@article {pmid42890887,
year = {2026},
author = {Tejesh, BSS and Bhaskar, K and Sivaji, M and Dakupati, R and Ram, SK and Sanki, PK and Ramakrishnan, M},
title = {A sliding window and Fourier decomposition method based 1D-CNN framework for motor imagery EEG classification.},
journal = {Frontiers in computational neuroscience},
volume = {20},
number = {},
pages = {1945160},
pmid = {42890887},
issn = {1662-5188},
abstract = {Electroencephalography (EEG) signal classification is fundamental requirement for effective motor imagery (MI) brain-computer interface (BCI) applications. However, current MI classification methods often depend on handcrafted spatial filtering or complex time-frequency transformations that increase computational complexity. To mitigate this limitation, we propose a unified architecture that integrates a sliding window (SW), a Fourier decomposition method (FDM), and a one-dimensional convolutional neural network (1D-CNN) for MI-EEG classification. The raw EEG data were segmented using the SW approach to augment the data and extract more temporal feature patterns from each trial. The segmented signal, containing time-domain features, was subsequently processed using a band-pass filter and FDM to extract frequency-based features. Here, FDM decomposes the signal into eight orthogonal Fourier Intrinsic Band Functions (FIBFs). A 1D-CNN architecture was employed to learn representations from the decomposed EEG signals. The proposed framework was evaluated on the BCI Competition III Dataset IVa (BCIC III IVa) and BCI Competition IV-2a (BCIC IV 2a) datasets. Our method achieved accuracies of 79.14% and 93.67% on the BCIC IV 2a and BCIC III IVa datasets, respectively, under the subject-dependent evaluation protocol. To evaluate cross-subject generalization, the proposed framework was validated using the Leave-One-Subject-Out (LOSO) protocol, achieving average classification accuracies of 76.49% on the BCIC III Dataset IVa and 55.50% on the BCIC IV 2a dataset. Furthermore, deployment on the NVIDIA Jetson Nano demonstrated complete pipeline latencies of 58.119 ms/trial and 85.055 ms/trial, corresponding to throughputs of 17.21 and 11.76 trials/s for the BCIC III IVa and BCIC IV 2a datasets, respectively. The results show the strong potential of the developed MI-EEG classification method to assist subjects with motor ailments.},
}

@article {pmid42890958,
year = {2026},
author = {Snowin, B and Hazeena, P and Narasimhan, L},
title = {Electroencephalography Changes in Emotional Recognition: A Signal Processing Approach Using Wavelet Features.},
journal = {Annals of neurosciences},
volume = {},
number = {},
pages = {09727531261483432},
pmid = {42890958},
issn = {0972-7531},
abstract = {BACKGROUND: motion recognition using electroencephalography (EEG) has gained significant attention in the fields of affective computing and human-computer interaction due to its ability to capture real-time neural responses. EEG signal variations associated with emotional states can be analysed using wavelet-based signal processing within the arousal-valence framework.

PURPOSE: This study aims to analyse EEG signal variations associated with emotional states using a wavelet-based signal processing approach within the arousal-valence framework.

METHODS: A prospective observational study was conducted on 48 healthy participants. Emotional states were induced using image, video and music stimuli categorised under the arousal-valence model. EEG signals were recorded using the standard 10-20 electrode placement system. The acquired signals were pre-processed and analysed using discrete wavelet transform (DWT) to extract time-frequency features, including energy and entropy. Statistical analysis was performed to evaluate hemispheric differences and gender-based variations.

RESULTS: Differences in EEG activity were observed between the left and right hemispheres across multiple emotional states (p < .05). Video and music stimuli produced stronger and more distinguishable EEG responses compared to image-based stimuli. Negative emotions demonstrated higher discriminative patterns than positive emotions. Gender-based variations were also observed in EEG responses.

CONCLUSION: Wavelet-based EEG feature analysis provides meaningful and discriminative information for emotion recognition and may provide a robust feature set for future machine learning-based emotion recognition systems. Further studies incorporating classification algorithms and larger data sets are required to validate their predictive performance.},
}

@article {pmid42893344,
year = {2026},
author = {Liu, J and He, Y and Liaw, HP and Russo, P and Gourdouparis, M and Huang, X and Chen, J and Liu, YH},
title = {A Compressive Neural Telemetry with Address-Event-μPacket Encoding and Duty-Cycling IR-UWB Transmitter.},
journal = {IEEE transactions on microwave theory and techniques},
volume = {74},
number = {8},
pages = {7429-7440},
pmid = {42893344},
issn = {0018-9480},
abstract = {This article presents a networked wireless telemetry for 384-sensor-pixel neural data acquisition, designed to support high throughput, low power consumption, and meter-scale communication distance. The proposed event-based Address-Event-μPacket (AEμP) encoder with differential binary phase shift keying (DBPSK) modulation addresses the challenging throughput-power trade-off for neural telemetry without compromising transmission range and neural signal fidelity. The encoder compresses data and minimizes protocol overhead as well as memory access. In addition, a duty-cycling IR-UWB transmitter is proposed as communication link for the event-based encoder. To support the multi-access operation, a duty-cycling frequency-locked loop (FLL) is adopted to provide fast-settling packet-level frequency control for frequency division multiplexing (FDM). The FLL achieves warm-start lock within 1 μs with 2.73-MHz RMS variation, meeting the frequency accuracy required for DBPSK at a 125-MHz symbol rate. The measured 3-access-node spectra comply with FCC/ETSI masks in 6-8.5 GHz. The neural signal integrity is validated by evaluating the RMS error between the reconstruction signals and the original in-vivo data captured by a high-pixel count Neuropixels probe. The presented AEμP achieves a 11.52× compression ratio. Fabricated in 65 nm CMOS with active area of 0.12 mm[2], the presented telemetry is capable of transmitting 384 pixels of neural data with low compression error (3.6-μV RMS), at record-low power of 1 mW, and with a 6.6-meter air communication range.},
}

