: 2024-01-29

Wu H, Hou Y, Yoon J, et al (2024)

Down-selection of biomolecules to assemble "reverse micelle" with perovskites.

Nature communications, 15(1):772.

Biological molecule-semiconductor interfacing has triggered numerous opportunities in applied physics such as bio-assisted data storage and computation, brain-computer interface, and advanced distributed bio-sensing. The introduction of electronics into biological embodiment is being quickly developed as it has great potential in providing adaptivity and improving functionality. Reciprocally, introducing biomaterials into semiconductors to manifest bio-mimetic functionality is impactful in triggering new enhanced mechanisms. In this study, we utilize the vulnerable perovskite semiconductors as a platform to understand if certain types of biomolecules can regulate the lattice and endow a unique mechanism for stabilizing the metastable perovskite lattice. Three tiers of biomolecules have been systematically tested and the results reveal a fundamental mechanism for the formation of a "reverse-micelle" structure. Systematic exploration of a large set of biomolecules led to the discovery of guiding principle for down-selection of biomolecules which extends the classic emulsion theory to this hybrid systems. Results demonstrate that by introducing biomaterials into semiconductors, natural phenomena typically observed in biological systems can also be incorporated into semiconducting crystals, providing a new perspective to engineer existing synthetic materials.

RevDate: 2024-01-26

Lamorie-Foote K, Kramer DR, Sundaram S, et al (2024)

Primary Somatosensory Cortex Organization for Engineering Artificial Somatosensation.

Neuroscience research pii:S0168-0102(24)00009-9 [Epub ahead of print].

Somatosensory deficits from stroke, spinal cord injury, or other neurologic damage can lead to a significant degree of functional impairment. The primary (SI) and secondary (SII) somatosensory cortices encode information in a medial to lateral organization. SI is generally organized topographically, with more discrete cortical representations of specific body regions. SII regions corresponding to anatomical areas are less discrete and may represent a more functional rather than topographic organization. Human somatosensory research continues to map cortical areas of sensory processing with efforts primarily focused on hand and upper extremity information in SI. However, research into SII and other body regions is lacking. In this review, we synthesize the current state of knowledge regarding the cortical organization of human somatosensation and discuss potential applications for brain computer interface. In addition to accurate individualized mapping of cortical somatosensation, further research is required to uncover the neurophysiological mechanisms of how somatosensory information is encoded in the cortex.

RevDate: 2024-01-26

Zhang S, An D, Liu J, et al (2023)

Dynamic decomposition graph convolutional neural network for SSVEP-based brain-computer interface.

Neural networks : the official journal of the International Neural Network Society, 172:106075 pii:S0893-6080(23)00736-0 [Epub ahead of print].

The SSVEP-based paradigm serves as a prevalent approach in the realm of brain-computer interface (BCI). However, the processing of multi-channel electroencephalogram (EEG) data introduces challenges due to its non-Euclidean characteristic, necessitating methodologies that account for inter-channel topological relations. In this paper, we introduce the Dynamic Decomposition Graph Convolutional Neural Network (DDGCNN) designed for the classification of SSVEP EEG signals. Our approach incorporates layerwise dynamic graphs to address the oversmoothing issue in Graph Convolutional Networks (GCNs), employing a dense connection mechanism to mitigate the gradient vanishing problem. Furthermore, we enhance the traditional linear transformation inherent in GCNs with graph dynamic fusion, thereby elevating feature extraction and adaptive aggregation capabilities. Our experimental results demonstrate the effectiveness of proposed approach in learning and extracting features from EEG topological structure. The results shown that DDGCNN outperforms other state-of-the-art (SOTA) algorithms reported on two datasets (Dataset 1: 54 subjects, 4 targets, 2 sessions; Dataset 2: 35 subjects, 40 targets). Additionally, we showcase the implementation of DDGCNN in the context of synchronized BCI robotic fish control. This work represents a significant advancement in the field of EEG signal processing for SSVEP-based BCIs. Our proposed method processes SSVEP time domain signals directly as an end-to-end system, making it easy to deploy. The code is available at https://github.com/zshubin/DDGCNN.

RevDate: 2024-01-26

Yao Y, Stebner A, Tuytelaars T, et al (2024)

Identifying temporal correlations between natural single-shot videos and EEG signals.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored.

APPROACH: We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features.

MAIN RESULTS: We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch (MM) task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects.

