@article {pmid39396023,
year = {2024},
author = {Zhou, H and Hong, T and Chen, X and Su, C and Teng, B and Xi, W and Cadet, JL and Yang, Y and Geng, F and Hu, Y},
title = {Glutamate concentration of medial prefrontal cortex is inversely associated with addictive behaviors: a translational study.},
journal = {Translational psychiatry},
volume = {14},
number = {1},
pages = {433},
pmid = {39396023},
issn = {2158-3188},
support = {81971245//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Glutamic Acid/metabolism ; *Prefrontal Cortex/metabolism/physiopathology ; Male ; Animals ; Rats ; Humans ; *Internet Addiction Disorder/physiopathology/metabolism ; *Behavior, Addictive/physiopathology/metabolism ; *Drug-Seeking Behavior/physiology ; Adult ; *Methamphetamine ; Young Adult ; Gyrus Cinguli/metabolism/physiopathology ; Female ; Disease Models, Animal ; Rats, Sprague-Dawley ; Translational Research, Biomedical ; Self Administration ; },
abstract = {In both preclinical and clinical settings, dysregulated frontostriatal circuits have been identified as the underlying neural substrates of compulsive seeking/taking behaviors manifested in substance use disorders and behavioral addictions including internet gaming disorder (IGD). However, the neurochemical substrates for these disorders remain elusive. The lack of comprehensive cognitive assessments in animal models has hampered our understanding of neural plasticity in addiction from these models. In this study, combining data from a rat model of compulsive taking/seeking and human participants with various levels of IGD severity, we investigated the relationship between regional glutamate (Glu) concentration and addictive behaviors. We found that Glu levels were significantly lower in the prelimbic cortex (PrL) of rats after 20-days of methamphetamine self-administration (SA), compared to controls. Glu concentration after a punishment phase negatively correlated with acute drug-seeking behavior. In addition, changes in Glu levels from a drug naïve state to compulsive drug taking patterns negatively correlated with drug-seeking during both acute and prolonged abstinence. The human data revealed a significant negative correlation between Glu concentration in the dorsal anterior cingulate cortex (dACC), the human PrL counterpart, and symptoms of IGD. Interestingly, there was a positive correlation between Glu levels in the dACC and self-control, as well as mindful awareness. Further analysis revealed that the dACC Glu concentration mediated the relationship between self-control/mindful awareness and IGD symptoms. These results provide convergent evidence for a protective role of dACC/PrL in addiction, suggesting interventions to enhance dACC glutamatergic functions as a potential strategy for addiction prevention and treatment.},
}

*Glutamic Acid/metabolism
*Prefrontal Cortex/metabolism/physiopathology
Male
Animals
Rats
Humans
*Internet Addiction Disorder/physiopathology/metabolism
*Behavior, Addictive/physiopathology/metabolism
*Drug-Seeking Behavior/physiology
Adult
*Methamphetamine
Young Adult
Gyrus Cinguli/metabolism/physiopathology
Female
Disease Models, Animal
Rats, Sprague-Dawley
Translational Research, Biomedical
Self Administration

@article {pmid39395910,
year = {2024},
author = {Fan, J and Wang, X and Xu, H},
title = {Sex-Differential Neural Circuits and Behavioral Responses for Empathy.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {39395910},
issn = {1995-8218},
}

@article {pmid39394849,
year = {2024},
author = {Li, Z and Meng, M},
title = {An SCA-based classifier for motor imagery EEG classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2024.2414069},
pmid = {39394849},
issn = {1476-8259},
abstract = {Efficient and accurate multi-class classification of electroencephalogram (EEG) signals poses a significant challenge in the development of motor imagery-based brain-computer interface (MI-BCI). Drawing inspiration from the sine cosine algorithm (SCA), a widely employed swarm intelligence algorithm for optimization problems, we proposed a novel population-based classification algorithm for EEG signals in this article. To fully leverage the characteristics contained in EEG signals, multi-scale sub-signals were constructed in terms of temporal windows and spectral bands simultaneously, and the common spatial pattern (CSP) features were extracted from each sub-signal. Subsequently, we integrated the multi-center optimal vectors mechanism into the classical SCA, resulting in the development of a multi-center SCA (MCSCA) classifier. During the classification stage, the label was assigned to the test trials by evaluating the Euclidean distance between their feature vectors and each optimal vector in MCSCA. Additionally, the weights of feature vectors were exploited to select the sub-signal of specific temporal windows and spectral bands for feature reduction, thereby declining computational effort and eliminating data redundancy. To validate the performance of the MCSCA classifier, we conducted four-class classification experiments using the BCI Competition IV dataset 2a, achieving an average classification accuracy of 71.89%. The experimental results show that the proposed algorithm offers a novel and effective approach for EEG classification.},
}

@article {pmid39391754,
year = {2024},
author = {Salari, V and O'Connor, R and Rodrigues, S and Oblak, D},
title = {Editorial: New approaches in Brain-Machine Interfaces with implants.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1485472},
pmid = {39391754},
issn = {1662-4548},
}

@article {pmid39391265,
year = {2024},
author = {Ren, C and Li, X and Gao, Q and Pan, M and Wang, J and Yang, F and Duan, Z and Guo, P and Zhang, Y},
title = {The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: a systematic review and meta-analysis.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1438095},
pmid = {39391265},
issn = {1662-5161},
abstract = {INTRODUCTION: Several clinical studies have demonstrated that brain-computer interfaces (BCIs) controlled functional electrical stimulation (FES) facilitate neurological recovery in patients with stroke. This review aims to evaluate the effectiveness of BCI-FES training on upper limb functional recovery in stroke patients.

METHODS: PubMed, Embase, Cochrane Library, Science Direct and Web of Science were systematically searched from inception to October 2023. Randomized controlled trials (RCTs) employing BCI-FES training were included. The methodological quality of the RCTs was assessed using the PEDro scale. Meta-analysis was conducted using RevMan 5.4.1 and STATA 18.

RESULTS: The meta-analysis comprised 290 patients from 10 RCTs. Results showed a moderate effect size in upper limb function recovery through BCI-FES training (SMD = 0.50, 95% CI: 0.26-0.73, I[2] = 0%, p < 0.0001). Subgroup analysis revealed that BCI-FES training significantly enhanced upper limb motor function in BCI-FES vs. FES group (SMD = 0.37, 95% CI: 0.00-0.74, I[2] = 21%, p = 0.05), and the BCI-FES + CR vs. CR group (SMD = 0.61, 95% CI: 0.28-0.95, I[2] = 0%, p = 0.0003). Moreover, BCI-FES training demonstrated effectiveness in both subacute (SMD = 0.56, 95% CI: 0.25-0.87, I[2] = 0%, p = 0.0004) and chronic groups (SMD = 0.42, 95% CI: 0.05-0.78, I[2] = 45%, p = 0.02). Subgroup analysis showed that both adjusting (SMD = 0.55, 95% CI: 0.24-0.87, I[2] = 0%, p = 0.0006) and fixing (SMD = 0.43, 95% CI: 0.07-0.78, I[2] = 46%, p = 0.02). BCI thresholds before training significantly improved motor function in stroke patients. Both motor imagery (MI) (SMD = 0.41 95% CI: 0.12-0.71, I[2] = 13%, p = 0.006) and action observation (AO) (SMD = 0.73, 95% CI: 0.26-1.20, I[2] = 0%, p = 0.002) as mental tasks significantly improved upper limb function in stroke patients.

DISCUSSION: BCI-FES has significant immediate effects on upper limb function in subacute and chronic stroke patients, but evidence for its long-term impact remains limited. Using AO as the mental task may be a more effective BCI-FES training strategy.

Identifier: CRD42023485744, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023485744.},
}

@article {pmid39391047,
year = {2024},
author = {Jang, M and Hays, M and Yu, WH and Lee, C and Caragiulo, P and Ramkaj, A and Wang, P and Phillips, AJ and Vitale, N and Tandon, P and Yan, P and Mak, PI and Chae, Y and Chichilnisky, EJ and Murmann, B and Muratore, DG},
title = {A 1024-Channel 268 nW/pixel 36×36 μm[2]/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces.},
journal = {IEEE journal of solid-state circuits},
volume = {59},
number = {4},
pages = {1123-1136},
pmid = {39391047},
issn = {0018-9200},
abstract = {This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 μm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 μVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 μm[2]) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.},
}

@article {pmid39389052,
year = {2024},
author = {Zheng, Z and Liu, Y and Mu, R and Guo, X and Feng, Y and Guo, C and Yang, L and Qiu, W and Zhang, Q and Yang, W and Dong, Z and Qiu, S and Dong, Y and Cui, Y},
title = {A small population of stress-responsive neurons in the hypothalamus-habenula circuit mediates development of depression-like behavior in mice.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2024.09.012},
pmid = {39389052},
issn = {1097-4199},
abstract = {Accumulating evidence has shown that various brain functions are associated with experience-activated neuronal ensembles. However, whether such neuronal ensembles are engaged in the pathogenesis of stress-induced depression remains elusive. Utilizing activity-dependent viral strategies in mice, we identified a small population of stress-responsive neurons, primarily located in the middle part of the lateral hypothalamus (mLH) and the medial part of the lateral habenula (LHbM). These neurons serve as "starter cells" to transmit stress-related information and mediate the development of depression-like behaviors during chronic stress. Starter cells in the mLH and LHbM form dominant connections, which are selectively potentiated by chronic stress. Silencing these connections during chronic stress prevents the development of depression-like behaviors, whereas activating these connections directly elicits depression-like behaviors without stress experience. Collectively, our findings dissect a core functional unit within the LH-LHb circuit that mediates the development of depression-like behaviors in mice.},
}

@article {pmid39386879,
year = {2024},
author = {Huang, Y and Yang, L and Yang, L and Xu, Z and Li, M and Shang, Z},
title = {Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy.},
journal = {Heliyon},
volume = {10},
number = {19},
pages = {e38113},
pmid = {39386879},
issn = {2405-8440},
abstract = {Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking.},
}

@article {pmid39385316,
year = {2024},
author = {Wang, D and Guo, X and Huang, Q and Wang, Z and Chen, J and Hu, S},
title = {Efficacy and Safety of Transcranial Direct Current Stimulation as an Add-On Trial Treatment for Acute Bipolar Depression Patients With Suicidal Ideation.},
journal = {CNS neuroscience & therapeutics},
volume = {30},
number = {10},
pages = {e70077},
doi = {10.1111/cns.70077},
pmid = {39385316},
issn = {1755-5949},
support = {81971271//National Natural Science Foundation of China/ ; 82201675//National Natural Science Foundation of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; LQ20H090012//Natural Science Foundation of Zhejiang Province/ ; 2020KY134//Health and Family Planning Commission of Zhejiang Province/ ; },
mesh = {Humans ; *Bipolar Disorder/therapy/psychology ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; Adult ; Double-Blind Method ; *Suicidal Ideation ; Middle Aged ; Treatment Outcome ; Psychiatric Status Rating Scales ; Quetiapine Fumarate/therapeutic use ; Young Adult ; Antipsychotic Agents/therapeutic use/adverse effects ; Combined Modality Therapy/methods ; },
abstract = {AIMS: Bipolar depression poses an overwhelming suicide risk. We aimed to examine the efficacy and safety of transcranial direct current stimulation (tDCS) combined with quetiapine in bipolar patients as a suicidal intervention.

METHODS: In a single-center, double-blind, treatment-naive bipolar depression patients with suicidal ideation were randomly assigned to quetiapine in combination with either active (n = 16) or sham (n = 15) tDCS over the left dorsolateral prefrontal cortex for three consecutive weeks. The 30-min, 2-mA tDCS was conducted twice a day on the weekday of the first week and then once a day on the weekdays of the two following weeks. Primary efficacy outcome measure was the change in the Beck Scale for Suicidal Ideation (BSSI). Secondary outcomes included changes on the 17-item Hamilton Depression Rating Scale (HDRS-17) and Montgomery-Asberg Depression Rating Scale (MADRS). Outcome was evaluated on Day 3 and weekend. Safety outcome was based on the reported adverse reactions.

RESULTS: Active tDCS was superior to sham tDCS on the BSSI at Day 3 and tended to sustain every weekend during the treatment process, compared to baseline. However, no difference between active and sham in HDRS-17 and MADRS was found. Response and remission rate also supported the antisuicide effect of tDCS, with higher response and remission rate in BSSI, but no antidepressant effect, compared to sham, over time. Regarding safety, active tDCS was well tolerated and all the adverse reactions reported were mild and limited to transient scalp discomfort.

CONCLUSION: The tDCS was effective as an antisuicide treatment for acute bipolar depression patients with suicidal ideation, with minimal side effects reported.},
}

Humans
*Bipolar Disorder/therapy/psychology
Male
Female
*Transcranial Direct Current Stimulation/methods
Adult
Double-Blind Method
*Suicidal Ideation
Middle Aged
Treatment Outcome
Psychiatric Status Rating Scales
Quetiapine Fumarate/therapeutic use
Young Adult
Antipsychotic Agents/therapeutic use/adverse effects
Combined Modality Therapy/methods

@article {pmid39368606,
year = {2024},
author = {Takemi, M and Tia, B and Kosugi, A and Castagnola, E and Ansaldo, A and Ricci, D and Fadiga, L and Ushiba, J and Iriki, A},
title = {Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation.},
journal = {Neuroscience},
volume = {560},
number = {},
pages = {263-271},
doi = {10.1016/j.neuroscience.2024.09.047},
pmid = {39368606},
issn = {1873-7544},
abstract = {Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state.},
}

@article {pmid39384601,
year = {2024},
author = {Li, D and Li, K and Xia, Y and Dong, J and Lu, R},
title = {Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {23549},
pmid = {39384601},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Neural Networks, Computer ; Imagination ; Brain/physiology ; },
abstract = {In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
*Algorithms
*Neural Networks, Computer
Imagination
Brain/physiology

@article {pmid39383883,
year = {2024},
author = {Ottenhoff, MC and Verwoert, M and Goulis, S and Wagner, L and van Dijk, JP and Kubben, PL and Herff, C},
title = {Global motor dynamics - invariant neural representations of motor behavior in distributed brain-wide recordings.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad851c},
pmid = {39383883},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.

APPROACH: Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.

MAIN RESULTS: We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.

SIGNIFICANCE: Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.},
}

@article {pmid39383715,
year = {2024},
author = {Li, K and Qian, L and Zhang, C and Li, R and Zeng, J and Xue, C and Deng, W},
title = {Deep transcranial magnetic stimulation for treatment-resistant obsessive-compulsive disorder: A meta-analysis of randomized-controlled trials.},
journal = {Journal of psychiatric research},
volume = {180},
number = {},
pages = {96-102},
doi = {10.1016/j.jpsychires.2024.09.043},
pmid = {39383715},
issn = {1879-1379},
abstract = {BACKGROUND: Deep transcranial magnetic stimulation (dTMS), an advancement of transcranial magnetic stimulation, was created to reach wider and possibly more profound regions of the brain. At present, there is insufficient high-quality evidence to support the effectiveness and safety of dTMS in treating obsessive-compulsive disorder (OCD).

OBJECTIVE: This study used a meta-analysis to evaluate the effectiveness and safety of dTMS for treating OCD.

METHODS: Four randomized controlled trials were found by searching PubMed, Embase, Web of Science, and Cochrane Library up to February 2024. The fixed effects meta-analysis model was used for the purpose of data merging in Stata17. The risk ratio (RR) value was used as the measure of effect size to compare response rates and dropout rates between active and sham dTMS.

RESULTS: The meta-analysis included four randomized-controlled trials involving 252 patients with treatment-resistant OCD. Active dTMS showed a notably greater rate of response on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) in comparison to sham dTMS after treatment (Y-BOCS: RR = 3.71, 95% confidence interval [CI] 2.06 to 6.69) and at the one-month follow-up (Y-BOCS: RR = 2.60, 95% CI 1.59 to 4.26). Subgroup analysis revealed that active dTMS with H-coils was more effective than sham dTMS (RR = 3.57, 95%CI 1.93 to 6.60). No serious adverse events were documented in the studies that were included.

CONCLUSION: The findings suggest that dTMS demonstrates notable efficacy and safety in treating patients with treatment-resistant OCD compared to sham dTMS, with sustained effectiveness noted throughout the one-month post-treatment period.},
}

@article {pmid39381774,
year = {2024},
author = {Pilacinski, A and Christ, L and Boshoff, M and Iossifidis, I and Adler, P and Miro, M and Kuhlenkötter, B and Klaes, C},
title = {Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1383089},
pmid = {39381774},
issn = {1662-5218},
abstract = {Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.},
}

@article {pmid39379912,
year = {2024},
author = {Chen, B and Dong, J and Guo, W and Li, T},
title = {Sex-specific associations between levels of high-sensitivity C-reactive protein and severity of depression: retrospective cross-sectional analysis of inpatients in China.},
journal = {BMC psychiatry},
volume = {24},
number = {1},
pages = {667},
pmid = {39379912},
issn = {1471-244X},
support = {82230046//Key Project of the National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: We aimed to clarify the controversial relationship between levels of high-sensitivity C-reactive protein (hs-CRP) and severity of depression in men and women.

