@article {pmid41635520,
year = {2025},
author = {Liu, Y and Xu, P and Hu, S},
title = {Resting-state gamma power in schizophrenia: a systematic review and meta-analysis.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1731645},
pmid = {41635520},
issn = {1664-0640},
abstract = {Gamma-band oscillations, generated by excitatory-inhibitory circuit interactions, are strongly implicated in schizophrenia, yet evidence on resting-state abnormalities remains inconsistent. We conducted a systematic review and meta-analysis of EEG and MEG studies comparing resting-state gamma activity in patients with schizophrenia and healthy controls, following PRISMA guidelines and assessing study quality with the Newcastle-Ottawa Scale. Twenty studies (n = 998 patients; n = 952 controls) were included. Standardized mean differences (Hedges' g) were calculated and pooled using random-effects models. Results demonstrated a significant elevation of whole-brain gamma power in schizophrenia (g=0.371; 95% CI = 0.119-0.622; P < 0.001; I[2] = 78.2%). Region-specific analyses showed increases in frontal and temporal cortices, with smaller or inconsistent effects in parietal, occipital, and default mode network (DMN) regions. Meta-regression revealed illness duration (β=1.13) and medication status (β=0.43) as positive predictors, while eyes-open resting conditions attenuated effects (β=-0.70), indicating that both clinical chronicity and methodological factors contribute to heterogeneity. Publication bias was not evident by Egger's test, although trim-and-fill suggested five potentially missing small-effect studies, reducing the pooled estimate to g=0.130. Sensitivity analyses confirmed that findings were not driven by outliers, and GRADE assessments rated the certainty of evidence as moderate for whole-brain gamma and low for regional outcomes. Taken together, these findings suggest that resting-state gamma power differences in schizophrenia represent a small and heterogeneous group-level effect, shaped by illness duration, medication status, and recording conditions. Rather than indicating a uniform abnormality, the results underscore substantial variability across studies and highlight the need for cautious interpretation. Future large-scale, longitudinal, and multimodal investigations-particularly in unmedicated and first-episode patients-are warranted to clarify the temporal dynamics, causal mechanisms, and potential translational relevance of resting-state gamma activity in schizophrenia.},
}

@article {pmid41632971,
year = {2026},
author = {Li, YY and Hu, AQ and Yi, LL and Mao, ZX and Lü, QY and Wang, J and Wei, W and Huang, YQ and Huang, S and Dai, WJ and Qiao, MX and Xu, JJ and Wang, Q and Li, XJ and Luo, FG and Deng, W and Hu, YZ and Li, T and Guo, WJ},
title = {Comparing the Associations of Internet Addiction and Internet Gaming Disorder With Psychopathological Symptoms: Cross-Sectional Study of Three Independent Adolescent Samples.},
journal = {Journal of medical Internet research},
volume = {28},
number = {},
pages = {e82414},
doi = {10.2196/82414},
pmid = {41632971},
issn = {1438-8871},
mesh = {Humans ; Cross-Sectional Studies ; Adolescent ; *Internet Addiction Disorder/psychology/epidemiology ; Male ; Female ; Young Adult ; *Video Games/psychology ; *Internet ; Surveys and Questionnaires ; *Behavior, Addictive/psychology/epidemiology ; China/epidemiology ; },
abstract = {BACKGROUND: Both internet gaming disorder (IGD) and internet addiction (IA) have been associated with diverse psychopathological symptoms. However, how the 2 conditions relate to each other and which is more strongly associated with psychopathology remain unclear.

OBJECTIVE: This study aimed to examine the association between IGD and IA and compare the strength of their associations with various types of psychopathological symptoms.

METHODS: This cross-sectional study surveyed 3 independent samples of Chinese adolescents: the first sample (S1) comprised 8194 first-year undergraduates at a comprehensive university in Chengdu, the second sample (S2) comprised 1720 students from a high school in Hangzhou, and the third sample (S3) comprised 551 inpatients aged 13 to 19 years recruited from 2 tertiary psychiatric hospitals in Hangzhou and Chengdu. IGD was defined as a score of 22 or more on the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), whereas IA was defined as a score of 50 or more on Young's 20-item Internet Addiction Test (IAT-20). Symptoms of depression, anxiety, psychoticism, paranoid ideation, and attention-deficit or hyperactivity were assessed using internationally validated scales including 9-item the Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, psychoticism and paranoid ideation subscales of the Symptom Checklist 90 (absent for S2), and Adult ADHD Self-Report Scale (absent for S1), through online surveys in S1 (October 2020) and S3 (January 2022 to February 2025) and via an offline survey in S2 (March 2024).

RESULTS: The prevalence estimates (95% CI) of IGD were 4.8% (4.3%-5.2%) in S1, 15.8% (14.0%-17.5%) in S2, and 32.3% (28.4%-36.2%) in S3, whereas prevalence estimates (95% CI) of IA were consistently higher across samples, ranging from 7.3% (6.8%-7.9%) in S1 and 18.8% (17.0%-20.6%) in S2 to 45.9% (41.8%-50.1%) in S3. The IGDS9-SF and the IAT-20 were moderately correlated (Pearson r=0.51-0.57; all P<.001) and were associated with the severity of most psychopathological symptom domains, with consistently stronger associations observed for IAT-20 scores. In multivariate models including all psychopathological symptoms as independent variables, the coefficients of determination (R², 95% CIs) were consistently higher for the IAT-20 than for the IGDS9-SF in S1 (0.33, 0.30-0.35 vs 0.13, 0.11-0.16) and S2 (0.44, 0.39-0.49 vs 0.23, 0.18-0.27), with a similar but nonsignificant pattern observed in S3 (0.13, 0.06-0.26 vs 0.06, 0.03-0.16). Post hoc analyses indicated that psychopathological symptoms were generally more severe in individuals with IA, either alone or comorbid with IGD, than in those with IGD only.

CONCLUSIONS: This study provides additional evidence that IGD and IA are distinct yet interrelated constructs, and further demonstrates that IA consistently exhibits stronger associations with the severity of psychopathological symptoms than IGD. These findings underscore the importance of recognizing and addressing compulsive and problematic online behaviors that extend beyond gaming, highlighting the need to refine diagnostic frameworks and prioritize targeted clinical interventions.},
}

Humans
Cross-Sectional Studies
Adolescent
*Internet Addiction Disorder/psychology/epidemiology
Male
Female
Young Adult
*Video Games/psychology
*Internet
Surveys and Questionnaires
*Behavior, Addictive/psychology/epidemiology
China/epidemiology

@article {pmid41632672,
year = {2026},
author = {Xu, Y and Vong, CM and Xu, Z and Fu, J and Li, J and Chen, C},
title = {Disentangled Multimodal Spatiotemporal Learning for Hybrid EEG-fNIRS Brain-Computer Interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3660692},
pmid = {41632672},
issn = {1558-2531},
abstract = {The hybrid EEG-fNIRS Brain-computer interface (BCI) combines the high temporal resolution of electroencephalography (EEG) with the high spatial resolution of functional near-infrared spectroscopy (fNIRS) to enable comprehensive brain activity detection. However, integrating these modalities to obtain highly discriminative features remains challenging. Most existing methods fail to effectively capture the spatiotemporal coupling features and correlations between EEG and fNIRS signals. Furthermore, these methods adopt a holistic learning paradigm for the representation of each modality, leading to unrefined and redundant multimodal representations. To address these challenges, we propose a disentangled multimodal spatiotemporal learning (DMSL) method for hybrid EEG-fNIRS BCI systems, which simultaneously performs multimodal spatiotemporal coupling and disentangled representation learning within a unified framework. Specifically, DMSL utilizes a compact convolutional module with one-dimensional temporal and spatial convolution layers to extract complex spatiotemporal patterns from each modality and introduces a multimodal attention interaction module to comprehensively capture the inter-modality correlations, enhancing the representations for each modality. Subsequently, DMSL designs an adaptive multi-branch graph convolutional module based on reconstructed channels to effectively capture the spatiotemporal coupling features, incorporating modality consistency and disparity constraints to disentangle common and modality-specific representations for each modality. These disentangled representations are finally adaptively fused to perform different task predictions. The proposed DMSL demonstrates state-of-the-art performance on publicly available datasets for mental arithmetic, motor imagery, and emotion recognition tasks, exceeding the best baselines by 2.34%, 0.59%, and 1.47%, respectively. These results demonstrate the effectiveness of DMSL in improving EEG-fNIRS decoding and its strong generalization ability in BCI applications.},
}

@article {pmid41631479,
year = {2026},
author = {Leung, J and Holanda, LJ and Wheeler, L and Chau, T},
title = {Wireless in-ear EEG system for auditory brain-computer interface applications in adolescents.},
journal = {Biomedical physics & engineering express},
volume = {12},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ae3b45},
pmid = {41631479},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/instrumentation/methods ; Adolescent ; Male ; Female ; *Wireless Technology/instrumentation ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Artifacts ; *Ear ; Acoustic Stimulation ; },
abstract = {In-ear electroencephalography (EEG) systems offer several practical advantages over scalp-based EEG systems for non-invasive brain-computer interface (BCI) applications. However, the difficulty in fabricating in-ear EEG systems can limit their accessibility for BCI use cases. In this study, we developed a portable, low-cost wireless in-ear EEG device using commercially available components. In-ear EEG signals (referenced to left mastoid) from 5 adolescent participants were compared to scalp-EEG collected simultaneously during an alpha modulation task, various artifact induction tasks, and an auditory word-streaming BCI paradigm. Spectral analysis confirmed that the proposed in-ear EEG system could capture significantly increased alpha activity during eyes-closed relaxation in 3 of 5 participants, with a signal-to-noise ratio of 2.34 across all participants. In-ear EEG signals were most susceptible to horizontal head movement, coughing and vocalization artifacts but were relatively insensitive to ocular artifacts such as blinking. For the auditory streaming paradigm, the classifier decoded the presented stimuli from in-ear EEG signals only in 1 of 5 participants. Classification of the attended stream did not exceed chance levels. Contrast plots showing the difference between attended and unattended streams revealed reduced amplitudes of in-ear EEG responses relative to scalp-EEG responses. Hardware modifications are needed to amplify in-ear signals and measure electrode-skin impedances to improve the viability of in-ear EEG for BCI applications.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/instrumentation/methods
Adolescent
Male
Female
*Wireless Technology/instrumentation
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Artifacts
*Ear
Acoustic Stimulation

@article {pmid41629357,
year = {2026},
author = {Wang, F and Chen, Y and Wang, P and Gong, A and Xu, J and Fu, Y},
title = {An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-06708-3},
pmid = {41629357},
issn = {2052-4463},
support = {Grant Nos. 62376112, 82172058, 81771926, 61763022, 62366026, and 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; Grant No. 2023M734315//China Postdoctoral Science Foundation/ ; },
abstract = {Non-invasive EEG-based brain-computer interfaces (BCI) for handwriting imagery can support the restoration of fine writing abilities in individuals with motor impairments. However, the development of high-performance decoding algorithms is constrained by scarce training datasets. To address this, we present the first open EEG dataset dedicated to handwriting imagery. The dataset comprises 32-channel EEG recordings (sampled at 1000 Hz) from 21 healthy participants across two sessions separated by at least 24 hours. A dual-paradigm design captures multidimensional neural features: a Chinese character stroke imagery task (five basic strokes, 200 trials per session) and a Pinyin single-vowel imagery task (six vowels, 240 trials per session). After rigorous quality screening, 18,480 standardized trials are provided, ensuring completeness, reliability, and adherence to the Brain Imaging Data Structure (BIDS) standard. This dataset enables the development and evaluation of algorithms for non-invasive BCI and supports research on restoring writing-based communication in individuals with motor impairments.},
}

@article {pmid41626718,
year = {2026},
author = {Saeed, S and Luo, Z and Wang, H and Xu, L and Zhang, X and Tang, A and Ma, X and Lai, J and Song, P and Hu, S},
title = {Mapping the Global Burden and Inequalities of Bipolar Disorder, 1990-2021, With Projections to 2050: A Systematic Analysis.},
journal = {Bipolar disorders},
volume = {28},
number = {1},
pages = {e70074},
doi = {10.1111/bdi.70074},
pmid = {41626718},
issn = {1399-5618},
support = {2023YFC2506200//National Key Research and Development Program of China/ ; JNL-2023001B//The Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021C03107//The Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//The Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//The Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; },
mesh = {Humans ; *Bipolar Disorder/epidemiology ; Male ; Female ; *Global Health/statistics & numerical data ; Adult ; Middle Aged ; *Global Burden of Disease/trends ; Incidence ; Prevalence ; Young Adult ; Socioeconomic Factors ; Adolescent ; Aged ; Sex Factors ; },
abstract = {BACKGROUND: Bipolar disorder is a severe mental disorder affecting millions worldwide, necessitating comprehensive policies and interventions.

AIMS: To provide assessment of global inequalities in the burden of bipolar disorder and their projected trajectories to 2050.

METHODS: Global Burden of Disease 2021 data from 204 countries and territories were analyzed, stratified by age, gender, and Socio-demographic Index (SDI) quintiles. Age-standardized prevalence (ASPR), incidence (ASIR), and years lived with disability (ASR YLD) per 100,000 population were calculated. Inequalities were assessed using the slope index of inequality (SII) and concentration index (CI), and ARIMA models were applied to project trends to 2050.

RESULTS: From 1990 to 2021, global incidence of BD increased, while prevalence and years lived with disability (YLDs) remained relatively stable (ASPR: 453.7 [95% UI: 381.6-540.8] to 454.6 [95% UI: 377.9-545.8]). Females consistently had higher prevalence than males (474.2 vs. 435.0 per 100,000 in 2021). High-SDI regions reported the highest rates, with Australasia reaching 1110.8 (95% UI: 940.3-1305.9). The SII for incidence rose slightly (10.87-11.38), while the CI declined (0.096-0.012), indicating increasing absolute but decreasing relative inequalities. Projections suggest a rising global burden, with female prevalence remaining higher and incidence rates converging between genders (global ASIR: 33.8 per 100,000).

CONCLUSION: Global inequalities in bipolar disorder persist, disproportionately affecting females and high-SDI regions. Projected trends indicate an increasing burden with a narrowing gender gap in incidence, emphasizing the need for targeted interventions and further research on long-term impacts, including the effects of COVID-19.},
}

Humans
*Bipolar Disorder/epidemiology
Male
Female
*Global Health/statistics & numerical data
Adult
Middle Aged
*Global Burden of Disease/trends
Incidence
Prevalence
Young Adult
Socioeconomic Factors
Adolescent
Aged
Sex Factors

@article {pmid41625625,
year = {2025},
author = {Muhsin, SM and Akbar, MA and Mustari, S and Alashaikh, MH and Chin, AHB},
title = {Human cognitive enhancement and reprogenetic technologies in Malaysia - A survey study of local Muslim undergraduate students' viewpoints.},
journal = {Frontiers in sociology},
volume = {10},
number = {},
pages = {1701007},
pmid = {41625625},
issn = {2297-7775},
abstract = {INTRODUCTION: Newly emerging human enhancement technologies such as brain chip implants, CRISPR-Cas9-based gene editing, and polygenic embryo screening (PES) alongside preimplantation genetic testing (PGT-P) are highly controversial in Islam. However, the prevailing sociocultural dynamics encourage their uptake. In the current era of declining fertility rates, increased parental investment in fewer children has resulted in a flourishing tuition industry, accompanied by heightened academic pressure on students and widespread parental anxiety. These emerging technologies can be employed for cognitive enhancement, thereby providing an expedient solution for parents and students navigating a highly competitive educational environment.

MATERIALS AND METHODS: To inform and facilitate future policy decision-making, an online survey was conducted among 575 undergraduate Muslim students at the International Islamic University Malaysia (IIUM) to assess their perspectives and opinions regarding these newly emerging technologies.

RESULTS: The findings indicated a significant level of opposition among respondents to the uptake of human enhancement technologies, with 54.8% opposing polygenic embryo screening, 69.2% opposing gene editing, and 75.3% opposing brain chip implants, reflecting substantial concerns about altering natural human attributes. The results also indicate that numerous Muslim respondents believe that Allah created humans flawlessly and purposefully, asserting that humanity lacks the authority to alter or amend this creation.

DISCUSSION/CONCLUSION: A three-pronged governance approach for human enhancement technologies is thus proposed, which encompasses (i) bioethical safeguards, (ii) public engagement and education, and (iii) economic accessibility. It is suggested that the Malaysian government should actively consult relevant stakeholders and various segments of the public before enacting future legislation on these technologies.},
}

@article {pmid41626035,
year = {2025},
author = {Jackson, MC and Azarraga, RB and Fraix, MP and Agrawal, DK},
title = {Stage-Based Communication Rehabilitation in Amyotrophic Lateral Sclerosis (ALS): A Review of Strategies for Enhancing Quality of Life.},
journal = {Archives of internal medicine research},
volume = {8},
number = {4},
pages = {359-371},
pmid = {41626035},
issn = {2688-5654},
support = {R25 AI179582/AI/NIAID NIH HHS/United States ; },
abstract = {Amyotrophic Lateral Sclerosis (ALS) is an incurable progressive degenerative neuromuscular disease. One way ALS affects patients is through dysarthria significantly impacting a patient's quality of life by affecting their ability to communicate. This makes maintaining relationships, identity and autonomy difficult, all of which affect psychological wellbeing - a determinant of the quality of life. Dysarthria makes communication difficult, and because the regions affected by ALS first are different for each patient, creating strategies for rehabilitating communication can be challenging. In this review we explore the different communication rehabilitation options available and organize them based on if they are usable based on the onset of intelligibility and locked in state. Interventions before the onset of intelligibility in the early stage are proactive measures such as voice banking and education which empower patient autonomy and a sense of control. Interventions between onset of intelligibility and the locked-in state in the middle stage are alternative and augmentative communication strategies varied in accessibility and usability in patients based on their preferences and functional ability. Late-stage interventions which work after a patient with ALS has entered a locked-in state, are the most technologically advanced alternative and augmentative communication devices and rehabilitate function inaccessible by other methods in this disease stage. While assessing patient values and recommending interventions which meet patient needs is most important in rehabilitation of communication in patient with ALS, using a stage-based approach to evaluate and recommend the treatment of dysarthria and communication rehabilitation will optimize quality of life throughout the progression of disease.},
}

@article {pmid41625473,
year = {2025},
author = {Jin, C and Yang, J and Liang, Z and Qiu, J and Zha, R and Yuan, Z and Shen, Y and Zhang, X},
title = {Navigating online emotion: affective patterns and depressive traits in youth digital engagement.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1736426},
pmid = {41625473},
issn = {1664-1078},
abstract = {INTRODUCTION: Youth digital engagement serves as a notable avenue for the expression of emotion and the construction of self among today's youth. This study aims to examine the patterns of youth online emotional expression and their association with individual psychological traits, particularly depressive tendencies.

METHODS: 23,966 Weibo posts published by 103 active youth users were sampled and analyzed. An integrative framework combining Russell's Circumplex Model with multi-level thematic analysis was applied to code each post for valence, arousal, trigger type and coping strategy. Youths also completed a standard depression-screening scale; scores were used to contrast high- versus low-depressive trait sub-groups.

RESULTS: The findings reveal that youth online emotional expression overall is characterized by a self-focused nature, high pleasure, and high arousal. The study also found that individual psychological traits influence emotional expression patterns. Individuals with depressive tendencies showed a significant propensity for higher emotional arousal expression and more no-trigger expression. Furthermore, no-trigger expression plays a mediating role in their emotional expression mechanism.

DISCUSSION: The study provides an integrative framework for youth digital engagement and highlights "no-trigger" expression as a mediator in the framework. These findings can guide early detection efforts and contribute to designing targeted digital mental health supports, as well as informing guidance for families and platform managers.},
}

@article {pmid41624131,
year = {2025},
author = {Guo, X and Li, P and Liu, H and Ding, S},
title = {A systematic review of the effects of brain-computer interface on lower limb motor function, balance function, and activities of daily living in stroke patients.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1641843},
pmid = {41624131},
issn = {1662-4548},
abstract = {OBJECTIVE: To systematically evaluate the effects of brain-computer interface (BCI) technology on lower limb motor function, balance function, and activities of daily living in stroke patients.

METHODS: This study followed the PRISMA guidelines and searched PubMed, Web of Science, EMbase, The Cochrane Library, CNKI, Wanfang, and VIP databases, with an additional manual search. The search period was from database inception to March 2024. The PEDro scale was used to assess the quality of the studies, the GRADE system was applied to evaluate the evidence quality for outcome measures, and Meta-analysis was conducted using Stata 17.0 software.

RESULTS: The systematic review included nine studies. The methodological quality, assessed using the PEDro scale, yielded an average score of 6.9, which corresponds to a moderate-to-low certainty of evidence. The Meta-analysis showed that BCI technology significantly improved lower limb motor function (MD = 3.52, 95% CI [2.03, 5.00], p < 0.001) and activities of daily living (MD = 6.08, 95% CI [1.81, 10.35], p = 0.01), but had no significant effect on balance function (MD = 4.82, 95% CI [-1.53, 11.16], p = 0.14). Subgroup analysis showed that the effect size in the acute and subacute phases was 3.89, and in the recovery phase, it was 3.12, both of which were statistically significant. In terms of intervention methods, the effect size for MI-BCI was 2.73, and for BCI-Robot, it was 4.60, both statistically significant. Regarding intervention dosage, the effect size for 2.5-10 h was 2.60, and for 12-20 h, it was 5.46, both statistically significant.