SIGNIFICANCE: This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.

RevDate: 2024-01-26

Pei Y, Xu J, Chen Q, et al (2024)

DTP-Net: Learning to Reconstruct EEG Signals in Time-Frequency Domain by Multi-scale Feature Reuse.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

Electroencephalography (EEG) signals are prone to contamination by noise, such as ocular and muscle artifacts. Minimizing these artifacts is crucial for EEG-based downstream applications like disease diagnosis and brain-computer interface (BCI). This paper presents a new EEG denoising model, DTP-Net. It is a fully convolutional neural network comprising Densely-connected Temporal Pyramids (DTPs) placed between two learnable time-frequency transformations. In the time-frequency domain, DTPs facilitate efficient propagation of multi-scale features extracted from EEG signals of any length, leading to effective noise reduction. Comprehensive experiments on two public semi-simulated datasets demonstrate that the proposed DTP-Net consistently outperforms existing state-of-the-art methods on metrics including relative root mean square error (RRMSE) and signal-to-noise ratio improvement (∆SNR). Moreover, the proposed DTP-Net is applied to a BCI classification task, yielding an improvement of up to 5.55% in accuracy. This confirms the potential of DTP-Net for applications in the fields of EEG-based neuroscience and neuro-engineering. An in-depth analysis further illustrates the representation learning behavior of each module in DTP-Net, demonstrating its robustness and reliability.

RevDate: 2024-01-29
CmpDate: 2024-01-29

Wang H, Xia Q, Dong Z, et al (2023)

Emotional distress and multimorbidity patterns in Chinese Han patients with osteoporosis: a network analysis.

Frontiers in public health, 11:1242091.

With the aging of the population, the prevalence of osteoporosis and multimorbidity is increasing. Patients with osteoporosis often experience varying levels of emotional distress, including anxiety and depression. However, few studies have explored the patterns of multiple conditions and their impact on patients' emotional distress. Here, we conducted a network analysis to explore the patterns of multimorbidities and their impact on emotional distress in 13,359 Chinese Han patients with osteoporosis. The results showed that multimorbidity was prevalent in Chinese patients with osteoporosis and increased with age, and was more frequent in males than in females, with the most common pattern of multimorbidity being osteoporosis and essential (primary) hypertension. Finally, we found that patients' emotional distress increased with the number of multimorbidities, especially in female patients, and identified eight multimorbidities with high correlation to patients' emotional distress.

RevDate: 2024-01-29
CmpDate: 2024-01-29

Henney MA, Carstensen M, Thorning-Schmidt M, et al (2024)

Brain stimulation with 40 Hz heterochromatic flicker extended beyond red, green, and blue.

Scientific reports, 14(1):2147.

Alzheimer's disease (AD) is associated with electrophysiological changes in the brain. Pre-clinical and early clinical trials have shown promising results for the possible therapy of AD with 40 Hz neurostimulation. The most notable findings used stroboscopic flicker, but this technique poses an inherent barrier for human applications due to its visible flickering and resulting high level of perceived discomfort. Therefore, alternative options should be investigated for entraining 40 Hz brain activity with light sources that appear less flickering. Previously, chromatic flicker based on red, green, and blue (RGB) have been studied in the context of brain-computer interfaces, but this is an incomplete representation of the colours in the visual spectrum. This study introduces a new kind of heterochromatic flicker based on spectral combinations of blue, cyan, green, lime, amber, and red (BCGLAR). These combinations are investigated by the steady-state visually evoked potential (SSVEP) response from the flicker with an aim of optimising the choice of 40 Hz light stimulation with spectrally similar colour combinations in BCGLAR space. Thirty healthy young volunteers were stimulated with heterochromatic flicker in an electroencephalography experiment with randomised complete block design. Responses were quantified as the 40 Hz signal-to-noise ratio and analysed using mixed linear models. The size of the SSVEP response to heterochromatic flicker is dependent on colour combinations and influenced by both visual and non-visual effects. The amber-red flicker combination evoked the highest SSVEP, and combinations that included blue and/or red consistently evoked higher SSVEP than combinations only with mid-spectrum colours. Including a colour from either extreme of the visual spectrum (blue and/or red) in at least one of the dyadic phases appears to be more important than choosing pairs of colours that are far from each other on the visual spectrum. Spectrally adjacent colour pairs appear less flickering to the perceiver, and thus the results motivate investigations into the limits for how alike the two phases can be and still evoke a 40 Hz response. Specifically, combining a colour on either extreme of the visual spectrum with another proximal colour might provide the best trade-off between flickering sensation and SSVEP magnitude.