METHODS: Medical records were retrospectively analyzed for 1,236 inpatients at our medical center who were diagnosed with depression at discharge between January 2018 and August 2022. Depression severity was assessed during hospitalization using the 24-item Hamilton Depression Rating Scale. Potential associations between severity scores and hs-CRP levels were explored using multivariate linear regression as well as smooth curve fitting to detect non-linear patterns.

RESULTS: In male patients, hs-CRP levels between 2.00 mg/L and 10.00 mg/L showed a non-linear association with depression severity overall (fully adjusted β = 1.69, 95% CI 0.65 to 2.72), as well as with severity of specific symptoms such as hopelessness, sluggishness, and cognitive disturbance. In female patients, hs-CRP levels showed a linear association with severity of cognitive disturbance (fully adjusted β = 0.07, 95% CI 0.01 to 0.12). These results remained significant after adjusting for age, body mass index, diabetes, hypertension, history of drinking, history of smoking, and estradiol levels.

DISCUSSION: Levels of hs-CRP show sex-specific associations with depression severity, particularly levels between 2.00 and 10.00 mg/L in men. These findings may help develop personalized anti-inflammatory treatments for depression, particularly for men with hs-CRP levels of 2.00-10.00 mg/L.},
}

@article {pmid39378126,
year = {2024},
author = {Li, J and Wu, W and Chen, J and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P},
title = {Development and safety of investigational and approved drugs targeting the RAS function regulation in RAS mutant cancers.},
journal = {Toxicological sciences : an official journal of the Society of Toxicology},
volume = {},
number = {},
pages = {},
doi = {10.1093/toxsci/kfae129},
pmid = {39378126},
issn = {1096-0929},
abstract = {The RAS gene family holds a central position in controlling key cellular activities such as migration, survival, metabolism, and other vital biological processes. The activation of RAS signaling cascades is instrumental in the development of various cancers. Although several RAS inhibitors have gained approval from the United States Food and Drug Administration (FDA) for their substantial antitumor effects, their widespread and severe adverse reactions significantly curtail their practical usage in the clinic. Thus, there exists a pressing need for a comprehensive understanding of these adverse events, ensuring the clinical safety of RAS inhibitors through the establishment of precise management guidelines, suitable intermittent dosing schedules, and innovative combination regimens. This review centers on the evolution of RAS inhibitors in cancer therapy, delving into the common adverse effects associated with these inhibitors, their underlying mechanisms, and the potential strategies for mitigation.},
}

@article {pmid39376540,
year = {2024},
author = {Giove, F and Zuo, XN and Calhoun, VD},
title = {Editorial: Insights in brain imaging methods: 2023.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1488845},
doi = {10.3389/fnins.2024.1488845},
pmid = {39376540},
issn = {1662-4548},
}

@article {pmid39375394,
year = {2024},
author = {Katoozian, D and Hosseini-Nejad, H and Dehaqani, MA},
title = {A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {23291},
pmid = {39375394},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Animals ; *Algorithms ; *Macaca mulatta ; Neurons/physiology ; Male ; Humans ; },
abstract = {In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm[2], and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.},
}

*Brain-Computer Interfaces
Animals
*Algorithms
*Macaca mulatta
Neurons/physiology
Male
Humans

@article {pmid39374625,
year = {2024},
author = {Abbasi, MAA and Abbasi, HF and Yu, X and Aziz, MZ and Yih, NTJY and Fan, Z},
title = {E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad83f4},
pmid = {39374625},
issn = {1741-2552},
abstract = {The advancements in Brain-Computer Interface (BCI) have substantially evolved people's lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Specifically, for E-SAT, ELM is employed both to imrpove self-attention module generalization ability for feature extraction and to optimize the model's parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performances on different MI EEG signals. Extensive experiments with different datasets, such as BCI Competition III Dataset IV-a, IV-b and BCI Competition IV Datasets 1,2a,2b,3, are conducted to verify the effectiveness of proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art (SOTA) existing methods in subject-specific classification on all the datasets, with an average classification accuracy of 99.8%,99.1%,98.9%,75.8%, 90.8%, and 95.4%, being achieved for each datasets, respectively. The experimental results not only show outstanding performance of E-SAT in feature extractions, but also demonstrate that it helps achieves the best results among nine other robust ones. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datatsets. .},
}

@article {pmid39374272,
year = {2024},
author = {Lv, R and Chang, W and Yan, G and Nie, W and Zheng, L and Guo, B and Sadiq, MT},
title = {A novel recognition and classification approach for motor imagery based on spatio-temporal features.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3464550},
pmid = {39374272},
issn = {2168-2208},
abstract = {Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.},
}

@article {pmid39373084,
year = {2024},
author = {Kong, F and He, F and Chisholm, RA},
title = {High beta diversity of gaps contributes to plot-level tree diversity in a tropical forest.},
journal = {Ecology},
volume = {},
number = {},
pages = {e4443},
doi = {10.1002/ecy.4443},
pmid = {39373084},
issn = {1939-9170},
support = {//China Scholarship Council/ ; },
abstract = {Canopy gaps are widely recognized as being crucial for maintaining the diversity of forest tree communities. But empirical studies have found mixed results because the differences in diversity between individual gaps and non-gaps are often small and statistically undetectable. One overlooked factor, however, is how small individual gap versus non-gap differences may accumulate across sites and potentially have a large effect on forest diversity at the plot scale. Our study investigated sapling richness, density, and composition in 124 treefall gaps, and 200 non-gap sites in the 50-ha tropical forest plot at Barro Colorado Island (BCI), Panama. Additionally, we analyzed species accumulation curves to understand how species richness increases with increasing stem numbers. We observed that sapling richness and density were only slightly higher in gaps 7 years after formation and statistically indistinguishable from non-gaps after 12 years. However, species accumulation curves across multiple gaps were substantially higher than those across non-gaps. Species composition showed small differences between individual gaps and non-gaps but differed significantly between collections of gaps and non-gaps. Specifically, 55 species specialized in 7-year-old gaps compared with 24 in non-gaps; of these, 23 gap-specialized species and zero non-gap species were pioneers. Our results indicate that tree species richness is higher in gaps because of both higher stem density and the presence of gap-specialized species. Our study has finally provided compelling evidence to support the idea that gaps enhance the overall diversity of tropical forest tree communities.},
}

@article {pmid39372246,
year = {2024},
author = {Sakel, M and Saunders, K and Ozolins, C and Biswas, R},
title = {Feasibility and Safety of a Home-based Electroencephalogram Neurofeedback Intervention to Reduce Chronic Neuropathic Pain: A Cohort Clinical Trial.},
journal = {Archives of rehabilitation research and clinical translation},
volume = {6},
number = {3},
pages = {100361},
pmid = {39372246},
issn = {2590-1095},
abstract = {OBJECTIVE: To evaluate the feasibility, safety, and potential health benefits of an 8-week home-based neurofeedback intervention.

DESIGN: Single-group preliminary study.

SETTING: Community-based.

PARTICIPANTS: Nine community dwelling adults with chronic neuropathic pain, 6 women and 3 men, with an average age of 51.9 years (range, 19-78 years) and with a 7-day average minimum pain score of 4 of 10 on the visual analog pain scale.

INTERVENTIONS: A minimum of 5 neurofeedback sessions per week (40min/session) for 8 consecutive weeks was undertaken with a 12-week follow-up baseline electroencephalography recording period.

MAIN OUTCOME MEASURES: Primary feasibility outcomes: accessibility, tolerability, safety (adverse events and resolution), and human and information technology (IT) resources required. Secondary outcomes: pain, sensitization, catastrophization, anxiety, depression, sleep, health-related quality of life, electroencephalographic activity, and simple participant feedback.

RESULTS: Of the 23 people screened, 11 were eligible for recruitment. One withdrew and another completed insufficient sessions for analysis, which resulted in 9 datasets analyzed. Three participants withdrew from the follow-up baselines, leaving 6 who completed the entire trial protocol. Thirteen adverse events were recorded and resolved: 1 was treatment-related, 4 were equipment-related, and 8 were administrative-related (eg, courier communication issues). The human and IT resources necessary for trial implementation were identified. There were also significant improvements in pain levels, depression, and anxiety. Six of 9 participants perceived minimal improvement or no change in symptoms after the trial, and 5 of 9 participants were satisfied with the treatment received.

CONCLUSIONS: It is feasible and safe to conduct a home-based trial of a neurofeedback intervention for people with chronic neuropathic pain, when the human and IT resources are provided and relevant governance processes are followed. Improvements in secondary outcomes merit investigation with a randomized controlled trial.},
}

@article {pmid39371523,
year = {2024},
author = {Jin, W and Zhu, X and Qian, L and Wu, C and Yang, F and Zhan, D and Kang, Z and Luo, K and Meng, D and Xu, G},
title = {Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.},
journal = {Frontiers in computational neuroscience},
volume = {18},
number = {},
pages = {1431815},
pmid = {39371523},
issn = {1662-5188},
abstract = {Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.},
}

@article {pmid39371161,
year = {2024},
author = {Angrick, M and Luo, S and Rabbani, Q and Joshi, S and Candrea, DN and Milsap, GW and Gordon, CR and Rosenblatt, K and Clawson, L and Maragakis, N and Tenore, FV and Fifer, MS and Ramsey, NF and Crone, NE},
title = {Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.09.18.24313755},
pmid = {39371161},
abstract = {Objective . Brain-Computer Interfaces (BCIs) hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training - a major challenge when translating such approaches to people who have already lost their voice. Approach . In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We then used the estimated speech segments to train a voice activity detection (VAD) model using only ECoG signals. We evaluated our approach using held-out open-loop recordings of a single dysarthric clinical trial participant living with ALS, and we compared the resulting performance to previous solutions trained with ground truth acoustic voice recordings. Main results . Our approach achieves a median error rate of around 0.5 seconds with respect to the actual spoken speech. Embedded into a real-time BCI, our approach is capable of providing VAD results with a latency of only 10 ms. Significance . To the best of our knowledge, our results show for the first time that speech activity can be predicted purely from unlabeled ECoG signals, a crucial step toward individuals who cannot provide this information anymore due to their neurological condition, such as patients with locked-in syndrome. Clinical Trial Information . ClinicalTrials.gov, registration number NCT03567213 .},
}

@article {pmid39369514,
year = {2024},
author = {Wang, Y and Han, M and Jing, L and Jia, Q and Lv, S and Xu, Z and Liu, J and Cai, X},
title = {Enhanced neural activity detection with microelectrode arrays modified by drug-loaded calcium alginate/chitosan hydrogel.},
journal = {Biosensors & bioelectronics},
volume = {267},
number = {},
pages = {116837},
doi = {10.1016/j.bios.2024.116837},
pmid = {39369514},
issn = {1873-4235},
abstract = {Microelectrode arrays (MEAs) are pivotal brain-machine interface devices that facilitate in situ and real-time detection of neurophysiological signals and neurotransmitter data within the brain. These capabilities are essential for understanding neural system functions, treating brain disorders, and developing advanced brain-machine interfaces. To enhance the performance of MEAs, this study developed a crosslinked hydrogel coating of calcium alginate (CA) and chitosan (CS) loaded with the anti-inflammatory drug dexamethasone sodium phosphate (DSP). By modifying the MEAs with this hydrogel and various conductive nanomaterials, including platinum nanoparticles (PtNPs) and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS), the electrical properties and biocompatibility of the electrodes were optimized. The hydrogel coating matches the mechanical properties of brain tissue more effectively and, by actively releasing anti-inflammatory drugs, significantly reduces post-implantation tissue inflammation, extends the electrodes' lifespan, and enhances the quality of neural activity detection. Additionally, this modification ensures high sensitivity and specificity in the detection of dopamine (DA), displaying high-quality dual-mode neural activity during in vivo testing and revealing significant functional differences between neuron types under various physiological states (anesthetized and awake). Overall, this study showcases the significant application value of bioactive hydrogels as excellent nanobiointerfaces and drug delivery carriers for long-term neural monitoring. This approach has the potential to enhance the functionality and acceptance of brain-machine interface devices in medical practice and has profound implications for future neuroscience research and the development of strategies for treating neurological diseases.},
}

@article {pmid39369011,
year = {2024},
author = {Peng, Z and Tong, L and Shi, W and Xu, L and Huang, X and Li, Z and Yu, X and Meng, X and He, X and Lv, S and Yang, G and Hao, H and Jiang, T and Miao, X and Ye, L},
title = {Multifunctional human visual pathway-replicated hardware based on 2D materials.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {8650},
pmid = {39369011},
issn = {2041-1723},
support = {62222404, 62304084 and 92248304//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Visual Pathways/physiology ; *Retina/physiology ; *Brain-Computer Interfaces ; Visual Cortex/physiology ; Tungsten/chemistry ; Robotics/instrumentation ; Selenium/chemistry ; Artificial Intelligence ; },
abstract = {Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red-green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain-computer interfaces, and intelligent robotics.},
}

Humans
*Visual Pathways/physiology
*Retina/physiology
*Brain-Computer Interfaces
Visual Cortex/physiology
Tungsten/chemistry
Robotics/instrumentation
Selenium/chemistry
Artificial Intelligence

@article {pmid39368632,
year = {2024},
author = {Hu, J and Chen, C and Wu, M and Zhang, J and Meng, F and Li, T and Luo, B},
title = {Assessing consciousness in acute coma using name-evoked responses.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111091},
doi = {10.1016/j.brainresbull.2024.111091},
pmid = {39368632},
issn = {1873-2747},
abstract = {Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients' relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject's own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.},
}

@article {pmid39366386,
year = {2024},
author = {Zhang, L and Wang, HL and Zhang, YF and Mao, XT and Wu, TT and Huang, ZH and Jiang, WJ and Fan, KQ and Liu, DD and Yang, B and Zhuang, MH and Huang, GM and Liang, Y and Zhu, SJ and Zhong, JY and Xu, GY and Li, XM and Cao, Q and Li, YY and Jin, J},
title = {Stress triggers irritable bowel syndrome with diarrhea through a spermidine-mediated decline in type I interferon.},
journal = {Cell metabolism},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cmet.2024.09.002},
pmid = {39366386},
issn = {1932-7420},
abstract = {Irritable bowel syndrome with diarrhea (IBS-D) is a common and chronic gastrointestinal disorder that is characterized by abdominal discomfort and occasional diarrhea. The pathogenesis of IBS-D is thought to be related to a combination of factors, including psychological stress, abnormal muscle contractions, and inflammation and disorder of the gut microbiome. However, there is still a lack of comprehensive analysis of the logical regulatory correlation among these factors. In this study, we found that stress induced hyperproduction of xanthine and altered the abundance and metabolic characteristics of Lactobacillus murinus in the gut. Lactobacillus murinus-derived spermidine suppressed the basal expression of type I interferon (IFN)-α in plasmacytoid dendritic cells by inhibiting the K63-linked polyubiquitination of TRAF3. The reduction in IFN-α unrestricted the contractile function of colonic smooth muscle cells, resulting in an increase in bowel movement. Our findings provided a theoretical basis for the pathological mechanism of, and new drug targets for, stress-exposed IBS-D.},
}

@article {pmid39366088,
year = {2024},
author = {Pan, Y and Sequestro, M and Golkar, A and Olsson, A},
title = {Handholding reduces the recovery of threat memories and magnifies prefrontal hemodynamic responses.},
journal = {Behaviour research and therapy},
volume = {183},
number = {},
pages = {104641},
doi = {10.1016/j.brat.2024.104641},
pmid = {39366088},
issn = {1873-622X},
abstract = {Human touch is a powerful means of social and affective regulation, promoting safety behaviors. Yet, despite its importance across human contexts, it remains unknown how touch can promote the learning of new safety memories and what neural processes underlie such effects. The current study used measures of peripheral physiology and brain activity to examine the effects of interpersonal touch during safety learning (extinction) on the recovery of previously learned threat. We observed that handholding during extinction significantly reduced threat recovery, which was reflected in enhanced prefrontal hemodynamic responses. This effect was absent when learners were instructed to hold a rubber ball, independent of the presence of their partners. Our findings indicate that social touch contributes to safety learning, potentially influencing threat memories via prefrontal circuitry.},
}

@article {pmid39367153,
year = {2024},
author = {Miroshnikov, A and Yakovlev, L and Syrov, N and Vasilyev, A and Berkmush-Antipova, A and Golovanov, F and Kaplan, A},
title = {Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.},
journal = {Brain topography},
volume = {38},
number = {1},
pages = {4},
pmid = {39367153},
issn = {1573-6792},
support = {21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; },
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Imagination/physiology ; Male ; Female ; Adult ; *Hemodynamics/physiology ; Young Adult ; *Brain Mapping/methods ; Touch Perception/physiology ; Touch/physiology ; Somatosensory Cortex/physiology/diagnostic imaging ; Brain/physiology/diagnostic imaging ; Motor Cortex/physiology/diagnostic imaging ; },
abstract = {Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.},
}

Humans
*Spectroscopy, Near-Infrared/methods
*Imagination/physiology
Male
Female
Adult
*Hemodynamics/physiology
Young Adult
*Brain Mapping/methods
Touch Perception/physiology
Touch/physiology
Somatosensory Cortex/physiology/diagnostic imaging
Brain/physiology/diagnostic imaging
Motor Cortex/physiology/diagnostic imaging

@article {pmid39366955,
year = {2024},
author = {Ullah, R and Xue, C and Wang, S and Qin, Z and Rauf, N and Zhan, S and Khan, NU and Shen, Y and Zhou, YD and Fu, J},
title = {Alternate-day fasting delays pubertal development in normal-weight mice but prevents high-fat diet-induced obesity and precocious puberty.},
journal = {Nutrition & diabetes},
volume = {14},
number = {1},
pages = {82},
pmid = {39366955},
issn = {2044-4052},
support = {82350410491//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82370863//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Diet, High-Fat/adverse effects ; *Puberty, Precocious/etiology/prevention & control ; Female ; Mice ; *Fasting ; *Obesity/prevention & control/etiology ; Sexual Maturation/physiology ; Pregnancy ; Mice, Inbred C57BL ; Growth Hormone/blood ; },
abstract = {BACKGROUND/OBJECTIVES: Childhood obesity, particularly in girls, is linked to early puberty onset, heightening risks for adult-onset diseases. Addressing childhood obesity and precocious puberty is vital to mitigate societal burdens. Despite existing costly and invasive medical interventions, introducing lifestyle-based alternatives is essential. Our study investigates alternate-day fasting's (ADF) impact on pubertal development in normal-weight and high-fat diet (HFD)-induced obese female mice.