CONCLUSION: Current evidence suggests that BCI-based interventions have a beneficial effect on lower limb motor function and activities of daily living in stroke patients. Interventions initiated during the acute or subacute phase, with a total dose exceeding 12 h, appear to be associated with superior outcomes. However, the certainty of this evidence is moderate to low, necessitating further validation. Future research should prioritize large-scale, high-quality randomized controlled trials to definitively establish the efficacy of BCI technology and elucidate its optimal implementation protocols.},
}

@article {pmid41623459,
year = {2026},
author = {Eyvazpour, R and Farrokhi, B and Erfanian, A},
title = {A general model based on Riemannian manifold for stable decoding movement trajectory from ECoG signals.},
journal = {iScience},
volume = {29},
number = {2},
pages = {114521},
pmid = {41623459},
issn = {2589-0042},
abstract = {Decoding continuous 3D hand trajectories from electrocorticographic (ECoG) signals holds potential for brain-computer interface (BCI) applications. However, inter-session variability poses a major challenge for generalization. In this study, we propose a framework that leverages Riemannian-based feature extraction combined with stacked long short-term memory (LSTM) network to enable transfer learning across multiple sessions. ECoG recordings from five monkeys performing reaching tasks are considered. Spatial cross-frequency covariance matrices are computed over the brain area for each of 10 frequency band power and projected onto a Riemannian manifold to extract features which are invariant to session variability. These features and spectral feature are then used to train staked LSTM network. The results show that the proposed method achieves a stable cross-session performance and outperforms baseline models which are trained on frequency features. These findings highlight the potential of combining geometric features with temporal deep learning models for generalized decoding in translational BCI systems.},
}

@article {pmid41623142,
year = {2026},
author = {de Borman, A and Dyck, BV and Rooy, KV and Carrette, E and Meurs, A and Roost, DV and Van Hulle, MM},
title = {Word classification across speech modes from low-density electrocorticography signals.},
journal = {Journal of neural engineering},
volume = {23},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ae3a1b},
pmid = {41623142},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; Male ; *Brain-Computer Interfaces ; Female ; *Speech/physiology ; Adult ; Middle Aged ; Young Adult ; Imagination/physiology ; Speech Perception/physiology ; },
abstract = {Objective.Speech brain-computer interfaces (BCIs) aim to provide an alternative means of communication for individuals who are not able to speak. Remarkable progress has been achieved to decode attempted speech in individuals with severe anarthria. In contrast, imagined speech remains challenging to decode. The underlying neural mechanisms and relations to other speech modes are still elusive.Approach.In this study, we collected low-density electrocorticography signals from ten participants during a word repetition task. Electrodes were implanted for presurgical epilepsy evaluation in participants with preserved speech abilities. Models were developed using linear discriminant analysis to classify five words in response to different speech modes. We compared models trained during speaking, listening, imagining speaking, mouthing and reading. The relations between speech modes were investigated by transferring and augmenting models across speech modes.Main results.As expected, performed speech achieved the highest word classification accuracy followed by listening, mouthing, imagining and reading. While the accuracies obtained were not high enough for practical application, model transfer and augmentation could be investigated across speech modes. Transferring or augmenting models from one speech mode to another mode could significantly improve model performance. In particular, patterns learned from performed and perceived speech could generalize to imagined speech, leading to significantly improved imagined speech performance in seven participants. For four participants, imagined speech could be decoded above chance exclusively when models were transferred or augmented with performed or perceived speech.Significance.Imagined speech is often preferred by speech BCI users over attempted speech, as it requires less effort and can be produced more quickly. Transferring models across speech modes has the potential to facilitate and boost the development of imagined speech decoders.},
}

Humans
*Electrocorticography/methods
Male
*Brain-Computer Interfaces
Female
*Speech/physiology
Adult
Middle Aged
Young Adult
Imagination/physiology
Speech Perception/physiology

@article {pmid41621526,
year = {2026},
author = {Zhao, Y and Zhang, Y and Li, T},
title = {Causal relationships between ADHD, ASD and brain structure: A mendelian randomization study.},
journal = {Progress in neuro-psychopharmacology & biological psychiatry},
volume = {},
number = {},
pages = {111631},
doi = {10.1016/j.pnpbp.2026.111631},
pmid = {41621526},
issn = {1878-4216},
abstract = {Neurodevelopmental disorders (NDDs) are debilitating conditions that impose significant burdens on individuals, families, and society. Despite evidence demonstrated altered brain structure in NDDs, definitive conclusions remain elusive. Using two-sample mendelian randomization (MR) and the latest GWAS findings, the current study aimed to elucidate the causal relationships between grey matter (GM), white matter (WM), subcortical regions, and two prevalent NDDs: attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Our findings identified two frontal regions as key neural substrates in NDDs. Specifically, an increased surface area (SA) of the superior frontal gyrus (SFG) was significantly associated with an enhanced risk of ADHD (P = 2.04E-13, β = 4.28E-02, SE = 5.82E-03), while a larger SA of the orbital frontal gyrus (OFG) was associated with a reduced risk of ASD (P = 1.98E-42, β = -9.8E-02; SE = 0.007). Regarding WM tracts, the mode of anisotropy (MO) in the inferior fronto-occipital fasciculus (IFO) emerged as a causal factor for ADHD (P = 3.36E-70, β = -18.35; SE = 1.04), whereas the MO in the retro-lenticular part of the internal capsule (RLIC) was implicated in ASD (P = 1.37E-04, β = -12.73, SE = 3.34). No reverse causal link, i.e., brain alteration caused by NDDs was identified. Further mediation analyses using functional MRI (fMRI) GWAS data revealed that brain functional activities mediated the relationship between structural brain changes and NDDs risk. In conclusion, our findings underscored the critical role of the frontal lobe and association and projection fibers in the pathophysiology of NDDs, provide novel insights into the neural mechanisms underlying ADHD and ASD.},
}

@article {pmid41621181,
year = {2026},
author = {Spinelli, R and Sanchis, I and de Orellana, M and Humpola, MV and Rietmann, Á and Siano, ÁS},
title = {A nature-inspired peptide from the Boana cordobae frog as a potent and reversible AChE inhibitor with anti-amyloid and neuroprotective activities.},
journal = {Bioorganic chemistry},
volume = {171},
number = {},
pages = {109566},
doi = {10.1016/j.bioorg.2026.109566},
pmid = {41621181},
issn = {1090-2120},
abstract = {Alzheimer's disease (AD) is a multifactorial and progressive neurodegenerative disorder for which no effective treatment currently exists. The development of multitarget-directed ligands (MTDLs) capable of simultaneously modulating several pathological pathways represents a rational strategy to address its complex etiology. In this study, we report the isolation, chemical synthesis, and functional characterization of BcI-4, a short cationic peptide identified from the skin secretion of the Argentinean frog Boana cordobae. The peptide exhibited potent and reversible inhibitory activity against acetylcholinesterase (AChE), with IC50 values of 1.10 and 0.9 μM for recombinant human and Electrophorus electricus AChE, respectively, acting through a non-competitive mechanism involving the peripheral anionic site (PAS). BcI-4 also inhibited AChE-induced β-amyloid (Aβ) aggregation, showed modest monoamine oxidase B (MAO-B) inhibition, and displayed both antioxidant and metal-chelating activities, including inhibition of lipid peroxidation. The peptide retained the multifuctional pharmacological profile previously observed for the crude extract of B. cordobae, with significantly enhanced potency and selectivity toward AChE. Moreover, BcI-4 was non-toxic in vitro (hemolysis and HeLa cell assays) and in vivo (Artemia salina test) even at the highest concentrations tested. Altogether, these findings position BcI-4 as a nature-inspired multitarget peptide with neuroprotective potential, combining reversible AChE inhibition, anti-amyloid, antioxidant, and MAO-B modulatory activities. BcI-4 represents a promising lead compound for the development of peptide-based therapeutics against AD.},
}

@article {pmid41621106,
year = {2026},
author = {Alhourani, A and Pouratian, N},
title = {Editorial. Defining value and function in miniaturized cortical arrays for human brain-computer interface applications.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E4},
doi = {10.3171/2025.11.FOCUS251041},
pmid = {41621106},
issn = {1092-0684},
}

@article {pmid41621105,
year = {2026},
author = {Vattipally, VN and Kramer, P and Troumouchi, K and Shiino, S and Abouelseoud, N and Joshi, K and Xu, R and Theodore, N and Brem, H and Bettegowda, C and Jantzie, LL and Robinson, S and Azad, TD and Kathuria, A},
title = {Engineered neuroglial organoids as living neural interfaces for restorative neurosurgery.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E5},
doi = {10.3171/2025.11.FOCUS25911},
pmid = {41621105},
issn = {1092-0684},
mesh = {*Organoids/transplantation/physiology ; Humans ; *Neuroglia/physiology/transplantation ; Animals ; *Brain-Computer Interfaces ; *Tissue Engineering/methods ; *Neurosurgical Procedures/methods ; Pluripotent Stem Cells ; Neurons/physiology ; },
abstract = {Acute and chronic CNS pathologies that result in tissue loss remain among the most intractable problems in neurosurgery, with current treatments focused on stabilization and neuroprotection rather than structural repair. Neural interfaces such as recording, stimulating, or replacing neural activity have demonstrated value in restoring function via prostheses and brain-computer interfaces, yet these approaches are constrained by electrode design, bandwidth, and limited biological integration. Engineered neuroglial organoids offer a complementary, biologically based interface strategy. Derived from pluripotent stem cells, neuroglial organoids arrive as 3D constructs containing neurons and glia in intrinsic architecture, capable of vascularization, synaptic connectivity, and integration with host tissue. Building on dissociated stem cell suspensions, organoids act not only as reservoirs of cells but also as living neural interfaces, receiving inputs from host circuits and generating functional outputs. Preclinical studies have demonstrated that transplanted organoids can couple to host sensory pathways, respond to stimulation, and support recovery of motor and cognitive functions. Moreover, emerging work coupling organoid grafts to brain-computer interfaces highlights the potential for closed-loop biological electronic systems, in which engineered devices provide precise recording and stimulation while organoids contribute adaptive, active biological circuits. This combination allows real-time bidirectional communication, allowing the graft to be both monitored and adapted to structurally and functionally integrate into host tissue. In this review, the authors examine neuroglial organoid transplantation through the lens of neural interfacing. They outline lessons from non-CNS organoid transplantation, summarize neurotrauma studies where grafts engage host circuits, and highlight opportunities to integrate organoids with electrodes, stimulation paradigms, and computational models. They also discuss challenges, namely vascularization, immune tolerance, surgical delivery, and manufacturing standards, that parallel those in neural device translation. For neurosurgeons, the appeal of neuroglial organoids lies not only in tissue replacement but in establishing a new class of biological neural interfaces, extending the reach of restorative neurosurgery. By merging living constructs with engineered devices, organoid-based strategies may enable hybrid restorative systems that restore function after neurological injury and disease.},
}

*Organoids/transplantation/physiology
Humans
*Neuroglia/physiology/transplantation
Animals
*Brain-Computer Interfaces
*Tissue Engineering/methods
*Neurosurgical Procedures/methods
Pluripotent Stem Cells
Neurons/physiology

@article {pmid41621104,
year = {2026},
author = {Johnson, TR and Moralle, S and Luo, Z and Taylor, DM},
title = {Implanting microelectrode arrays in the bottom of the central sulcus targeting somatosensory area 3a for restoration of proprioception.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E8},
doi = {10.3171/2025.11.FOCUS25916},
pmid = {41621104},
issn = {1092-0684},
mesh = {Animals ; Macaca mulatta ; Microelectrodes ; *Electrodes, Implanted ; *Somatosensory Cortex/surgery/physiology/diagnostic imaging ; *Proprioception/physiology ; *Brain-Computer Interfaces ; Male ; Stereotaxic Techniques ; Magnetic Resonance Imaging ; },
abstract = {OBJECTIVE: The long-term goal of this work is to develop a sensorimotor brain-machine interface (BMI) in which intended movements are decoded from the motor cortex and proprioceptive feedback is delivered via intracortical microstimulation of Brodmann's area 3a. A vital step toward this goal is to demonstrate in rhesus macaques a novel surgical approach for the precise and safe implantation of custom-length microelectrode arrays into area 3a at the bottom of the central sulcus.

METHODS: Preoperative planning combined high-resolution 7-T MR and CT imaging to generate 3D models of the cortices of 2 subjects. These models were used to fabricate 3D-printed skull replicas and to define a stereotactic trajectory that provided the shortest perpendicular path to the base of the central sulcus, where Brodmann's area 3a resides. Custom variable-length microwire electrode arrays were designed to span this target region. The flexibility of the microwires precluded the standard impact-insertion approach used with stiffer electrodes. Therefore, a custom vacuum-powered microdrive holder that moved with the pulsating brain was developed to maintain electrode orientation and to allow slow, controlled insertion along the planned trajectory. After implantation, the craniotomy was closed, and a skull-mounted recording chamber was secured. Postoperative verification of array placement was performed using CT imaging and neural recordings.

RESULTS: In both animals, imaging revealed that the base of the central sulcus was positioned anterior to its dorsal opening, making a precentral implant trajectory the shortest and most direct path to the bottom of the central sulcus. The integrated imaging and 3D modeling approach enabled accurate stereotactic placement of custom microelectrode arrays using the novel vacuum-assisted microdrive, as confirmed by postoperative CT imaging. Both surgical procedures were completed without complication, and isolatable neuronal spikes were recorded from multiple channels in each subject. In both animals, neural activity was modulated by passive movements of the arm.

CONCLUSIONS: Intracortical microelectrode implants for BMI applications have traditionally been limited to short (1.5-mm) electrodes targeting cortical sites exposed on the brain surface. The surgical methodology described here enables safe and accurate implantation of custom-length arrays into deep sulcal targets such as Brodmann's area 3a. By expanding access to previously inaccessible cortical regions, this approach broadens the potential neural information available for future BMI applications.},
}

Animals
Macaca mulatta
Microelectrodes
*Electrodes, Implanted
*Somatosensory Cortex/surgery/physiology/diagnostic imaging
*Proprioception/physiology
*Brain-Computer Interfaces
Male
Stereotaxic Techniques
Magnetic Resonance Imaging

@article {pmid41621103,
year = {2026},
author = {Lehner, KR and Luo, S and Greene, B and Angrick, M and Candrea, D and Husari, KS and Barth, K and Dister, J and Anushiravani, R and Miller, JS and Ho, E and Rincon-Torroella, J and Rapoport, B and Comair, Y and Crone, NE},
title = {Initial experience with the precision neuroscience Layer 7 micro-electrocorticography interface for real-time intraoperative neural decoding.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E3},
doi = {10.3171/2025.11.FOCUS25908},
pmid = {41621103},
issn = {1092-0684},
mesh = {Humans ; Adult ; *Electrocorticography/methods/instrumentation ; *Brain-Computer Interfaces ; Male ; Female ; Young Adult ; *Brain Neoplasms/surgery ; *Intraoperative Neurophysiological Monitoring/methods ; Craniotomy/methods ; Speech/physiology ; Brain Mapping/methods ; *Motor Cortex/physiology/surgery ; },
abstract = {OBJECTIVE: The aim of this study was to evaluate the feasibility of using the Layer 7 Cortical Interface, a high-density micro-electrocorticography (μECoG) array, for intraoperative neural recordings and real-time brain-computer interface (BCI) applications, including speech decoding and cursor control.

METHODS: Four patients (age range 23-43 years) who underwent awake craniotomy for tumor resection near the eloquent cortex were enrolled. The Layer 7 µECoG device (1024 channels, approximately 1.5-cm2 coverage) was placed on the motor cortex following standard cortical mapping. Intraoperative tasks included a joystick-controlled center-out movement paradigm (n = 3) and an auditory-cued speech repetition task (n = 1). Neural data were recorded at 20 kHz, preprocessed, and used to train decoders intraoperatively. A transformer-based model was applied for real-time speech synthesis and a convolutional neural network was trained for speech classification, while a convolutional recurrent neural network was trained to classify 2D cursor direction.

RESULTS: All 4 patients tolerated the procedure without device-related adverse events. The mean electrode impedances across 6 arrays (6144 channels) ranged from 1.21 to 1.99 MΩ, with 954-990 channels per array retained for analysis. In the speech task, a 4-word classification model achieved 77.5% accuracy, and a real-time synthesis model was able to distinguish speech and silence during approximately 20 minutes of data recording in the operating room. In the motor task, a 4-direction classification model achieved 78%-84% accuracy. Recordings remained stable during tumor resection.

CONCLUSIONS: The Layer 7 Cortical Interface device enabled high-resolution nonpenetrating cortical recordings that supported real-time speech classification and cursor control within the limited timeframe of an intraoperative session. These findings highlight the potential clinical applications of high-density µECoG for functional mapping, diagnostic assessment, and future chronic BCI systems for patients with motor and communication impairments.},
}

Humans
Adult
*Electrocorticography/methods/instrumentation
*Brain-Computer Interfaces
Male
Female
Young Adult
*Brain Neoplasms/surgery
*Intraoperative Neurophysiological Monitoring/methods
Craniotomy/methods
Speech/physiology
Brain Mapping/methods
*Motor Cortex/physiology/surgery

@article {pmid41621102,
year = {2026},
author = {Mortezaei, A and Al-Saidi, N and Taghlabi, KM and Hussein, A and Hallak, H and Pouratian, N and Faraji, AH},
title = {Brain-computer interfaces in poststroke rehabilitation: a meta-analysis of randomized clinical trials.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E7},
doi = {10.3171/2025.11.FOCUS25913},
pmid = {41621102},
issn = {1092-0684},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Randomized Controlled Trials as Topic/methods ; *Stroke/therapy/physiopathology ; Recovery of Function/physiology ; },
abstract = {OBJECTIVE: Stroke is a leading cause of long-term disability, with conventional rehabilitation often failing to achieve substantial motor recovery, particularly in patients with severe paresis or in chronic stages. Brain-computer interfaces (BCIs) offer a novel rehabilitation approach by translating neural signals into real-time external feedback. The authors performed a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy and safety of noninvasive BCIs for poststroke motor rehabilitation.

METHODS: A systematic literature review was performed based on the PRISMA guidelines using 3 databases. Eligible RCTs enrolled stroke patients receiving noninvasive BCI-assisted motor rehabilitation compared with conventional therapies. The primary outcome was the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) improvement. Secondary outcomes included the Action Research Arm Test (ARAT), Motor Activity Log (MAL), Modified Barthel Index (MBI), and Modified Ashworth Scale (MAS). Effect sizes were pooled using random-effects models and expressed as mean differences (MDs), standardized MDs (SMDs), or odds ratios, each with corresponding 95% confidence intervals (CIs).

RESULTS: Thirty-two RCTs comprising 1187 patients were included with no heterogeneity or significant imbalances in baseline characteristics across groups. A BCI was significantly superior in FMA-UE score improvement compared with controls (MD 3.85, 95% CI 2.84-4.86; p < 0.01), with benefits sustained at follow-up. Within-group analyses revealed greater improvement in the BCI arm from follow-up to baseline (MD 8.18, 95% CI 5.77-10.60; p < 0.01). A BCI was also associated with higher ARAT (MD 7.18, 95% CI 2.4-12.0; p < 0.01) and MAL (SMD 0.59, 95% CI 0.34-0.85; p < 0.01) scores, although between-group differences for these endpoints were not statistically significant. For the MBI, a subgroup analysis did not demonstrate significant differences, but a sensitivity analysis revealed a significant improvement in the BCI group (p = 0.042). There were no significant differences in the within- and between-group analyses of the MAS. A subgroup analysis suggested a synergistic benefit with the BCI combined with neuromuscular electrical stimulation. Adverse events were infrequent and generally mild; 2 withdrawals in the BCI group were reported due to seizure and electrode allergy. Notably, all heterogeneity was successfully resolved through sensitivity analyses, supporting the robustness of the findings.

CONCLUSIONS: Noninvasive BCI-assisted rehabilitation is a safe and effective adjunct to conventional therapy, enhancing motor recovery after stroke. While all included RCTs evaluated noninvasive systems, the potential value and efficacy of invasive and minimally invasive BCIs may require further consideration.},
}

Humans
*Brain-Computer Interfaces
*Stroke Rehabilitation/methods
*Randomized Controlled Trials as Topic/methods
*Stroke/therapy/physiopathology
Recovery of Function/physiology

@article {pmid41620439,
year = {2026},
author = {Gong, Q and Fu, X and Feng, D and Rao, S and Pütz, B and Müller-Myhsok, B and Wei, L and Shen, C and Zhang, Y and Xu, L and Chen, W and Yang, K and Chen, D and Lv, X and Yan, Z and Luo, D and Wei, P and Jiang, H and Chen, W},
title = {Randomized, double-blind, sham-controlled pilot trial of theta-band transcranial alternating current stimulation during cognitive training in mild Alzheimer's disease.},
journal = {Translational psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41398-026-03822-z},
pmid = {41620439},
issn = {2158-3188},
support = {82071181//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82101581//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82371453//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Cognitive deficits are a hallmark of Alzheimer's disease (AD), and effective treatments remain elusive. Transcranial alternating current stimulation (tACS), a non-invasive technique, has shown potential in improving cognitive function across various populations, but further research is needed to investigate its efficacy in AD. In a randomized, double-blind, sham-controlled pilot trial, 36 mild AD patients received active or sham theta-tACS (8 Hz, 1.6 mA, 20-min daily) during n-back task for two weeks, followed by a 10-week follow-up. Cognitive assessments and resting-state EEG were analyzed at baseline, after-treatment, and follow-up. The results showed that the active group demonstrated significant cognitive improvements after treatment (MMSE: t (15) =-3.273, p = 0.005, Cohen's d = 0.82), particularly in short-term memory (MMSE-recall: Z = -2.11, p = 0.035, r = 0.53), with maintained benefits after 10 weeks. In contrast, the sham group exhibited long-term cognitive decline (MMSE: t (4) = 3.586, p = 0.023, Cohen's d = -1.60). EEG analysis revealed reduced gamma power (t (23) = 2.689, p = 0.013, Cohen's d = 1.077) and theta connectivity in active group, particularly in the frontotemporal regions (F4/F7: t (23) = 2.467, p = 0.021, Cohen's d = 0.988; F4/T3: t (23) = 2.465, p = 0.022, Cohen's d = 0.987), which was correlated with cognitive improvements (R = -0.57, p = 0.043). In conclusion, tACS combining cognitive training may offer cognitive benefits in mild AD by modulating neural activity, though further studies are needed to clarify its mechanisms.},
}

@article {pmid41620194,
year = {2026},
author = {Graham, F and Hutchinson, DW and Moon, TJ and Wang, J and Flores-Jimenez, H and Druschel, L and Ogunnaike, L and Gao, Y and Smith, T and DeTillio, S and Goelz, C and Bhalotia, A and Newman, L and Hess-Dunning, A and Capadona, JR and Karathanasis, E},
title = {Lipid Nanoparticle-Mediated Cd14 siRNA Delivery Ameliorates the Acute Inflammatory Response to Intracortical Microelectrode Implantation.},
journal = {Acta biomaterialia},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.actbio.2026.01.055},
pmid = {41620194},
issn = {1878-7568},
abstract = {Intracortical microelectrodes (IMEs) are an integral component of brain computer interfaces (BCIs) designed to study and treat neurological disorders. Unfortunately, IMEs tend to fail prematurely due in part to the macrophage-mediated inflammation in response to implantation injury and the persistent foreign body reaction. Previous work has established that cluster of differentiation 14 (CD14) is implicated in the neuroinflammatory response to IME implants. CD14 is a conserved damage-associated coreceptor that facilitates immune activation in the presence of inflammatory damage-associated stimuli. We sought to mitigate the inflammatory response to IME implantation by suppressing CD14 expression on macrophages using a lipid nanoparticle (LNP) loaded with Cd14-specific siRNA. We tested the efficacy of the LNP-mediated gene delivery in cultured murine macrophages and in an in vivo mouse model with IME implants. Our in vitro findings indicated that the LNPs suppress inflammatory cytokine secretion. The in vivo studies showed efficient targeting of the LNPs to the desired cell populations with the majority of LNPs found in blood-circulating macrophages and infiltrating macrophages at the intracortical implant site. Our results show that the LNPs efficiently silence expression of the targeted Cd14 gene. Suppression of the CD14 protein led to reduced infiltration of immune cells to the brain parenchyma, as well as a significant decrease of the inflammatory response to implantation within the first 24 hours after implantation, as determined by flow cytometry and transcriptomics. Together our results suggest that LNP-mediated gene therapy can specifically regulate one of the dominant drivers of the innate immune response to IME implantation. STATEMENT OF SIGNIFICANCE: Brain-computer interfaces rely on implanted electrodes to record and stimulate neural activity, but these devices often fail early because the body mounts an inflammatory immune response against them. Here, we focused on a central immune receptor, CD14, as a key driver of the inflammatory response to implants. Using lipid nanoparticles to deliver gene-silencing RNA, we were able to suppress CD14 expression in macrophages both in culture and in a mouse model with implanted electrodes. This targeted approach reduced immune cell infiltration and inflammation around implants. Our findings demonstrate that lipid nanoparticle-mediated gene therapy can selectively weaken the brain's innate immune response to implants, offering a promising strategy to improve the longevity and performance of neural interfaces.},
}