RevDate: 2024-01-29
CmpDate: 2024-01-29

Liu H, Wei P, Wang H, et al (2024)

An EEG motor imagery dataset for brain computer interface in acute stroke patients.

Scientific data, 11(1):131.

The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient rehabilitation. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. The dataset consists of four types of data: 1) the motor imagery instructions, 2) raw recording data, 3) pre-processed data after removing artefacts and other manipulations, and 4) patient characteristics. This is the first open dataset to address left- and right-handed motor imagery in acute stroke patients. We believe that the dataset will be very helpful for analysing brain activation and designing decoding methods that are more applicable for acute stroke patients, which will greatly facilitate research in the field of motor imagery-BCI.

RevDate: 2024-01-25

Shokri M, Gogliettino AR, Hottowy P, et al (2024)

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach.

Journal of neural engineering [Epub ahead of print].

Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts. Methods: Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts. Results: We applied our method to high-density multi-electrode recordings from the primate retina in an ex vivo setup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability (R^2 = 0.951 for human 1 and R^2 = 0.944 for human 2). Conclusion: Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies. .

RevDate: 2024-01-25

Zhong Y, Yao L, Pan G, et al (2024)

Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.

RevDate: 2024-01-26
CmpDate: 2024-01-26

Calzone MR, MD Grossman (2024)

Blunt cardiac injury in the hemodynamically stable patient.

JAAPA : official journal of the American Academy of Physician Assistants, 37(2):35-38.

Blunt cardiac injury (BCI) describes a spectrum of problems including severe, potentially life-threatening injuries from trauma. Pericardial effusion is an example of a BCI that has generally been assumed to imply serious underlying injury to the heart and should be considered hemopericardium until proven otherwise. A standard of care has been established to screen for BCI and treat hemodynamically unstable patients with an acute pericardial effusion presumably related to BCI. Less agreement exists on definitive treatment for hemodynamically stable patients with pericardial effusion after blunt cardiac trauma. This case study explores a new treatment for small to moderate hemopericardium in a stable patient after BCI.

RevDate: 2024-01-27
CmpDate: 2024-01-26

Liang S, Huang Z, Wang Y, et al (2024)

Using a longitudinal network structure to subgroup depressive symptoms among adolescents.

BMC psychology, 12(1):46.

BACKGROUND: Network modeling has been proposed as an effective approach to examine complex associations among antecedents, mediators and symptoms. This study aimed to investigate whether the severity of depressive symptoms affects the multivariate relationships among symptoms and mediating factors over a 2-year longitudinal follow-up.

METHODS: We recruited a school-based cohort of 1480 primary and secondary school students over four semesters from January 2020 to December 2021. The participants (n = 1145) were assessed at four time points (ages 10-13 years old at baseline). Based on a cut-off score of 5 on the 9-item Patient Health Questionnaire at each time point, the participants were categorized into the non-depressive symptom (NDS) and depressive symptom (DS) groups. We conducted network analysis to investigate the symptom-to-symptom influences in these two groups over time.

RESULTS: The global network metrics did not differ statistically between the NDS and DS groups at four time points. However, network connection strength varied with symptom severity. The edge weights between learning anxiety and social anxiety were prominently in the NDS group over time. The central factors for NDS and DS were oversensitivity and impulsivity (3 out of 4 time points), respectively. Moreover, both node strength and closeness were stable over time in both groups.

CONCLUSIONS: Our study suggests that interrelationships among symptoms and contributing factors are generally stable in adolescents, but a higher severity of depressive symptoms may lead to increased stability in these relationships.

RevDate: 2024-01-24

Diez P, Orosco L, Garcés Correa A, et al (2024)

Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study.

Medical & biological engineering & computing [Epub ahead of print].