METHODS: Four groups of female mice were utilized, with dams initially fed control chow during and before pregnancy. Post-parturition, two groups continued on control chow, while two switched to an HFD. Offspring diets mirrored maternal exposure. One control and one HFD group were subjected to ADF. Morphometry and hormone analyses at various time points were performed.

RESULTS: Our findings demonstrate that ADF in normal-weight mice led to reduced body length, weight, uterine, and ovarian weights, accompanied by delayed puberty and lower levels of sex hormones and growth hormone (GH). Remarkably, GH treatment effectively prevented ADF-induced growth reduction but did not prevent delayed puberty. Conversely, an HFD increased body length, induced obesity and precocious puberty, and altered sex hormones and leptin levels, which were counteracted by ADF regimen. Our data indicate ADF's potential in managing childhood obesity and precocious puberty.

CONCLUSIONS: ADF reduced GH and sex hormone levels, contributing to reduced growth and delayed puberty, respectively. Therefore, parents of normal-weight children should be cautious about prolonged overnight fasting. ADF prevented HFD-induced obesity and precocious puberty, offering an alternative to medical approaches; nevertheless, further studies are needed for translation into clinical practice.},
}

Animals
*Diet, High-Fat/adverse effects
*Puberty, Precocious/etiology/prevention & control
Female
Mice
*Fasting
*Obesity/prevention & control/etiology
Sexual Maturation/physiology
Pregnancy
Mice, Inbred C57BL
Growth Hormone/blood

@article {pmid39365711,
year = {2024},
author = {Wang, Y and Wang, J and Wang, W and Su, J and Bunterngchit, C and Hou, ZG},
title = {TFTL: A Task-Free Transfer Learning Strategy for EEG-based Cross-Subject & Cross-Dataset Motor Imagery BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3474049},
pmid = {39365711},
issn = {1558-2531},
abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly user experience, while the inadequacy of EEG data hinders the performance of deep learning models. To address these challenges, a task-free transfer learning strategy (TFTL) for EEG-based cross-subject & cross-dataset MI-BCI is proposed for calibration time reduction and multi-center data co-modeling.

METHODS: TFTL strategy consists of data alignment, shared feature extractor, and specific classifiers, in which the label predictor for MI tasks classification, as well as domain and dataset discriminator for inter-subject variability reduction are concurrently optimized for knowledge transfer from subjects across different datasets to the target subject. Moreover, only resting data of the target subject is used for subject-specific model construction to achieve task-free.

RESULTS: We employed three deep learning methods (ShallowConvNet, EEGNet, and TCNet-Fusion) as baseline approaches to evaluate the effectiveness of the proposed strategy on five datasets (BCIC IV Dataset 2a, Dataset 1, Physionet MI, Dreyer 2023, and OpenBMI). The results demonstrate a significant improvement with the inclusion of the TFTL strategy compared to the baseline methods, reaching a maximum enhancement of 15.67% with a statistical significance (p=2.4e-5<0.05). Moreover, task-free resulted in MI trials needed for calibration being 0 for all datasets, which significantly alleviated the calibration burden for patients before usage.

CONCLUSION/SIGNIFICANCE: The proposed TFTL strategy effectively addresses challenges posed by prolonged calibration periods and insufficient EEG data, thus promoting MI-BCI from laboratory to clinical application.},
}

@article {pmid39362975,
year = {2024},
author = {Yu, H and Cao, W and Fang, T and Jin, J and Pei, G},
title = {EEG β oscillations in aberrant data perception under cognitive load modulation.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {22995},
pmid = {39362975},
issn = {2045-2322},
support = {LGG21G010002//Zhejiang Provincial Natural Science Foundation of China/ ; LQ22C090007//Zhejiang Provincial Natural Science Foundation of China/ ; 72271166//National Natural Science Foundation of China/ ; 72401263//National Natural Science Foundation of China/ ; 2023KFKT003//Open Research Project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University/ ; },
mesh = {Humans ; Male ; *Cognition/physiology ; Female ; *Electroencephalography ; Adult ; Young Adult ; Decision Making/physiology ; Beta Rhythm/physiology ; Brain/physiology ; Perception/physiology ; },
abstract = {Data-driven decision making (DDDM) is becoming an indispensable component of work across various fields, and the perception of aberrant data (PAD) has emerged as an essential skill. Nonetheless, the neural processing mechanisms underpinning PAD remain incompletely elucidated. Direct evidence linking neural oscillations to PAD is currently lacking, and the impact of cognitive load remains ambiguous. We address this issue using EEG time-frequency analysis. Data were collected from 21 healthy participants. The experiment employed a 2 (low vs. high cognitive load) × 2 [PAD+ (aberrant data accurately identified as aberrant) vs. PAD- (non-aberrant data correctly recognized as normal)] within-subject laboratory design. Results indicate that upper β band oscillations (26-30 Hz) were significantly enhanced in the PAD + condition compared to PAD-, with consistent activity observed in the frontal (p < 0.001, [Formula: see text] = 0.41) and parietal lobes (p = 0.028, [Formula: see text] = 0.22) within the 300-350 ms time window. Additionally, as cognitive load increased, the time window of β oscillations for distinguishing PAD+ from PAD- shifted earlier. This study enriches our understanding of the PAD neural basis by exploring the distribution of neural oscillation frequencies, decision-making neural circuits, and the windowing effect induced by cognitive load. These findings have significant implications for elucidating the pathological mechanisms of neurodegenerative disorders, as well as in the initial screening, intervention, and treatment of diseases.},
}

Humans
Male
*Cognition/physiology
Female
*Electroencephalography
Adult
Young Adult
Decision Making/physiology
Beta Rhythm/physiology
Brain/physiology
Perception/physiology

@article {pmid39358539,
year = {2024},
author = {Drew, L},
title = {United States sets the pace for implantable brain-computer interfaces.},
journal = {Nature},
volume = {634},
number = {8032},
pages = {S8-S10},
doi = {10.1038/d41586-024-03046-5},
pmid = {39358539},
issn = {1476-4687},
mesh = {Humans ; *Brain-Computer Interfaces/statistics & numerical data/trends ; *Electrodes, Implanted/statistics & numerical data/trends ; United States ; },
}

Humans
*Brain-Computer Interfaces/statistics & numerical data/trends
*Electrodes, Implanted/statistics & numerical data/trends
United States

@article {pmid39358411,
year = {2024},
author = {Andreu-Sánchez, C and Martín-Pascual, MÁ and Gruart, A and Delgado-García, JM},
title = {Differences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {22921},
pmid = {39358411},
issn = {2045-2322},
support = {PID2021-122446NB-100//Ministerio de Ciencia e Innovación/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovación/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovación/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovación/ ; BIO-122//Junta de Andalucía/ ; BIO-122//Junta de Andalucía/ ; },
mesh = {Humans ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Somatosensory Cortex/physiology ; *Hand Strength/physiology ; Young Adult ; Psychomotor Performance/physiology ; Photic Stimulation ; Brain Waves/physiology ; Visual Perception/physiology ; Brain-Computer Interfaces ; Motor Activity/physiology ; },
abstract = {Mu rhythm (∼8-12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects' brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces.},
}

Humans
Male
Female
Adult
*Electroencephalography/methods
*Somatosensory Cortex/physiology
*Hand Strength/physiology
Young Adult
Psychomotor Performance/physiology
Photic Stimulation
Brain Waves/physiology
Visual Perception/physiology
Brain-Computer Interfaces
Motor Activity/physiology

@article {pmid39358021,
year = {2024},
author = {Graczyk, E and Hutchison, B and Valle, G and Bjanes, D and Gates, D and Raspopovic, S and Gaunt, R},
title = {Clinical Applications and Future Translation of Somatosensory Neuroprostheses.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {40},
pages = {},
pmid = {39358021},
issn = {1529-2401},
mesh = {Humans ; *Somatosensory Cortex/physiology ; Feedback, Sensory/physiology ; Translational Research, Biomedical/trends/methods ; Neural Prostheses ; Brain-Computer Interfaces/trends ; Electric Stimulation/methods ; Prostheses and Implants/trends ; },
abstract = {Somatosensory neuroprostheses restore, replace, or enhance tactile and proprioceptive feedback for people with sensory impairments due to neurological disorders or injury. Somatosensory neuroprostheses typically couple sensor inputs from a wearable device, prosthesis, robotic device, or virtual reality system with electrical stimulation applied to the somatosensory nervous system via noninvasive or implanted interfaces. While prior research has mainly focused on technology development and proof-of-concept studies, recent acceleration of clinical studies in this area demonstrates the translational potential of somatosensory neuroprosthetic systems. In this review, we provide an overview of neurostimulation approaches currently undergoing human testing and summarize recent clinical findings on the perceptual, functional, and psychological impact of somatosensory neuroprostheses. We also cover current work toward the development of advanced stimulation paradigms to produce more natural and informative sensory feedback. Finally, we provide our perspective on the remaining challenges that need to be addressed prior to translation of somatosensory neuroprostheses.},
}

Humans
*Somatosensory Cortex/physiology
Feedback, Sensory/physiology
Translational Research, Biomedical/trends/methods
Neural Prostheses
Brain-Computer Interfaces/trends
Electric Stimulation/methods
Prostheses and Implants/trends

@article {pmid39353205,
year = {2024},
author = {Zhang, Y and Xing, H and Li, J and Han, F and Fan, S and Zhang, Y},
title = {Bioinspired Artificial Intelligent Nociceptive Alarm System Based on Fibrous Biomemristors.},
journal = {ACS sensors},
volume = {},
number = {},
pages = {},
doi = {10.1021/acssensors.4c01568},
pmid = {39353205},
issn = {2379-3694},
abstract = {With the advancement of modern medical and brain-computer interface devices, flexible artificial nociceptors with tactile perception hold significant scientific importance and exhibit great potential in the fields of wearable electronic devices and biomimetic robots. Here, a bioinspired artificial intelligent nociceptive alarm system integrating sensing monitoring and transmission functions is constructed using a silk fibroin (SF) fibrous memristor. This memristor demonstrates high stability, low operating power, and the capability to simulate synaptic plasticity. As a result, an artificial pressure nociceptor based on the SF fibrous memristor can detect both fast and chronic pain and provide a timely alarm in the event of a fall or prolonged immobility of the carrier. Further, an array of artificial pressure nociceptors not only monitors the pressure distribution across various parts of the carrier but also provides direct feedback on the extent of long-term pressure to the carrier. This work holds significant implications for medical support in biological carriers or targeted maintenance of electronic carriers.},
}

@article {pmid39352826,
year = {2024},
author = {Jin, J and Chen, W and Xu, R and Liang, W and Wu, X and He, X and Wang, X and Cichocki, A},
title = {Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3472097},
pmid = {39352826},
issn = {2168-2208},
abstract = {Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant aspects of realizing efficient and stable interaction, has received a great deal of intensive research. However, the current decoding methods based on deep learning are still dominated by single-scale serial convolution, which leads to insufficient extraction of abundant information from motor imagery signals. To overcome such challenges, we propose a new end-to-end convolutional neural network based on multiscale spatial-temporal feature fusion (MSTFNet) for EEG classification of motor imagery. The architecture of MSTFNet consists of four distinct modules: feature enhancement module, multiscale temporal feature extraction module, spatial feature extraction module and feature fusion module, with the latter being further divided into the depthwise separable convolution block and efficient channel attention block. Moreover, we implement a straightforward yet potent data augmentation strategy to bolster the performance of MSTFNet significantly. To validate the performance of MSTFNet, we conduct cross-session experiments and leave-one-subject-out experiments. The cross-session experiment is conducted across two public datasets and one laboratory dataset. On the public datasets of BCI Competition IV 2a and BCI Competition IV 2b, MSTFNet achieves classification accuracies of 83.62% and 89.26%, respectively. On the laboratory dataset, MSTFNet achieves 86.68% classification accuracy. Besides, the leave-one-subject-out experiment is performed on the BCI Competition IV 2a dataset, and MSTFNet achieves 66.31% classification accuracy. These experimental results outperform several state-of-the-art methodologies, indicate the proposed MSTFNet's robust capability in decoding EEG signals associated with motor imagery.},
}

@article {pmid39356668,
year = {2024},
author = {Simony, E and Grossman, S and Malach, R},
title = {Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {41},
pages = {e2319709121},
doi = {10.1073/pnas.2319709121},
pmid = {39356668},
issn = {1091-6490},
mesh = {*Brain/physiology ; *Biological Evolution ; *Models, Neurological ; Humans ; Entorhinal Cortex/physiology ; Animals ; Neurons/physiology ; Neural Networks, Computer ; Nerve Net/physiology ; },
abstract = {Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other. Our argument is based on an extension of the concept of convergent evolution-well established in biology-to the domain of artificial systems. Applying this concept to different areas and levels of the cortical hierarchy can be a powerful tool for elucidating the functional role of well-known cortical selectivities. Importantly, we further demonstrate that such parallels can uncover novel functionalities by showing that grid cells in the entorhinal cortex can be modeled to function as a set of basis functions in a lossy representation such as the well-known JPEG compression. Thus, contrary to common intuition, here we illustrate that finding parallels with artificial systems provides novel and informative insights, particularly in those cases that are far removed from realistic brain biology.},
}

*Brain/physiology
*Biological Evolution
*Models, Neurological
Humans
Entorhinal Cortex/physiology
Animals
Neurons/physiology
Neural Networks, Computer
Nerve Net/physiology

@article {pmid39355672,
year = {2024},
author = {Smith, K and Pilger, A and Amorim, MLM and Mircic, S and Reining, Z and Ristow, N and Miller, D and Leonhardt, A and Donovan, JC and Meier, M and Marzullo, TC and Serbe-Kamp, E and Steiner, AP and Gage, GJ},
title = {Low-Cost Classroom and Laboratory Exercises for Investigating Both Wave and Event-Related Electroencephalogram Potentials.},
journal = {Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience},
volume = {22},
number = {3},
pages = {A197-A206},
pmid = {39355672},
issn = {1544-2896},
abstract = {Electroencephalography (EEG) has given rise to a myriad of new discoveries over the last 90 years. EEG is a noninvasive technique that has revealed insights into the spatial and temporal processing of brain activity over many neuroscience disciplines, including sensory, motor, sleep, and memory formation. Most undergraduate students, however, lack laboratory access to EEG recording equipment or the skills to perform an experiment independently. Here, we provide easy-to-follow instructions to measure both wave and event-related EEG potentials using a portable, low-cost amplifier (Backyard Brains, Ann Arbor, MI) that connects to smartphones and PCs, independent of their operating system. Using open-source software (SpikeRecorder) and analysis tools (Python, Google Colaboratory), we demonstrate tractable and robust laboratory exercises for students to gain insights into the scientific method and discover multidisciplinary neuroscience research. We developed 2 laboratory exercises and ran them on participants within our research lab (N = 17, development group). In our first protocol, we analyzed power differences in the alpha band (8-13 Hz) when participants alternated between eyes open and eyes closed states (n = 137 transitions). We could robustly see an increase of over 50% in 59 (43%) of our sessions, suggesting this would make a reliable introductory experiment. Next, we describe an exercise that uses a SpikerBox to evoke an event-related potential (ERP) during an auditory oddball task. This experiment measures the average EEG potential elicited during an auditory presentation of either a highly predictable ("standard") or low-probability ("oddball") tone. Across all sessions in the development group (n=81), we found that 64% (n=52) showed a significant peak in the standard response window for P300 with an average peak latency of 442ms. Finally, we tested the auditory oddball task in a university classroom setting. In 66% of the sessions (n=30), a clear P300 was shown, and these signals were significantly above chance when compared to a Monte Carlo simulation. These laboratory exercises cover the two methods of analysis (frequency power and ERP), which are routinely used in neurology diagnostics, brain-machine interfaces, and neurofeedback therapy. Arming students with these methods and analysis techniques will enable them to investigate this laboratory exercise's variants or test their own hypotheses.},
}