@article {pmid41619890,
year = {2026},
author = {Zhou, W and Chen, Y and Cen, K and Li, Z and Huang, C and Su, W and Li, P},
title = {Calcium carboxymethyl cellulose/quaternary ammonium chitosan self-gelling powder with good biocompatibility for wound hemostasis.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {150610},
doi = {10.1016/j.ijbiomac.2026.150610},
pmid = {41619890},
issn = {1879-0003},
abstract = {In this study, a multifunctional self-gelling hemostatic powder (CQA) was designed using natural biomaterials by integrating the antioxidant and biocompatible properties of Aloe vera gel (AV) with the hemostatic efficacy of calcium carboxymethylcellulose (Ca-CMC) and the antibacterial activity of quaternary ammonium chitosan (QCS). The CQA powder rapidly absorbs moisture upon contact with blood, forming a physically sealing hydrogel network through electrostatic and hydrogen bonding interactions. In vitro evaluations revealed that the optimized formulation, CQA0.3, exhibits outstanding adsorption capacity, antioxidant activity, and biocompatibility. Compared to commercial chitosan-based hemostatic powder (CS), CQA0.3 demonstrated significantly enhanced procoagulant performance, with a blood clotting index (BCI) of 8.48% versus 56.65% for CS, and promoted accelerated blood cell adhesion. In whole-blood coagulation assays, the CQA0.3 group achieved rapid clotting within 180 s, while bleeding persisted in the CS group beyond 210 s. In practical hemorrhage models, CQA0.3 reduced blood loss to 94.0 ± 8.7 mg, substantially lower than both the CQ group (225.7 ± 6.03 mg) and the CS group (292.7 ± 14.46 mg). These findings highlight the potential of CQA0.3 as a safe, efficient, and adaptable hemostatic agent for emergency and clinical applications, combining rapid gelation, high biocompatibility, and excellent wound adaptability.},
}

@article {pmid41618548,
year = {2026},
author = {Ma, Y and Li, H and Li, W and Jiang, D and Li, H and Xuan, X and Li, M},
title = {Noninvasive Graphene Brain-Computer Interface Integrating EEG Recording and Acoustic-Optical Stimulation for Rhythm Intervention.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e05327},
doi = {10.1002/adhm.202505327},
pmid = {41618548},
issn = {2192-2659},
support = {52271341//National Nature Science Foundation of China/ ; 62271350//National Nature Science Foundation of China/ ; },
abstract = {Noninvasive wearable stimulation-acquisition integrated brain-computer interfaces (BCIs) have significant application value in neurological rehabilitation and health monitoring. However, their widespread adoption depends on the development of long-term, stable dry/semi-dry electrodes and lightweight hardware. In this study, a sodium-doped vertical graphene (Na-VG) electrode that utilized sweat and tissue fluids as electrolytes was developed. When applied with ultrapure water, an extremely low electrode-skin impedance of 4.22 ± 0.50 kΩ was detected at 10 Hz. The 20-channel EEG cap assembled with the Na-VG electrodes maintained a high α-rhythm response of 5.06-14.22 dB in the signal-to-noise ratio of whole-brain EEG signals during a 36-day stability evaluation. Furthermore, a wearable Na-VG headband BCI combining sound-light stimulation and EEG acquisition was developed. Healthy individuals wearing this system, under the coordinated intervention of 40 Hz differential-frequency sound stimulation and 10 Hz light stimulation, showed changes in the frequency and amplitude of the α-rhythm. This improvement increased the proportion of moderate-levels of the vigilance index, neural activity, heart rate, emotion, and arousal index to 84-100%, with a precision of 98.73%. These results provide novel long-term, lightweight strategies and matching software and hardware for the monitoring and noninvasive intervention of emotional and cognitive-related diseases.},
}

@article {pmid41618424,
year = {2026},
author = {Campion, S and Navarro-Suné, X and Rivals, I and Morélot-Panzini, C and Serresse, L and Chavez, M and Demoule, A and Niérat, MC and Raux, M and Similowski, T},
title = {SSVEP-based brain-computer interface enabling graded dyspnoea self-report: proof-of-concept study in healthy volunteers.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01846-y},
pmid = {41618424},
issn = {1743-0003},
abstract = {BACKGROUND: Mechanically ventilated patients may experience respiratory suffering, which is difficult to assess when verbal communication is impaired. We evaluated the performance of a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) designed to enable self-reporting of dyspnoea in this context.

METHODS: Forty-nine healthy volunteers were studied under five respiratory conditions: normal breathing (NB), inspiratory resistive loading (IRL), inspiratory threshold loading (ITL), CO₂ inhalation (CO₂), and a return to NB as wash-out (NBWO). Respiratory discomfort was evaluated using a visual analogue scale (VAS). Two BCIs models were tested: a detection BCI (D-BCI), designed to discriminate between 'breathing is OK' and 'breathing is difficult', and a quantification BCI in the form of a LED-based analogue scale (LAS), composed of five light-emitting diodes. Visual stimuli were delivered at different frequency sets: 12-15 Hz, 15-20 Hz, and 20-30 Hz for the D-BCI; low frequencies (13-17-19-23-29 Hz) and high frequencies (41-43-47-53-59 Hz) for the LAS. Performance was assessed using receiver operating characteristic (ROC) curves; the area under the ROC curve (AUC) was the primary outcome.

RESULTS: Participants reported significant respiratory discomfort during IRL, ITL, and CO₂ conditions in the D-BCI groups, and during ITL and CO₂ in the LAS groups, as reflected by higher dyspnoea VAS scores compared to NB. The best-performing frequency sets were 20-30 Hz for the D-BCI (AUC 0.89 [0.89-0.90]) and low frequencies for the LAS (AUC 0.84 [0.83-0.85]).

CONCLUSIONS: This study demonstrates that an SSVEP-based BCI can sucessfully detect and quantify experimentally induced dyspnoea in healthy individuals. Further research is needed to evaluate its clinical applicability for assessing dyspnoea in non-communicative patients.},
}

@article {pmid41617748,
year = {2026},
author = {Samuel, J and Murugan, TK and Govindaraj, L and Balaji, M and SenthilKumar, V and Sundararajan, S},
title = {Adversarial robust EEG-based brain-computer interfaces using a hierarchical convolutional neural network.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-34024-0},
pmid = {41617748},
issn = {2045-2322},
abstract = {Brain-Computer Interfaces (BCIs) based on electroencephalography (EEG) are widely used in motor rehabilitation, assistive communication, and neurofeedback due to their non-invasive nature and ability to decode movement-related neural activity. Recent advances in deep learning, particularly convolutional neural networks, have improved the accuracy of motor imagery (MI) and motor execution (ME) classification. However, EEG-based BCIs remain vulnerable to adversarial attacks, in which small, imperceptible perturbations can alter classifier predictions, posing risks in safety-critical applications such as rehabilitation therapy and assistive device control. To address this issue, this study proposes a three-level Hierarchical Convolutional Neural Network (HCNN) designed to improve both classification performance and adversarial robustness. The framework decodes motor intention through a structured hierarchy: Level 1 distinguishes MI from ME, Level 2 differentiates unilateral and bilateral motor tasks, and Level 3 performs fine-grained movement classification. The model is evaluated on the publicly available BCI Competition IV-2a dataset, which contains multi-class MI EEG recordings from nine healthy subjects. Robustness is assessed under gradient-based adversarial attacks, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and DeepFool, across varying perturbation strengths, with adversarial training incorporated during learning. Experimental results show that the proposed HCNN achieves a clean-data accuracy of 91.2% and exhibits reduced performance degradation under adversarial attacks compared with conventional CNN baselines. These results indicate that hierarchical architectures offer a viable approach for improving the reliability of EEG-based BCIs. All experiments were conducted exclusively on the BCI Competition IV-2a dataset using EEG data from healthy subjects.},
}

@article {pmid41616114,
year = {2026},
author = {Yang, J and Huo, J and Liu, M and Feng, C and Zhang, Y and Pan, G and Meng, W and Han, R},
title = {vEMINR: Ultra-Fast Isotropic Reconstruction for Volume Electron Microscopy With Implicit Neural Representation.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e11922},
doi = {10.1002/advs.202511922},
pmid = {41616114},
issn = {2198-3844},
support = {2021YF F0704300//National Key Research and Development Program of China/ ; 62206158//National Natural Science Foundation of China/ ; 32371248//National Natural Science Foundation of China/ ; 2024CANAD-MES-061//Dubai Future Foundation/ ; //Fundamental Research Funds for the Central Universities/ ; 2025A1515010342//Natural Science Foundation of Guangdong Province/ ; ts20230204//Instrument Improvement Funds of Shandong University Public Technology Platform/ ; ZR2023YQ057//Natural Science Foundation of Shandong Province/ ; ZR2022QF097//Natural Science Foundation of Shandong Province/ ; //Shandong University Young Scholar Future Plan to W.M./ ; },
abstract = {Volume electron microscopy (vEM) is a powerful technique that enables 3D visualization of biological structures at the nanometer scale. However, vEM imaging relies on sequential scanning of 2D images, and due to section thickness limitations, the axial resolution is significantly lower than the lateral resolution. In this paper, we propose the vEMINR, an ultra-fast isotropic reconstruction method based on implicit neural representation (INR). This method enhances the reconstruction quality of vEM images by learning the true degradation patterns of low-resolution images, and significantly accelerates the reconstruction process by utilizing the efficient parameterization and a continuous function representation of INR. In experiments on 11 public datasets, vEMINR outperforms mainstream methods with over tenfold faster reconstruction and higher accuracy. vEMINR substantially improved the accuracy of organelle and neuron reconstruction from vEM. Overall, the excellent reconstruction time efficiency of vEMINR enables high-throughput processing of terabyte-scale vEM datasets while maintaining reconstruction accuracy. We believe that it will play a significant role in large-scale vEM image reconstruction and related research fields.},
}

@article {pmid41615974,
year = {2026},
author = {Ding, W and Liu, A and Wu, L and Cui, H and Fang, B and Chen, X},
title = {Data Augmentation for Subject-Independent SSVEP-BCIs via Simultaneous Spatial-Energy Representation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3659606},
pmid = {41615974},
issn = {1558-2531},
abstract = {OBJECTIVE: Data augmentation is important for enhancing subject-independent classification in deep learning (DL) approaches for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) using electroencephalography (EEG). However, current augmentation techniques often inadequately exploit individual-specific style characteristics, limiting the model's robustness against inter-subject style variability. To tackle this problem, this study proposes a novel data augmentation method called Simultaneous Spatial-Energy Representation (SSER).

METHODS: SSER employs singular value decomposition (SVD) to extract spatial and energy representations from EEG signals, effectively capturing style characteristics. These representations are independently mixed across source domains during signal reconstruction, generating novel domains that cover a broader range of styles. This strategy promotes the learning of domain-invariant features and enhances the model's robustness to style variability.

RESULTS: Comprehensive experiments on public datasets demonstrate that SSER outperforms state-of-the-art data augmentation techniques and generalizes well across various DL models. Furthermore, self-collected offline and online experiments involving 30 subjects provide additional evidence of the method's effectiveness.

CONCLUSION: By simultaneously manipulating spatial and energy representations, SSER offers a richer characterization of EEG signal style variability, leading to superior performance.

SIGNIFICANCE: The proposed innovative data augmentation method advances subject-independent classification, facilitating the broader application of EEG-based BCIs in real-world scenarios.},
}

@article {pmid41554845,
year = {2026},
author = {Carević, I and Bajto, JŠ and Grubor, M and Štirmer, N},
title = {Wood biomass ash as a clinker substitute in advancing next-generation blended cement: Croatian case study.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {3932},
pmid = {41554845},
issn = {2045-2322},
support = {Project No. 101058162//HORIZON-CL4-2021-TWIN-TRANSITION-01/ ; },
abstract = {This research investigates the use of wood biomass ash (WBA) as a supplementary cementitious material (SCM) in blended cement formulations containing 6 and 12 wt% of bottom WBA. Motivated by the need to advance low-carbon cement production, reduce reliance on imported materials, and incorporate waste management strategies, the study explores sustainable pathways for cement manufacturing. Experimental results show that the 6 wt% WBA blend (BLEND BC-II) achieves a compressive strength of 59.3 MPa after 28 days, surpassing the reference CEM II, whereas the 12 wt% WBA blend (BLEND BC-I) also delivers favourable mechanical and durability performance, including a chloride diffusion coefficient of 15.85 × 10[-12] m[2]/s, capillary absorption of 0.68 g/m[2]·h[1]/[2], and gas permeability of 0.50 × 10[-16] m[2]. Volume stability tests of the 12 wt% WBA blend confirm that autogenous deformations remain below − 0.017 mm/m after 90 days, indicating effective mitigation of shrinkage and reliable dimensional stability. When combined with other SCMs, WBA further improves long-term mechanical performance. Despite challenges related to compositional variability and infrastructure requirements, WBA incorporation can reduce environmental impact and support low-carbon cement production. Achieving net-zero emissions extends beyond quantitative targets, requiring the restoration of balance between resource use, material efficiency, and environmental sustainability. These findings demonstrate that WBA is a viable SCM, advancing sustainable and resilient cement manufacturing.},
}

@article {pmid41613455,
year = {2025},
author = {Sun, Y and Wang, S and Gong, Y},
title = {Terahertz's silent revolution in physics, engineering, and life science: Beyond the spectrum.},
journal = {Fundamental research},
volume = {5},
number = {5},
pages = {1930-1932},
pmid = {41613455},
issn = {2667-3258},
abstract = {Terahertz technology is revolutionizing photonics, biomedicine, and communications by merging non-ionizing radiation with molecular sensitivity and material penetration. Advances in metamaterials, adaptive antennas, and AI-driven systems address historical limitations in emission efficiency and atmospheric attenuation, enabling secure high-capacity networks and precision biomedical applications. Reconfigurable beamforming and hybrid channel models enhance wireless reliability, while ultra-sensitive biosensors and neuromodulation techniques pioneer non-invasive diagnostics and therapies for neurodegenerative and psychiatric disorders. Terahertz's dual role in molecular sensing and neural modulation establishes closed-loop "detect-treat" paradigms, bridging material science and neuroscience. Challenges remain in optimizing clinical application and hybrid system scalability, yet its capacity to probe carrier dynamics, protein interactions, and neural circuits positions Terahertz as a universal platform for 6G networks, personalized medicine, and brain-machine interfaces. By unifying physics-aware engineering with biological insights, terahertz technology transcends traditional boundaries, offering transformative solutions for healthcare, secure connectivity, and industrial innovation.},
}

@article {pmid41613156,
year = {2025},
author = {Mohammadpour, H and Power, SD},
title = {Investigating singing imagery as an additional or alternative control task for EEG-based Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1736711},
pmid = {41613156},
issn = {1662-5161},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) provide a movement-free means of communication and control, typically based on motor imagery (MI) tasks of hand, foot, or tongue movements. Most BCI studies focus on classifying up to four such tasks, which limits the number of available commands and restricts overall system functionality. Expanding the range of reliable mental tasks would directly increase the number of possible commands and thereby enhance the practical utility of BCIs. Singing imagery (SI) may offer an intuitive alternative or additional task to complement conventional MI paradigms.

METHODS: EEG data were recorded from 14 participants performing right-hand, left-hand, foot, and tongue MI, SI, and rest. Features were extracted using filter bank common spatial patterns (FBCSP), and tasks were classified with a random forest algorithm across 2-, 4-, 5-, and 6-class scenarios. Subjective data regarding participants' perceived task difficulty and general task preferences was also collected.

RESULTS: Classification accuracies with SI included were comparable to subsets of conventional MI tasks in 2-, 4-, and 5-class scenarios. In the 6-class scenario, average accuracy was approximately 60%, with six participants exceeding 70%, the level often cited as being necessary for effective BCI control. It is reasonable to expect performance to improve further with more advanced analysis methods and participant training.

CONCLUSION: These promising results suggest that singing imagery can serve as both an additional and an alternative task in MI-BCIs. In lower-class systems, SI may provide a valuable option for generating commands, particularly for users who may find some conventional MI tasks less intuitive. When combined with the established MI tasks, SI could increase the number of possible commands, thereby extending the functional capacity of BCI systems. Overall, this work demonstrates the potential of SI to broaden the repertoire of mental tasks available for BCI control and to advance the development of more flexible, powerful, and user-centered BCI applications.},
}

@article {pmid41612341,
year = {2026},
author = {Powell, J and Zhou, A},
title = {Brain-computer interface commercialization.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {23},
number = {1},
pages = {45},
pmid = {41612341},
issn = {1743-0003},
}

@article {pmid41611752,
year = {2026},
author = {Aars, J and Ieno, EN and Andersen, M and Derocher, AE and Wiig, Ø and Zuur, AF},
title = {Body condition among Svalbard Polar bears Ursus maritimus during a period of rapid loss of sea ice.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {2182},
pmid = {41611752},
issn = {2045-2322},
mesh = {Animals ; *Ursidae/physiology ; *Ice Cover ; Female ; Male ; Arctic Regions ; Svalbard ; Ecosystem ; Climate Change ; },
abstract = {Polar bears are only found in Arctic areas with sufficient access to sea ice and seals on which they prey. Studies have highlighted negative effects on condition and demographics in areas where sea ice cover is declining due to warmer climate, but condition of the Barents Sea polar bear population have not been examined yet. Loss of sea ice rate has been considerably higher here than in other areas with polar bears. We investigated variation in body condition index (BCI) among 770 adult bears, 1188 captures, in March-May 1995-2019, in Svalbard, Norway (western part of the Barents Sea). We assessed how intrinsic (female reproductive state, age) and both males and females, BCI declined until 2000, but increased afterwards, during a period with rapid loss of sea ice. In models including sea ice metrics and climate (Arctic Oscillation), there was no support for the predicted negative effect of warmer weather and habitat loss. This indicates a complex relationship between habitat, ecosystem structure, energy intake, and energy expenditure. Increases in some prey species, including harbour seals, reindeer, and walrus, may partly offset reduced access to seals. Our findings underline the importance not to extrapolate findings across populations.},
}

Animals
*Ursidae/physiology
*Ice Cover
Female
Male
Arctic Regions
Svalbard
Ecosystem
Climate Change

@article {pmid41611285,
year = {2026},
author = {Zan, T and Gao, YS},
title = {[Reconstruction of superficial organs: a leap from structural restoration to functional rehabilitation].},
journal = {Zhonghua shao shang yu chuang mian xiu fu za zhi},
volume = {42},
number = {1},
pages = {26-33},
pmid = {41611285},
issn = {2097-1109},
support = {T2024076//National High-Level Talents Program/ ; 82272264, 82472557//General Program of National Natural Science Foundation of China/ ; 2023ZZ02023//Shanghai Plastic Surgery Research Center of Shanghai Priority Research Center/ ; },
mesh = {Humans ; *Tissue Engineering/methods ; *Regenerative Medicine/methods ; *Plastic Surgery Procedures/methods ; Printing, Three-Dimensional ; Artificial Intelligence ; Tissue Scaffolds ; },
abstract = {The core objective of superficial organ reconstruction is to perfectly restore the organ's morphological structure and biological function. Currently, significant progress has been achieved in structural construction, blood supply assurance, and morphological and functional reconstruction of superficial organ reconstruction, primarily relying on approaches including surgical techniques, tissue engineering, and regenerative medicine. In the future, with the integration and application of cutting-edge technologies such as gene editing, artificial intelligence, three-dimensional printing, and brain-computer interfaces, superficial organ reconstruction is poised to enter a new historical stage characterized by high intelligence, precision, and comprehensive functional restoration. This article focuses on superficial organ reconstruction, systematically outlines its concept, challenges, and current development status, and proposes future perspectives for this field.},
}

Humans
*Tissue Engineering/methods
*Regenerative Medicine/methods
*Plastic Surgery Procedures/methods
Printing, Three-Dimensional
Artificial Intelligence
Tissue Scaffolds

@article {pmid41525560,
year = {2026},
author = {Siviero, I and Vale, N and Menegaz, G and Ramos-Murguialday, A and Francesca Storti, S},
title = {Artificial Intelligence and Wearable Technologies for Upper Limb Neurorehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {732-749},
doi = {10.1109/TNSRE.2026.3651949},
pmid = {41525560},
issn = {1558-0210},
mesh = {Humans ; *Wearable Electronic Devices ; *Artificial Intelligence ; *Neurological Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Electromyography ; Electroencephalography ; Brain-Computer Interfaces ; },
abstract = {Non-invasive neural interfaces (NIs) are increasingly investigated in upper limb neurorehabilitation, where they exploit biosignals, such as electroencephalography (EEG) and electromyography (EMG), to decode motor intentions using artificial intelligence (AI). Yet, traditional systems are complex and difficult to use outside the clinic. Wearable devices have the potential for innovative neurorehabilitation solutions thanks to their comfort, easy-to-use and long-term monitoring. However, current AI approaches require adaptation to the technical constraints of wearable devices, and the related state-of-the-art is not clearly explained and summarized. In this work, a systematic literature review on 51 studies was conducted analyzing them according to five important concepts: biosignals, wearable devices, AI-driven methods, upper limb, and clinical applications. The review highlights methodological heterogeneity, a variety of wearable sensor configurations, and open challenges related to accuracy, robustness, and clinical validation. Finally, we discuss how explainable AI (XAI) and generative AI (GenAI) may contribute to improve the interpretability and personalization of future neurorehabilitation systems.},
}

Humans
*Wearable Electronic Devices
*Artificial Intelligence
*Neurological Rehabilitation/methods/instrumentation
*Upper Extremity/physiopathology
Electromyography
Electroencephalography
Brain-Computer Interfaces

@article {pmid41462297,
year = {2025},
author = {Zhao, Z and Duan, X and Luo, J and He, Z and Zhang, Y and Wang, M and Qin, J and Lin, S and Chen, H},
title = {Spatiotemporal dynamics of neuronal subtypes and their interactions with glia following intracortical electrode implantation.},
journal = {Biology direct},
volume = {21},
number = {1},
pages = {13},
pmid = {41462297},
issn = {1745-6150},
support = {32201095//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Chronically implanted electrodes offer a promising approach for treating neurological disorders via brain-computer interfaces, yet their long-term efficacy is compromised by the neuroinflammatory foreign body response. While neurons are central to both electrode function and inflammatory regulation, their specific responses post-implantation remain poorly characterized. Here, we combined single-nucleus RNA sequencing (snRNA-seq) and immunofluorescence to delineate the spatiotemporal dynamics of neuronal subtypes in the rat motor cortex at 3, 25, and 50 days after electrode implantation.

RESULTS: We identified 22 distinct neuronal subpopulations, among which clusters 5, 6, and 8 emerged as injury-responsive subtypes during the acute phase (3 days), exhibiting a specific upregulation of Tmsb4x, a key regulator of neuronal plasticity and repair. Furthermore, our analysis revealed activated signaling pathways mediating neuron-glia communication, most notably the Ptn-Sdc4 and Il34/Csf1-Csf1R axes between neurons and astrocytes.

CONCLUSIONS: These findings provide a high-resolution map of neuronal adaptation to intracortical implants, uncovering previously unknown repair-associated neuronal subtypes and specific ligand-receptor pairs that coordinate the neuroinflammatory microenvironment, which offers novel insights and potential therapeutic targets for improving the biocompatibility and long-term stability of neural electrodes.