Fatigue deteriorates the performance of a brain-computer interface (BCI) system; thus, reliable detection of fatigue is the first step to counter this problem. The fatigue evaluated by means of electroencephalographic (EEG) signals has been studied in many research projects, but widely different results have been reported. Moreover, there is scant research when considering the fatigue on steady-state visually evoked potential (SSVEP)-based BCI. Therefore, nowadays, fatigue detection is not a completely solved topic. In the current work, the issues found in the literature that led to the differences in the results are identified and saved by performing a new experiment on an SSVEP-based BCI system. The experiment was long enough to produce fatigue in the users, and different SSVEP stimulation ranges were used. Additionally, the EEG features commonly reported in the literature (EEG rhythms powers, SNR, etc.) were calculated as well as newly proposed features (spectral features and Lempel-Ziv complexity). The analysis was carried out on O1, Oz and O2 channels. This work found a tendency of displacement from high-frequency rhythms to low-frequency ones, and thus, better EEG features should present a similar behaviour. Then, the 'relative power' of EEG rhythms, the rates (θ + α)/β, α/β and θ/β, some spectral features (central and mean frequencies, asymmetry and kurtosis coefficients, etc.) and Lempel-Ziv complexity are proposed as reliable EEG features for fatigue detection. Hence, this set of features may be used to construct a more trustworthy fatigue index.

RevDate: 2024-01-27
CmpDate: 2024-01-26

Wei S, Jiang A, Sun H, et al (2024)

Shape-changing electrode array for minimally invasive large-scale intracranial brain activity mapping.

Nature communications, 15(1):715.

Large-scale brain activity mapping is important for understanding the neural basis of behaviour. Electrocorticograms (ECoGs) have high spatiotemporal resolution, bandwidth, and signal quality. However, the invasiveness and surgical risks of electrode array implantation limit its application scope. We developed an ultrathin, flexible shape-changing electrode array (SCEA) for large-scale ECoG mapping with minimal invasiveness. SCEAs were inserted into cortical surfaces in compressed states through small openings in the skull or dura and fully expanded to cover large cortical areas. MRI and histological studies on rats proved the minimal invasiveness of the implantation process and the high chronic biocompatibility of the SCEAs. High-quality micro-ECoG activities mapped with SCEAs from male rodent brains during seizures and canine brains during the emergence period revealed the spatiotemporal organization of different brain states with resolution and bandwidth that cannot be achieved using existing noninvasive techniques. The biocompatibility and ability to map large-scale physiological and pathological cortical activities with high spatiotemporal resolution, bandwidth, and signal quality in a minimally invasive manner offer SCEAs as a superior tool for applications ranging from fundamental brain research to brain-machine interfaces.

RevDate: 2024-01-24

Yang H, T Yanagisawa (2024)

Is Phantom Limb Awareness Necessary for the Treatment of Phantom Limb Pain?.

Neurologia medico-chirurgica [Epub ahead of print].

Phantom limb pain is attributed to abnormal sensorimotor cortical representations. Various feedback treatments have been applied to induce the reorganization of the sensorimotor cortical representations to reduce pain. We developed a training protocol using a brain-computer interface (BCI) to induce plastic changes in the sensorimotor cortical representation of phantom hand movements and demonstrated that BCI training effectively reduces phantom limb pain. By comparing the induced cortical representation and pain, the mechanisms worsening the pain have been attributed to the residual phantom hand representation. Based on our data obtained using neurofeedback training without explicit phantom hand movements and hand-like visual feedback, we suggest a direct relationship between cortical representation and pain. In this review, we summarize the results of our BCI training protocol and discuss the relationship between cortical representation and phantom limb pain. We propose a treatment for phantom limb pain based on real-time neuroimaging to induce appropriate cortical reorganization by monitoring cortical activities.

RevDate: 2024-01-24

Li C, Luo H, Lin X, et al (2024)

Laser-driven noncontact bubble transfer printing via a hydrogel composite stamp.

Proceedings of the National Academy of Sciences of the United States of America, 121(5):e2318739121.

Transfer printing that enables heterogeneous integration of materials into spatially organized, functional arrangements is essential for developing unconventional electronic systems. Here, we report a laser-driven noncontact bubble transfer printing via a hydrogel composite stamp, which features a circular reservoir filled with hydrogel inside a stamp body and encapsulated by a laser absorption layer and an adhesion layer. This composite structure of stamp provides a reversible thermal controlled adhesion in a rapid manner through the liquid-gas phase transition of water in the hydrogel. The ultrasoft nature of hydrogel minimizes the influence of preload on the pick-up performance, which offers a strong interfacial adhesion under a small preload for a reliable damage-free pick-up. The strong light-matter interaction at the interface induces a liquid-gas phase transition to form a bulge on the stamp surface, which eliminates the interfacial adhesion for a successful noncontact printing. Demonstrations of noncontact transfer printing of microscale Si platelets onto various challenging nonadhesive surfaces (e.g., glass, key, wrench, steel sphere, dry petal, droplet) in two-dimensional or three-dimensional layouts illustrate the unusual capabilities for deterministic assembly to develop unconventional electronic systems such as flexible inorganic electronics, curved electronics, and micro-LED display.