@article {pmid39355516,
year = {2024},
author = {von Groll, VG and Leeuwis, N and Rimbert, S and Roc, A and Pillette, L and Lotte, F and Alimardani, M},
title = {Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {11},
number = {3},
pages = {87-97},
pmid = {39355516},
issn = {2326-263X},
abstract = {The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance.},
}

@article {pmid39354145,
year = {2024},
author = {Lian, YN and Cao, XW and Wu, C and Pei, CY and Liu, L and Zhang, C and Li, XY},
title = {Deconstruction the feedforward inhibition changes in the layer III of anterior cingulate cortex after peripheral nerve injury.},
journal = {Communications biology},
volume = {7},
number = {1},
pages = {1237},
pmid = {39354145},
issn = {2399-3642},
mesh = {Animals ; *Gyrus Cinguli/physiopathology ; *Peripheral Nerve Injuries/physiopathology ; Mice ; Male ; Mice, Inbred C57BL ; Neural Inhibition ; Neurons/physiology ; Peroneal Nerve/injuries/physiopathology ; Thalamus/physiopathology ; },
abstract = {The anterior cingulate cortex (ACC) is one of the critical brain areas for processing noxious information. Previous studies showed that peripheral nerve injury induced broad changes in the ACC, contributing to pain hypersensitivity. The neurons in layer 3 (L3) of the ACC receive the inputs from the mediodorsal thalamus (MD) and form the feedforward inhibition (FFI) microcircuits. The effects of peripheral nerve injury on the MD-driven FFI in L3 of ACC are unknown. In our study, we record the enhanced excitatory synaptic transmissions from the MD to L3 of the ACC in mice with common peroneal nerve ligation, affecting FFI. Chemogenetically activating the MD-to-ACC projections induces pain sensitivity and place aversion in naive mice. Furthermore, chemogenetically inactivating MD-to-ACC projections decreases pain sensitivity and promotes place preference in nerve-injured mice. Our results indicate that the peripheral nerve injury changes the MD-to-ACC projections, contributing to pain hypersensitivity and aversion.},
}

Animals
*Gyrus Cinguli/physiopathology
*Peripheral Nerve Injuries/physiopathology
Mice
Male
Mice, Inbred C57BL
Neural Inhibition
Neurons/physiology
Peroneal Nerve/injuries/physiopathology
Thalamus/physiopathology

@article {pmid39352734,
year = {2024},
author = {Gao, Y and Cai, YC and Liu, DY and Yu, J and Wang, J and Li, M and Xu, B and Wang, T and Chen, G and Northoff, G and Bai, R and Song, XM},
title = {GABAergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {39352734},
issn = {2050-084X},
support = {2021ZD0200401//STI 2030 - Major Projects/ ; 2022ZD0206000//STI 2030 - Major Projects/ ; 61876222//The National Natural Science Foundation of China/ ; 82222032//The National Natural Science Foundation of China/ ; U1909205//The National Natural Science Foundation of China/ ; 785907//Horizon 2020 Framework Programme/ ; 20YJC880095//Humanities and Social Sciences Ministry of Education/ ; 18YJA190001//Humanities and Social Sciences Ministry of Education/ ; 2022C03096//The Key R&D Program of Zhejiang/ ; 2022ZJJH02-06//The Key R&D Program of Zhejiang/ ; 32000761//The National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Frontal Lobe/physiology/diagnostic imaging ; Male ; *gamma-Aminobutyric Acid/metabolism ; *Intelligence/physiology ; Female ; Young Adult ; *Magnetic Resonance Imaging ; Adult ; Magnetic Resonance Spectroscopy/methods ; Space Perception/physiology ; },
abstract = {The prevailing opinion emphasizes fronto-parietal network (FPN) is key in mediating general fluid intelligence (gF). Meanwhile, recent studies show that human MT complex (hMT+), located at the occipito-temporal border and involved in 3D perception processing, also plays a key role in gF. However, the underlying mechanism is not clear, yet. To investigate this issue, our study targets visuo-spatial intelligence, which is considered to have high loading on gF. We use ultra-high field magnetic resonance spectroscopy (MRS) to measure GABA/Glu concentrations in hMT+ combining resting-state fMRI functional connectivity (FC), behavioral examinations including hMT+ perception suppression test and gF subtest in visuo-spatial component. Our findings show that both GABA in hMT+ and frontal-hMT+ functional connectivity significantly correlate with the performance of visuo-spatial intelligence. Further, serial mediation model demonstrates that the effect of hMT+ GABA on visuo-spatial gF is fully mediated by the hMT+ frontal FC. Together our findings highlight the importance in integrating sensory and frontal cortices in mediating the visuo-spatial component of general fluid intelligence.},
}

Humans
*Frontal Lobe/physiology/diagnostic imaging
Male
*gamma-Aminobutyric Acid/metabolism
*Intelligence/physiology
Female
Young Adult
*Magnetic Resonance Imaging
Adult
Magnetic Resonance Spectroscopy/methods
Space Perception/physiology

@article {pmid39351695,
year = {2024},
author = {Wang, PS and Yang, XX and Wei, Q and Lv, YT and Wu, ZY and Li, HF},
title = {Clinical characterization and founder effect analysis in Chinese amyotrophic lateral sclerosis patients with SOD1 common variants.},
journal = {Annals of medicine},
volume = {56},
number = {1},
pages = {2407522},
pmid = {39351695},
issn = {1365-2060},
mesh = {Humans ; *Amyotrophic Lateral Sclerosis/genetics ; *Superoxide Dismutase-1/genetics ; Male ; Female ; Middle Aged ; *Founder Effect ; *Asian People/genetics ; Adult ; *Haplotypes ; Aged ; China/epidemiology ; Exome Sequencing ; Genetic Association Studies ; Mutation ; Age of Onset ; Phenotype ; East Asian People ; },
abstract = {OBJECTIVE: In the Asian population, SOD1 variants are the most common cause of amyotrophic lateral sclerosis (ALS). To date, more than 200 variants have been reported in SOD1. This study aimed to summarize the genotype-phenotype correlation and determine whether the patients carrying common variants derive from a common ancestor.

METHODS: A total of 103 sporadic ALS (SALS) and 11 familial ALS (FALS) probands were included and variants were screened by whole exome sequencing. Functional analyses were performed on fibroblasts derived from patients with SOD1 p.V48A and control. Haplotype analysis was performed in the probands with p.H47R or p.V48A and their familial members.

RESULTS: A total of 25 SOD1 variants were identified in 44 probands, in which p.H47R, p.V48A and p.C112Y variants were the most common variants. 94.3% and 60% of patients with p.H47R or p.V48A had lower limb onset with predominant lower motor neurons (LMNs) involvement. Patients with p.H47R had a slow progression and prolonged survival time, while patients with p.V48A exhibited a duration of 2-5 years. Patients with p.C112Y variant showed remarkable phenotypic variation in age at onset and disease course. SOD1[V48A] fibroblasts showed mutant SOD1 aggregate formation, enhanced intracellular reactive oxygen species level, and decreased mitochondrial membrane potential compared to the control fibroblast. Haplotype analysis showed that seven families had two different haplotypes. p.H47R and p.V48A variants did not originate from a common founder.

CONCLUSIONS: Our study expanded the understanding of the genotype-phenotype correlation of ALS with SOD1 variants and revealed that the common p.H47R or p.V48A variant did not have a founder effect.},
}

Humans
*Amyotrophic Lateral Sclerosis/genetics
*Superoxide Dismutase-1/genetics
Male
Female
Middle Aged
*Founder Effect
*Asian People/genetics
Adult
*Haplotypes
Aged
China/epidemiology
Exome Sequencing
Genetic Association Studies
Mutation
Age of Onset
Phenotype
East Asian People

@article {pmid39350409,
year = {2024},
author = {Pang, M and Yao, H and Bao, K and Xu, R and Xi, R and Peng, R and Zhi, H and Zhang, K and He, R and Du, Y and Su, Y and Liu, X and Ming, D},
title = {Phenolic Glycoside Monomer from Reed Rhizome Inhibits Melanin Production via PI3K-Akt and Ras-Raf-MEK-ERK Pathways.},
journal = {Current medicinal chemistry},
volume = {},
number = {},
pages = {},
doi = {10.2174/0109298673341645240919072455},
pmid = {39350409},
issn = {1875-533X},
abstract = {INTRODUCTION: Melanogenesis, the process responsible for melanin production, is a critical determinant of skin pigmentation. Dysregulation of this process can lead to hyperpigmentation disorders.

METHOD: In this study, we identified a novel Reed Rhizome extract, (1'S, 2'S)-syringyl glycerol 3'-O-β-D-glucopyranoside (compound 5), and evaluated its anti-melanogenic potential in zebrafish models and in vitro assays. Compound 5 inhibited melanin synthesis by 36.66% ± 14.00% and tyrosinase in vivo by 48.26% ± 6.94%, surpassing the inhibitory effects of arbutin. Network pharmacological analysis revealed key targets, including HSP90AA1, HRAS, and PIK3R1, potentially involved in the anti-melanogenic effects of compound 5.

RESULTS: Molecular docking studies supported the interactions between compound 5 and these targets. Further, gene expression analysis in zebrafish indicated that compound 5 up-regulates hsp90aa1.1, hrasa, and pik3r1, and subsequently down-regulating mitfa, tyr, and tyrp1, critical genes in melanogenesis.

CONCLUSION: These findings suggest that compound 5 inhibits melanin production via PI3K-Akt and Ras-Raf-MEK-ERK signaling pathways, positioning it as a promising candidate for the treatment of hyperpigmentation.},
}

@article {pmid39350194,
year = {2024},
author = {Du, YC and Ma, LH and Li, QF and Ma, Y and Dong, Y and Wu, ZY},
title = {Genotype-phenotype correlation and founder effect analysis in southeast Chinese patients with sialidosis type I.},
journal = {Orphanet journal of rare diseases},
volume = {19},
number = {1},
pages = {362},
pmid = {39350194},
issn = {1750-1172},
support = {82230062, 82071260//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Mucolipidoses/genetics ; Male ; Female ; *Founder Effect ; China/epidemiology ; Haplotypes/genetics ; Child, Preschool ; Polymorphism, Single Nucleotide/genetics ; Neuraminidase/genetics ; Child ; Mutation/genetics ; Genotype ; Infant ; Genetic Association Studies ; Asian People/genetics ; Adolescent ; East Asian People ; },
abstract = {BACKGROUND: Sialidosis type 1 (ST-1) is a rare autosomal recessive disorder caused by mutation in the NEU1 gene. However, limited reports on ST-1 patients in the Chinese mainland are available.

METHODS: This study reported the genetic and clinical characteristics of 10 ST-1 patients from southeastern China. A haplotype analysis was performed using 21 single nucleotide polymorphism (SNP) markers of 500 kb flanking the recurrent c.544 A > G in 8 families harboring the mutation. Furthermore, this study summarized and compared previously reported ST-1 patients from Taiwan and mainland China.

RESULTS: Five mutations within NEU1 were found, including two novel ones c.557 A > G and c.799 C > T. The c.544 A > G mutation was most frequent and identified in 9 patients, 6 patients were homozygous for c.544 A > G. Haplotype analysis revealed a shared haplotype surrounding c.544 A > G was identified, suggesting a founder effect presenting in southeast Chinese population. Through detailed assessment, 52 ST-1 patients from 45 families from Taiwan and mainland China were included. Homozygous c.544 A > G was the most common genotype and found in 42.2% of the families, followed by the c.544 A > G/c.239 C > T compound genotype, which was observed in 22.2% of the families. ST-1 patients with the homozygous c.544 A > G mutation developed the disease at a later age and had a lower incidence of cherry-red spots significantly.

CONCLUSION: The results contribute to gaps in the clinical and genetic features of ST-1 patients in southeastern mainland China and provide a deeper understanding of this disease to reduce misdiagnosis.},
}

Humans
*Mucolipidoses/genetics
Male
Female
*Founder Effect
China/epidemiology
Haplotypes/genetics
Child, Preschool
Polymorphism, Single Nucleotide/genetics
Neuraminidase/genetics
Child
Mutation/genetics
Genotype
Infant
Genetic Association Studies
Asian People/genetics
Adolescent
East Asian People

@article {pmid39349841,
year = {2024},
author = {Fan, J and Gao, Z},
title = {Promoting glymphatic flow: A non-invasive strategy using 40 Hz light flickering.},
journal = {Purinergic signalling},
volume = {},
number = {},
pages = {},
pmid = {39349841},
issn = {1573-9546},
support = {2070974//National Natural Science Foundation of China/ ; 2070974//National Natural Science Foundation of China/ ; 2021R52021//Key Research and Development Program of Zhejiang Province/ ; 2021R52021//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {The glymphatic system is critical for brain homeostasis by eliminating metabolic waste, whose disturbance contributes to the accumulation of pathogenic proteins in neurodegenerative diseases. Promoting glymphatic clearance is a potential and attractive strategy for several brain disorders, including neurodegenerative diseases. Previous studies have uncovered that 40 Hz flickering augmented glymphatic flow and facilitated sleep (Zhou et al. in Cell Res 34:214-231, 2024) since sleep drives waste clearance via glymphatic flow (Xie et al. in Science 342:373-377, 2013). However, it remains unclear whether 40 Hz light flickering directly increased glymphatic flow or indirectly by promoting sleep. A recent article published in Cell Discovery by Chen et al. (Sun et al. in Cell Discov 10:81, 2024) revealed that 40 Hz light flickering facilitated glymphatic flow, by promoting the polarization of astrocytic aquaporin-4 (AQP4) and vasomotion through upregulated adenosine-A2A receptor (A2AR) signaling, independent of sleep. These findings suggest that 40 Hz light flickering may be used as a non-invasive approach to control the function of the glymphatic-lymphatic system, to help remove metabolic waste in the brain, thereby presenting a potential strategy for neurodegenerative disease treatment.},
}

@article {pmid39349588,
year = {2024},
author = {Chen, X and Cao, L and Haendel, BF},
title = {Right visual field advantage in orientation discrimination is influenced by biased suppression.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {22687},
pmid = {39349588},
issn = {2045-2322},
support = {201908060012//China Scholarship Council/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People's Republic of China/ ; 32271078//National Natural Science Foundation of China/ ; 677819/ERC_/European Research Council/International ; },
mesh = {Humans ; *Visual Fields/physiology ; Male ; Female ; Adult ; *Reaction Time/physiology ; *Electroencephalography/methods ; Young Adult ; Orientation/physiology ; Orientation, Spatial/physiology ; Evoked Potentials/physiology ; Photic Stimulation ; Visual Perception/physiology ; Functional Laterality/physiology ; Walking/physiology ; Attention/physiology ; },
abstract = {Visual input is not equally processed over space. In recent years, a right visual field advantage during free walking and standing in orientation discrimination and contrast detection task was reported. The current study investigated the underlying mechanism of the previously reported right visual field advantage. It particularly tested if the advantage is driven by a stronger suppression of distracting input from the left visual field or improved processing of targets from the right visual field. Combing behavioural and electrophysiological measurements in a mobile EEG and augmented reality setup, human participants (n = 30) in a standing and a walking condition performed a line orientation discrimination task with stimulus eccentricity and distractor status being manipulated. The right visual field advantage, as demonstrated in accuracy and reaction time, was influenced by the distractor status. Specifically, the right visual field advantage was only observed when the target had an incongruent line orientation with the distractor. Neural data further showed that the right visual field advantage was paralleled by a strong modulation of neural activity in the right hemisphere (i.e. contralateral to the distractor). A significant positive correlation between this right hemispheric event related potential (ERP) and behavioural measures (accuracy and reaction time) was found exclusively for trials in which a target was presented on the right and an incongruent distractor was presented on the left. The right hemispheric ERP component further predicted the strength of the right visual field advantage. Notably, the lateralised brain activity and the right visual field advantage were both independent of stimulus eccentricity and the movement state of participants. Overall, our findings suggest an important role of spatially biased suppression of left distracting input in the right visual field advantage as found in orientation discrimination.},
}

Humans
*Visual Fields/physiology
Male
Female
Adult
*Reaction Time/physiology
*Electroencephalography/methods
Young Adult
Orientation/physiology
Orientation, Spatial/physiology
Evoked Potentials/physiology
Photic Stimulation
Visual Perception/physiology
Functional Laterality/physiology
Walking/physiology
Attention/physiology