GRAPHICAL ABSTRACT: [Image: see text]

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13062-025-00719-7.},
}

@article {pmid41610453,
year = {2026},
author = {Eguinoa, R and San Martín, R and Luna, P and Herrojo-Ruiz, M and Vidaurre, C},
title = {An EEG correlation framework to study state anxiety and learning under uncertainty.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3f58},
pmid = {41610453},
issn = {1741-2552},
abstract = {Objective.Recent developments in computational neuroscience have shed light on the neural processes underlying altered decision-making under uncertainty in anxiety. These disruptions are partly attributed to impaired encoding of precision-weighted prediction errors (pwPEs), which guide belief updating during learning and decision-making, as described by hierarchical Bayesian models. In this paper, we introduce a gamified paradigm for collecting decision-making data, together with a framework for extracting EEG features linked to computationally relevant variables, drawing on principles from neurofeedback and brain-computer interface research. This approach aims to develop tools that target functionally meaningful brain networks involved in decision-making, with the potential to inform future neurofeedback interactions.Approach.Forty healthy participants performed a volatile decision-making task in a game-based, immersive environment. EEG data were analysed to identify spatial filters whose theta- and alpha-band power correlated with pwPEs and state anxiety scores. Both intra-subject (trial-wise pwPEs) and intersubject (state anxiety) analyses were conducted to uncover distinct neural signatures.Main results.The intra-subject analysis revealed that pwPEs were significantly and positively correlated with theta power, and significantly and negatively correlated with alpha power - supporting the hypothesis that these oscillatory patterns underlie belief updating. In contrast, the inter-subject analysis showed that higher state anxiety was associated with reduced theta and increased alpha power, consistent with attenuated learning and impaired adaptation in anxious individuals. These findings align with theoretical models of hierarchical Bayesian inference and prior evidence of anxiety-related disruptions in uncertainty processing.Significance.The findings validate the proposed EEG framework for identifying neural markers related to belief updating and anxiety-related learning impairments. This approach lays the foundation for personalized neurofeedback procedures that target maladaptive decision-making in anxiety, with the added benefit of using immersive task paradigms for better engagement and translational potential for real-world applications.},
}

@article {pmid41609332,
year = {2026},
author = {Cicciarella, R and Willems, EP and Markham, B and Bizzozzero, MR and Phillips, W and Allen, SJ and Krützen, M and Christiansen, F},
title = {Validation of aerial photogrammetry methods to measure body size, condition and mass in small cetaceans.},
journal = {The Journal of physiology},
volume = {},
number = {},
pages = {},
doi = {10.1113/JP290419},
pmid = {41609332},
issn = {1469-7793},
support = {//A.H. Schultz Foundation/ ; 310030_204974/SNSF_/Swiss National Science Foundation/Switzerland ; },
abstract = {Accurate morphometric measurements are essential for estimating body size and condition in animals. These characteristics are, in turn, key to eco-physiological studies, wildlife management and conservation. For free-ranging cetaceans, however, collecting non-invasive morphometric data is challenging. Unoccupied aerial vehicle (UAV) photogrammetry offers a promising solution but requires ground-truthing to assess accuracy and precision. Similarly, morphometric-based indices of body condition must be validated against the animals' true body condition. Here we validated UAV-derived estimates of body size and condition in bottlenose dolphins (Tursiops spp.) under human care by comparing photogrammetry-based measurements of body length, width, height and girth from both stationary and swimming individuals with manual measurements. The two methods showed negligible differences, with UAV-based data yielding lower variability, confirming both high measurement accuracy and precision. Using UAV-derived measurements we calculated a volume-based body condition index (BCI) and compared it with a mass-based BCI, a standard metric in ecological research. The two indices showed a near-perfect fit, demonstrating that volume-based metrics reliably reflect true body condition in small cetaceans. Body density decreased with increasing body condition, consistent with higher fat-to-muscle ratios. By combining UAV-derived body volume with predicted density, based on their body condition, we accurately estimated individual body mass (mean error = 6.4%). This study provides a comprehensive validation of UAV-based photogrammetry to estimate body size, condition and mass in small cetaceans, highlighting its value as a non-invasive and cost-effective tool for ecological and conservation research. KEY POINTS: Measuring body size and condition in free-ranging dolphins is difficult, yet essential to understand their physiology, energy reserves and health. We used unoccupied aerial vehicles (UAV) to obtain accurate, non-invasive body measurements of bottlenose dolphins and compared them with direct manual measurements. UAV-based photogrammetry produced highly precise and accurate estimates of body length, girth and overall body volume, even for freely swimming animals. A UAV-derived, volume-based body condition index matched traditional mass-based indices and enabled accurate estimation of body mass. These results validate UAV photogrammetry as a reliable, ethical and cost-effective method for assessing body size, condition and mass in small cetaceans, thereby advancing ecological and physiological research in the wild.},
}

@article {pmid41606732,
year = {2026},
author = {Hu, L and Ye, L and Ye, H and Liu, X and Xiong, K and Zhang, Y and Zheng, Z and Jiang, H and Chen, C and Shen, C and Wang, Z and Zhou, J and Wu, Y and Huang, K and Zhu, J and Chen, Z and Ding, M and Weiss, S and Yang, D and Wang, S},
title = {Harmonic patterns embedded in ictal EEG signals in focal epilepsy: new insight into the epileptogenic zone.},
journal = {BMC medicine},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12916-026-04665-7},
pmid = {41606732},
issn = {1741-7015},
support = {82171437//The National Natural Science Foundation of China/ ; LD24H090003//The Natural Science Foundation of Zhejiang Province/ ; },
abstract = {BACKGROUND: Localization of the epileptogenic zone (EZ) requires further refinement. We identified a unique ictal spectral structure, the "harmonic pattern" (H pattern), which potentially serves as a novel biomarker for localizing the EZ. This study aimed to analyze the clinical significance of the H pattern and to explore its underlying waveform features.

METHODS: Seventy patients with drug-resistant focal epilepsy, undergoing stereo-EEG (SEEG) evaluation and surgery, were included. Time-frequency maps (TFM) were generated using Morlet wavelet transform analysis. The H pattern was defined as multiple equidistant, high-density bands with varying frequencies on TFM. The upper quartile was employed to confirm contacts expressing dominant H pattern (dH pattern). Bispectral analysis and transfer function modeling were employed to assess nonlinear properties and signal propagation, respectively. The performance of the dH pattern in evaluating the EZ was compared with other ictal biomarkers.

RESULTS: Regardless of seizure onset patterns, the H pattern commonly occurred during early or late seizure propagation among 57 patients (81.4%). It harbored within specific EEG segments characterized by fast activity and irregular polyspikes. The H pattern often appeared simultaneously across different brain regions at a consistent fundamental frequency, highlighting a crucial stage in seizure propagation characterized by inter-regional synchronization. The dH pattern demonstrated greater nonlinearity compared to the non-dH pattern, as evidenced by bispectral analysis. The waveforms associated with the dH pattern were more stereotyped and showed increased skewness and/or asymmetry. Notably, the complete removal of areas exhibiting the dH pattern, but not high epileptogenicity index (≥ 0.3) or seizure onset zone, was independently associated with seizure freedom after surgery.

CONCLUSIONS: The H pattern provides unique insights into ictal neural dynamics. Additionally, it is a novel and alternative approach for measuring the EZ over an extended ictal time window.},
}

@article {pmid41606342,
year = {2026},
author = {Xu, Z and Wang, H and Yu, J and Deng, Y and Tian, X and Ni, R and Xia, F and Yang, L and Xu, C and Zhang, L and Luo, R and Chen, P and Zhang, X and Liu, Y and Hou, J and Zhang, M and Chen, S and Su, L and Sun, H and He, Y and Chen, D and Chen, X and Miao, Z and Xie, J and Liu, X and Zhao, J and Ke, B and Tian, X and Zeng, L and Zhang, L and Tang, X and Yang, S and Liu, J and Wang, X and Yan, W and Shao, Z},
title = {Psychedelics elicit their effects by 5-HT2A receptor-mediated Gi signalling.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {41606342},
issn = {1476-4687},
abstract = {Psychedelics are undergoing a renaissance as potential therapy for psychiatric disorders, with more than 200 clinical trials being studied across several countries[1-3]. However, the precise mechanisms by which these drugs bring about benefits and the potential clinical risks are not yet fully understood. The serotonin 2A receptor (5-HT2AR) was reported to be a Gq-coupled receptor and the primary interoceptive target of psychedelics[4,5]. Here we compared psychedelics and their non-hallucinogenic analogues (nHAs) using in vitro and in vivo approaches, finding that 5-HT2AR-mediated non-canonical Gi signalling is essential for hallucinogenic effect. We further presented five cryo-electron microscopy structures of 5-HT2AR-Gi/Gq in complex with psychedelics or nHAs. Structural analysis and pharmacological investigation revealed that a special contact between nHAs with 5-HT2AR mediated the signalling bias. Building on this insight, we identified a 2,5-dimethoxy-4-iodoamphetamine derivative, DOI-NBOMe, which exhibits potent and selective Gq-biased activity, and demonstrates promising therapeutic effects in mouse models without hallucinogenic effect. Our finding uncovers the functional mechanisms underlying the Gi signalling mediated by 5-HT2AR and provides valuable insights for designing psychedelic-based drugs with minimized risk from hallucinogenic effects.},
}

@article {pmid41605490,
year = {2026},
author = {Chen, H and Yun, G},
title = {Efficacy of Brain-Computer Interface Therapy for Upper Limb Rehabilitation in Chronic Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials.},
journal = {Journal of medical Internet research},
volume = {28},
number = {},
pages = {e79132},
doi = {10.2196/79132},
pmid = {41605490},
issn = {1438-8871},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Randomized Controlled Trials as Topic ; *Upper Extremity/physiopathology ; Activities of Daily Living ; Chronic Disease ; *Stroke/physiopathology ; },
abstract = {BACKGROUND: Over 50% of people with chronic stroke experience persistent upper limb dysfunction. Brain-computer interface (BCI) therapy, creating a sensorimotor loop via neural feedback, is a promising alternative; yet, its optimal application remains unclear.

OBJECTIVE: This meta-analysis evaluates BCI's efficacy on motor function, tone, and activities of daily living (ADL) in chronic stroke and identifies optimal feedback modalities and intervention parameters.

METHODS: We systematically searched Cochrane Library, Embase, PubMed, Scopus, Web of Science, and Wanfang Data from inception to October 2025 for randomized controlled trials (RCTs) comparing BCI-based training to control interventions in adults with chronic stroke. Primary outcomes were upper limb motor function (Fugl-Meyer Assessment for upper extremity [FMA-UE], Action Research Arm Test [ARAT]), muscle tone (Modified Ashworth Scale [MAS]), and ADL (Modified Barthel Index [MBI], Motor Activity Log [MAL]). Screening, data extraction, and risk-of-bias assessment were performed independently. Meta-analysis used a random-effects model with Hartung-Knapp-Sidik-Jonkman adjustment. Pooled mean differences (MDs) with 95% CIs and 95% prediction intervals (PIs) were calculated. Subgroup analyses examined feedback modalities, intervention intensity, and follow-up effects. Sensitivity analysis was also conducted.

RESULTS: From 3529 records, 21 RCTs (650 participants) were included. BCI training significantly improved motor function (FMA-UE: MD 2.50, 95% CI 0.60-4.40; P=.01; 95% PI -2.52 to 7.22) and ADL performance (MBI: MD 8.38, 95% CI 2.23-14.53; P=.02; 95% PI -3.92 to 20.53; MAL: MD 2.09, 95% CI 0.42-3.76; P=.03; 95% PI -0.69 to 4.54). No significant effects were observed for fine motor skills (ARAT: MD 0.18, 95% CI -0.27 to 0.62; P=.30; 95% PI -3.64 to 3.99) or muscle tone (MAS: MD -0.48, 95% CI -1 to 0.03; P=.06; 95% PI -1.27 to 0.35). Subgroup analyses revealed that BCI-functional electrical stimulation (FES) yielded the greatest improvement in motor recovery (FMA-UE: MD 5, 95% CI 1.86-8.13; P=.01). The optimal intervention protocol was identified as 30-minute sessions, administered 4-5 times per week over 2 weeks (total of 10-12 sessions). However, benefits were not sustained at follow-up.

CONCLUSIONS: Low- to moderate-certainty evidence suggests that BCI training, particularly the BCI-FES paradigm, can improve upper limb motor function and ADL in people with chronic stroke on average. However, wide prediction intervals indicate the effect may vary substantially across settings, ranging from negligible to beneficial. Subgroup analyses suggested a potential optimal protocol of 30-minute sessions, 4-5 times per week for 2 weeks, but these findings are limited by the small number of studies in each subgroup and the high risk of bias in several included trials. Therefore, this proposed protocol should be viewed as preliminary and requires validation in future, high-quality RCTs. Future research should also focus on identifying patient subgroups most likely to benefit and on strategies to sustain long-term gains.

TRIAL REGISTRATION: PROSPERO CRD420251063808; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251063808.},
}

Humans
*Brain-Computer Interfaces
*Stroke Rehabilitation/methods
Randomized Controlled Trials as Topic
*Upper Extremity/physiopathology
Activities of Daily Living
Chronic Disease
*Stroke/physiopathology

@article {pmid41605384,
year = {2026},
author = {Jiang, H and Fu, H and Wei, Q and Wang, Y},
title = {A hierarchical bilayer sponge dressing based on QCMCS@GO/PLA for synergistic wound healing via hemostasis and anti-adhesion.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {150565},
doi = {10.1016/j.ijbiomac.2026.150565},
pmid = {41605384},
issn = {1879-0003},
abstract = {To address the challenges of inefficient hemostasis, high risk of bacterial infection, and biofilm formation in wound management, this study developed a bilayered sponge dressing composed of quaternized carboxymethyl chitosan@ graphene oxide/polylactic acid (QCMCS@GO/PLLA) with triple functionalities: coagulation, antibacterial activity, and anti-adhesion. A hierarchical structure was constructed using freeze-drying and electrospinning techniques: the bottom layer is a QCMCS@GO composite sponge, where graphene oxide (GO) enhances mechanical strength and enriches coagulation factors, while the quaternized carboxymethyl chitosan (QCMCS) promotes platelet activation and intrinsic coagulation pathway via its cationic properties; the top layer consists of electrospun polylactic acid (PLLA) nanofibers that serve as a superhydrophobic physical barrier to effectively inhibit bacterial adhesion. The material exhibits high porosity (>92%) and rapid liquid absorption (≥95% within 40 ms). In vitro experiments demonstrated that the dressing significantly accelerated whole blood coagulation (time reduced by 52.3%), optimized the blood clotting index (BCI = 4.7%), and enhanced thrombus formation through FXII contact activation. It achieved bacterial eradication rates of 99.94% against Staphylococcus aureus and 99.61% against Escherichia coli, while reducing bacterial adhesion on the surface by 91.8%. The dressing showed excellent biocompatibility (hemolysis rate 2.3%, cell proliferation rate 138%). In a rat liver injury model, it shortened hemostatic time by 63.2% and reduced blood loss by 76.5% compared to commercial gelatin sponges. This study provides a novel strategy for developing multifunctional wound dressings.},
}

@article {pmid41605140,
year = {2026},
author = {Ravi, A and Jiang, N and Tung, J},
title = {EEG-Based Gait Phase Decoding from Combined Action Observation and Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3659011},
pmid = {41605140},
issn = {1558-0210},
abstract = {Gait recovery is a crucial component of stroke rehabilitation. While Brain-Computer Interfaces (BCIs) decoding motor intent from motor imagery (MI) have shown success, their application in the area of gait phase decoding remains limited. Combining Action Observation (AO) and MI paradigms have demonstrated enhanced motor cortex activation compared to AO or MI alone. This study investigated the feasibility of decoding swing and stance phass of gait from electroencephalogaphy (EEG), via a proposed feature extraction and classification method. A novel dataset, utilizing the Combined AO, MI, and Steady-State Motion Visual Evoked Potential (SSMVEP) (CAMS-BCI) paradigm, was collected from twenty healthy volunteers. Employing an innovative labelling technique, three different classification methods were compared. Among them, broad band EEG features with a linear classifier achieved the highest average f1-score of 0.77 in gait phase classification. Additionally, the methods achieved an overall accuracy of 70% in classifying individual Swing and Stance phases based on the CAMS stimulus responses. These findings provide valuable insights for the development of novel BCI feedback mechanisms specifically targeting different phases of gait. Implementing them in future designs can potentially enhance gait recovery outcomes in post-stroke rehabilitation.},
}

@article {pmid41604716,
year = {2026},
author = {Lu, J and Liu, Y and Zhang, X and Han, J and Fan, Z and Yu, N},
title = {A principal brain-region analysis framework based on evolutionary decomposition for fNIRS brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3eb7},
pmid = {41604716},
issn = {1741-2552},
abstract = {Functional near-infrared spectroscopy (fNIRS) is an emerging technique for brain-computer interfaces (BCIs) due to its advantages in spatial resolution, robustness to artifacts, portability and usability for long-term monitoring, etc. Existing BCI methods take a holistic approach to all signal-collecting channels and corresponding brain regions, while the task-related brain regions and their interactions have not been well explored. Approach. This paper proposes a principal brain-region analysis (PBA) framework to incorporate the functional contribution as well as collaboration of task-specific brain regions (TSBRs) to boost BCI performance. Firstly, the identification of TSBRs is formulated as an optimization problem by maximizing classification accuracy under spatial constraints on brain regions of interest. Then, an evolutionary decomposition algorithm is constructed by combining spatial nondominated operators and genetic iterative computation, identifying TSBRs from the whole brain regions. Afterwards, classifiers are trained by neuroimaging features in the decomposed TSBRs in combination with stacking to generate the final predictions. Results. The proposed PBA method was evaluated on two public datasets for fNIRSbased BCIs, significantly enhancing the classification accuracy for the sliding slopebased method by 8.91% and 6.03% and the sliding mean concentration change method by 13.62% and 6.15%, respectively. Significance. Principal brain-region analysis establishes a pivotal framework to fundamentally advance the accuracy and explainability of BCIs.},
}

@article {pmid41604633,
year = {2026},
author = {Bialostocki, LS and Adhia, DB and Mudiyanselage, DR and Smith, ML and Cakmak, YO and De Ridder, D and Mani, R and Mathew, J},
title = {Authors' Reply: Bridging Neurofeedback and Structural Connectivity in Chronic Pain.},
journal = {JMIR research protocols},
volume = {15},
number = {},
pages = {e89007},
doi = {10.2196/89007},
pmid = {41604633},
issn = {1929-0748},
}

@article {pmid41604568,
year = {2026},
author = {Acar, A and Yahya, D and Tekirdaş, E},
title = {Bridging Neurofeedback and Structural Connectivity in Chronic Pain.},
journal = {JMIR research protocols},
volume = {15},
number = {},
pages = {e87420},
doi = {10.2196/87420},
pmid = {41604568},
issn = {1929-0748},
}

@article {pmid41605144,
year = {2026},
author = {Wei, S and Yu, H and Huang, Y and Meng, J and Xu, R and Jung, TP and Xu, M and Ming, D},
title = {Predicting Attention Decline: An Integrated Beta-Band and SSVEP Approach for Visual Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3658740},
pmid = {41605144},
issn = {1558-0210},
abstract = {Real-time monitoring of sustained attention fluctuations during continuous complex tasks is vital for enhancing task performance and preventing accidents. Attention modulates neurons in the visual cortex in various ways to improve the visual sensitivity at an attended location. EEG-based brain-computer interfaces (BCIs) offer one of the most effective approaches for monitoring the state of human individuals. Whether transient responses evoked by brief stimuli, steady-state responses elicited by prolonged stimuli, or spontaneous neural oscillations, researchers can extract recognized electrophysiological features that reflect attention levels. However, unimodal features face inherent limitations, such as the low signal-to-noise ratio of transient responses and susceptibility of spontaneous rhythms to electrophysiological interference. Nevertheless, few studies have explored multimodal feature fusion for attention state monitoring. Here, we developed an innovative continuous go/no-go task to concurrently evoke both event-related potential (ERP) and steady-state visual evoked potential (SSVEP), while modulating spontaneous oscillatory activities through attentional engagement. To maximize the attentional modulation effect, we integrated the contrast-response functions of the modulation effect of attention on SSVEP and implemented 12 stimulus contrast levels to identify optimal visual stimulation intensity. Results from 25 subjects demonstrated that the decline in sustained attention during a continuous task was predictable before behavioral mistakes. Classification performance peaked at 31.60% stimulus contrast condition using the fused features combining spontaneous beta-band oscillations and SSVEP responses (average: 74.48%; best: 90.83%). These findings advance the development of more robust real-time attention monitoring systems based on BCI technology.},
}

@article {pmid41603967,
year = {2026},
author = {Schumacher, X and Frazzini, V and Adam, C and Dupont, S and Bielle, F and Guesdon, A and Mere, M and Nguyen-Michel, VH and Navarro, V and Mathon, B},
title = {Safety and efficacy of sEEG-guided resective surgery in patients with MRI-negative drug-resistant epilepsy.},
journal = {Neurosurgical review},
volume = {49},
number = {1},
pages = {166},
pmid = {41603967},
issn = {1437-2320},
}

@article {pmid41603128,
year = {2026},
author = {Shimizu, S and Osawa, T and Sato, M and Yamada, S and Harabayashi, T and Miki, J and Kobayashi, T and Hashine, K and Kawashima, A and Matsumoto, T and Mochizuki, T and Taoka, R and Urabe, F and Tatarano, S and Sawada, A and Kojima, T and Takahashi, A and Yokomizo, A and Suekane, S and Hashimoto, K and Hashimoto, Y and Yatsuda, J and Morita, K and Kobayashi, K and Satake, Y and Sazawa, A and Matsui, Y and Ito, YM and Nishiyama, H and Kitamura, H and Shinohara, N and Fukuhara, S and , },
title = {Validation of the 7-Item Quality of Life Disease-Specific Impact Scale in Patients Undergoing Radical Cystectomy for Bladder Cancer: A Cross-Sectional Study.},
journal = {International journal of urology : official journal of the Japanese Urological Association},
volume = {33},
number = {2},
pages = {e70364},
doi = {10.1111/iju.70364},
pmid = {41603128},
issn = {1442-2042},
mesh = {Humans ; Cross-Sectional Studies ; *Quality of Life ; Male ; *Urinary Bladder Neoplasms/surgery/psychology ; *Cystectomy/psychology/adverse effects ; Female ; Aged ; Middle Aged ; Reproducibility of Results ; Surveys and Questionnaires ; Psychometrics ; Body Image ; Aged, 80 and over ; Factor Analysis, Statistical ; Self Report ; },
abstract = {OBJECTIVES: To validate, for the first time in patients with bladder cancer who underwent radical cystectomy, the recently developed 7-item Quality of Life Disease-specific Impact Scale (QDIS-7), a brief, unidimensional instrument designed for cross-condition comparisons.

METHODS: In this cross-sectional study conducted at 24 facilities, patients aged ≥ 20 years who were 3 months post-radical cystectomy for bladder cancer completed self-reported questionnaires. The enrollment period was from January 2020 to October 2022. Quality of life measures included the QDIS-7, the Bladder Cancer Index (BCI), and the Body Image Scale (BIS). Confirmatory factor analysis was performed to test the hypothesized one-factor structure of the QDIS-7. Internal consistency reliability was assessed using Cronbach's alpha coefficient. Criterion-based validity was evaluated using Spearman's correlation coefficients (ρ) between the QDIS-7 scores and the BCI bother subdomains and BIS scores.