RevDate: 2024-01-25

Yu Z, Bu T, Zhang Y, et al (2024)

Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust relationship between stimuli and neural responses. Neural decoding aims to reconstruct the original stimuli that trigger neural responses. With the recent upsurge of artificial intelligence, neural decoding provides an insightful perspective for designing novel algorithms of brain-machine interface. For humans, vision is the dominant contributor to the interaction between the external environment and the brain. In this study, utilizing the retinal neural spike data collected over multi trials with visual stimuli of two movies with different levels of scene complexity, we used a neural network decoder to quantify the decoded visual stimuli with six different metrics for image quality assessment establishing comprehensive inspection of decoding. With the detailed and systematical study of the effect and single and multiple trials of data, different noise in spikes, and blurred images, our results provide an in-depth investigation of decoding dynamical visual scenes using retinal spikes. These results provide insights into the neural coding of visual scenes and services as a guideline for designing next-generation decoding algorithms of neuroprosthesis and other devices of brain-machine interface.

RevDate: 2024-01-25

Martínez-Saez MC, Ros L, López-Cano M, et al (2023)

Effect of popular songs from the reminiscence bump as autobiographical memory cues in aging: a preliminary study using EEG.

Frontiers in neuroscience, 17:1300751.

INTRODUCTION: Music has the capacity to evoke emotions and memories. This capacity is influenced by whether or not the music is from the reminiscence bump (RB) period. However, research on the neural correlates of the processes of evoking autobiographical memories through songs is scant. The aim of this study was to analyze the differences at the level of frequency band activation in two situations: (1) whether or not the song is able to generate a memory; and (2) whether or not the song is from the RB period.

METHODS: A total of 35 older adults (22 women, age range: 61-73 years) listened to 10 thirty-second musical clips that coincided with the period of their RB and 10 from the immediately subsequent 5 years (non-RB). To record the EEG signal, a brain-computer interface (BCI) with 14 channels was used. The signal was recorded during the 30-seconds of listening to each music clip.

RESULTS: The results showed differences in the activation levels of the frequency bands in the frontal and temporal regions. It was also found that the non-retrieval of a memory in response to a song clip showed a greater activation of low frequency waves in the frontal region, compared to the trials that did generate a memory.

DISCUSSION: These results suggest the importance of analyzing not only brain activation, but also neuronal functional connectivity at older ages, in order to better understand cognitive and emotional functions in aging.

RevDate: 2024-01-23

Vitória MA, Fernandes FG, van den Boom M, et al (2024)

Decoding Single and Paired Phonemes Using 7T Functional MRI.

Brain topography [Epub ahead of print].

Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speech by training classifiers based on the activity in the sensorimotor cortex related to the production of individual phonemes. To address this, we investigated the decodability of trials with individual and paired phonemes (pronounced consecutively with one second interval) using activity in the sensorimotor cortex. Fifteen participants pronounced 3 different phonemes and 3 combinations of two of the same phonemes in a 7T functional MRI experiment. We confirmed that support vector machine (SVM) classification of single and paired phonemes was possible. Importantly, by combining classifiers trained on single phonemes, we were able to classify paired phonemes with an accuracy of 53% (33% chance level), demonstrating that activity of isolated phonemes is present and distinguishable in combined phonemes. A SVM searchlight analysis showed that the phoneme representations are widely distributed in the ventral sensorimotor cortex. These findings provide insights about the neural representations of single and paired phonemes. Furthermore, it supports the notion that speech BCI may be feasible based on machine learning algorithms trained on individual phonemes using intracranial electrode grids.

RevDate: 2024-01-23

Suematsu N, Vazquez AL, TD Kozai (2024)

Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex.

bioRxiv : the preprint server for biology pii:2024.01.01.573814.