@article {pmid39349502,
year = {2024},
author = {Fan, Y and Tao, Y and Wang, J and Gao, Y and Wei, W and Zheng, C and Zhang, X and Song, XM and Northoff, G},
title = {Irregularity of visual motion perception and negative symptoms in schizophrenia.},
journal = {Schizophrenia (Heidelberg, Germany)},
volume = {10},
number = {1},
pages = {82},
pmid = {39349502},
issn = {2754-6993},
support = {LR23E070001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82001410//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Schizophrenia (SZ) is a severe psychiatric disorder characterized by perceptual, emotional, and behavioral abnormalities, with cognitive impairment being a prominent feature of the disorder. Recent studies demonstrate irregularity in SZ with increased variability on the neural level. Is there also irregularity on the psychophysics level like in visual perception? Here, we introduce a methodology to analyze the irregularity in a trial-by-trial way to compare the SZ and healthy control (HC) subjects. In addition, we use an unsupervised clustering algorithm K-means + + to identify SZ subgroups in the sample, followed by validation of the subgroups based on intraindividual visual perception variability and clinical symptomatology. The K-means + + method divided SZ patients into two subgroups by measuring durations across trials in the motion discrimination task, i.e., high, and low irregularity of SZ patients (HSZ, LSZ). We found that HSZ and LSZ subgroups are associated with more negative and positive symptoms respectively. Applying a mediation model in the HSZ subgroup, the enhanced irregularity mediates the relationship between visual perception and negative symptoms. Together, we demonstrate increased irregularity in visual perception of a HSZ subgroup, including its association with negative symptoms. This may serve as a promising marker for identifying and distinguishing SZ subgroups.},
}

@article {pmid39347924,
year = {2024},
author = {Yan, ZN and Liu, PR and Zhou, H and Zhang, JY and Liu, SX and Xie, Y and Wang, HL and Yu, JB and Zhou, Y and Ni, CM and Huang, L and Ye, ZW},
title = {Brain-computer Interaction in the Smart Era.},
journal = {Current medical science},
volume = {},
number = {},
pages = {},
pmid = {39347924},
issn = {2523-899X},
abstract = {The brain-computer interface (BCI) system serves as a critical link between external output devices and the human brain. A monitored object's mental state, sensory cognition, and even higher cognition are reflected in its electroencephalography (EEG) signal. Nevertheless, unprocessed EEG signals are frequently contaminated with a variety of artifacts, rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging, not to mention the manual adjustment thereof. Over the last few decades, the rapid advancement of artificial intelligence (AI) technology has contributed to the development of BCI technology. Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals, thereby expanding the range of potential interactions between the human brain and computers. As a result, the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients' physical and psychological status, thereby contributing to improvements in their health and quality of life.},
}

@article {pmid39346532,
year = {2024},
author = {Zou, J and Chen, H and Chen, X and Lin, Z and Yang, Q and Tie, C and Wang, H and Niu, L and Guo, Y and Zheng, H},
title = {Noninvasive closed-loop acoustic brain-computer interface for seizure control.},
journal = {Theranostics},
volume = {14},
number = {15},
pages = {5965-5981},
pmid = {39346532},
issn = {1838-7640},
mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; *Seizures/physiopathology/therapy ; *Electroencephalography/methods ; Rats, Sprague-Dawley ; Vagus Nerve Stimulation/methods ; Disease Models, Animal ; Male ; Hippocampus/physiopathology ; Vagus Nerve/physiology ; Epilepsy/therapy/physiopathology ; Brain/physiopathology/physiology ; },
abstract = {Rationale: The brain-computer interface (BCI) is core tasks in comprehensively understanding the brain, and is one of the most significant challenges in neuroscience. The development of novel non-invasive neuromodulation technique will drive major innovations and breakthroughs in the field of BCI. Methods: We develop a new noninvasive closed-loop acoustic brain-computer interface (aBCI) for decoding the seizure onset based on the electroencephalography and triggering ultrasound stimulation of the vagus nerve to terminate seizures. Firstly, we create the aBCI system and decode the onset of seizure via a multi-level threshold model based on the analysis of wireless-collected electroencephalogram (EEG) signals recorded from above the hippocampus. Then, the different acoustic parameters induced acoustic radiation force were used to stimulate the vagus nerve in a rat model of epilepsy-induced by pentylenetetrazole. Finally, the results of epileptic EEG signal triggering ultrasound stimulation of the vagus nerve to control seizures. In addition, the mechanism of aBCI control seizures were investigated by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In a rat model of epilepsy, the aBCI system selectively actives mechanosensitive neurons in the nodose ganglion while suppressing neuronal excitability in the hippocampus and amygdala, and stops seizures rapidly upon ultrasound stimulation of the vagus nerve. Physical transection or chemical blockade of the vagus nerve pathway abolish the antiepileptic effects of aBCI. In addition, aBCI shows significant antiepileptic effects compared to conventional vagus nerve electrical stimulation in an acute experiment. Conclusions: Closed-loop aBCI provides a novel, safe and effective tool for on-demand stimulation to treat abnormal neuronal discharges, opening the door to next generation non-invasive BCI.},
}

Animals
*Brain-Computer Interfaces
Rats
*Seizures/physiopathology/therapy
*Electroencephalography/methods
Rats, Sprague-Dawley
Vagus Nerve Stimulation/methods
Disease Models, Animal
Male
Hippocampus/physiopathology
Vagus Nerve/physiology
Epilepsy/therapy/physiopathology
Brain/physiopathology/physiology

@article {pmid39346088,
year = {2024},
author = {Zhu, L and Zhang, Q and Ni, K and Yang, XJ and Jin, K and Wei, W and Preece, DA and Li, BM and Cai, XL},
title = {Assessing Emotion Regulation Difficulties Across Negative and Positive Emotions: Psychometric Properties and Clinical Applications of the Perth Emotion Regulation Competency Inventory in the Chinese Context.},
journal = {Psychology research and behavior management},
volume = {17},
number = {},
pages = {3299-3311},
pmid = {39346088},
issn = {1179-1578},
abstract = {BACKGROUND: Abnormalities of regulating positive and negative emotion have been documented in patients with mental disorders. Valid and reliable psychological instruments for measuring emotion regulation across different valences are needed. The Perth Emotion Regulation Competency Inventory (PERCI) is a 32-item self-report measure recently developed to compressively assess emotion regulation ability across both positive and negative valences.

PURPOSE: This study aimed to validate the Chinese PERCI in a large non-clinical sample and examine the clinical utility in patients with major depressive disorder (MDD).

METHODS: The Chinese PERCI was administered to 1090 Chinese participants (mean age = 20.64 years, 773 females). The factor structure, internal consistency, test-retest reliability, convergent validity, concurrent validity, and predictive validity were examined. Moreover, a MDD group (n = 50) and a matched healthy control group (n = 50) were recruited. Group comparisons and the linear discriminant analysis were conducted to assess the clinical relevance of the PERCI.

RESULTS: Confirmatory factor analysis supported the intended eight-factor structure of the PERCI in the Chinese population. The PERCI showed high internal consistency, test-retest reliability, as well as good convergent and concurrent validity. The MDD group had significantly higher PERCI scores than the healthy control group. Linear discriminant function comprised of the eight factors successfully distinguish patients with MDD from their matched controls.

CONCLUSION: The Chinese version of the PERCI is a valid and reliable instrument to compressively measure emotion regulation across positive and negative valences in the general Chinese population and patients with depression.},
}

@article {pmid39345641,
year = {2024},
author = {Karpowicz, BM and Ye, J and Fan, C and Tostado-Marcos, P and Rizzoglio, F and Washington, C and Scodeler, T and de Lucena, D and Nason-Tomaszewski, SR and Mender, MJ and Ma, X and Arneodo, EM and Hochberg, LR and Chestek, CA and Henderson, JM and Gentner, TQ and Gilja, V and Miller, LE and Rouse, AG and Gaunt, RA and Collinger, JL and Pandarinath, C},
title = {Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.09.15.613126},
pmid = {39345641},
issn = {2692-8205},
abstract = {Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices. https://snel-repo.github.io/falcon/.},
}

@article {pmid39345497,
year = {2024},
author = {Gedela, NSS and Salim, S and Radawiec, RD and Richie, J and Chestek, C and Draelos, A and Pelled, G},
title = {Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.09.13.612676},
pmid = {39345497},
issn = {2692-8205},
abstract = {The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.},
}

@article {pmid39345413,
year = {2024},
author = {Wu, M and Yang, Y and Zhang, J and Efimov, AI and Vazquez-Guardado, A and Li, X and Zhang, K and Wang, Y and Gu, J and Zeng, L and Liu, J and Riahi, M and Yoon, H and Kim, M and Zhang, H and Lee, M and Kang, J and Ting, K and Cheng, S and Zhang, W and Banks, A and Good, CH and Cox, JM and Pinto, L and Huang, Y and Kozorovitskiy, Y and Rogers, JA},
title = {Patterned wireless transcranial optogenetics generates artificial perception.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.09.20.613966},
pmid = {39345413},
issn = {2692-8205},
abstract = {Synthesizing perceivable artificial neural inputs independent of typical sensory channels remains a fundamental challenge in the development of next-generation brain-machine interfaces. Establishing a minimally invasive, wirelessly effective, and miniaturized platform with long-term stability is crucial for creating a clinically meaningful interface capable of mediating artificial perceptual feedback. In this study, we demonstrate a miniaturized fully implantable wireless transcranial optogenetic encoder designed to generate artificial perceptions through digitized optogenetic manipulation of large cortical ensembles. This platform enables the spatiotemporal orchestration of large-scale cortical activity for remote perception genesis via real-time wireless communication and control, with optimized device performance achieved by simulation-guided methods addressing light and heat propagation during operation. Cue discrimination during operant learning demonstrates the wireless genesis of artificial percepts sensed by mice, where spatial distance across large cortical networks and sequential order-based analyses of discrimination performance reveal principles that adhere to general perceptual rules. These conceptual and technical advancements expand our understanding of artificial neural syntax and its perception by the brain, guiding the evolution of next-generation brain-machine communication.},
}

@article {pmid39345118,
year = {2024},
author = {Rustamov, N and Souders, L and Sheehan, L and Carter, A and Leuthardt, EC},
title = {IpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Rehabilitation in Chronic Stroke.},
journal = {Neurorehabilitation and neural repair},
volume = {},
number = {},
pages = {15459683241287731},
doi = {10.1177/15459683241287731},
pmid = {39345118},
issn = {1552-6844},
abstract = {BACKGROUND: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation.

OBJECTIVES: This study investigated the effectiveness of contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery driven by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity, and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.

METHODS: Twenty-six prospectively enrolled chronic hemiparetic stroke patients performed a therapeutic BCI task for 12 weeks. Motor function assessment data and resting state electroencephalogram signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.

RESULTS: Chronic stroke patients achieved significant motor improvement in both proximal and distal upper extremity with BCI therapy. Motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3/C4 motor electrodes and positively correlated with motor recovery across BCI therapy sessions.

CONCLUSIONS: BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients, which significantly correlated with theta-gamma CFC increases in the motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven rehabilitation in chronic stroke patients.

TRIAL REGISTRATION: Advarra Study: https://classic.clinicaltrials.gov/ct2/show/NCT04338971 and Washington University Study: https://classic.clinicaltrials.gov/ct2/show/NCT03611855.},
}

@article {pmid39342695,
year = {2024},
author = {Wang, J and Ning, X and Xu, W and Li, Y and Jia, Z and Lin, Y},
title = {Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106742},
doi = {10.1016/j.neunet.2024.106742},
pmid = {39342695},
issn = {1879-2782},
abstract = {Affective brain-computer interface is an important part of realizing emotional human-computer interaction. However, existing objective individual differences among subjects significantly hinder the application of electroencephalography (EEG) emotion recognition. Existing methods still lack the complete extraction of subject-invariant representations for EEG and the ability to fuse valuable information from multiple subjects to facilitate the emotion recognition of the target subject. To address the above challenges, we propose a Multi-source Selective Graph Domain Adaptation Network (MSGDAN), which can better utilize data from different source subjects and perform more robust emotion recognition on the target subject. The proposed network extracts and selects the individual information specific to each subject, where public information refers to subject-invariant components from multi-source subjects. Moreover, the graph domain adaptation network captures both functional connectivity and regional states of the brain via a dynamic graph network and then integrates graph domain adaptation to ensure the invariance of both functional connectivity and regional states. To evaluate our method, we conduct cross-subject emotion recognition experiments on the SEED, SEED-IV, and DEAP datasets. The results demonstrate that the MSGDAN has superior classification performance.},
}

@article {pmid39342624,
year = {2024},
author = {Benioudakis, ES and Kalaitzaki, A and Karlafti, E and Ahanov, O and Kapageridou, E and Savopoulos, C and Didangelos, T},
title = {Dimensionality and psychometric properties of the Greek version of the Diabetes Impact and Device Satisfaction (DIDS) scale.},
journal = {Psychiatrike = Psychiatriki},
volume = {},
number = {},
pages = {},
doi = {10.22365/jpsych.2024.017},
pmid = {39342624},
issn = {1105-2333},
abstract = {Type 1 diabetes mellitus (T1D) is a chronic condition with rising prevalence. The only treatment for individuals with T1D to prevent diabetes-related complications is exogenous insulin administration. Diabetes-related technology has significantly contributed to the management of T1D by reducing the burden of living with diabetes and providing greater flexibility in insulin management during daily activities. This study presents the psychometric properties of the Greek translation of the Diabetes Impact and Device Satisfaction (DIDS) Scale, which assesses satisfaction with the use of an insulin delivery device and the impact of diabetes management on individuals with T1D. A sample of 101 adults with T1D, mostly females (71.3%), with a mean age of 38.4 years (± 11.7), completed the translated Greek version of DIDS (DIDS-Gr). Exploratory factor analysis revealed three factors: 'Device Satisfaction', 'Diabetes Management Impact', and (new factor) 'Device Usability'. The internal consistency indices (Cronbach's alpha) for the subscales were 0.86, 0.71, and 0.60, respectively. Furthermore, convergent validity was demonstrated with moderate to high positive correlations between the DIDS-Grand the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) and its subscales, while divergent validity was also confirmed with weaker correlations with the depression subscale of the Hospital Anxiety and Depression Scale (HADS). Additionally, test-retest reliability and differential validity were present in our study. Therefore, DIDS-Gr is a valid and reliable measure for assessing the impact of diabetes on individuals with T1D and the satisfaction with the use of an insulin delivery device in Greece.},
}

@article {pmid39341475,
year = {2024},
author = {Luo, C and Ding, N},
title = {Cortical encoding of hierarchical linguistic information when syllabic rhythms are obscured by echoes.},
journal = {NeuroImage},
volume = {300},
number = {},
pages = {120875},
doi = {10.1016/j.neuroimage.2024.120875},
pmid = {39341475},
issn = {1095-9572},
abstract = {In speech perception, low-frequency cortical activity tracks hierarchical linguistic units (e.g., syllables, phrases, and sentences) on top of acoustic features (e.g., speech envelope). Since the fluctuation of speech envelope typically corresponds to the syllabic boundaries, one common interpretation is that the acoustic envelope underlies the extraction of discrete syllables from continuous speech for subsequent linguistic processing. However, it remains unclear whether and how cortical activity encodes linguistic information when the speech envelope does not provide acoustic correlates of syllables. To address the issue, we introduced a frequency-tagging speech stream where the syllabic rhythm was obscured by echoic envelopes and investigated neural encoding of hierarchical linguistic information using electroencephalography (EEG). When listeners attended to the echoic speech, cortical activity showed reliable tracking of syllable, phrase, and sentence levels, among which the higher-level linguistic units elicited more robust neural responses. When attention was diverted from the echoic speech, reliable neural tracking of the syllable level was also observed in contrast to deteriorated neural tracking of the phrase and sentence levels. Further analyses revealed that the envelope aligned with the syllabic rhythm could be recovered from the echoic speech through a neural adaptation model, and the reconstructed envelope yielded higher predictive power for the neural tracking responses than either the original echoic envelope or anechoic envelope. Taken together, these results suggest that neural adaptation and attentional modulation jointly contribute to neural encoding of linguistic information in distorted speech where the syllabic rhythm is obscured by echoes.},
}

@article {pmid39338869,
year = {2024},
author = {Miladinović, A and Accardo, A and Jarmolowska, J and Marusic, U and Ajčević, M},
title = {Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338869},
issn = {1424-8220},
support = {V-A Italia-Slovenia 2014-2020//Interreg/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Support Vector Machine ; *Stroke/physiopathology ; Male ; Female ; Algorithms ; Middle Aged ; Stroke Rehabilitation/methods ; Aged ; Discriminant Analysis ; Time Factors ; },
abstract = {Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI systems. We investigated the impact of time window duration on classification accuracy and false positive rate, employing Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) on data acquired from six post-stroke patients and on the external BCI IVa dataset. EEG signals were recorded and processed using the Common Spatial Patterns (CSP) algorithm for feature extraction. Our results indicate that longer time windows generally enhance classification accuracy and reduce false positives across all classifiers, with LDA performing the best. However, to maintain the real-time responsiveness, crucial for practical applications, a balance must be struck. The results suggest an optimal time window of 1-2 s, offering a trade-off between classification performance and excessive delay to guarantee the system responsiveness. These findings underscore the importance of temporal optimization in MI-BCI systems to improve usability in real rehabilitation scenarios.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
*Support Vector Machine
*Stroke/physiopathology
Male
Female
Algorithms
Middle Aged
Stroke Rehabilitation/methods
Aged
Discriminant Analysis
Time Factors