RESULTS: In total, 205 patients (median age, 71 years; 78.5% male) were included. The QDIS-7 score showed no floor or ceiling effects. Confirmatory factor analysis supported the one-factor model (factor loadings, 0.71-0.94). Internal consistency reliability was high (Cronbach's alpha, 0.94). The QDIS-7 score showed moderate correlations with the BIS and the BCI urinary and bowel bother subdomain scores (ρ = 0.654, -0.560, and -0.475, respectively).

CONCLUSIONS: The QDIS-7 effectively captured urinary and bowel symptom burden and body image impairment in patients undergoing radical cystectomy for bladder cancer. Its brevity, strong psychometric properties, and capacity for comparisons across conditions support its use in patient-centered research.

TRAIL REGISTRATION: UMIN-CTR (UMIN000039538).},
}

Humans
Cross-Sectional Studies
*Quality of Life
Male
*Urinary Bladder Neoplasms/surgery/psychology
*Cystectomy/psychology/adverse effects
Female
Aged
Middle Aged
Reproducibility of Results
Surveys and Questionnaires
Psychometrics
Body Image
Aged, 80 and over
Factor Analysis, Statistical
Self Report

@article {pmid41600429,
year = {2026},
author = {Falk, M and Shleev, S},
title = {Advances in (Bio)Sensors for Physiological Monitoring: A Special Issue Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {2},
pages = {},
doi = {10.3390/s26020633},
pmid = {41600429},
issn = {1424-8220},
mesh = {Humans ; Monitoring, Physiologic/methods/instrumentation ; Wearable Electronic Devices ; *Biosensing Techniques/methods ; Brain-Computer Interfaces ; },
abstract = {Physiological monitoring has become an inherently interdisciplinary field, merging advances in engineering, chemistry, biology, medicine, and data analytics to create sensors that continuously track the vital signals of the body. These developments are enabling more personalized and preventive healthcare, as wearable (bio)sensors and intelligent algorithms can detect subtle physiological changes in real-time. In the Special Issue 'Advances in (Bio)Sensors for Physiological Monitoring', researchers from diverse domains contributed 18 papers showcasing cutting-edge sensor technologies and applications for health and performance monitoring. In this review, we summarize these contributions by grouping them into logical themes based on their focus: (1) cardiovascular and autonomic monitoring, (2) glucose and metabolic monitoring, (3) wearable sensors for movement and musculoskeletal health, (4) neurophysiological monitoring and brain-computer interfaces, and (5) innovations in sensor technology and methods. This thematic organization highlights the breadth of the research, spanning from fundamental sensor hardware to data-driven analytics, and underscores how modern (bio)sensors are breaking traditional boundaries in healthcare.},
}

Humans
Monitoring, Physiologic/methods/instrumentation
Wearable Electronic Devices
*Biosensing Techniques/methods
Brain-Computer Interfaces

@article {pmid41600372,
year = {2026},
author = {Zhang, B and You, X and Liu, Y and Xu, J and Xu, S},
title = {Multi-Level Perception Systems in Fusion of Lifeforms: Classification, Challenges and Future Conceptions.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {2},
pages = {},
doi = {10.3390/s26020576},
pmid = {41600372},
issn = {1424-8220},
support = {2024YFC3406302//the National Key R&D Program of China/ ; 12204273//the National Natural Science Foundation of China/ ; ZR2024MF107//the Natural Science Foundation of Shandong Province, China/ ; 2017YFA0701302//the National Key R&D Program of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Perception/physiology ; },
abstract = {The emerging paradigm of "fusion of lifeforms" represents a transformative shift from conventional human-machine interfaces toward deeply integrated symbiotic systems, where biological and artificial components co-adapt structurally, energetically, informationally, and cognitively. This review systematically classifies multi-level perception systems within fusion of lifeforms into four functional categories: sensory and functional restoration, beyond-natural sensing, endogenous state sensing, and cognitive enhancement. We survey recent advances in neuroprosthetics, sensory augmentation, closed-loop physiological monitoring, and brain-computer interfaces, highlighting the transition from substitution to fusion. Despite significant progress, critical challenges remain, including multi-source heterogeneous integration, bandwidth and latency limitations, power and thermal constraints, biocompatibility, and system-level safety. We propose future directions such as layered in-body communication networks, sustainable energy strategies, advanced biointerfaces, and robust safety frameworks. Ethical considerations regarding self-identity, neural privacy, and legal responsibility are also discussed. This work aims to provide a comprehensive reference and roadmap for the development of next-generation fusion of lifeforms, ultimately steering human-machine integration from episodic functional repair toward sustained, multi-level symbiosis between biological and artificial systems.},
}

Humans
*Brain-Computer Interfaces
*Perception/physiology

@article {pmid41600345,
year = {2026},
author = {Dalgaard, KS and Lavesen, ER and Sulkjær, CS and Stevenson, AJT and Jochumsen, M},
title = {Specificity of Pairing Afferent and Efferent Activity for Inducing Neural Plasticity with an Associative Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {2},
pages = {},
doi = {10.3390/s26020549},
pmid = {41600345},
issn = {1424-8220},
mesh = {Humans ; *Neuronal Plasticity/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Evoked Potentials, Motor/physiology ; Transcranial Magnetic Stimulation ; Wrist/physiology ; Electric Stimulation ; Movement/physiology ; Young Adult ; },
abstract = {Brain-computer interface-based (BCI) training induces neural plasticity and promotes motor recovery in stroke patients by pairing movement intentions with congruent electrical stimulation of the affected limb, eliciting somatosensory afferent feedback. However, this training can potentially be refined further to enhance rehabilitation outcomes. It is not known how specific the afferent feedback needs to be with respect to the efferent activity from the brain. This study investigated how corticospinal excitability, a marker of neural plasticity, was modulated by four types of BCI-like interventions that varied in the specificity of afferent feedback relative to the efferent activity. Fifteen able-bodied participants performed four interventions: (1) wrist extensions paired with radial nerve peripheral electrical stimulation (PES) (matching feedback), (2) wrist extensions paired with ulnar nerve PES (non-matching feedback), (3) wrist extensions paired with sham radial nerve PES (no feedback), and (4) palmar grasps paired with radial nerve PES (partially matching feedback). Each intervention consisted of 100 pairings between visually cued movements and PES. The PES was triggered based on the peak of maximal negativity of the movement-related cortical potential associated with the visually cued movement. Before, immediately after, and 30 min after the intervention, transcranial magnetic stimulation-elicited motor-evoked potentials were recorded to assess corticospinal excitability. Only wrist extensions paired with radial nerve PES significantly increased the corticospinal excitability with 57 ± 49% and 65 ± 52% immediately and 30 min after the intervention, respectively, compared to the pre-intervention measurement. In conclusion, maximizing the induction of neural plasticity with an associative BCI requires that the afferent feedback be precisely matched to the efferent brain activity.},
}

Humans
*Neuronal Plasticity/physiology
*Brain-Computer Interfaces
Male
Female
Adult
Evoked Potentials, Motor/physiology
Transcranial Magnetic Stimulation
Wrist/physiology
Electric Stimulation
Movement/physiology
Young Adult

@article {pmid41600232,
year = {2026},
author = {Wu, W and Liu, L and Chen, W and Chen, Y and Wang, X and Cichocki, A and Lu, Y and Jin, J},
title = {MS-TSEFNet: Multi-Scale Spatiotemporal Efficient Feature Fusion Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {2},
pages = {},
doi = {10.3390/s26020437},
pmid = {41600232},
issn = {1424-8220},
support = {2022ZD0208900//STI 2030-major projects/ ; 62176090//National Natural Science Foundation of China/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; BE2022064-1//Project of Jiangsu Province Science and Technology Plan Special Fund in 2022/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Deep Learning ; Brain/physiology ; },
abstract = {Motor imagery signal decoding is an important research direction in the field of brain-computer interfaces, which aim to judge the motor imagery state of an individual by analyzing electroencephalogram (EEG) signals. Deep learning technology has been gradually applied to EEG classification, which can automatically extract features. However, when processing complex EEG signals, the existing decoding models cannot effectively fuse features at different levels, resulting in limited classification performance. This study proposes a multi-scale spatiotemporal efficient feature fusion network (MS-TSEFNet), which learns the dynamic changes in EEG signals at different time scales through multi-scale convolution modules and combines the spatial attention mechanism to efficiently capture the spatial correlation between electrodes in EEG signals. In addition, the network adopts an efficient feature fusion strategy to deeply fuse features at different levels, thereby improving the expression ability of the model. In the task of motor imagery signal decoding, MS-TSEFNet shows higher accuracy and robustness. We use the public BCIC-IV2a, BCIC-IV2b and ECUST datasets for evaluation. The experimental results show that the average classification accuracy of MS-TSEFNet reaches 80.31%, 86.69% and 71.14%, respectively, which is better than the current state-of-the-art algorithms. We conducted an ablation experiment to further verify the effectiveness of the model. The experimental results showed that each module played an important role in improving the final performance. In particular, the combination of the multi-scale convolution module and the feature fusion module significantly improved the model's ability to extract the spatiotemporal features of EEG signals.},
}

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

@article {pmid41598333,
year = {2026},
author = {Wankner, MC and Visser-Vandewalle, V and Andrade, P and Heiden, P},
title = {Cervical Spinal Cord Stimulation for Functional Rehabilitation After Spinal Cord Injury: A Systematic Review of Preclinical and Clinical Studies.},
journal = {Life (Basel, Switzerland)},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/life16010179},
pmid = {41598333},
issn = {2075-1729},
abstract = {Cervical spinal cord injury causes severe functional impairment with limited spontaneous recovery, and while spinal cord stimulation has emerged as a promising neuromodulatory strategy, evidence for cervical applications remains fragmented. To address this gap, we conducted a systematic review synthesizing preclinical and clinical evidence on cervical spinal cord stimulation for functional rehabilitation following spinal cord injury. The review was registered on PROSPERO (CRD420251088804) and conducted in accordance with PRISMA guidelines, with PubMed, Embase, IEEE Xplore, and Web of Science searched from inception to July 2025 for animal and human studies of cervical spinal cord stimulation, including epidural, intraspinal, and transcutaneous approaches, reporting functional neurological outcomes. Risk of bias was assessed using the Cochrane RoB 2 and ROBINS-I tools, and due to substantial heterogeneity, results were synthesized narratively. Thirty-one studies comprising 119 animals and 156 human participants, met inclusion criteria. Across studies, outcome measures such as GRASSP, ISNCSCI, and dynamometry consistently demonstrated improvements in hand strength, dexterity, and voluntary motor activation. Several studies also reported gains in sensory and autonomic function, whereas respiratory outcomes were infrequently assessed. Adjunctive interventions, including cortical stimulation, brain-computer interface priming, and task-specific training frequently augmented recovery. Adverse events were generally mild, although overall risk of bias was predominantly serious. Overall, cervical spinal cord stimulation demonstrates preliminary assistive and therapeutic effects on motor recovery, with additional sensory, autonomic, and potential respiratory benefits.},
}

@article {pmid41597853,
year = {2026},
author = {Ding, Y and Ding, J and Yang, Z and Fan, X and Chen, W},
title = {A Surface-Mount Substrate-Integrated Waveguide Bandpass Filter Based on MEMS Process and PCB Artwork for Robotic Radar Applications.},
journal = {Micromachines},
volume = {17},
number = {1},
pages = {},
doi = {10.3390/mi17010072},
pmid = {41597853},
issn = {2072-666X},
support = {F2025508015//Natural Science Foundation of Hebei Province/ ; 3142025013//Fundamental Research Funds for the Central Universities/ ; 2025011037//Science and Technology Support Project of Langfang/ ; 3142023023//Fundamental Research Funds for the Central Universities/ ; },
abstract = {To address the pressing need for compact and highly reliable perception systems in autonomous mobile robots, a compact bandpass filter (BPF) integrating slot-line resonator with substrate-integrated waveguide (SIW) technology for robotic millimeter-wave radar front ends was proposed. By integrating slot-line resonators between adjacent SIW cavities, the proposed design effectively increases the filtering order without increasing the layout area. This approach not only generates extra transmission poles but also creates a sharp transmission zero at the upper stopband, thereby significantly enhancing out-of-band rejection. This characteristic is crucial for robotic radar operating in complex and dynamic environments, as it effectively suppresses out-of-band interference and improves the system signal-to-noise ratio and detection reliability. To validate the performance, a prototype filter operating in the 24.25-27.5 GHz passband was fabricated. The measured results show good agreement with simulations, demonstrating low insertion loss, compact size, and wide stopband. Finally, to validate its compatibility with robotic radar modules, the chip was assembled onto a PCB using surface-mount technology. The responses of the bare die and the packaged module were then compared to evaluate the impact of integration on the overall RF performance. The proposed design offers a key filtering solution for next-generation high-performance, miniaturized robotic perception platforms.},
}

@article {pmid41596724,
year = {2026},
author = {Yen, C and Chiang, MC},
title = {Neuroimaging-Guided Insights into the Molecular and Network Mechanisms of Chronic Pain and Neuromodulation.},
journal = {International journal of molecular sciences},
volume = {27},
number = {2},
pages = {},
doi = {10.3390/ijms27021080},
pmid = {41596724},
issn = {1422-0067},
support = {NSTC 113-2314-B-030-007, NSTC 113-2515-S-030-001, and NSTC 114-2918-I-030-001//National Science and Technology Council/ ; },
mesh = {Humans ; *Chronic Pain/diagnostic imaging/metabolism/therapy/physiopathology ; *Neuroimaging/methods ; Brain/diagnostic imaging/metabolism/physiopathology ; Transcranial Magnetic Stimulation ; Deep Brain Stimulation ; Animals ; Positron-Emission Tomography ; },
abstract = {Chronic pain is a pervasive and debilitating condition that affects millions of individuals worldwide. Unlike acute pain, which serves a protective physiological role, chronic pain persists beyond routine tissue healing and often arises without a discernible peripheral cause. Accumulating evidence indicates that chronic pain is not merely a symptom but a disorder of the central nervous system, underpinned by interacting molecular, neurochemical, and network-level alterations. Molecular neuroimaging using PET and MR spectroscopy has revealed dysregulated excitatory-inhibitory balance (glutamate/GABA), altered monoaminergic and opioidergic signaling, and neuroimmune activation (e.g., TSPO-indexed glial activation) in key pain-related regions such as the insula, anterior cingulate cortex, thalamus, and prefrontal cortex. Converging multimodal imaging-including functional MRI, diffusion MRI, and EEG/MEG-demonstrates aberrant activity and connectivity across the default mode, salience, and sensorimotor networks, alongside structural remodeling in cortical and subcortical circuits. Parallel advances in neuromodulation, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), deep brain stimulation (DBS), and emerging biomarker-guided closed-loop approaches, provide tools to perturb these maladaptive circuits and to test mechanistic hypotheses in vivo. This review integrates neuroimaging findings with molecular and systems-level mechanistic insights into chronic pain and its modulation, highlighting how imaging markers can link biochemical signatures to neural dynamics and guide precision pain management and individualized therapeutic strategies.},
}

Humans
*Chronic Pain/diagnostic imaging/metabolism/therapy/physiopathology
*Neuroimaging/methods
Brain/diagnostic imaging/metabolism/physiopathology
Transcranial Magnetic Stimulation
Deep Brain Stimulation
Animals
Positron-Emission Tomography

@article {pmid41596014,
year = {2026},
author = {Liang, J and Zhou, Y and Ma, K and Jia, Y and Zhang, Y and Han, B and Xiang, M},
title = {Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {1},
pages = {},
doi = {10.3390/bioengineering13010084},
pmid = {41596014},
issn = {2306-5354},
support = {U23A20485//National Natural Science Foundation of China/ ; 62501039//National Natural Science Foundation of China/ ; 2021ZD0300503//Innovation Program for Quantum Science and Technology/ ; BX20240465//China National Postdoctoral Program for Innovative Talents/ ; LMS25H180004//Zhejiang Provincial Natural Science Foundation Exploration Project/ ; },
abstract = {Bio-electric fields-manifested as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG), and Electrooculogram (EOG)-are fundamental to modern medical diagnostics but often suffer from severe data imbalance, scarcity, and environmental noise. Generative Adversarial Networks (GANs) offer a powerful, nonlinear solution to these modeling hurdles. This review presents a comprehensive survey of GAN methodologies specifically tailored for bio-electric signal processing. We first establish a theoretical foundation by detailing GAN principles, training mechanisms, and critical structural variants, including advancements in loss functions and conditional architectures. Subsequently, the paper extensively analyzes applications ranging from high-fidelity signal synthesis and noise reduction to multi-class classification. Special attention is given to clinical anomaly detection, specifically covering epilepsy, arrhythmia, depression, and sleep apnea. Furthermore, we explore emerging applications such as modal transformation, Brain-Computer Interfaces (BCI), de-identification for privacy, and signal reconstruction. Finally, we critically evaluate the computational trade-offs and stability issues inherent in current models. The study concludes by delineating prospective research avenues, emphasizing the necessity of interdisciplinary synergy to advance personalized medicine and intelligent diagnostic systems.},
}

@article {pmid41594981,
year = {2025},
author = {Jiao, M and Ding, Z and Huang, C and Xu, Y and Zhong, B and Chen, H},
title = {The Effects of Computerized Cognitive Training via Tablet and Computer Platforms on Cognitive Function in Patients with Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/bs16010040},
pmid = {41594981},
issn = {2076-328X},
support = {No.2022ZD0210800//the Science and Technology Innovation 2030-"Brain Science and Brain-like Research" Major Project/ ; No. 20&ZD045//the Emerging Enhancement Technology under Perspective of Humanistic Philosophy, supported by the National Office for Philosophy and Social Science/ ; No.32171046//the Emerging Enhancement Technology under Perspective of Humanistic Philosophy, supported by the National Office for Philosophy and Social Science/ ; 226-2024-00207, 226-2024-00118//the Fundamental Research Funds for the Central Universities/ ; WX23A99//the Wuhan Medical Research Project 2023 (Healthy Development)/ ; 2024020801020405//the 2024 Wuhan Natural Science Foundation Exploration Plan Municipal Medical Institutions Clinical Research Key Project/ ; },
abstract = {Background: Mild cognitive impairment (MCI) is a high-risk prodromal stage of dementia. While tablet/computer-based computerized cognitive training (CCT) is widely used, its efficacy and gamification's role need clarification. Objective: This study aimed to evaluate the effect of tablet/computer-based CCT on global cognition in older adults with MCI and explore the impact of gamification. Methods: We systematically searched five databases for RCTs (through October 2025) involving individuals aged ≥55 with MCI. The intervention was task-based CCT via tablets/computers. Primary outcome was global cognition. We used random-effects meta-analysis and subgroup analyses. Results: Nineteen RCTs (1013 participants) were included. CCT demonstrated a significant, moderate positive effect on global cognition (Hedges' g = 0.57, 95% CI [0.36, 0.78]). A trend suggesting greater benefits with higher gamification was observed: high (g = 0.71), medium (g = 0.46), and low (g = 0.45) degrees. However, subgroup differences were not statistically significant (p = 0.4333). Results were robust in sensitivity analyses. Conclusions: Tablet/computer-based CCT effectively improves global cognition in MCI. The potential additive value of gamification highlights its promise for enhancing engagement and effects, warranting further investigation in larger trials. This systematic review was registered with PROSPERO (CRD420251231618).},
}

@article {pmid41594816,
year = {2026},
author = {Li, M and Xia, J and Pan, J and Zhao, S and Zhang, X and Jin, H and Dong, S},
title = {SleepMFormer: An Efficient Attention Framework with Contrastive Learning for Single-Channel EEG Sleep Staging.},
journal = {Brain sciences},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/brainsci16010095},
pmid = {41594816},
issn = {2076-3425},
support = {2021ZD0200401//STI2030-Major projects/ ; 2024C03001//Zhejiang Province Key R & D programs/ ; 2025C01137//Zhejiang Province Key R & D programs/ ; 2022R52042//Zhejiang Province high level talent special support plan/ ; 2025ZFJH01//Fundamental Research Funds for the Central Universities/ ; },
abstract = {BACKGROUND/OBJECTIVES: Sleep stage classification is crucial for assessing sleep quality and diagnosing related disorders. Electroencephalography (EEG) is currently recognized as a primary method for sleep stage classification. High-performance automatic sleep staging methods based on EEG leverage the powerful contextual modeling capabilities of Transformer Encoder architectures. However, the global self-attention mechanism in Transformers incurs significant computational overhead, substantially hindering the training and inference efficiency of automatic sleep staging algorithms.

METHODS: To address these issues, we introduce an end-to-end framework for automatic sleep stage classification using single-channel EEG: SleepMFormer. At the algorithmic level, SleepMFormer adopts a task-driven simplification of the Transformer encoder to improve attention efficiency while preserving sequence modeling capability. At the training level, supervised contrastive learning is incorporated as an auxiliary strategy to enhance representation robustness. From an engineering perspective, these design choices enable efficient training and inference under resource-constrained settings.

RESULTS: When integrated with the SleePyCo backbone, the proposed framework achieves competitive performance on three widely used public datasets: Sleep-EDF, PhysioNet, and SHHS. Notably, SleepMFormer reduces training and inference time by up to 33% compared to conventional self-attention-based models. To further validate the generalizability of MaxFormer, we conduct additional experiments using DeepSleepNet and TinySleepNet as alternative feature extractors. Experimental results demonstrate that MaxFormer consistently maintains performance across different model architectures.