Objective . Intracortical microstimulation can be an effective method for restoring sensory perception in contemporary brain-machine interfaces. However, the mechanisms underlying better control of neuronal responses remain poorly understood, as well as the relationship between neuronal activity and other concomitant phenomena occurring around the stimulation site. Approach . Different microstimulation frequencies were investigated in vivo on Thy1-GCaMP6s mice using widefield and two-photon imaging to evaluate the evoked excitatory neural responses across multiple spatial scales as well as the induced hemodynamic responses. Specifically, we quantified stimulation-induced neuronal activation and depression in the mouse visual cortex and measured hemodynamic oxyhemoglobin and deoxyhemoglobin signals using mesoscopic-scale widefield imaging. Main results . Our calcium imaging findings revealed a preference for lower-frequency stimulation in driving stronger neuronal activation. A depressive response following the neural activation preferred a slightly higher frequency stimulation compared to the activation. Hemodynamic signals exhibited a comparable spatial spread to neural calcium signals. Oxyhemoglobin concentration around the stimulation site remained elevated during the post-activation (depression) period. Somatic and neuropil calcium responses measured by two-photon microscopy showed similar dependence on stimulation parameters, although the magnitudes measured in soma was greater than in neuropil. Furthermore, higher-frequency stimulation induced a more pronounced activation in soma compared to neuropil, while depression was predominantly induced in soma irrespective of stimulation frequencies. Significance . These results suggest that the mechanism underlying depression differs from activation, requiring ample oxygen supply, and affecting neurons. Our findings provide a novel understanding of evoked excitatory neuronal activity induced by intracortical microstimulation and offer insights into neuro-devices that utilize both activation and depression phenomena to achieve desired neural responses.

RevDate: 2024-01-23

Oby ER, Degenhart AD, Grigsby EM, et al (2024)

Dynamical constraints on neural population activity.

bioRxiv : the preprint server for biology pii:2024.01.03.573543.

The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface (BCI) to challenge monkeys to violate the naturally-occurring time courses of neural population activity that we observed in motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.

RevDate: 2024-01-23

Huang S, Paul U, Gupta S, et al (2024)

U.S. public perceptions of the sensitivity of brain data.

Journal of law and the biosciences, 11(1):lsad032 pii:lsad032.

As we approach an era of potentially widespread consumer neurotechnology, scholars and organizations worldwide have started to raise concerns about the data privacy issues these devices will present. Notably absent in these discussions is empirical evidence about how the public perceives that same information. This article presents the results of a nationwide survey on public perceptions of brain data, to inform discussions of law and policy regarding brain data governance. The survey reveals that the public may perceive certain brain data as less sensitive than other 'private' information, like social security numbers, but more sensitive than some 'public' information, like media preferences. The findings also reveal that not all inferences about mental experiences may be perceived as equally sensitive, and perhaps not all data should be treated alike in ethical and policy discussions. An enhanced understanding of public perceptions of brain data could advance the development of ethical and legal norms concerning consumer neurotechnology.

RevDate: 2024-01-26
CmpDate: 2024-01-26

Kim SJ, Lee DH, Kwak HG, et al (2024)

Toward Domain-Free Transformer for Generalized EEG Pre-Training.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 32:482-492.

Electroencephalography (EEG) signals are the brain signals acquired using the non-invasive approach. Owing to the high portability and practicality, EEG signals have found extensive application in monitoring human physiological states across various domains. In recent years, deep learning methodologies have been explored to decode the intricate information embedded in EEG signals. However, since EEG signals are acquired from humans, it has issues with acquiring enormous amounts of data for training the deep learning models. Therefore, previous research has attempted to develop pre-trained models that could show significant performance improvement through fine-tuning when data are scarce. Nonetheless, existing pre-trained models often struggle with constraints, such as the necessity to operate within datasets of identical configurations or the need to distort the original data to apply the pre-trained model. In this paper, we proposed the domain-free transformer, called DFformer, for generalizing the EEG pre-trained model. In addition, we presented the pre-trained model based on DFformer, which is capable of seamless integration across diverse datasets without necessitating architectural modification or data distortion. The proposed model achieved competitive performance across motor imagery and sleep stage classification datasets. Notably, even when fine-tuned on datasets distinct from the pre-training phase, DFformer demonstrated marked performance enhancements. Hence, we demonstrate the potential of DFformer to overcome the conventional limitations in pre-trained model development, offering robust applicability across a spectrum of domains.

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Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

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