@article {pmid39338854,
year = {2024},
author = {Frosolone, M and Prevete, R and Ognibeni, L and Giugliano, S and Apicella, A and Pezzulo, G and Donnarumma, F},
title = {Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338854},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
*Algorithms
Signal Processing, Computer-Assisted
Imagination/physiology

@article {pmid39338748,
year = {2024},
author = {Gulyás, D and Jochumsen, M},
title = {Detection of Movement-Related Brain Activity Associated with Hand and Tongue Movements from Single-Trial Around-Ear EEG.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338748},
issn = {1424-8220},
mesh = {Humans ; *Tongue/physiology ; *Electroencephalography/methods ; *Movement/physiology ; *Brain-Computer Interfaces ; *Hand/physiology ; Male ; Adult ; Female ; Brain/physiology ; Support Vector Machine ; Young Adult ; Ear/physiology ; },
abstract = {Movement intentions of motor impaired individuals can be detected in laboratory settings via electroencephalography Brain-Computer Interfaces (EEG-BCIs) and used for motor rehabilitation and external system control. The real-world BCI use is limited by the costly, time-consuming, obtrusive, and uncomfortable setup of scalp EEG. Ear-EEG offers a faster, more convenient, and more aesthetic setup for recording EEG, but previous work using expensive amplifiers detected motor intentions at chance level. This study investigates the feasibility of a low-cost ear-EEG BCI for the detection of tongue and hand movements for rehabilitation and control purposes. In this study, ten able-bodied participants performed 100 right wrist extensions and 100 tongue-palate movements while three channels of EEG were recorded around the left ear. Offline movement vs. idle activity classification of ear-EEG was performed using temporal and spectral features classified with Random Forest, Support Vector Machine, K-Nearest Neighbours, and Linear Discriminant Analysis in three scenarios: Hand (rehabilitation purpose), hand (control purpose), and tongue (control purpose). The classification accuracies reached 70%, 73%, and 83%, respectively, which was significantly higher than chance level. These results suggest that a low-cost ear-EEG BCI can detect movement intentions for rehabilitation and control purposes. Future studies should include online BCI use with the intended user group in real-life settings.},
}

Humans
*Tongue/physiology
*Electroencephalography/methods
*Movement/physiology
*Brain-Computer Interfaces
*Hand/physiology
Male
Adult
Female
Brain/physiology
Support Vector Machine
Young Adult
Ear/physiology

@article {pmid39338733,
year = {2024},
author = {Huang, J and Chang, Y and Li, W and Tong, J and Du, S},
title = {A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338733},
issn = {1424-8220},
support = {No.81705//Shengzhi Du/ ; Grant No.NCRCOP20230013//Yinghui Chang/ ; No. 2021ZD011//Jigang Tong/ ; No. 62303244//Wenyu Li/ ; No. 22JCQNJC01440//Wenyu Li/ ; 111-2221-E-A49 -126 -MY3//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 112-2218-E-A49-027//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2218-E-002-013//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2640-E-A49-009//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2218-E-A49-028//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 112UC2N006//Satellite Communications and AIoT Research Center/The Co-operation Platform of the Industry-Academia Innovation School, National Yang Ming Chiao Tung University (NYCU), Taiwan R.OC./ ; },
mesh = {*Electroencephalography/methods ; *Neural Networks, Computer ; Humans ; *Imagination/physiology ; *Semantics ; *Brain-Computer Interfaces ; *Algorithms ; Perception/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding.},
}

*Electroencephalography/methods
*Neural Networks, Computer
Humans
*Imagination/physiology
*Semantics
*Brain-Computer Interfaces
*Algorithms
Perception/physiology
Signal Processing, Computer-Assisted

@article {pmid39338628,
year = {2024},
author = {Mohajelin, F and Sheykhivand, S and Shabani, A and Danishvar, M and Danishvar, S and Lahijan, LZ},
title = {Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338628},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions-the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle.},
}

*Electroencephalography/methods
Humans
*Emotions/physiology
*Neural Networks, Computer
*Brain-Computer Interfaces
*Algorithms
Signal Processing, Computer-Assisted
Brain/physiology

@article {pmid39338620,
year = {2024},
author = {Razzaq, Z and Brahimi, N and Rehman, HZU and Khan, ZH},
title = {Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338620},
issn = {1424-8220},
mesh = {*Fuzzy Logic ; *Robotics/methods ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Algorithms ; Brain/physiology ; Neural Networks, Computer ; Machine Learning ; },
abstract = {Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders-such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury-by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot.},
}

*Fuzzy Logic
*Robotics/methods
Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
Algorithms
Brain/physiology
Neural Networks, Computer
Machine Learning

@article {pmid39335353,
year = {2024},
author = {Matulyte, G and Parciauskaite, V and Bjekic, J and Pipinis, E and Griskova-Bulanova, I},
title = {Gamma-Band Auditory Steady-State Response and Attention: A Systemic Review.},
journal = {Brain sciences},
volume = {14},
number = {9},
pages = {},
pmid = {39335353},
issn = {2076-3425},
abstract = {Auditory steady-state response (ASSR) is the result of the brain's ability to follow and entrain its oscillatory activity to the phase and frequency of periodic auditory stimulation. Gamma-band ASSR has been increasingly investigated with intentions to apply it in neuropsychiatric disorders diagnosis as well as in brain-computer interface technologies. However, it is still debatable whether attention can influence ASSR, as the results of the attention effects of ASSR are equivocal. In our study, we aimed to systemically review all known articles related to the attentional modulation of gamma-band ASSRs. The initial literature search resulted in 1283 papers. After the removal of duplicates and ineligible articles, 49 original studies were included in the final analysis. Most analyzed studies demonstrated ASSR modulation with differing attention levels; however, studies providing mixed or non-significant results were also identified. The high versatility of methodological approaches including the utilized stimulus type and ASSR recording modality, as well as tasks employed to modulate attention, were detected and emphasized as the main causality of result inconsistencies across studies. Also, the impact of training, inter-individual variability, and time of focus was addressed.},
}

@article {pmid39332212,
year = {2024},
author = {Deng, Y and Ji, Z and Wang, Y and Zhou, SK},
title = {OS-SSVEP: One-shot SSVEP classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106734},
doi = {10.1016/j.neunet.2024.106734},
pmid = {39332212},
issn = {1879-2782},
abstract = {It is extremely challenging to classify steady-state visual evoked potentials (SSVEPs) in scenarios characterized by a huge scarcity of calibration data where only one calibration trial is available for each stimulus target. To address this challenge, we introduce a novel approach named OS-SSVEP, which combines a dual domain cross-subject fusion network (CSDuDoFN) with the task-related and task-discriminant component analysis (TRCA and TDCA) based on data augmentation. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the information from the single available calibration trial of the target subject. Specifically, CSDuDoFN uses multi-reference least-squares transformation (MLST) to map data from both the source subjects and the target subject into the domain of sine-cosine templates, thereby reducing cross-subject domain gap and benefiting transfer learning. In addition, CSDuDoFN is fed with both transformed and original data, with an adequate fusion of their features occurring at different network layers. To capitalize on the calibration trial of the target subject, OS-SSVEP utilizes source aliasing matrix estimation (SAME)-based data augmentation to incorporate into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of CSDuDoFN, eTRCA, and TDCA are combined for the SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on the third. Further, it is worth noting that our method follows a different technical route from the current state-of-the-art (SOTA) method and the two are complementary. The performance is significantly improved when our method is combined with the SOTA method. This study underscores the potential to integrate the SSVEP-based brain-computer interface (BCI) into daily life. The corresponding source code is accessible at https://github.com/Sungden/One-shot-SSVEP-classification.},
}

@article {pmid39332118,
year = {2024},
author = {Crell, MR and Müller-Putz, GR},
title = {Handwritten character classification from EEG through continuous kinematic decoding.},
journal = {Computers in biology and medicine},
volume = {182},
number = {},
pages = {109132},
doi = {10.1016/j.compbiomed.2024.109132},
pmid = {39332118},
issn = {1879-0534},
abstract = {The classification of handwritten letters from invasive neural signals has lately been subject of research to restore communication abilities in people with limited movement capacities. This study explores the classification of ten letters (a,d,e,f,j,n,o,s,t,v) from non-invasive neural signals of 20 participants, offering new insights into the neural correlates of handwriting. Letters were classified with two methods: the direct classification from low-frequency and broadband electroencephalogram (EEG) and a two-step approach comprising the continuous decoding of hand kinematics and the application of those in subsequent classification. The two-step approach poses a novel application of continuous movement decoding for the classification of letters from EEG. When using low-frequency EEG, results show moderate accuracies of 23.1% for ten letters and 39.0% for a subset of five letters with highest discriminability of the trajectories. The two-step approach yielded significantly higher performances of 26.2% for ten letters and 46.7% for the subset of five letters. Hand kinematics could be reconstructed with a correlation of 0.10 to 0.57 (average chance level: 0.04) between the decoded and original kinematic. The study shows the general feasibility of extracting handwritten letters from non-invasively recorded neural signals and indicates that the proposed two-step approach can improve performances. As an exploratory investigation of the neural mechanisms of handwriting in EEG, we found significant influence of the written letter on the low-frequency components of neural signals. Differences between letters occurred mostly in central and occipital channels. Further, our results suggest movement speed as the most informative kinematic for the decoding of short hand movements.},
}

@article {pmid39331412,
year = {2024},
author = {Wang, S and Jiang, Q and Liu, H and Yu, C and Li, P and Pan, G and Xu, K and Xiao, R and Hao, Y and Wang, C and Song, J},
title = {Mechanically adaptive and deployable intracortical probes enable long-term neural electrophysiological recordings.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {40},
pages = {e2403380121},
doi = {10.1073/pnas.2403380121},
pmid = {39331412},
issn = {1091-6490},
support = {2021ZD0200401//STI 2030-Major Projects/ ; U21A20502//MOST | National Natural Science Foundation of China (NSFC)/ ; 12225209//MOST | National Natural Science Foundation of China (NSFC)/ ; U20A6001//MOST | National Natural Science Foundation of China (NSFC)/ ; 12321002//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Animals ; Rats ; *Electrodes, Implanted ; Electrophysiological Phenomena ; Polymers/chemistry ; Cerebral Cortex/physiology ; Neurons/physiology ; Rats, Sprague-Dawley ; Brain/physiology ; },
abstract = {Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications.},
}

Animals
Rats
*Electrodes, Implanted
Electrophysiological Phenomena
Polymers/chemistry
Cerebral Cortex/physiology
Neurons/physiology
Rats, Sprague-Dawley
Brain/physiology

@article {pmid39331031,
year = {2024},
author = {Wang, W and Liu, Y and Wang, G and Cheng, Q and Ming, D},
title = {Oscillatory cortico-cortical connectivity during tactile discrimination between dynamic and static stimulation.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {9},
pages = {},
doi = {10.1093/cercor/bhae277},
pmid = {39331031},
issn = {1460-2199},
support = {2023YFC3603800//National Key Research and Development Program of China/ ; 62273251//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography ; *Touch Perception/physiology ; Adult ; Young Adult ; Cerebral Cortex/physiology ; Discrimination, Psychological/physiology ; Touch/physiology ; Neural Pathways/physiology ; Physical Stimulation ; Brain Mapping ; Evoked Potentials/physiology ; },
abstract = {Fine sensory modalities play an essential role in perceiving the world. However, little is known about how the cortico-cortical distinguishes between dynamic and static tactile signals. This study investigated oscillatory connectivity during a tactile discrimination task of dynamic and static stimulation via electroencephalogram (EEG) recordings and the fast oscillatory networks across widespread cortical regions. While undergoing EEG recordings, the subject felt an electro-tactile presented by a 3-dot array. Each block consisted of 3 forms of stimulation: Spatio-temporal (dynamic), Spatial (static), and Control condition (lack of electrical stimulation). The average event-related potential for the Spatial and Spatio-temporal conditions exhibited statistically significant differences between 25 and 75, 81 and 121, 174 and 204 and 459 and 489 ms after stimulus onset. Based on those times, the sLORETA approach was used to reconstruct the inverse solutions of EEG. Source localization appeared superior parietal at around 25 to 75 ms, in the primary motor cortex at 81 to 121 ms, in the central prefrontal cortex at 174 to 204 and 459 to 489 ms. To better assess spectral brain functional connectivity, we selected frequency ranges with correspondingly significant differences: for static tactile stimulation, these are concentrated in the Theta, Alpha, and Gamma bands, whereas for dynamic stimulation, the relative energy change bands are focused on the Theta and Alpha bands. These nodes' functional connectivity analysis (phase lag index) showed 3 distinct distributed networks. A tactile information discrimination network linked the Occipital lobe, Prefrontal lobe, and Postcentral gyrus. A tactile feedback network linked the Prefrontal lobe, Postcentral gyrus, and Temporal lobe. A dominant motor feedforward loop network linked the Parietal cortex, Prefrontal lobe, Frontal lobe, and Parietal cortex. Processing dynamic and static tactile signals involves discriminating tactile information, motion planning, and cognitive decision processing.},
}

Humans
Male
Female
*Electroencephalography
*Touch Perception/physiology
Adult
Young Adult
Cerebral Cortex/physiology
Discrimination, Psychological/physiology
Touch/physiology
Neural Pathways/physiology
Physical Stimulation
Brain Mapping
Evoked Potentials/physiology

@article {pmid39330094,
year = {2024},
author = {Ruan, Z and Li, H},
title = {Two Levels of Integrated Information Theory: From Autonomous Systems to Conscious Life.},
journal = {Entropy (Basel, Switzerland)},
volume = {26},
number = {9},
pages = {},
pmid = {39330094},
issn = {1099-4300},
support = {22&ZD034//the Major Program of National Social Science Fund of China/ ; },
abstract = {Integrated Information Theory (IIT) is one of the most prominent candidates for a theory of consciousness, although it has received much criticism for trying to live up to expectations. Based on the relevance of three issues generalized from the developments of IITs, we have summarized the main ideas of IIT into two levels. At the second level, IIT claims to be strictly anchoring consciousness, but the first level on which it is based is more about autonomous systems or systems that have reached some other critical complexity. In this paper, we argue that the clear gap between the two levels of explanation of IIT has led to these criticisms and that its panpsychist tendency plays a crucial role in this. We suggest that the problems of IIT are far from being "pseudoscience", and by adding more necessary elements, when the first level is combined with the second level, IIT can genuinely move toward an appropriate theory of consciousness that can provide necessary and sufficient interpretations.},
}

@article {pmid39329668,
year = {2024},
author = {Tan, X and Wang, D and Xu, M and Chen, J and Wu, S},
title = {Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {9},
pages = {},
pmid = {39329668},
issn = {2306-5354},
support = {12275295//National Natural Science Foundation of China/ ; GZC20230189//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; },
abstract = {Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain-computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding.},
}

@article {pmid39329659,
year = {2024},
author = {Hiwaki, O},
title = {Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {9},
pages = {},
pmid = {39329659},
issn = {2306-5354},
support = {22KK-128//Mazda Motor Corporation (Japan)/ ; },
abstract = {Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain-machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain.},
}

@article {pmid39329584,
year = {2024},
author = {Mounesi Rad, S and Danishvar, S},
title = {Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {9},
number = {9},
pages = {},
pmid = {39329584},
issn = {2313-7673},
abstract = {Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain-computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). To achieve this objective, a dry EEG electrode is created using the silver-copper sintering technique, which is assessed through Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis (EDXA) evaluations. Subsequently, a database is generated utilizing the designated electrode, which is based on the musical stimulus. The collected data are fed into an improved deep network for automatic feature selection/extraction and classification. The deep network architecture is structured by combining type 2 fuzzy sets (FT2) and deep convolutional graph networks. The fabricated electrode demonstrated superior performance, efficiency, and affordability compared to other electrodes (both wet and dry) in this study. Furthermore, the dry EEG electrode was examined in noisy environments and demonstrated robust resistance across a diverse range of Signal-To-Noise ratios (SNRs). Furthermore, the proposed model achieved a classification accuracy of 99% for distinguishing between positive and negative emotions, an improvement of approximately 2% over previous studies. The manufactured dry EEG electrode is very economical and cost-effective in terms of manufacturing costs when compared to recent studies. The proposed deep network, combined with the fabricated dry EEG electrode, can be used in real-time applications for long-term recordings that do not require gel.},
}

@article {pmid39328074,
year = {2024},
author = {Sawyer, A and Cooke, L and Breyman, E and Spohn, S and Edelman, S and Saravanan, K and Putrino, D},
title = {Meeting the Needs of People With Severe Quadriplegia in the 21st Century: The Case for Implanted Brain-Computer Interfaces.},
journal = {Neurorehabilitation and neural repair},
volume = {},
number = {},
pages = {15459683241282783},
doi = {10.1177/15459683241282783},
pmid = {39328074},
issn = {1552-6844},
abstract = {BACKGROUND: In recent decades, there has been a widespread adoption of digital devices among the non-disabled population. The pervasive integration of digital devices has revolutionized how the majority of the population manages daily activities. Most of us now depend on digital platforms and services to conduct activities across the domains of communication, finance, healthcare, and work. However, a clear disparity exists for people who live with severe quadriplegia, who largely lack access to tools that would enable them to perform daily tasks digitally and communicate effectively with their environment.