CONCLUSIONS: Overall, SleepMFormer introduces an efficient and practical framework for automatic sleep staging, demonstrating strong potential for related clinical applications.},
}

@article {pmid41594762,
year = {2025},
author = {Liu, Y and Xue, W and Yang, L and Li, M},
title = {Deep Learning-Based EEG Emotion Recognition: A Review.},
journal = {Brain sciences},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/brainsci16010041},
pmid = {41594762},
issn = {2076-3425},
support = {252102311095//Key Scientific and Technological Projects of Henan Province/ ; 2025T180781//China Postdoctoral Science Foundation/ ; 62301496//National Natural Science Foundation of China/ ; GZC20232447//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; },
abstract = {Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human-computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality-individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods.},
}

@article {pmid41594729,
year = {2025},
author = {Tyler, WJ},
title = {Transcutaneous Auricular Vagus Nerve Stimulation for Treating Emotional Dysregulation and Inflammation in Common Neuropsychiatric Disorders.},
journal = {Brain sciences},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/brainsci16010008},
pmid = {41594729},
issn = {2076-3425},
support = {FA8650-18-2-5402//United States Air Force Research Laboratory/ ; },
abstract = {Development of new therapeutic approaches and strategies for common neuropsychiatric disorders, including Major Depressive Disorder, anxiety disorders, and Post-Traumatic Stress Disorder, represent a significant global health challenge. Recent research indicates that emotional dysregulation and persistent inflammation are closely linked and serve as key pathophysiological features of these conditions. Emotional dysregulation is mechanistically coupled to locus coeruleus and norepinephrine (LC-NE) or noradrenergic system activity. Stemming from chronic stress, persistently elevated activity of the LC-NE system leads to hypervigilance, anxious states, and depressed mood. Concurrently, these symptoms are marked by systemic inflammation as indicated by elevated pro-inflammatory cytokines, and central neuroinflammation indicated by microglial activation in brain regions and networks involved in mood regulation and emotional control. In turn, chronic inflammation increases sympathetic tone and LC-NE activity resulting in a vortex of psychoneuroimmunological dysfunction that worsens mental health. Transcutaneous auricular vagus nerve stimulation (taVNS) in a non-invasive neuromodulation method uniquely positioned to address both noradrenergic dysfunction and chronic inflammation in neuropsychiatric applications. Evidence spanning the past decade demonstrates taVNS works via two complementary mechanisms. An ascending pathway engages vagal afferents projecting to the LC-NE system in the brain stem, which has been shown to modulate cortical arousal, cognitive function, mood, and stress responses. Through descending circuits, taVNS also modulates the cholinergic anti-inflammatory pathway to suppress the production of pro-inflammatory cytokines like TNF-α and IL-6 mitigating poor health outcomes caused by inflammation. By enhancing both central brain function and peripheral immune responses, taVNS has shown significant potential for recalibrating perturbed affective-cognitive processing. The present article describes and discusses recent evidence suggesting that taVNS offers a promising network-based paradigm for restoring psychoneuroimmunological homeostasis in common neuropsychiatric conditions.},
}

@article {pmid41594269,
year = {2026},
author = {Wang, H and Xu, S and Guo, R and Han, J and Huang, MC},
title = {Neurosense: Bridging Neural Dynamics and Mental Health Through Deep Learning for Brain Health Assessment via Reaction Time and p-Factor Prediction.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/diagnostics16020293},
pmid = {41594269},
issn = {2075-4418},
support = {N/A//Kunshan Municipal Government research funding/ ; },
abstract = {Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health assessment framework using electroencephalography (EEG) to non-invasively capture neural dynamics. Methods: Our Dual-path Spatio-Temporal Adaptive Gated Encoder (D-STAGE) architecture processes temporal and spatial EEG features in parallel through Transformer-based and graph convolutional pathways, integrating them via adaptive gating mechanisms. We introduce a two-stage paradigm: first training on cognitive task EEG for reaction time prediction to acquire cognitive performance-related representations, then featuring parameter-efficient adapter-based transfer learning to estimate p-factor-a transdiagnostic psychopathology dimension. The adapter-based transfer achieves competitive performance using only 1.7% of parameters required for full fine-tuning. Results: The model achieves effective reaction time prediction from EEG signals. Transfer learning from cognitive tasks to mental health assessment demonstrates that cognitive efficiency representations can be adapted for p-factor prediction, outperforming direct training approaches while maintaining parameter efficiency. Conclusions: The Neurosense framework reveals hierarchical relationships between neural dynamics, cognitive efficiency, and mental health dimensions, establishing foundations for a promising computational framework for mental health assessment applications.},
}

@article {pmid41593817,
year = {2026},
author = {Bahadir, S and Robinson, JT and Morse, LR and Yoo, PB and Kern, R and Makowski, NS and Kozai, TDY and Fudim, M and Barbe, MF and Lim, HH and Chang, EH and Zanos, S},
title = {The sixth bioelectronic medicine summit: Neurotechnologies for individuals and communities.},
journal = {Bioelectronic medicine},
volume = {12},
number = {1},
pages = {3},
pmid = {41593817},
issn = {2332-8886},
support = {NIH SPARC U41 - NS129436/NH/NIH HHS/United States ; NIH-NIGMS R01GM143362/NH/NIH HHS/United States ; NIH-NINDS 1R01NS136685/NH/NIH HHS/United States ; },
}

@article {pmid41592346,
year = {2026},
author = {Suo, X and Li, W and Liao, X and Wu, Y and Zhang, H},
title = {A study of cortical activation and corticomuscular coupling enhancement following pre-task electrical stimulation in motor imagery.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3e17},
pmid = {41592346},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been extensively studied. However, their widespread application is limited by the difficulty in extracting motor intentions from electroencephalography (EEG) signals, leading to low recognition rates. Additionally, the phenomenon of motor imagery blindnes s in some individuals further limits its applicability. Previous studies have attempted to improve motor imagery ability through electrical stimulation. However, applying electrical stimulation during motor imagery may introduce EEG artifacts and interfere with participants' concentration. The goal of this study is to investigate a new experimental paradigm. The new experimental paradigm improves motor imagery ability through pre-task electrical stimulation while preventing participant distraction or EEG artifacts.

APPROACH: This study implemented two paradigms: motor imagery with pre-task electrical stimulation (MI-ES) and motor imagery-only (MI-Only). Electrical stimulation was applied over hand muscle groups. Electromyography (EMG) and 64-channel EEG signals were simultaneously recorded under two experimental conditions.

MAIN RESULTS: We analyzed cortical activities and correlations between different brain regions under the two experimental conditions. Participants in the MI-ES condition exhibited a higher level of brain activation compared to the MI-Only condition. Additionally, in the MI-ES condition, the correlation between participants' EEG and EMG signals increased after electrical stimulation, indicating that the activation level of the motor-related cortex increased. A novel convolutional spiking neural network was applied to classify motor intentions, with participants achieving higher accuracy under the MI-ES condition, demonstrating enhanced motor imagery ability through pre-task electrical stimulation.

SIGNIFICANCE: This research demonstrates that pre-task electrical stimulation significantly enhances motor imagery ability, while also increasing cortical activation and corticomuscular coupling without introducing EEG artifacts or attentional interference.},
}

@article {pmid41590267,
year = {2025},
author = {Mehmood, F and Rehman, SU and Mehmood, A and Kim, YJ},
title = {Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review.},
journal = {Biosensors},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/bios16010015},
pmid = {41590267},
issn = {2079-6374},
support = {20022793//KEIT/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Nervous System Diseases/diagnosis ; *Artificial Intelligence ; *Ocular Motility Disorders/diagnosis ; Machine Learning ; Deep Learning ; },
abstract = {Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis for the diagnosis, classification, and monitoring of neurological conditions, including oculomotor-related disorders. Following the PRISMA guidelines, a structured literature search was conducted across major scientific databases, resulting in the inclusion of 15 peer-reviewed studies published over the last decade. The reviewed works encompass a range of neurological and ocular-related disorders and employ diverse AI models, from conventional ML algorithms to advanced DL architectures capable of learning complex spatiotemporal representations of neural signals. Key trends in feature extraction, signal representation, model design, and validation strategies are synthesized here to highlight methodological advancements and common challenges. While the reviewed studies demonstrate the growing potential of AI-enhanced EEG analysis for supporting clinical decision-making, limitations such as small sample sizes, heterogeneous datasets, and limited external validation remain prevalent. Addressing these challenges through standardized methodologies, larger multi-center datasets, and robust validation frameworks will be essential for translating EEG-driven AI approaches into reliable clinical applications. Overall, this review provides a comprehensive overview of current methodologies and future directions for AI-driven EEG analysis in neurological and oculomotor disorder assessment.},
}

Humans
*Electroencephalography/methods
*Nervous System Diseases/diagnosis
*Artificial Intelligence
*Ocular Motility Disorders/diagnosis
Machine Learning
Deep Learning

@article {pmid41590101,
year = {2026},
author = {He, M and Huang, Y and Cui, Z and Cheng, Z and Cao, W and Wang, G and Yao, W and Feng, M},
title = {Construction of Flexible Kaolin/Chitin Composite Aerogels and Their Properties.},
journal = {Gels (Basel, Switzerland)},
volume = {12},
number = {1},
pages = {},
doi = {10.3390/gels12010076},
pmid = {41590101},
issn = {2310-2861},
support = {253A7631D//Hebei Science and Technology Department Project/ ; 51503177//National Natural Science Foundation of China/ ; No. CNP-C-240204//Sichuan Province Engineering Technology Research Center of Novel CN Polymeric Materials/ ; },
abstract = {In this work, kaolin/chitin (K/Ch) composite aerogels with different mass ratios were successfully fabricated via a freeze-drying approach. The influence of kaolin content on the microstructure, properties and hemostatic performance of the composite aerogels was systematically investigated. The results demonstrated that the incorporation of kaolin endowed the chitin-based aerogels with tunable porous structures, excellent water absorption capacity (up to 4282% for K0.25/Ch2), and enhanced water retention (73.7% for K2/Ch2 at 60 min). Moreover, the K/Ch composite aerogels exhibited good biodegradability, no cytotoxicity (cell viability > 91.9%), and no hemolysis (hemolysis rate < 1.5% at all test concentrations). In vitro hemostatic evaluations revealed that the composite aerogels exhibited rapid blood coagulation (blood clotting time of 16 s for K2/Ch2) with a blood coagulation index (BCI) as low as 0.5%, which was attributed to the synergistic effect of the physical adsorption of chitin and the coagulation cascade activation by kaolin. These findings indicated that the K/Ch composite aerogels could be used as novel natural hemostatic materials for potential effective and rapid hemostasis.},
}

@article {pmid41587494,
year = {2026},
author = {Jia, J and Zhang, R and Yuan, D and Yu, D and Li, P},
title = {Theoretical and applied research on spatio-temporal graph attention networks for single-trial P300 detection.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3d68},
pmid = {41587494},
issn = {1741-2552},
abstract = {Accurate detection of single-trial P300 ERPs (event-related potentials) is crucial for developing high-performance non-invasive BCIs (brain-computer interfaces). However, this task remains challenging because of the low SNR (signal-to-noise ratio) of EEG (electroencephalography) and the limited ability of existing models to concurrently capture the complex non-Euclidean spatiotemporal dynamics of brain signals. Approach: We propose a novel ST-GraphTRNet (spatiotemporal graph transformer network). This architecture synergis-tically integrates temporal convolutions for local feature extraction, graph convolutions to explicitly model the neurophysio-logical spatial relationships between EEG electrodes, and a temporal transformer with a self-attention mechanism to capture global, long-range temporal dependencies across the entire signal. Main Results: Extensive evaluation of four public P300 datasets demonstrates that ST-GraphTRNet significantly outper-forms SOTA (state-of-the-art) benchmarks under both within-subject and cross-subject paradigms. Crucially, interpretability analyses via t-SNE (T-distributed Stochastic Neighbor Embedding) and Grad-CAM (Gradient-weighted Class Activation Mapping) revealed that the model's decisions aligned with established neurophysiological priors, focusing on parietal elec-trodes approximately 300 ms post-stimulus. Significance: This study provides a powerful and interpretable framework for single-trial ERPs decoding. By effectively integrating the strengths of CNNs (Convolutional Neural Networks), GNNs (Craph Neural Networks), and Transformers, a new benchmark for building high-accuracy, generalizable, and clinically viable BCIs is established, moving closer to the goal of plug-and-play systems that require minimal user-specific calibration. .},
}

@article {pmid41586177,
year = {2026},
author = {Chin, AHB and Roslan, R and Alsomali, N and Al-Balas, Q and Salhab, BBS and Muhsin, SM},
title = {Islamic Bioethics Viewpoint on Elective Brain Chip Implants and Brain-Computer Interfaces for Enhancing Academic Performance in Competitive Examinations.},
journal = {Asian bioethics review},
volume = {18},
number = {1},
pages = {79-92},
pmid = {41586177},
issn = {1793-9453},
abstract = {The first implantation of a brain chip into a human paralysis patient by Neuralink demonstrated much potential for treating debilitating neurological diseases and injuries. Nevertheless, brain chips can also be implanted in healthy people to provide an interface between the human brain with computers, robotic machines, and novel artificial intelligence platforms, which generates new ethical issues. The focus here is on the development of brain chip implants that can significantly improve memory, intelligence, and cognition, thereby boosting performance in national examinations for university admissions and securing civil service jobs, thus providing a "game-changer" and "shortcut" for many students and parents. Given that Islam is a major world religion, constituting a significant portion of the global population, it is crucial for the biomedical industry to comprehend Islamic perspectives on emerging medical technologies, which will enable it to more effectively cater to a substantial and growing demographic. We thus critically examine whether the application of brain chip technology to enhance academic performance in highly competitive examinations is consistent with Islamic principles. Based on the Islamic jurisprudential framework, such an application for intellectual enhancement of normal and healthy people without any mental impairment may conflict with the injunction to preserve intellect (Hifz al-Aql) and "consideration of consequences" (murā'āt al-ma'ālāt) in Islam. It may also be viewed as tampering with Allah's creation (Taghyir Khalq Allah). Gaining such unfair advantages in competitive examinations will likely be viewed as unethical, by transgressing the core Islamic precepts of Amanah (trustworthiness), Al-'Adl (justice), Ikhlas (sincerity), and Mujahadah (striving).},
}

@article {pmid41585948,
year = {2025},
author = {Wang, S and Wang, R and Chang, L and Wu, J and Hu, L},
title = {AMANet: a data-augmented multi-scale temporal attention convolutional network for motor imagery classification.},
journal = {Frontiers in neurorobotics},
volume = {19},
number = {},
pages = {1704111},
pmid = {41585948},
issn = {1662-5218},
abstract = {Motor imagery brain-computer interface (MI-BCI) has garnered considerable attention due to its potential for neural plasticity. However, the limited number of MI-EEG samples per subject and the susceptibility of features to noise and artifacts posed significant challenges for achieving high decoding performance. To address this problem, a Data-Augmented Multi-Scale Temporal Attention Convolutional Network (AMANet) was proposed. The network mainly consisted of four modules. First, the data augmentation module comprises three steps: sliding-window segmentation to increase sample size, Common Spatial Pattern (CSP) extraction of discriminative spatial features, and linear scaling to enhance network robustness. Then, multi-scale temporal convolution was incorporated to dynamically extract temporal and spatial features. Subsequently, the ECA attention mechanism was integrated to realize the adaptive adjustment of the weights of different channels. Finally, depthwise separable convolution was utilized to fully integrate and classify the deep extraction of temporal and spatial features. In 10-fold cross-validation, the results show that AMANet achieves classification accuracies of 84.06 and 85.09% on the BCI Competition IV Datasets 2a and 2b, respectively, significantly outperforming baseline models such as Incep-EEGNet. On the High-Gamma dataset, AMANet attains a classification accuracy of 95.48%. These results demonstrate the excellent performance of AMANet in motor imagery decoding tasks.},
}

@article {pmid41585347,
year = {2025},
author = {Zhang, M and Wang, T and Zhu, Z},
title = {Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1737839},
pmid = {41585347},
issn = {1662-5188},
}

@article {pmid41584816,
year = {2026},
author = {Kim, DU and Yoo, MA and Choi, SI and Kim, MY and Kim, SP},
title = {Toward zero-calibration MEG brain-computer interfaces based on event-related fields.},
journal = {Biomedical engineering letters},
volume = {16},
number = {1},
pages = {67-76},
pmid = {41584816},
issn = {2093-985X},
abstract = {Magnetoencephalography (MEG) offers high spatiotemporal resolution, but its application in practical brain-computer interface (BCI) systems remains limited partially due to the need for user-specific calibration and inter-subject variability. We present a zero-calibration MEG-based BCI based on event-related fields (ERFs) by leveraging spatial filters and deep learning techniques. First, we developed an on-line ERF-based MEG BCI with a visual oddball paradigm, achieving the mean classification accuracy of 94.29% and an information transfer rate (ITR) of 20.47 bits/min. Using the resulting multi-subject dataset, we applied xDAWN spatial filtering and trained a deep convolutional neural network (DeepConvNet) to classify target versus non-target responses. To simulate real-world plug-and-play use, zero-calibration performance was evaluated using a leave-one-subject-out (LOSO) cross-validation approach. The combination of xDAWN and DeepConvNet achieved the average accuracy of 80.32% and ITR of 12.75 bits/min, respectively, demonstrating cross-subject generalization. These results underscore the feasibility of zero-calibration MEG BCIs for more practical use.},
}

@article {pmid41582168,
year = {2026},
author = {Mannan, MMN and Palipana, DB and Mulholland, K and Jurd, E and Lloyd, ECR and Quinn, ARJ and Crossley, CB and Rabbi, MF and Lloyd, DG and Teng, YD and Pizzolato, C},
title = {Virtual reality mediated brain-computer interface training improves sensorimotor neuromodulation in unimpaired and post spinal cord injury individuals.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-36431-3},
pmid = {41582168},
issn = {2045-2322},
abstract = {Real-time brain-computer interfaces (BCIs) that decode electroencephalograms (EEG) during motor imagery (MI) are powerful adjuncts to rehabilitation after neurotrauma. Further, immersive virtual reality (VR) could complement BCIs by delivering visual and auditory sensory feedback (VR biofeedback) congruent to user's MI, enabling task-oriented therapies. Yet, therapeutic outcomes rely on user's proficiency in evoking MI to attain volitional BCI-commanded VR interaction. While previous studies have explored multi-session BCIs, we investigated the impact of longitudinal training on sensorimotor neuromodulation using BCI combined with VR-mediated externally-cued and self-paced lower-limb MI tasks. The EEG-based BCI was coupled with real-time VR biofeedback congruent with the MI task. Over multiple training sessions in laboratory conditions, five unimpaired individuals progressively learnt to improve control over their EEG during MI virtual walking, corresponding with increased BCI classification accuracy. Further, similar improvements were found with four individuals with chronic complete spinal cord injury (SCI) using the system in real-world neurorehabilitation settings. These findings demonstrate that unimpaired and SCI impaired individuals learnt to control their sensorimotor EEG associated with MI tasks through VR-mediated BCI training, which was associated with improved BCI classification accuracy. Our findings highlight the potential of VR-mediated BCIs in enhancing neuromodulation, providing a foundation for future rehabilitation therapies.},
}

@article {pmid41581018,
year = {2026},
author = {Liao, X and Gao, G},
title = {Strategies for improving recovery of consciousness after acute brain injury.},
journal = {Current opinion in critical care},
volume = {},
number = {},
pages = {},
doi = {10.1097/MCC.0000000000001365},
pmid = {41581018},
issn = {1531-7072},
abstract = {PURPOSE OF REVIEW: Advances in critical care have improved survival rates after severe brain injuries, yet many patients experience prolonged disorders of consciousness, resulting in significant care burdens and ethical challenges. Therefore, a systematic review of current treatment strategies for these disorders following acute brain injury is essential to provide evidence-based guidance for clinicians, ultimately aiming to enhance patient prognosis and quality of life.

RECENT FINDINGS: Research has rapidly evolved beyond traditional drugs like amantadine and zolpidem, with significant breakthroughs in neuromodulation techniques such as spinal cord stimulation, transcranial direct current stimulation, and brain-computer interfaces. These innovations are reshaping clinical practice by transitioning from theoretical concepts to validated interventions, enabling more precise, individualized treatment protocols. This shift moves clinical management from empirical medication toward targeted neural circuit modulation, while technologies detecting covert consciousness are helping redefine diagnostic standards. The differential effects of these interventions are also advancing fundamental research, deepening understanding of consciousness networks and shifting focus from single targets to whole brain dynamic regulation.

SUMMARY: These developments collectively highlight the need for integrated multimodal assessment and multilevel interventions, pointing toward a future of personalized, precision medicine for arousal promotion that offers tangible hope for improving patient recovery outcomes and quality of life.},
}

@article {pmid41580794,
year = {2026},
author = {Niu, X and Yuan, M and Zhang, J and Yang, J and Yu, Q and Wang, D},
title = {Noninvasive BCI-based cognitive rehabilitation in poststroke cognitive impairment: study protocol for a randomized controlled trial.},
journal = {Trials},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13063-026-09449-1},
pmid = {41580794},
issn = {1745-6215},
abstract = {BACKGROUND: Poststroke cognitive impairment (PSCI) significantly reduces quality of life and survival rates. Current interventions face challenges in efficacy and accessibility. Noninvasive brain-computer interface (BCI) technology may enhance neural plasticity and cognitive recovery through real-time neurofeedback, offering a safe and accessible approach for poststroke cognitive rehabilitation. This trial aims to evaluate the efficacy of BCI-based cognitive training and explore its neural mechanisms.

METHODS: A prospective, randomized, double-blind, controlled, single-center trial will enroll 66 PSCI patients. Participants will be randomized into the intervention group or control group. Interventions will be administered 5 days/week for 4 weeks. Primary outcome is as follows: The 4-week post-intervention MoCA scores; secondary outcomes are as follows: 3-month follow-up MoCA scores, attention, memory, executive function, neurophysiological markers, and daily living function. Assessments will be conducted at baseline (T0W), post-intervention (T4W), and 3-month follow-up (T16W).

DISCUSSION: Results will provide evidence for BCI's clinical utility and neuroplasticity mechanisms, guiding personalized neurorehabilitation strategies.

TRIAL STATUS: The protocol version used for this study is Version 3.0, dated May 8, 2025. Recruitment is scheduled to begin on June 10, 2025, and is expected to be completed by May 8, 2026.

TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2500107318. Registered on 8 August 2025.},
}

@article {pmid41243805,
year = {2026},
author = {Colucci, A and Vermehren, M and Angerhöfer, C and Peekhaus, N and Kim, WS and Chang, WK and Hömberg, V and Paik, NJ and Soekadar, SR},
title = {Hybrid Brain/Neural Exoskeleton Enables Bimanual ADL Training in Routine Stroke Rehabilitation.},
journal = {Stroke},
volume = {57},
number = {2},
pages = {505-510},
pmid = {41243805},
issn = {1524-4628},
mesh = {Humans ; Middle Aged ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Aged ; Female ; *Activities of Daily Living ; Adult ; Aged, 80 and over ; *Exoskeleton Device ; *Stroke/physiopathology/complications ; Pilot Projects ; Electroencephalography ; Young Adult ; Electrooculography ; *Brain/physiopathology ; Adolescent ; },
abstract = {BACKGROUND: Severe upper limb motor impairment is one of the most disabling consequences of stroke. Although brain-controlled rehabilitation technologies, such as brain/neural exoskeletons (B/NE), have been shown to be effective in promoting motor recovery, their clinical adoption remains limited because of insufficient integration of B/NE into existing clinical workflows. Here, we introduce and validate a fully portable B/NE system that overcomes this limitation by relying on brain (electroencephalography) and ocular (electrooculography) signals to restore bimanual activities of daily living within a novel therapeutic framework.

METHODS: In this pilot study, we tested the feasibility of the novel approach in 5 stroke survivors (mean age, 51 years; SD=14.8) undergoing inpatient neurorehabilitation. Stroke survivors aged 18 to 80 years, who exhibited hemiparesis and sufficient cognitive ability to understand and follow instructions, were invited to participate in a 1-hour training session. This session included system setup and calibration, followed by performing B/NE-supported, self-paced bimanual activities of daily living. As primary outcome measures, we assessed control accuracy, the ability to reliably modulate electroencephalography and electrooculography signals, and time to initialize, defined as the time required to react to cues and initiate the task, serving as a measure of control intuitiveness. In addition, participants' B/NE control performance during assisted training of bimanual activities of daily living, as well as setup preparation time, were assessed via direct observation.

RESULTS: Participants demonstrated reliable control accuracy in using both brain (mean, 83%; SD=15.36) and ocular (mean=100%) signals, as well as intuitive control (time to initialize <2 s). All participants reliably controlled the B/NE performing a battery of 10 bimanual activities of daily living. Moreover, setup and calibration times remained below 20 minutes.