OBJECTIVES: The purpose of this piece is to (i) highlight the unmet needs of people with severe quadriplegia (including cases for medical necessity and perspectives from the community), (ii) present the current landscape of assistive technology for people with severe quadriplegia, (iii) make the case for implantable BCIs (how they address needs and why they are a good solution relative to other assistive technologies), and (iv) present future directions.

RESULTS: There are technologies that are currently available to this population, but these technologies are certainly not usable with the same level of ease, efficiency, or autonomy as what has been designed for the non-disabled community. This hinders the ability of people with severe quadriplegia to achieve digital autonomy, perpetuating social isolation and limiting the expression of needs, opinions, and preferences.

CONCLUSION: Most importantly, the gap in digital equality fundamentally undermines the basic human rights of people with severe quadriplegia.},
}

@article {pmid39326769,
year = {2024},
author = {Wu, X and Xie, C and Cheng, F and Li, Z and Li, R and Xu, D and Kim, H and Zhang, J and Liu, H and Liu, M},
title = {Comparative evaluation of interpretation methods in surface-based age prediction for neonates.},
journal = {NeuroImage},
volume = {300},
number = {},
pages = {120861},
doi = {10.1016/j.neuroimage.2024.120861},
pmid = {39326769},
issn = {1095-9572},
abstract = {Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods-regional age prediction and the perturbation-based saliency map approach-for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.},
}

@article {pmid39326569,
year = {2024},
author = {Hurley, ET and Twomey-Kozack, J and Doyle, TR and Meyer, LE and Meyer, AM and Lorentz, SG and Bradley, KE and Dickens, JF and Klifto, CS},
title = {Bioinductive Collagen Implant Has Potential To Improve Rotator Cuff Healing - A Systematic Review.},
journal = {Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.arthro.2024.09.028},
pmid = {39326569},
issn = {1526-3231},
abstract = {PURPOSE: The purpose of this study was to systematically review the literature to evaluate the clinical studies on bioinductive collagen implant (BCI) for the treatment of rotator cuff tears.

METHODS: A literature search of MEDLINE, Embase, and the Cochrane Library was performed based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Clinical studies reporting following BCI for rotator cuff tears were included. Quantitive and qualitative data was evaluated.

RESULTS: A total of 21 studies were included. In patients with full thickness tears, 7 of the 8 studies with pre- to postoperative ASES scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 75%-100% of patients meeting the MCID. In those with partial thickness tears, 7 of the 8 studies with pre- to postoperative ASES scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 54.4%-100% of patients meeting the MCID. For studies that quantified percent increases in tendon thickness, the reported increases ranged from 13% in 44% full thickness tears, and 14% to 60% in partial thickness tears. There were 6 studies that evaluated rotator cuff re-tears after BCI treatment in the full thickness cohort, with rates reported ranging from 0-9%. There were 5 studies that evaluated rotator cuff re-tears after BCI treatment in the partial thickness cohort, with rates reported ranging from 0-18%. Two of the included studies found that BCI was cost-effective due to the increased tendon healing with cost savings of $5,338-$13,061 per healed rotator cuff tendon.

CONCLUSION: The literature on rotator cuff tear augmentation with BCI has shown consistently reported good results. Additionally, there was evidence of low retear rates and consistently improved tendon thickness with BCI, with two randomized controlled trials showing improved tendon healing with BCI. However, there appears to be a higher rate of adhesive capsulitis reported.

LEVEL OF EVIDENCE: Level IV, Systematic review of Level I, III and IV studies.},
}

@article {pmid39326451,
year = {2024},
author = {Erbslöh, A and Buron, L and Ur-Rehman, Z and Musall, S and Hrycak, C and Löhler, P and Klaes, C and Seidl, K and Schiele, G},
title = {Technical survey of end-to-end signal processing in BCIs using invasive MEAs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8031},
pmid = {39326451},
issn = {1741-2552},
abstract = {Modern brain-computer interfaces and neural implants allow interaction between the tissue, the user and the environment, where people suffer from neurodegenerative diseases or injuries. This interaction can be achieved by using penetrating/invasive microelectrodes for extracellular recordings and stimulation, such as Utah or Michigan arrays. The application-specific signal processing of the extracellular recording enables the detection of interactions and enables user interaction. For example, it allows to read out movement intentions from recordings of brain signals for controlling a prosthesis or an exoskeleton. To enable this, computationally complex algorithms are used in research that cannot be executed onchip or on embedded systems. Therefore, an optimization of the end-to-end processing pipeline, from the signal condition on the electrode array over the analog pre-processing to spike-sorting and finally the neural decoding process, is necessary for hardware inference in order to enable a local signal processing in real-time and to enable a compact system for achieving a high comfort level. is This paper presents a survey of system architectures and algorithms for end-to-end signal processing pipelines of neural activity on the hardware of such neural devices, including (i) on-chip signal pre-processing, (ii) spike-sorting on-chip or on embedded hardware and (iii) neural decoding on workstations. A particular focus for the hardware implementation is on low-power electronic design and artifact-robust algorithms with low computational effort and very short latency. For this, current challenges and possible solutions with support of novel machine learning techniques are presented in brief. In addition, we describe our future vision for next-generation BCIs.},
}

@article {pmid39326449,
year = {2024},
author = {Harel, A and Shriki, O},
title = {Task-guided attention increases non-linearity of steady-state visually evoked potentials.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8032},
pmid = {39326449},
issn = {1741-2552},
abstract = {Attention is a multifaceted cognitive process, with nonlinear dynamics playing a crucial role. In this study, we investigated the involvement of nonlinear processes in top-down visual attention by employing a contrast-modulated sequence of letters and numerals, encircled by a consistently flickering white square on a black background - a setup that generated steady-state visually evoked potentials. Nonlinear processes are recognized for eliciting and modulating the harmonics of constant frequencies. We examined the fundamental and harmonic frequencies of each stimulus to evaluate the underlying nonlinear dynamics during stimulus processing. In line with prior research, our findings indicate that the power spectrum density of EEG responses is influenced by both task presence and stimulus contrast. By utilizing the Rhythmic Entrainment Source Separation (RESS) technique, we discovered that actively searching for a target within a letter stream heightened the amplitude of the fundamental frequency and harmonics related to the background flickering stimulus. While the fundamental frequency amplitude remained unaffected by stimulus contrast, a lower contrast led to an increase in the second harmonic's amplitude. We assessed the relationship between the contrast response function and the nonlinear-based harmonic responses. Our findings contribute to a more nuanced understanding of the nonlinear processes impacting top-down visual attention while also providing insights into optimizing brain-computer interfaces. .},
}

@article {pmid39326392,
year = {2024},
author = {Magee, P and Ienca, M and Farahany, N},
title = {Beyond neural data: Cognitive biometrics and mental privacy.},
journal = {Neuron},
volume = {112},
number = {18},
pages = {3017-3028},
doi = {10.1016/j.neuron.2024.09.004},
pmid = {39326392},
issn = {1097-4199},
mesh = {Humans ; *Privacy ; *Cognition/physiology ; *Brain-Computer Interfaces/ethics ; Wearable Electronic Devices ; Biometry/methods ; Confidentiality/ethics ; },
abstract = {Innovations in wearable technology and artificial intelligence have enabled consumer devices to process and transmit data about human mental states (cognitive, affective, and conative) through what this paper refers to as "cognitive biometrics." Devices such as brain-computer interfaces, extended reality headsets, and fitness wearables offer significant benefits in health, wellness, and entertainment through the collection and processing and cognitive biometric data. However, they also pose unique risks to mental privacy due to their ability to infer sensitive information about individuals. This paper challenges the current approach to protecting individuals through legal protections for "neural data" and advocates for a more expansive legal and industry framework, as recently reflected in the draft UNESCO Recommendation on the Ethics of Neurotechnology, to holistically address both neural and cognitive biometric data. Incorporating this broader and more inclusive approach into legislation and product design can facilitate responsible innovation while safeguarding individuals' mental privacy.},
}

Humans
*Privacy
*Cognition/physiology
*Brain-Computer Interfaces/ethics
Wearable Electronic Devices
Biometry/methods
Confidentiality/ethics

@article {pmid39321842,
year = {2024},
author = {Dillen, A and Omidi, M and Ghaffari, F and Vanderborght, B and Roelands, B and Romain, O and Nowé, A and De Pauw, K},
title = {A shared robot control system combining augmented reality and motor imagery brain-computer interfaces with eye tracking.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad7f8d},
pmid = {39321842},
issn = {1741-2552},
abstract = {Objective: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals. Approach: A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user's gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system's usability, focusing on its effectiveness and efficiency. Main results:Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system's feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen's Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 minutes to complete the evaluation tasks. The success rate dropped below 0.5 when a 5-minute cutoff time was selected. Significance: These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications in the future.},
}

@article {pmid39321841,
year = {2024},
author = {Wen, X and Jia, S and Han, D and Dong, Y and Gao, C and Cao, R and Hao, Y and Guo, Y and Cao, R},
title = {Filter banks guided correlational convolutional neural network for SSVEPs based BCI classification.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad7f89},
pmid = {39321841},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/classification ; Humans ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Electroencephalography/methods/classification ; Adult ; Male ; Photic Stimulation/methods ; Female ; },
abstract = {Objective.In the field of steady-state visual evoked potential brain computer interfaces (SSVEP-BCIs) research, convolutional neural networks (CNNs) have gradually been proved to be an effective method. Whereas, majority works apply the frequency domain characteristics in long time window to train the network, thus lead to insufficient performance of those networks in short time window. Furthermore, only the frequency domain information for classification lacks of other task-related information.Approach.To address these issues, we propose a time-frequency domain generalized filter-bank convolutional neural network (FBCNN-G) to improve the SSVEP-BCIs classification performance. The network integrates multiple frequency information of electroencephalogram (EEG) with template and predefined prior of sine-cosine signals to perform feature extraction, which contains correlation analyses in both template and signal aspects. Then the classification is performed at the end of the network. In addition, the method proposes the use of filter banks divided into specific frequency bands as pre-filters in the network to fully consider the fundamental and harmonic frequency characteristics of the signal.Main results.The proposed FBCNN-G model is compared with other methods on the public dataset Benchmark. The results manifest that this model has higher accuracy of character recognition accuracy and information transfer rates in several time windows. Particularly, in the 0.2 s time window, the mean accuracy of the proposed method reaches62.02%±5.12%, indicating its superior performance.Significance.The proposed FBCNN-G model is critical for the exploitation of SSVEP-BCIs character recognition models.},
}

*Brain-Computer Interfaces/classification
Humans
*Evoked Potentials, Visual/physiology
*Neural Networks, Computer
*Electroencephalography/methods/classification
Adult
Male
Photic Stimulation/methods
Female

@article {pmid39321840,
year = {2024},
author = {Arif, M and Rehman, FU and Sekanina, L and Malik, AS},
title = {A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad7f8e},
pmid = {39321840},
issn = {1741-2552},
abstract = {Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces (BCI). Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.},
}

@article {pmid39321611,
year = {2024},
author = {Wang, X and Qi, H},
title = {Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis.},
journal = {Computer methods and programs in biomedicine},
volume = {257},
number = {},
pages = {108425},
doi = {10.1016/j.cmpb.2024.108425},
pmid = {39321611},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.

METHODS: The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.

RESULTS: The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001).

CONCLUSIONS: The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.},
}

@article {pmid39321571,
year = {2024},
author = {Cioffi, E and Hutber, A and Molloy, R and Murden, S and Yurkewich, A and Kirton, A and Lin, JP and Gimeno, H and McClelland, VM},
title = {EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {167},
number = {},
pages = {143-166},
doi = {10.1016/j.clinph.2024.08.009},
pmid = {39321571},
issn = {1872-8952},
abstract = {OBJECTIVE: Therapeutic interventions for children and young people with dystonia and dystonic/dyskinetic cerebral palsy are limited. EEG-based neurofeedback is emerging as a neurorehabilitation tool. This scoping review maps research investigating EEG-based sensorimotor neurofeedback in adults and children with neurological motor impairments, including augmentative strategies.

METHODS: MEDLINE, CINAHL and Web of Science databases were searched up to 2023 for relevant studies. Study selection and data extraction were conducted independently by at least two reviewers.

RESULTS: Of 4380 identified studies, 133 were included, only three enrolling children. The most common diagnosis was adult-onset stroke (77%). Paradigms mostly involved upper limb motor imagery or motor attempt. Common neurofeedback modes included visual, haptic and/or electrical stimulation. EEG parameters varied widely and were often incompletely described. Two studies applied augmentative strategies. Outcome measures varied widely and included classification accuracy of the Brain-Computer Interface, degree of enhancement of mu rhythm modulation or other neurophysiological parameters, and clinical/motor outcome scores. Few studies investigated whether functional outcomes related specifically to the EEG-based neurofeedback.

CONCLUSIONS: There is limited evidence exploring EEG-based sensorimotor neurofeedback in individuals with movement disorders, especially in children. Further clarity of neurophysiological parameters is required to develop optimal paradigms for evaluating sensorimotor neurofeedback.

SIGNIFICANCE: The expanding field of sensorimotor neurofeedback offers exciting potential as a non-invasive therapy. However, this needs to be balanced by robust study design and detailed methodological reporting to ensure reproducibility and validation that clinical improvements relate to induced neurophysiological changes.},
}

@article {pmid39320995,
year = {2024},
author = {Huang, H and Chen, J and Xiao, J and Chen, D and Zhang, J and Pan, J and Li, Y},
title = {Real-Time Attention Regulation and Cognitive Monitoring Using a Wearable EEG-based BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3468351},
pmid = {39320995},
issn = {1558-2531},
abstract = {OBJECTIVE: Attention regulation is an essential ability in daily life that affects learning and work efficiency and is closely related to mental health. The effectiveness of brain-computer interface (BCI) systems in attention regulation has been proven, but most of these systems rely on bulky and expensive equipment and are still in the experimental stage. This study proposes a wearable BCI system for real-time attention regulation and cognitive monitoring.

METHODS: The BCI system integrates a wearable singlechannel electroencephalogram (EEG) headband with wireless data streaming for real-time analysis. Twenty healthy subjects participated in the long-term attention regulation experiment and were evenly divided into an experimental group and a control group based on the presence of realtime neurofeedback. The neurofeedback is represented by output value of attention, which calculated from singlechannel EEG data. Before and after the regulation sessions, baseline assessments were conducted for each subject, incorporating multi-channel EEG data analysis and cognitive behavioral evaluations, to verify the effectiveness of system for attention regulation.

RESULTS: The online experimental results indicate that the average attention level in the experimental group increased from 0.625 to 0.812, while no significant improvement was observed in the control group. Further comparative analysis revealed the reasons for the enhancement of attention regulation ability in terms of both brain network patterns and cognitive performance.

SIGNIFICANCE: The proposed wearable BCI system is effective at improving attention regulation ability and could be a promising tool for assisting people with attention disorders.},
}

@article {pmid39318665,
year = {2024},
author = {Chai, C and Yang, X and Gao, X and Shi, J and Wang, X and Song, H and Chen, YH and Sawan, M},
title = {Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {12},
number = {},
pages = {1452865},
pmid = {39318665},
issn = {2296-4185},
abstract = {Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution. Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.},
}

@article {pmid39316474,
year = {2024},
author = {Li, X and Yang, Z and Tu, X and Wang, J and Huang, J},
title = {MFRC-Net: Multi-Scale Feature Residual Convolutional Neural Network for Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3467090},
pmid = {39316474},
issn = {2168-2208},
abstract = {Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). However, the majority of studies prioritize enhancing the accuracy of decoding methods, often overlooking the magnitude and computational resource demands of deep learning models. In this study, we propose a novel lightweight Multi-Scale Feature Residual Convolutional Neural Network (MFRC-Net). MFRC-Net primarily consists of two blocks: temporal multi-scale residual convolution blocks and cross-domain dual-stream spatial convolution blocks. The former captures dynamic changes in EEG signals across various time scales through multi-scale grouped convolution and backbone temporal convolution skip connections; the latter improves local spatial feature extraction and calibrates feature mapping through the introduction of cross-domain spatial filtering layers. Furthermore, by specifically optimizing the loss function, MFRC-Net effectively reduces sensitivity to outliers. Experiment results on the BCI Competition IV 2a dataset and the SHU dataset demonstrate that, with a parameter size of only 13K, MFRC-Net achieves accuracy of 85.1% and 69.3%, respectively, surpassing current state-of-the-art models. The integration of temporal multi-scale residual convolution blocks and crossdomain dual-stream spatial convolution blocks in lightweight models significantly boosts performance, as evidenced by ablation studies and visualizations.},
}

@article {pmid39316274,
year = {2024},
author = {Dinov, ID},
title = {Neuroinformatics Applications of Data Science and Artificial Intelligence.},
journal = {Neuroinformatics},
volume = {},
number = {},
pages = {},
pmid = {39316274},
issn = {1559-0089},
abstract = {Leveraging vast neuroimaging and electrophysiological datasets, AI algorithms are uncovering patterns that offer unprecedented insights into brain structure and function. Neuroinformatics, the fusion of neuroscience and AI, is advancing technologies like brain-computer interfaces, AI-driven cognitive enhancement, and personalized neuromodulation for treating neurological disorders. These developments hold potential to improve cognitive functions, restore motor abilities, and create human-machine collaborative systems. Looking ahead, the convergence of neuroscience and AI is set to transform cognitive modeling, decision-making, and mental health interventions. This fusion mirrors the quest for nuclear fusion energy, both driven by the need to unlock profound sources of understanding. As STEM disciplines continue to drive core developments of foundational models of the brain, neuroinformatics promises to lead innovations in augmented intelligence, personalized healthcare, and effective decision-making systems.},
}

@article {pmid39314938,
year = {2024},
author = {Bjånes, DA and Kellis, S and Nickl, R and Baker, B and Aflalo, T and Bashford, L and Chivukula, S and Fifer, MS and Osborn, LE and Christie, B and Wester, BA and Celnik, PA and Kramer, D and Pejsa, K and Crone, NE and Anderson, WS and Pouratian, N and Lee, B and Liu, CY and Tenore, F and Rieth, L and Andersen, RA},
title = {Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2246 days.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {39314938},
support = {U01 NS098975/NS/NINDS NIH HHS/United States ; UG3 NS107688/NS/NINDS NIH HHS/United States ; },
abstract = {MOTIVATION: The clinical success of brain-machine interfaces depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. Therefore, there is a need to quantify any damage that microelectrodes sustain when they are chronically implanted in the human cortex.

METHODS: Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from Neuroport arrays chronically implanted in the cortex of three people with tetraplegia for 956-2246 days. We analyzed eleven multi-electrode arrays in total: eight arrays with platinum (Pt) electrode tips and three with sputtered iridium oxide tips (SIROF); one Pt array was left in sterile packaging, serving as a control. The arrays were implanted/explanted across three different clinical sites surgeries (Caltech/UCLA, Caltech/USC and APL/Johns Hopkins) in the anterior intraparietal area, Brodmann's area 5, motor cortex, and somatosensory cortex.Human experts rated the electron micrographs of electrodes with respect to five damage metrics: the loss of metal at the electrode tip, the amount of separation between the silicon shank and tip metal, tissue adherence or bio-material to the electrode, damage to the shank insulation and silicone shaft. These metrics were compared to functional outcomes (recording quality, noise, impedance and stimulation ability).

RESULTS: Despite higher levels of physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt electrodes (measured by SNR), at the time of explant. Additionally, 1 kHz impedance (measured in vivo prior to explant) significantly correlated with all physical damage metrics, recording, and stimulation performance for SIROF electrodes (but not Pt), suggesting a reliable measurement of in vivo degradation.We observed a new degradation type, primarily occurring on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, tip metalization damage was not significantly higher due to stimulation or amount of charge. Physical damage was centralized to specific regions of an array often with differences between outer and inner electrodes. This is consistent with degradation due to contact with the biologic milieu, influenced by variations in initial manufactured state. From our data, we hypothesize that erosion of the silicon shank often precedes damage to the tip metal, accelerating damage to the electrode / tissue interface.

CONCLUSIONS: These findings link quantitative measurements, such as impedance, to the physical condition of the microelectrodes and their capacity to record and stimulate. These data could lead to improved manufacturing or novel electrode designs to improve long-term performance of BMIs making them are vitally important as multi-year clinical trials of BMIs are becoming more common.},
}

@article {pmid39314865,
year = {2024},
author = {Luo, X},
title = {Effects of motor imagery-based brain-computer interface-controlled electrical stimulation on lower limb function in hemiplegic patients in the acute phase of stroke: a randomized controlled study.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1394424},
pmid = {39314865},
issn = {1664-2295},
abstract = {BACKGROUND: Lower limb motor dysfunction is one of the most serious consequences of stroke; however, there is insufficient evidence for optimal rehabilitation strategies. Improving lower limb motor function through effective rehabilitation strategies is a top priority for stroke patients. Neuroplasticity is a key factor in the recovery of motor function. The extent to which neuroplasticity-based rehabilitation therapy using brain-computer interface (BCI) is effective in treating lower limb motor dysfunction in acute ischemic stroke patients has not been extensively investigated.

OBJECTIVE: This study aimed to assess the impact of BCI rehabilitation on lower limb motor dysfunction in individuals with acute ischemic stroke by evaluating motor function, walking ability, and daily living activities.

METHODS: This study was conducted in a randomized controlled trial, involving 64 patients with acute ischemic stroke who experienced lower limb motor dysfunction. All patients were divided into two groups, with 32 patients assigned to the control group was given conventional rehabilitation once a day for 70 min, 5 times a week for 2 weeks, and the experimental group (n = 32) was given BCI rehabilitation on top of the conventional rehabilitation for 1 h a day, 30 min of therapy in the morning and an additional 30 min in the afternoon, for a total of 20 sessions over a two-week period. The primary outcome was lower extremity motor function, which was assessed using the lower extremity portion of the Fugl-Meyer Rating Scale (FMA-LE), and the secondary endpoints were the Functional Ambulation Scale (FAC), and the Modified Barthel index (MBI).

RESULTS: After 20 sessions of treatment, both groups improved in motor function, walking function, and activities of daily living, and the improvements in FMA-LE scores (p < 0.001), FAC (p = 0.031), and MBI (p < 0.001) were more pronounced in the experimental group compared with the control group.

CONCLUSION: Conventional rehabilitation therapy combined with BCI rehabilitation therapy can improve the lower limb motor function of hemiplegic patients with stroke, enhance the patient's ability to perform activities of daily living, and promote the improvement of walking function, this is an effective rehabilitation policy to promote recovery from lower extremity motor function disorders.},
}

@article {pmid39314275,
year = {2024},
author = {Ma, X and Rizzoglio, F and Bodkin, KL and Miller, LE},
title = {Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39314275},
issn = {2692-8205},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; },
abstract = {OBJECTIVE: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.

APPROACH: We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to.

MAIN RESULTS: We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network (RNN) decoder with 10-12 clusters.

SIGNIFICANCE: This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.},
}

@article {pmid39314155,
year = {2024},
author = {Cai, Q and Meng, L and Quan, M and Wang, L and Ren, J and Zheng, C and Yang, J and Ming, D},
title = {Progress of research in the application of ultrasound technology for the treatment of Alzheimer's disease.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-24-00539},
pmid = {39314155},
issn = {1673-5374},
abstract = {Alzheimer's disease is a common neurodegenerative disorder defined by decreased reasoning abilities, memory loss, and cognitive deterioration. The presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer's disease. The use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent years. As a safe and feasible therapeutic and drug-delivery method, ultrasound has shown promise in improving cognitive deficits. This article provides a summary of the application of ultrasound technology for treating Alzheimer's disease over the past 5 years, including standalone ultrasound treatment, ultrasound combined with microbubbles or drug therapy, and magnetic resonance imaging-guided focused ultrasound therapy. Emphasis is placed on the benefits of introducing these treatment methods and their potential mechanisms. We found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-β plaque deposition. We believe that ultrasound is an effective therapy for Alzheimer's disease, and this review provides a theoretical basis for future ultrasound treatment methods.},
}

@article {pmid39314138,
year = {2024},
author = {Lv, Y and Li, H},
title = {Blood diagnostic and prognostic biomarkers in amyotrophic lateral sclerosis.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-24-00286},
pmid = {39314138},
issn = {1673-5374},
abstract = {Amyotrophic lateral sclerosis is a devastating neurodegenerative disease for which the current treatment approaches remain severely limited. The principal pathological alterations of the disease include the selective degeneration of motor neurons in the brain, brainstem, and spinal cord, as well as abnormal protein deposition in the cytoplasm of neurons and glial cells. The biological markers under extensive scrutiny are predominantly located in the cerebrospinal fluid, blood, and even urine. Among these biomarkers, neurofilament proteins and glial fibrillary acidic protein most accurately reflect the pathologic changes in the central nervous system, while creatinine and creatine kinase mainly indicate pathological alterations in the peripheral nerves and muscles. Neurofilament light chain levels serve as an indicator of neuronal axonal injury that remain stable throughout disease progression and are a promising diagnostic and prognostic biomarker with high specificity and sensitivity. However, there are challenges in using neurofilament light chain to differentiate amyotrophic lateral sclerosis from other central nervous system diseases with axonal injury. Glial fibrillary acidic protein predominantly reflects the degree of neuronal demyelination and is linked to non-motor symptoms of amyotrophic lateral sclerosis such as cognitive impairment, oxygen saturation, and the glomerular filtration rate. TAR DNA-binding protein 43, a pathological protein associated with amyotrophic lateral sclerosis, is emerging as a promising biomarker, particularly with advancements in exosome-related research. Evidence is currently lacking for the value of creatinine and creatine kinase as diagnostic markers; however, they show potential in predicting disease prognosis. Despite the vigorous progress made in the identification of amyotrophic lateral sclerosis biomarkers in recent years, the quest for definitive diagnostic and prognostic biomarkers remains a formidable challenge. This review summarizes the latest research achievements concerning blood biomarkers in amyotrophic lateral sclerosis that can provide a more direct basis for the differential diagnosis and prognostic assessment of the disease beyond a reliance on clinical manifestations and electromyography findings.},
}

@article {pmid39313602,
year = {2024},
author = {Yin, X and Yang, C and Dong, H and Liang, J and Lin, M},
title = {Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {39313602},
issn = {1741-0444},
support = {2024A1515012810//Basic and Applied Basic Research Foundation of Guangdong Province/ ; },
abstract = {The uncalibrated brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP) can omit the training process and is closer to the practical application. Filter bank canonical correlation analysis (FBCCA), as a classical approach of uncalibrated SSVEP-based BCI, extracts the fundamental and harmonic ingredients through filter bank decomposition. Nevertheless, this method fails to fully leverage the temporal feature of the signal. The paper suggested utilizing reconstructed data with temporal delay in the computation of the canonical correlation coefficient, and the different combinations of the time-delayed embedding and FBCCA were discussed. We selected the data from seven participants in the Benchmark dataset for parameter optimization and evaluated the method across all participants. The experimental results showed that only embedding the time-delayed version into the first subband (FBdCCA) was better than embedding it into all subbands (FBdCCA(all)), and the accuracy of FBdCCA surpassed that of FBCCA significantly. This suggests that the approach of time-delayed embedding can further enhance the performance of FBCCA.},
}

@article {pmid39312420,
year = {2024},
author = {Wang, J and Kim, SJ and Wu, W and Lee, J and Hinton, H and Gertner, RS and Jung, HS and Park, H and Ham, D},
title = {A Cyto-silicon Hybrid System with On-chip Closed-loop Modulation.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2024.3466549},
pmid = {39312420},
issn = {1940-9990},
abstract = {We introduce a bioelectronic interface between biological electrogenic cells and a mixed-signal CMOS integrated circuit with an array of surface electrodes, where not only is the CMOS electrode array capable of electrophysiological recording and stimulation of the cells with 1,024 recording and stimulation channels, but it can also provide low-latency artificial signal pathways from cells it records to cells it stimulates. This on-chip closed-loop modulation has an intrinsic latency less than 5 μs. To demonstrate the utility of the on-chip closed loop modulation as an artificial feedback pathway between biological cells, we develop a silicon-cardiomyocyte self-sustained oscillator with a tunable frequency to which both the relevant part of the CMOS chip and cells are locked, and also a silicon-neuron interface with a silicon inhibitory connection between neuronal cells. This line of cyto-silicon hybrid system, where the boundary between biological and semiconductor systems is blurred, may find applications in prosthesis, brain-machine interface, and fundamental biology research.},
}

@article {pmid39312418,
year = {2024},
author = {Lee, G and Jang, J and Song, K and Kim, TW},
title = {A 6-9 GHz 1.28 Gbps 76 mW Amplitude and Synchronized Time Shift Keying IR-UWB CMOS Transceiver for Brain Computer Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2024.3465533},
pmid = {39312418},
issn = {1940-9990},
abstract = {This paper proposes a low-power, high-speed impulse radio-ultra-wideband (IR-UWB) transceiver for brain computer interfaces (BCIs) using amplitude and synchronized time shift keying technique (ASTSK). The proposed IR-UWB transmitter (Tx) generates two pulses (sync pulse and data pulse) per symbol rate. The time difference between two pulses is used for synchronized time shift keying and the amplitude of the two pulses is used for amplitude shift keying. The receiver (Rx) demodulates the time difference with a low power time-to-digital converter (TDC) and peak detector (PD) based amplitude demodulation is suggested to relax analog-to-digital converter (ADC) burden for low power receiver. Especially the Tx-based synchronized operation eliminates the need for complex clock circuitry such as phase-lock loop (PLL) and reference crystal oscillator. Therefore, it can achieve low power and high-speed operation. The prototype, fabricated in 65 nm CMOS, has a frequency range of 6-9 GHz, communication speed of 1.28 Gbps, and power consumption of 18 mW (Tx) and 58 mW (Rx). This work is a fully integrated RF transceiver adapted for high-order modulation and designed to include the receiver.},
}

@article {pmid39310800,
year = {2024},
author = {Vanderheiden, G and Marte, C and Bahram, S},
title = {Rethinking Our Approach to Accessibility in the Era of Rapidly Emerging Technologies.},
journal = {Human aspects of IT for the aged population : 10th International Conference, ITAP 2024, held as part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings. Part I. ITAP (Conference) ...},
volume = {14725},
number = {},
pages = {306-323},
pmid = {39310800},
support = {90REGE0008/ACL/ACL HHS/United States ; },
abstract = {Accessibility has always played catch-up to the detriment of people with disabilities - and this appears to be exacerbated by the rapid advancements in technology. A key question becomes, can we better predict where technology will be in 10 or 20 years and develop a plan to be better positioned to make these new technologies accessible when they make it to market? To attempt to address this question, a "Future of Interface Workshop" was convened in February 2023, chaired by Vinton Cerf and Gregg Vanderheiden that brought together leading researchers in artificial intelligence, brain-computer interfaces, computer vision, and VR/AR/XR, and disability to both a) identify barriers these new technologies might present and how to address them, and b) how these new technologies might be tapped to address current un- or under-addressed problems and populations. This paper provides an overview of the results of the workshop as well as the current version of the R&D Agenda work that was initiated at the conference. It will also present an alternate approach to accessibility that is being proposed based on the new emerging technologies.},
}

@article {pmid39309467,
year = {2024},
author = {Stacy, NI and Smith, R and Sullivan, KE and Nelson, SE and Nolan, EC and De Voe, RS and Witherington, BE and Perrault, JR},
title = {Health assessment of nesting loggerhead sea turtles (Caretta caretta) in one of their largest rookeries (central eastern Florida coast, USA).},
journal = {Conservation physiology},
volume = {12},
number = {1},
pages = {coae064},
pmid = {39309467},
issn = {2051-1434},
abstract = {Reproduction is a physiologically demanding process for sea turtles. Health indicators, including morphometric indices and blood analytes, provide insight into overall health, physiology and organ function for breeding sea turtles as a way to assess population-level effects. The Archie Carr National Wildlife Refuge (ACNWR) on Florida's central eastern coast is critical nesting habitat for loggerhead sea turtles (Caretta caretta), but health variables from this location have not been documented. Objectives of the study were to (1) assess morphometrics and blood analyte data (including haematology, plasma biochemistry, protein electrophoresis, β-hydroxybutyrate, trace nutrients, vitamins and fatty acid profiles) from loggerheads nesting on or near the beaches of the ACNWR, (2) investigate correlations of body condition index (BCI) with blood analytes and (3) analyse temporal trends in morphometric and blood analyte data throughout the nesting season. Morphometric and/or blood analyte data are reported for 57 nesting loggerheads encountered between 2016 and 2019. Plasma copper and iron positively correlated with BCI. Mass tended to decline across nesting season, whereas BCI did not. Many blood analytes significantly increased or decreased across nesting season, reflecting the catabolic state and haemodynamic variations of nesting turtles. Twenty-three of 34 fatty acids declined across nesting season, which demonstrates the physiological demands of nesting turtles for vitellogenesis and reproductive activities, thus suggesting potential utility of fatty acids for the assessment of foraging status and phases of reproduction. The findings herein are relevant for future spatiotemporal and interspecies comparisons, investigating stressor effects and understanding the physiological demands in nesting sea turtles. This information provides comparative data for individual animals in rescue or managed care settings and for assessment of conservation strategies.},
}

@article {pmid39308547,
year = {2024},
author = {Villa, J and Cury, J and Kessler, L and Tan, X and Richter, CP},
title = {Enhancing biocompatibility of the brain-machine interface: A review.},
journal = {Bioactive materials},
volume = {42},
number = {},
pages = {531-549},
pmid = {39308547},
issn = {2452-199X},
abstract = {In vivo implantation of microelectrodes opens the door to studying neural circuits and restoring damaged neural pathways through direct electrical stimulation and recording. Although some neuroprostheses have achieved clinical success, electrode material properties, inflammatory response, and glial scar formation at the electrode-tissue interfaces affect performance and sustainability. Those challenges can be addressed by improving some of the materials' mechanical, physical, chemical, and electrical properties. This paper reviews materials and designs of current microelectrodes and discusses perspectives to advance neuroprosthetics performance.},
}