CONCLUSIONS: These findings highlight the compatibility of the novel B/NE with existing clinical workflows and its feasibility to enable B/NE-supported stroke neurorehabilitation by facilitating seamless integration into clinical practice.},
}

Humans
Middle Aged
*Stroke Rehabilitation/methods/instrumentation
Male
Aged
Female
*Activities of Daily Living
Adult
Aged, 80 and over
*Exoskeleton Device
*Stroke/physiopathology/complications
Pilot Projects
Electroencephalography
Young Adult
Electrooculography
*Brain/physiopathology
Adolescent

@article {pmid41585048,
year = {2023},
author = {Rodriguez-Calienes, A and Oliver, M and Hassan, AE and Vivanco-Suarez, J and Divani, AA and Ribo, M and Petersen, N and Abraham, M and Fifi, J and Guerrero, WR and Malik, AM and Siegler, JE and Nguyen, T and Sheth, S and Yoo, A and Linares, G and Janjua, N and Quispe-Orozco, D and Galecio-Castillo, M and Alhajala, H and Malaga, M and Farooqui, M and Jovin, T and Jumaa, M and Ortega-Gutierrez, S},
title = {Safety of Intravenous Cangrelor Versus Dual Oral Antiplatelet Loading Therapy in Endovascular Treatment of Tandem Lesions: An Observational Cohort Study.},
journal = {Stroke (Hoboken, N.J.)},
volume = {3},
number = {6},
pages = {e001020},
pmid = {41585048},
issn = {2694-5746},
abstract = {BACKGROUND: Procedural intravenous cangrelor has been proposed as an effective platelet inhibition strategy for stenting in acute ischemic stroke. We aimed to compare the safety profile of low-dose intravenous cangrelor versus dual oral antiplatelet therapy (DAPT) loading in patients with acute cervical tandem lesions.

METHODS: We retrospectively identified cases from an international multicenter cohort who underwent intraprocedural administration of intravenous cangrelor (15 μg/kg followed by an infusion of 2 μg/kg per min) or DAPT loading during acute tandem lesions intervention. Safety outcomes included rates of symptomatic intracranial hemorrhage, parenchymal hematoma type 2, petechial hemorrhage, and in-stent thrombosis. Inverse probability of treatment weighting matching was used to reduce confounding.

RESULTS: From 691 patients, we included 195 patients, 30 of whom received intravenous cangrelor and 165 DAPT. The DAPT regimens were aspirin+clopidogrel (93.3%) or aspirin+ticagrelor (6.6%). After inverse probability of treatment weighting, the patients treated with cangrelor were not at greater odds of symptomatic intracranial hemorrhage (odds ratio [OR], 1.30 [95% CI, 0.09-17.3]; P=0.837), symptomatic intracranial hemorrhage-parenchymal hematoma type 2 (OR, 0.54 [95% CI, 0.05-4.98]; P=0.589), or petechial hemorrhage (OR, 1.11 [95% CI, 0.38-3.28]; P=0.836). Similarly, the rate of in-stent thrombosis was not significantly different between the 2 groups (1.8% versus 0%; P=0.911).

CONCLUSION: Cangrelor at the half dose of the myocardial infarction protocol showed a similar safety profile compared with the commonly used DAPT loading protocols in patients with acute tandem lesions. Further studies with larger samples are warranted to elucidate the safety of antiplatelet therapy in tandem lesions.},
}

@article {pmid41580462,
year = {2026},
author = {Huang, Q and Chen, D and Pereira, AC and Leonard, A and Ellis, CL and Velthuis, H and Dimitrov, M and Ponteduro, FM and Wong, NML and Kowalewski, L and Pretzsch, CM and Daly, E and Murphy, DGM and McAlonan, GM},
title = {Differential GABA dynamics across brain functional networks in autism.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-09563-5},
pmid = {41580462},
issn = {2399-3642},
support = {2024A1515011690//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Differences in the ϒ-aminobutyric acid (GABA) system contribute to an excitatory-inhibitory imbalance in autism, particularly affecting sensory processing. However, the brain's broader response to interventions targeting GABA pathways in individuals with autism remains poorly understood. This study tested the hypothesis that GABAergic control of information transfer across large-scale brain functional networks is altered in autism. We conducted a phase-amplitude coupling (PAC) analysis of resting-state EEG signals within and between these networks. Responses were compared after double-blind, randomized oral administration of either a placebo or 15/30 mg of arbaclofen, a GABAB receptor agonist. Twenty-four non-autistic (9 males; 19-53 years) and 15 autistic participants (9 males; 20-51 years) completed 93 study visits. Autistic participants exhibited significantly higher theta-beta PAC, especially within the limbic network. High-dose arbaclofen shifted PAC metrics in visual and somatomotor networks towards non-autistic levels, but had minimal effects on networks related to higher cognitive functions. Interestingly, altered PAC within and between networks in the limbic system of autistic participants was normalized by low-dose arbaclofen, yet reemerged after high-dose administration. These findings provide compelling evidence for altered GABAergic responsivity in autism, helping explain some of the challenges in prescribing medications for autistic individuals, such as paradoxical reactions and dose sensitivity.},
}

@article {pmid41579859,
year = {2026},
author = {Huang, C and Tao, H and Zhou, Y and Wu, Q and Li, M and Liu, A and Zhu, T and Yu, C and Li, P and Huang, S and Guo, H and Hu, J and Wang, G},
title = {Pregnenolone promotes immune evasion through blocking endogenous retrovirus expression.},
journal = {Cell metabolism},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cmet.2025.12.020},
pmid = {41579859},
issn = {1932-7420},
abstract = {Research into steroid hormones shaping tumor biology has gained increasing attention. Using multiple mouse tumor models, we show that pregnenolone promoted tumor progression and reduced sensitivity to immunotherapy. Pregnenolone levels were markedly elevated in maternal mice experiencing mating deficiency. Mechanistically, pregnenolone directly binds Kap1 and inhibits Trim39-mediated ubiquitination at K750, leading to Kap1 stabilization and repression of endogenous retrovirus (ERV) expression and type-I interferon production. Furthermore, pharmacological antagonism of pregnenolone effectively suppressed tumor growth and enhanced immunotherapy efficacy. These findings reveal a previously unrecognized link between mating-associated steroid metabolism and tumor immune regulation and identify pregnenolone signaling as a potential therapeutic target in cancer.},
}

@article {pmid41576955,
year = {2026},
author = {Ning, C and Fu, G and Zhang, YY and Meyniel, F and Wang, L},
title = {Macaque prefrontal cortex integrates multiple components for metacognitive judgments of working memory.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.11.014},
pmid = {41576955},
issn = {1097-4199},
abstract = {The ability to evaluate one's own memory is known as metamemory. Whether metamemory is inherent to memory strength or requires additional computation in the brain remains largely unknown. We investigated the metacognitive mechanism of working memory (WM) using two-photon calcium imaging in the prefrontal cortex (PFC) of macaque monkeys, memorizing spatial sequences of varying difficulties. In some trials, after viewing the sequence, monkeys could opt out of retrieval for a smaller reward, reflecting their confidence in WM (meta-WM). We discovered that PFC neurons encoded WM strength by jointly representing the remembered locations and their associated uncertainties. Additional factors-trial history and arousal-encoded in baseline activity also predicted opt-out decisions, serving as cues for meta-WM. We further identified a code for meta-WM itself that integrated WM strength with these cues. Thus, the PFC neural geometry implements metacognitive computations, integrating WM strength with cues into a meta-WM signal to guide behavior.},
}

@article {pmid41574639,
year = {2026},
author = {Guo, Z and Ye, R and Guan, L and He, W and Qiu, S and Shao, X and Fang, J and Fang, J and Du, J},
title = {Differential roles of EA-TRAPed cells in the anterior cingulate cortex across various intervention times in inflammatory pain.},
journal = {Animal models and experimental medicine},
volume = {},
number = {},
pages = {},
doi = {10.1002/ame2.70118},
pmid = {41574639},
issn = {2576-2095},
support = {//The National Natural Science Fund of China (82374561, 82174490, 81873360), the Research Project of Zhejiang Chinese Medical University (2022JKZKTS44, 2022FSYYZZ07), and the Zhejiang Medical and Health Science and Technology Program (2021RC098)/ ; },
abstract = {BACKGROUND: The analgesic effects of multiple electroacupuncture (EA) sessions and single EA sessions differ significantly in pain management. Area 24b (A24b) of the anterior cingulate cortex (ACC) is crucial in pain processing. EA relieves pain by targeting and modulating the neuronal activity within this subregion. However, whether the cumulative effect of EA antinociception is connected to A24b mechanisms has remained unclear.

METHODS: In our study, we used the Complete Freund's Adjuvant (CFA) model to induce inflammatory pain and the Spared Nerve Injury (SNI) model to induce neuropathic pain, and adult male C57BL/6, FosTRAP, and FosTRAP:Ai9 mice were used as experimental subjects to investigate the cumulative effect of EA antinociception and whether multiple EA sessions and a single EA session regulate different neuronal populations in the A24b.

RESULTS: We observed that EA effectively alleviated pain in mice, with three EA sessions yielding superior analgesic effects compared to a single session. Using chemical genetics combined with FosCreER technology to activate EA-TRAPed cells in the A24b, we found that pain relief was more pronounced with three EA sessions. Moreover, chemogenetic inhibition of EA-TRAPed cells in the A24b reversed the analgesic effects of a single EA session but not those of three EA sessions. Fluorescent in situ hybridization results indicated that three EA sessions significantly increased the number of GABAergic neurons in the A24b compared with a single session. Additionally, retrograde tracing revealed that the A24b circuit that monosynaptically innervates EA-TRAPed cells included projections from the central lateral nucleus (CL), lateral mediodorsal thalamic nucleus (MDL), lateral habenula (LHb), dorsal raphe nucleus (DR), caudal linear nucleus of the raphe (CLi), dorsal tuberomamillary nucleus (DTM), periventricular hypothalamic nucleus (Pe) and hippocampal fields CA1, CA2, and CA3. These findings suggest that multiple EA sessions and single EA sessions activated different neuronal populations in the A24b. The enhanced analgesic effect of multiple EA sessions may be attributed to an increase in the proportion of GABAergic neurons within the A24b.

CONCLUSIONS: Multiple and single EA sessions recruit distinct neuronal populations in A24b, with the stronger analgesic effect of repeated EA linked to a higher proportion of GABAergic neurons in this region.},
}

@article {pmid41573300,
year = {2025},
author = {Nieves-Méndez, C},
title = {From neurotechnology to the classroom: the promise of brain-computer interfaces for training systems engineers.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1733768},
pmid = {41573300},
issn = {1662-5161},
abstract = {This perspective article explores the transformative potential of brain-Computer Interfaces (BCI) in undergraduate systems engineering programs, a domain characterized by high attrition and a widening gap between rapid technological innovation and slower pedagogical change. I argue that BCI, by enabling real-time detection of cognitive states such as mental workload, attention, and frustration, can evolve from laboratory tools to central pedagogical instruments for adaptive, student-centered education. I review the state-of-the-art methods, which demonstrate the technical feasibility of low-cost electroencephalography (EEG) devices and machine learning algorithms that classify cognitive states with high accuracy in controlled settings. Building on this evidence, I outline concrete applications in three dimensions: formative assessment, dynamic curricular adaptation, and cognitive inclusion, with a specific emphasis on preventing dropout in foundational courses such as algorithms. I also examine ethical, technical, and pedagogical challenges, and propose a scalable, ethically grounded pilot model tailored for universities, particularly in Latin America. This study reports no empirical results. It synthesizes the existing evidence and proposes a roadmap for research and educational action.},
}

@article {pmid41571161,
year = {2026},
author = {Artigas, R and Ruiz, S and Montalba, C and Peñafiel, C and Figueroa, R and Irarrazaval, P},
title = {Decreased levels of N-Acetylaspartyglutamate, myo-inositol, and syllo-inositol, in cortical brain regions of women exposed to adverse childhood experiences.},
journal = {Magnetic resonance imaging},
volume = {128},
number = {},
pages = {110621},
doi = {10.1016/j.mri.2026.110621},
pmid = {41571161},
issn = {1873-5894},
abstract = {Adverse Childhood Experiences (ACE), including abuse and neglect, can have lasting negative effects on health, decreasing lifespan and increasing the risk of chronic diseases. While research on ACE's impact on brain biochemistry is limited, Magnetic Resonance Spectroscopy (MRS) provides a non-invasive way to study these alterations. This study aims to identify neurochemical patterns linked to ACE exposure using J-edited MRS methods. 43 female participants (18 Low-ACE and 25 High-ACE), aged 19 to 31, were recruited. ACE exposure was assessed using the Maltreatment and Abuse Chronology of Exposure (MACE) test. MRS was conducted on a 3.0 T scanner, with J-edited single-voxel 1H-MRS from the Anterior Cingulate Cortex (ACC), Pre-Frontal Cortex (PFC), and hippocampus. Metabolite quantification was carried out using the Osprey pipeline and analyzed using univariate and multivariate methods. Univariate analysis showed reduced N-Acetylaspartyglutamate (NAAG) and syllo-Inositol (sI) levels in the ACC (p = 0.06) and PFC (p = 0.057), respectively, among High-ACE participants. Logistic Regression identified lower NAAG, GABA, glutathione (GSH), and myo-Inositol (mI) in the ACC, and differences in sI, lactate, NAAG, and GSH in the PFC, within the High-ACE group. Random Forest and Support Vector Machines confirmed NAAG, mI, and sI as possible ACE biomarkers. Throughout this study, cortical regions consistently showed reduced levels of NAAG, mI, and sI in the High-ACE group, suggesting a potential link to ACE. These findings improve our understanding of neurochemical changes associated with ACE, aiding in the identification of at-risk individuals and in the development of strategies to prevent long-term health effects.},
}

@article {pmid41570674,
year = {2026},
author = {Fu, X and Jiang, W and Liu, R and Müller-Putz, GR and Guan, C},
title = {EEG-to-gait decoding via phase-aware representation learning.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {198},
number = {},
pages = {108608},
doi = {10.1016/j.neunet.2026.108608},
pmid = {41570674},
issn = {1879-2782},
abstract = {Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This study presents NeuroDyGait, a two-stage, phase-aware EEG-to-gait decoding framework that explicitly models temporal continuity and domain relationships. To address challenges of causal, phase-consistent prediction and cross-subject variability, Stage I learns semantically aligned EEG-motion embeddings via relative contrastive learning with a cross-attention-based metric, while Stage II performs domain relation-aware decoding through dynamic fusion of session-specific heads. Comprehensive experiments on two benchmark datasets (GED and FMD) show substantial gains over baselines, including a recent 2025 model EEG2GAIT. The framework generalizes to unseen subjects and maintains inference latency below 5 ms per window, satisfying real-time BCI requirements. Visualization of learned attention and phase-specific cortical saliency maps further reveals interpretable neural correlates of gait phases. Future extensions will target rehabilitation populations and multimodal integration.},
}

@article {pmid41570350,
year = {2026},
author = {Sun, J and Xie, R and Yu, J and Ji, L and Jia, T and Pan, Y and Li, C},
title = {Dynamic modulation of corticomuscular coherence during ankle dorsiflexion after stroke: towards hybrid BCI for lower-limb rehabilitation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3c41},
pmid = {41570350},
issn = {1741-2552},
abstract = {OBJECTIVE: Hybrid brain-computer interface (BCI) systems incorporate electroencephalography (EEG) and electromyography (EMG) signals to extract corticomuscular coherence (CMC) features, enabling self-modulation of neural communication. While promising for stroke rehabilitation, the neurophysiological mechanism underlying hybrid BCI therapy remains poorly understood. To address this gap, we characterized post-stroke CMC dynamics during ankle dorsiflexion and further established their relationship with functional motor recovery.

APPROACH: We acquired synchronous EEG and high-density EMG (HD-EMG) recordings from 13 subacute stroke patients (with their affected limb) before and after three-week rehabilitation, and 9 age-matched healthy controls (using their dominant limb) during isometric ankle dorsiflexion. Using multivariate coupling analysis, we computed EEG and EMG projection vectors to identify optimal coupling patterns. Subsequently, we derived CMC spectra and topographies through coherence analysis to characterize corticomuscular interactions at spatial and spectral scales.

MAIN RESULTS: Compared to healthy controls, stroke patients demonstrated reduced beta-band CMC patterns, particularly within the sensorimotor areas involved in the foot movement. No significant differences in CMC patterns were observed between stroke patients before and after rehabilitation training. Further analysis revealed significant correlation between betaband CMC changes and clinical improvements measured by the Berg Balance Scale (BBS).

SIGNIFICANCE: Beta-band CMC is a potential neurophysiological biomarker of motor recovery following stroke. These findings provide novel insights into the disrupted corticomuscular communication underlying post-stroke motor dysfunction, while offering mechanistic evidence to guide the design and implementation of hybrid BCI systems that target these specific biomarkers for therapeutic intervention.},
}

@article {pmid41529302,
year = {2026},
author = {Yue, H and Ruan, H and Zhao, Y},
title = {LMSA-net: a lightweight multi-scale attention network for eeg-based emotion recognition.},
journal = {Biomedical physics & engineering express},
volume = {12},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ae3763},
pmid = {41529302},
issn = {2057-1976},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; Algorithms ; *Signal Processing, Computer-Assisted ; *Attention ; *Neural Networks, Computer ; },
abstract = {Electroencephalogram (EEG)-based emotion recognition holds great potential in affective computing, mental health assessment, and human-computer interaction. However, EEG signals are non-stationary, noisy, and composed of multiple frequency bands, making direct feature learning from raw data particularly challenging. While end-to-end models alleviate the need for manual feature engineering, advancing the performance frontier of lightweight architectures remains a crucial and complex challenge for practical deployment. To address these issues, we propose LMSA-Net (Lightweight Multi-Scale Attention Network), a lightweight, interpretable, and end-to-end model that directly learns spatio-temporal features from raw EEG signals. The architecture integrates learnable channel weighting for adaptive spatial encoding, multi-scale temporal separable convolution for rhythm-specific feature extraction, and Sim Attention Module for parameter-free saliency enhancement. Our proposed LMSA-Net is evaluated on three benchmark datasets, SEED, SEED-IV, and DEAP, under subject-dependent protocols. It achieves top performance on SEED (65.53% accuracy), competitive results on SEED-IV (48.52% accuracy), and strong performance in arousal classification on DEAP, demonstrating good generalization. Ablation studies confirm the critical role of each proposed module. Frequency analysis reveals that our multi-scale temporal kernels inherently specialize in distinct EEG rhythms, validating their neurophysiological alignment. Lightweight design is evidenced by minimal parameters (7.64K) and low latency, ideal for edge deployment. Interpretability analysis further shows the model's focus on emotion-related brain regions. LMSA-Net thus delivers an efficient, interpretable, and high-performing solution. The code is available athttps://github.com/rhr0411/LMSA-Net.git.},
}

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

@article {pmid41566808,
year = {2026},
author = {Huang, C and Bai, J and Lin, K and Li, X and Guo, D and Song, J and Wang, J and Chen, Z and Wang, C},
title = {Exploring Back Muscle Activities in Chronic Low Back Pain Patients Using a Large-Area Stretchable Electrode Array With Full-Back Coverage.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e04815},
doi = {10.1002/adhm.202504815},
pmid = {41566808},
issn = {2192-2659},
support = {12302223//National Natural Science Foundation of China/ ; 2024E10108//Zhejiang Key Laboratory of Intelligent Rehabilitation and Translational Neuroelectronics/ ; 2022C03038//Key R&D Program of Zhejiang Province/ ; BLB19J014//Key R&D Program of Zhejiang Province/ ; 2022M710126//China Postdoctoral Science Foundation/ ; LGF20H170004//Zhejiang Province Basic Public Welfare Research Project/ ; BX20220268//National Postdoctoral Program for Innovative Talents/ ; },
abstract = {Exploring the back muscle activity in chronic low back pain (CLBP) patients is crucial for the quantitative assessment of their neuromuscular function. However, hindered by the lack of stretchable, large-area electrode arrays capable of spanning the entire back, existing studies have primarily focused on the erector spinae and multifidus muscles in the lower back. Here, we report a large-area, stretchable high-density surface electromyography (HD-sEMG) electrode array designed to cover both lower and upper back regions, enabling comprehensive characterization of back muscle activity in CLBP patients. The array comprises 64 channels arranged in a half-dumbbell configuration, inspired by the anatomical distribution of the erector spinae and multifidus muscles. Notably, this array demonstrates unprecedented operability, scalability for mass production, and reliable data acquisition capabilities. 128-channel HD-sEMG signals were acquired from both healthy controls and CLBP patients during the Biering-Sørensen test, a standardized lumbar endurance protocol. Statistical analyses of sEMG-derived metrics and topographic maps revealed significant intergroup differences in muscle activity, as well as regional variations between the lower back, upper back, and full-back segments, particularly in contraction time and fatigue-related metric changes. These findings offer novel insights into the neuromuscular dysfunction in CLBP, potentially illuminating the underlying physiological adaptations associated with CLBP.},
}

@article {pmid41566501,
year = {2026},
author = {Prasad, NK and Perry, NJ and Goldring, AL and Fleisher, LA and Petrossian, L and Leuthardt, EC and Souders, L and Wilk, SJ},
title = {A retrospective analysis of post-stroke rehabilitation with real world use of brain-computer interface.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01880-4},
pmid = {41566501},
issn = {1743-0003},
}

@article {pmid41565807,
year = {2026},
author = {Wang, Z and Chen, K and Shi, X and Du, Q and Ai, Y and Li, P and Yong, L and Sun, X and Wang, N and Hu, X and Lu, C and Tang, C and Wang, L and Zheng, Y and Zhang, Y and Guo, H and Pu, Z and Wang, X and Zhang, Y and Jiang, H and Liu, Y and Tang, Z and You, L and Deng, J and Che, R and Gao, Y and Zhang, S and Wang, B and Sun, X and Qin, J and Huang, Y and Shen, L and Ge, J and Zeng, X and Chen, L and Chen, P and Peng, H},
title = {Fibre integrated circuits by a multilayered spiral architecture.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {41565807},
issn = {1476-4687},
abstract = {Fibre electronic devices are transforming traditional fibres and garments into new-generation wearables that can actively interact with human bodies and the environment to shape future life[1-5]. Fibre electronic devices have achieved almost all of the desired functions, such as powering[6,7], sensing[8,9] and display[10,11] functions. However, viable information-processing fibres, which lie at the heart of building intelligent interactive fibre systems similar to any electronic product, remain the missing piece of the puzzle[12-15]. Here we fill this gap by creating a fibre integrated circuit (FIC) with unprecedented microdevice density and multimodal processing capacity. The integration density reaches 100,000 transistors per centimetre, which effectively satisfies the requirements for interactive fibre systems. The FICs can not only process digital and analogue signals similar to typical commercial arithmetic chips but also achieve high-recognition-accuracy neural computing similar to that of the state-of-the-art in-memory image processors. The FICs are stable under harsh service conditions that bulky and planar counterparts have difficulty withstanding, such as repeated bending and abrasion for 10,000 cycles, stretching to 30%, twisting at an angle of 180° cm[-1] and even crushing by a container truck weighing 15.6 tons. The realization of FICs enables closed-loop systems in a single fibre, without the need for any external rigid and bulky information processors. We demonstrate that this fully flexible fibre system paves the way for the interaction pattern desired in many cutting-edge applications, for example, brain-computer interfaces, smart textiles and virtual-reality wearables. This work presents new insights that can promote the development of fibre devices towards intelligent systems.},
}

@article {pmid41565681,
year = {2026},
author = {Zhu, L and Li, R and Qian, M and Lv, F and Hong, H and Li, Y and Zhou, Y and Li, Z and Lei, J and Zou, W and Guan, MX and Zhang, Y and Zhao, G and Ma, H and Gong, J and Kang, L},
title = {A Glial Hub-and-Spoke Circuitry in C. elegans orchestrates bidirectional thermosensation.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-68766-w},
pmid = {41565681},
issn = {2041-1723},
support = {2021ZD0203303//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
abstract = {Thermosensation is evolutionarily conserved for survival, yet the roles of glia in temperature coding and circuit dynamics remain poorly understood. Here, we identify C. elegans AMsh glia as dual-mode thermosensory hubs that autonomously detect temperature fluctuations by co-expressing the heat receptor GCY-28 (guanylate cyclase) and cold receptor GLR-3 (ionotropic glutamate receptor). Thermal changes induce spatiotemporal calcium dynamics in glia, driving GABA release to bidirectionally modulate neural circuits: enhancing AFD-mediated warmth detection through the excitatory receptor EXP-1 and suppressing ASH-dependent cold avoidance via the inhibitory receptor LGC-38. This GABAergic hub-and-spoke architecture regulates a broad range of thermal behaviors, including thermal avoidance, thermal resistance, and thermal preference. These findings establish glia as critical interpreters of environmental cues, highlighting their dual roles as sensors and modulators in sensory processing and providing a paradigm for understanding conserved glial mechanisms in neural circuit dynamics and behavioral plasticity.},
}

@article {pmid41565527,
year = {2026},
author = {Sun, Y and Liu, W and Zhang, H and Du, Z and Liu, H and Ma, K and Li, D and Wang, S and Fan, S and Li, L and Zheng, D and Shen, G},
title = {An ultrasoft, breathable, and multichannel ear-computer interface patch.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2025.12.042},
pmid = {41565527},
issn = {2095-9281},
abstract = {Brain-computer interface (BCI) presented by the non-invasive electroencephalography (EEG) cap/band or implantable chips enabling people to fast and reliable control computers or mobile devices with thoughts has redefined the boundaries of human capabilities. However, the existing cap/band-adhered sticky gel usually needs to be tightly fixed on the scalp through the hair to ensure intimate contact, which inconveniences the user. And the implantable chips represented by Neuralink gave a living example of how BCI can make quadriplegic live better, but the destructive unacceptable for healthy people. Here we proposed a multichannel wearable ear-computer interface (ECI) patch worn behind the ears for direct communication and control via brain activity. The 8-channel ECI patch based on MXene electrode was prepared by a facile direct inject print approach on the soft, thin, and breathable medical film that enables superior adherence. The fatigue induction experiments tested by the ECI patch offer an average classification accuracy of 90.5%, showing effective monitoring of the fatigue state. Participants wearing the ECI patch also perform the 4-target steady state visual evoked potential (SSVEP) BCI classification offline and online experiment, the online 4-route tasks reap a comparable average accuracy of 93.5% to the commercial cap. Moreover, the complex route task relied on the subjects who gave commands while observing the unmanned vehicle completed 3 times, demonstrating the reliability and possibility of the ECI patch.},
}

@article {pmid41564094,
year = {2025},
author = {Zafar, R and Abdulrab, H},
title = {Deep Learning Unveils Health Predictions From EEG and MRI Data.},
journal = {IEEE pulse},
volume = {16},
number = {5},
pages = {27-34},
doi = {10.1109/MPULS.2025.3618430},
pmid = {41564094},
issn = {2154-2317},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiology ; Neuroimaging/methods ; Brain-Computer Interfaces ; },
abstract = {The field of neuroscience and neuroimaging has been revolutionized with the use of artificial intelligence (AI), as it helps in enhancing the detection of brain activities and accurately diagnosing neurological disorders using various modalities. There are different modalities that help in measuring brain activities, but the most common and widely used are functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The advanced AI approaches, like deep learning (DL) models, give a new opportunity to various fields, including brain research. This research investigates various AI-driven techniques used for the detection and exploration of the human brain using fMRI and EEG. The AI methods include different machine learning (ML) and DL techniques used to interpret neural activities. Basically, the AI-based models, which also include ML and DL, identify the patterns and detect the abnormalities with higher accuracy, which is helpful in many applications, including brain decoding, monitoring cognitive states, brain-computer interface (BCI), and diagnosis of various diseases. This research provides a comprehensive overview of AI applications in neuroimaging, highlights key applications in cognitive neuroscience and medical imaging, along with a discussion of challenges and future directions. The AI impact of the transformation of neuroimaging research is comprehensively discussed with examples to enhance comprehension.},
}

Humans
*Deep Learning
*Electroencephalography/methods
*Magnetic Resonance Imaging/methods
*Brain/diagnostic imaging/physiology
Neuroimaging/methods
Brain-Computer Interfaces

@article {pmid41561967,
year = {2025},
author = {Sebastián-Romagosa, M and Cho, W and Ortner, R and Sieghartsleitner, S and Guger, M and von Oertzen, TJ and Meuth, SG and Laureys, S and Allison, BZ and Guger, C},
title = {Toward Brain-Computer Interface motor rehabilitation for people with Multiple Sclerosis.},
journal = {Frontiers in medicine},
volume = {12},
number = {},
pages = {1661972},
pmid = {41561967},
issn = {2296-858X},
abstract = {BACKGROUND: Multiple Sclerosis (MS) is a chronic neurodegenerative disease in which the immune system attacks the myelin sheaths around nerves. People with MS (pwMS) often experience pain, fatigue, cognitive dysfunction, and reduced mobility. Today, MS is incurable, and treatments can at best slow the progression of the disease and manage symptoms. We conducted a preliminary, single-arm study using a motor-imagery brain-computer interface (MI-BCI) with functional electrical stimulation (FES) and virtual reality avatar targeting gait in pwMS.

METHODS: Twenty-six pwMS were enrolled; 24 completed 30 BCI sessions. Outcomes were assessed at Baseline, immediately post-treatment (Post1, week 13) and during follow-up (Post2, week 17; Post3, week 37). Change from baseline was analyzed using mixed models for repeated measures (with log-ratio models for skewed measures) and multiplicity control. This uncontrolled study is hypothesis-generating.

RESULTS: Patients treated with the BCI-based intervention obtained significant improvements that were largely maintained to 6 months after the therapy. The walking endurance, assessed by the 6-minute walking test (6MWT), increased by 37.3 m (95% CI 21.50-53.10) after the treatment (p < 0.001), exceeding the minimal clinically important difference (MCID). This improvement in the walking endurance was maintained during the following 6 months after the intervention. Mobility/speed improved: TUG and T25FW times decreased by -15.5% and -16.4% after the last BCI session (both p < 0.001), with benefits persisting after 6 months. Spasticity (MAS) declined by about 1 point, and patient-reported outcomes improved statistically and clinically (MSIS-29 10.18 points, MFIS 7.29 points). Pairwise post-visit contrasts were not significant, consistent with maintenance. Exploratory models found no consistent MS-subtype effect on 6MWT change and suggested larger gains with higher baseline EDSS. Two discontinuations were due to participant availability, not concerns with fatigue or safety.

CONCLUSION: In this preliminary, single-arm study, a MI-BCI + FES system was associated with statistically significant, clinically meaningful gains in gait endurance, mobility/speed, spasticity, and patient-reported outcomes, sustained up to 6 months after the intervention.},
}

@article {pmid41560829,
year = {2026},
author = {Li, Z and Li, T and Ge, R and Chen, F and Du, C and Wang, D and Wang, L},
title = {Thermo-responsive, on-demand adhesive and tissue-conformal hydrogel electrodes for organ repair and brain-computer interfaces.},
journal = {Materials today. Bio},
volume = {36},
number = {},
pages = {102705},
pmid = {41560829},
issn = {2590-0064},
abstract = {Implantable bioelectronic devices, such as brain-computer interfaces (BCIs), face persistent challenges in achieving stable, rapid, and reversible adhesion on wet tissues due to hydration layers and mechanical mismatch, which can cause interfacial failure and unstable signals. Here, we report a conductive hydrogel interface with tissue-adaptive, temperature-controllable adhesion. The material is synthesized via dynamic co-entanglement of poly(acrylic acid) and poly(lipoic acid) with LA-NHS, establishing a dual physico-chemical anchoring mechanism that enables efficient tissue integration in aqueous environments. The hydrogel penetrates tissue microstructures within 5 s, withstands burst pressures >213 mmHg, exhibits <10 % swelling, ∼2784 % extensibility, and a low modulus of 41 kPa, thereby conforming to soft, irregular surfaces and reducing interfacial mismatch. Its temperature-triggered adhesion allows safe detachment and repositioning without apparent tissue damage, supporting repeated applications. In vivo and ex vivo tests confirm rapid hemostasis in mouse liver and tail injury models, effective sealing of porcine gastric, bladder, and intestinal defects, and stable electrocorticography and electrocardiography recordings. Moreover, the hydrogel demonstrates high cytocompatibility (>90 %), <5 % hemolysis, reactive oxygen species scavenging, and ∼90 % antibacterial efficiency. By integrating rapid wet adhesion, mechanical compliance, electrical functionality, and bioprotective features, this hydrogel provides a versatile platform for next-generation bioelectronic interfaces and soft therapeutic devices.},
}

@article {pmid41559987,
year = {2026},
author = {Xu, Q and Shao, Z and Ma, D and Zhai, X and Wang, Y and Dou, W and Pan, Y},
title = {Predicting rehabilitation outcomes of unilateral stroke after brain-computer interface training based on magnetic resonance imaging data.},
journal = {Medicine},
volume = {105},
number = {3},
pages = {e46280},
doi = {10.1097/MD.0000000000046280},
pmid = {41559987},
issn = {1536-5964},
support = {2022YFC3601100//National Key R&D Program of China/ ; 2022YFC3601105//National Key R&D Program of China/ ; Z221100003522016//Beijing Science and Technology Program/ ; 2022-2Z-2242//Capital’s Funds for Health Improvement and Research/ ; 1001020124//Tsinghua University Precision Medicine Research Program/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; Male ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging/methods ; Female ; Middle Aged ; Aged ; *Stroke/diagnostic imaging/complications/physiopathology ; Prognosis ; Recovery of Function ; Adult ; Treatment Outcome ; China ; *Hemiplegia/rehabilitation/etiology ; },
abstract = {Stroke remains a significant cause of disability globally, with a noticeable prevalence in China. Post-stroke rehabilitation, particularly through brain-computer interface (BCI) methods, plays a vital role in enhancing motor function recovery. However, the efficacy of BCI rehabilitation might be hindered by challenges in individualized program of prognosis prediction. This study aimed to develop prognostic prediction models for unilateral hemiplegia after BCI rehabilitation, utilizing both clinical and functional magnetic resonance imaging (fMRI) data, in order to enhance treatment efficiency and optimize patient outcomes. The study included 40 stroke patients (22 left hemisphere affected and 18 right hemisphere affected) who underwent BCI rehabilitation training at the Beijing Tsinghua Changgung Hospital (Beijing, China). Data related to patients' demographics, disease duration, and assessment scores were collected. Based on the improvement in the Fugl-Meyer assessment of the upper extremity (FMA-UE) rating scale, patients were categorized into responder and non-responder groups. Linear regression and its variants, including multivariate logistic regression and optimal subset regression, were utilized to predict the post-treatment scores based on both fMRI and clinical data. The accuracy and R-squared value of the models were assessed using leave-one-out cross-validation (LOOCV). The linear regression model using imaging data exhibited a remarkable performance with a classification accuracy of 100% and R2 (LOOCV) exceeding 0.94. In contrast, the model relying solely on clinical data achieved a classification accuracy of <80%. These results clearly demonstrated the potential of employing imaging data and machine learning methods to effectively predict the effectiveness of BCI rehabilitation. This study assessed the effectiveness of neuroimaging in predicting the efficacy of BCI rehabilitation for unilateral stroke patients. The developed model could serve as a foundation for enhancing our comprehension of rehabilitation outcomes, especially in uniqueness of left and right stroke, and ultimately improving patient well-being. The findings underscored the potential of neuroimaging data in optimizing BCI rehabilitation, leading to the enhanced recovery of motor function in unilateral stroke patients.},
}

Humans
*Stroke Rehabilitation/methods
Male
*Brain-Computer Interfaces
*Magnetic Resonance Imaging/methods
Female
Middle Aged
Aged
*Stroke/diagnostic imaging/complications/physiopathology
Prognosis
Recovery of Function
Adult
Treatment Outcome
China
*Hemiplegia/rehabilitation/etiology

@article {pmid41559146,
year = {2026},
author = {Du, M and Shi, P and Liu, Z and Xu, Y and Liu, X and Liu, W and Liu, S and Ming, D},
title = {Naturalistic facial dynamics enable quantitative clinical assessment of atypical expression phenotypes in children with autism spectrum disorder.},
journal = {NPJ digital medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41746-026-02375-1},
pmid = {41559146},
issn = {2398-6352},
support = {23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; },
abstract = {Existing facial-expression studies in children with autism spectrum disorder (ASD) rely mainly on discrete, task-driven measures that overlook the sustained emotional fluctuations and ambiguous expressions in naturalistic interactions. This study quantified atypical facial expression patterns in ASD during spontaneous, unscripted interactions. We analyzed 184 naturalistic video sessions from 99 children with ASD and 85 typically developing (TD) peers and extracted three features capturing spontaneous dynamics: emotion variation (temporal stability of emotional states), expression intensity (magnitude of facial muscle activation), and facial coordination (synchrony across facial muscles). These features integrated holistic and processual representations across coarse- and fine-grained levels, enabling detailed quantification of facial patterns. Compared with TD peers, the ASD group exhibited increased prominence of anger, altered emotion transition probabilities, heightened activation in non-core facial muscles, and atypical facial coordination (p < 0.05). These findings reveal subtle facial dynamics inaccessible to traditional approaches and provide a quantitative explanation for the hard-to-describe atypical expressions. Using the fused feature set, ASD classification reached 92.4% accuracy and 0.977 AUC. Regression analyses further predicted symptom severity with mean absolute errors of 13.94 on the ABC scale and 3.84 on the CABS scale. These quantitative and interpretable markers show promise for large-scale ASD screening in naturalistic settings.},
}

@article {pmid41559106,
year = {2026},
author = {Qamar, WUR and Abibullaev, B},
title = {Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {2503},
pmid = {41559106},
issn = {2045-2322},
support = {040225FD4727//Nazarbayev University under Faculty development competitive research grants program/ ; 040225FD4727//Nazarbayev University under Faculty development competitive research grants program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; },
abstract = {High inter-subject variability and the non-stationary nature of EEG signals pose significant challenges for subject-independent Brain-Computer Interfaces (BCIs) leading to poor model generalization. Differences in neural activity patterns, electrode placements, and external noise further degrade performance making it difficult to develop BCIs that remain reliable across users without extensive recalibration. This study presents a Compact Convolutional Swin Transformer (CCST) to address this issue by using hierarchical window based self-attention combined with convolutional feature extraction to efficiently capture both local electrode interactions and global temporal dependencies. This multi-scale feature representation enhances generalization across subjects, a critical factor for real world BCI deployment. We evaluated CCST on the BCI Competition IV (2a, 2b) and PhysioNet MI datasets using Leave-One-Subject-Out (LOSO) cross-validation achieving state-of-the-art classification accuracies of 68.27%, 76.61%, and 71.70% respectively. Our statistical analysis using the Wilcoxon signed-rank test with Bonferroni correction confirms significant performance improvements over benchmark models. Additionally, CCST achieves a reduction in parameters and a decrease in FLOPs compared to full self-attention models making it more efficient for real-time BCI applications. These results establish CCST as a scalable and efficient framework for adaptive subject-independent BCIs with promising applications in neurorehabilitation, assistive technology, and cognitive training.},
}

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

@article {pmid41557503,
year = {2026},
author = {Deng, GC and Liu, L and Liu, BY and Jing, R and Wu, C and Li, YY and Feng, X and Li, KY and Wang, JH and Liu, YJ and Yu, YQ and Chen, JD and Yang, HB and Li, XY and Duan, S and Sun, L},
title = {Transthyretin-mediated regulation of neuropathic pain and anxiety-like behavior in the lateral parabrachial nucleus.},
journal = {Cell reports},
volume = {45},
number = {1},
pages = {116860},
doi = {10.1016/j.celrep.2025.116860},
pmid = {41557503},
issn = {2211-1247},
abstract = {Neuropathic pain presents a complex challenge in clinical treatment due to its multifaceted etiology and frequent comorbidities with anxiety. Despite its prevalence, the underlying molecular, cellular, and circuit mechanisms remain poorly understood. The lateral parabrachial nucleus (LPBN) is a critical center that regulates both pain perception and the emotional aspects. In this study, single-cell sequencing shows upregulated transthyretin (TTR) in neuropathic pain models. Through bidirectional conditional knockout (cKO) and overexpression of TTR in LPBN neurons of mice, we confirm that TTR in LPBN glutamatergic neurons serves as a necessary and sufficient regulator of pain-anxiety comorbidity. Furthermore, TTR plays a pivotal role in pain regulation by binding to its receptor, receptor for advanced glycation end products (RAGE), thereby influencing neuroinflammation and neuronal excitability through the NF-κB signaling pathway. These results highlight potential molecular targets for the treatment of neuropathic pain.},
}

@article {pmid40768444,
year = {2026},
author = {Scholten, K and Xu, H and Lu, Z and Jiang, W and Jin, Z and Ortigoza-Diaz, J and Petrossians, A and Orler, S and Gallonio, R and Liu, X and Song, D and Meng, E},
title = {A Comprehensive Research Dissemination Model for Polymer-Based Neural Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {73},
number = {2},
pages = {934-944},
doi = {10.1109/TBME.2025.3596222},
pmid = {40768444},
issn = {1558-2531},
support = {U24 NS113647/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Rats ; *Polymers/chemistry ; Microelectrodes ; *Electrodes, Implanted ; Electroencephalography/instrumentation ; Equipment Design ; Rats, Sprague-Dawley ; *Brain-Computer Interfaces ; Hippocampus/physiology ; },
abstract = {OBJECTIVE: Implantable polymer microelectrode arrays (pMEAs) offer stable integration with neural tissue but are not widely available. An academic resource model is explored as a means of standardizing and disseminating pMEAs.

METHODS: The resource is based on a multi-project wafer model, originally developed in the semiconductor industry, allowing the simultaneous microfabrication of pMEAs with arbitrary designs. This model leverages innovations in design, manufacturing, and packaging to produce custom penetrating, surface, and cuff-type form-factors in batch and at low cost. Device quality is verified through benchtop testing and chronic electrophysiological recording in rats.

RESULTS: To date, over 1000 pMEAs (more than 50 designs) were provided to 45 academic labs. Implanted penetrating arrays in the hippocampus achieved high quality, chronic recordings from freely moving rats. Surface arrays reliably recorded electroencephalogram signals from the cortex and evoked potentials from the somatosensory cortex in awake rats.

CONCLUSION: Efficient production of custom pMEAs for research is possible through a unique resource model inspired by the semiconductor industry.

SIGNIFICANCE: Greater access to pMEAs enables researchers to conduct new experiments across different regions of the nervous system, accelerating discoveries.},
}

Animals
Rats
*Polymers/chemistry
Microelectrodes
*Electrodes, Implanted
Electroencephalography/instrumentation
Equipment Design
Rats, Sprague-Dawley
*Brain-Computer Interfaces
Hippocampus/physiology

@article {pmid41557476,
year = {2026},
author = {Kristen, R and Lenarz, T and Keintzel, T and Sprinzl, G and Köstler, C and Knoelke, N and Busch, S and Raffelsberger, T and Magele, A and Schörg, P and Wiek, RJ},
title = {Lifetime Real-World Evidence on Safety and Performance of the First Active Transcutaneous Bone Conduction Implant (BCI), the Bonebridge Covering Conductive to Mixed Hearing Loss (CMHL), and Single-Sided Deafness (SSD): Results From a Long-Term Retrospective Analysis.},
journal = {Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004837},
pmid = {41557476},
issn = {1537-4505},
abstract = {OBJECTIVE: Confirm the safety and performance of the first partially implantable active transcutaneous Bone Conduction Implant (tBCI) in patients who have been implanted for a minimum of 5 years before 2023.

SETTING: Otolaryngology departments of 4 German and Austrian hospitals.

STUDY DESIGN: Retrospective, multicenter, longitudinal, open-label case series study. Patients: 186 ears treated for conductive and mixed hearing loss (CMHL), or single-sided deafness (SSD) implanted for 5 years (151 aged 18 y or older, 35 aged 5 to 17 y) at the time of implantation.

INTERVENTION: Implantation of the Bonebridge (BB) BCI 601, a partially implantable active middle ear implant (AMEI).

MAIN OUTCOME MEASURES: Patients' audiometric pure-tone average (PTA4) (0.5, 1, 2, 4 kHz) thresholds (bone conduction, sound field) and speech perception (word recognition scores) were retrospectively collected up to 10 years 10 months postoperatively. Complications were recorded with focus on revision surgery and explantations. Subgroups were adults and children.

RESULTS: Safety was established by stable bone conduction (BC) thresholds 5 years after implantation or later with mean paired differences of -5.33 dB for adults and -8.05 dB for children and underscored by a low number of technical failures and high survival rates 10 years after implantation. Paired mean sound field PTA4 thresholds and word recognition scores significantly improved as tested by post hoc analysis 5 years or later after implantation, with functional gains for CMHL of 23.44 dB (adults), 27.69 dB (children), and word recognition scores of 58.22% (adults), 80.00% (children). Furthermore, mean sound field PTA4 thresholds and word recognition scores remain significantly improved over time at 36.37 dB HL and 68.75% 5 years or later after implantation as tested with linear mixed-effects model.

CONCLUSIONS: The findings of this study demonstrate that this tBCI remains safe and effective for up to 10 years.},
}

@article {pmid41555272,
year = {2026},
author = {An, Q and Cao, M and Zhang, J and Liu, D and Jialin, A and Chen, D and Wang, J and Wang, C and Zhao, X and Wang, D and Li, K and Zhang, D and Deng, W},
title = {Spatiotemporal disruption of prefrontal dynamics during affective association in depression: an fNIRS case-control study.},
journal = {BMC psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12888-026-07800-z},
pmid = {41555272},
issn = {1471-244X},
}

@article {pmid41554257,
year = {2026},
author = {Althoff, J and Nogueira, W},
title = {Selective auditory attention decoding in bilateral cochlear implant users to music instruments.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3a1a},
pmid = {41554257},
issn = {1741-2552},
abstract = {Electroencephalography (EEG) data can be used to decode an attended sound source in normal-hearing (NH) listeners, even for music stimuli. This information could steer the sound processing strategy for cochlear implants (CIs) users, potentially improving their music listening experience. The aim of this study was to investigate whether selective auditory attention decoding (SAAD) could be performed in CI users for music stimuli. Approach: High-density EEG was recorded from 8 NH and 8 CI users. Duets containing a clarinet and cello were dichotically presented. A linear decoder was trained to reconstruct audio features of the attended instrument from EEG data. The estimated attended instrument was selected based on which of the two instruments had a higher correlation to the reconstructed instrument. EEG recordings are challenging in CI users, as these devices introduce strong electrical artifacts. We also propose a new artifact rejection technique that employs ICA calculating ICs and automating their selection for removal, which we termed ASICA. Main results: We showed that it was possible to perform SAAD for music in CI users. The decoding accuracies were 59.4 \% for NH listeners and 60 \% for CI users with the proposed algorithm. Using the proposed algorithm, the correlation coefficients between the reconstructed audio feature and the attended audio feature were improved in conditions where artifact was dominating. Significance: Results indicate that selective auditory attention to musical instruments can be effectively decoded, and that this decoding is enhanced by the new artifact reduction algorithm, particularly in scenarios where the cochlear implant's electrical artifact has greater influence. Moreover, these results could be relevant as an objective measure of music perception or for a brain computer interface that improves music enjoyment. Additionally we showed that the stimulation artifact can be suppressed. The ethic's committee of the MHH approved this study (8874_BO_K_2020).},
}