@article {pmid42894183,
year = {2026},
author = {Wu, Y and Dong, Q and Chen, J and Huang, C and Zhang, M},
title = {Temporal synchrony promotes motor recovery and ipsilesional cortical reorganization in BCI-based stroke rehabilitation: a randomized controlled trial.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {23},
number = {1},
pages = {},
pmid = {42894183},
issn = {1743-0003},
support = {XWRCHT20220045//Xuzhou Key medical Talents Project/ ; 2025TD14//New Round of Xuzhou" Pengcheng Talent Program"- High-level Healthcare Talent Recruitment and Development Project/ ; KC25081//Medical and Health General Program of Xuzhou Science and Technology Bureau/ ; },
mesh = {Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Imagination/physiology ; Neuronal Plasticity/physiology ; *Recovery of Function/physiology ; Robotics ; Spectroscopy, Near-Infrared ; Stroke/physiopathology ; *Stroke Rehabilitation/methods ; *Cerebral Cortex/physiology ; },
abstract = {BACKGROUND: The therapeutic mechanisms of brain-computer interface (BCI) rehabilitation remain partially understood. Specifically, it is unclear whether functional gains are driven primarily by active neural engagement or strictly by the temporal contingency between motor intention and sensory feedback. This study aimed to deconstruct the BCI paradigm to isolate the specific contributions of these core components to functional recovery and cortical reorganization in subacute stroke.

METHODS: Seventy-seven subacute stroke patients were randomized into three intervention arms. Group C (synchronous) received robotic assistance strictly contingent on real-time detection of task engagement during motor imagery; Group B (asynchronous) performed identical motor imagery tasks but received temporally uncoupled robotic assistance; and Group A (sham) received sham motor imagery detection and preset passive robotic assistance. The intervention lasted for 3 weeks. The primary clinical outcome was Fugl-Meyer Assessment-Upper Extremity (FMA-UE) scores. Functional near-infrared spectroscopy (fNIRS) was included as an exploratory neuroimaging measure to provide preliminary information on cortical responses associated with the intervention.

RESULTS: Sixty-eight participants were included in the final analysis. After adjustment for baseline values, Group C showed significantly higher post-intervention FMA-UE scores than both Group A (adjusted mean difference = 7.47, 95% CI [1.94, 13.00], P = 0.004) and Group B (adjusted mean difference = 9.72, 95% CI [4.14, 15.30], P < 0.001). Group C also showed significantly higher post-intervention MBI scores than both Group A (adjusted mean difference = 21.15, 95% CI [12.49, 29.81], P < 0.001) and Group B (adjusted mean difference = 21.27, 95% CI [12.42, 30.12], P < 0.001). Exploratory neuroimaging analyses showed that only the change in ipsilesional sensorimotor cortex laterality index during motor imagery was significantly correlated with motor improvement (ρ = 0.404, P = 0.002, q = 0.012), whereas no resting-state or task-related fNIRS group comparisons remained significant after false discovery rate correction.

CONCLUSIONS: Synchronous BCI training was associated with superior clinical improvement compared with asynchronous and sham conditions in subacute stroke. The benefit was observed only when robotic assistance was contingent on real-time task performance, despite all three groups receiving comparable device exposure and motor imagery instruction. The neuroimaging results remain exploratory and should be confirmed in larger studies with prospectively defined imaging endpoints. Trial registration This trial was registered under the Chinese Clinical Trial Registry (ChiCTR2400091197) and was prospectively registered on October 21, 2024. Ethics Approval: Approved by the Ethics Committee of Xuzhou Rehabilitation Hospital (XK-LSW-20241017-009).},
}

Aged
Female
Humans
Male
Middle Aged
*Brain-Computer Interfaces
Imagination/physiology
Neuronal Plasticity/physiology
*Recovery of Function/physiology
Robotics
Spectroscopy, Near-Infrared
Stroke/physiopathology
*Stroke Rehabilitation/methods
*Cerebral Cortex/physiology

@article {pmid42895891,
year = {2026},
author = {Pitt, KM and Sawall, J and Butler, E and Bubak, A and Behboodi, A},
title = {Bridging the Gap Between Design and Access: Toward Visual Scene Displays for Brain-Computer Interface Use in Augmentative and Alternative Communication by Children.},
journal = {Journal of speech, language, and hearing research : JSLHR},
volume = {},
number = {},
pages = {1-19},
doi = {10.1044/2026_JSLHR-26-00267},
pmid = {42895891},
issn = {1558-9102},
abstract = {PURPOSE: Brain-computer interface (BCI) may support communication and computer interaction through neural signals such as the P300 event-related potential (ERP). As a step toward integrating photographic visual scene displays (VSDs) in BCI, this study examined the feasibility of incorporating contextual, naturalistic scenes in which communication items were embedded asymmetrically.

METHOD: To evaluate the effects of contextual scene use, 24 neurotypical children aged 8-13 years completed three experimental conditions: (1) a contextual scene with asymmetrical item placement, (2) an asymmetrical arrangement of the same items without contextual information, and (3) a standard grid. Electroencephalogram data were analyzed for the N170, P300, and N400 ERP components, and offline (predicted) BCI augmentative and alternative communication accuracy was calculated.

RESULTS: The results showed that asymmetry increased frontal and centro-parietal P300 amplitudes, and the scene enhanced occipital N170. Offline BCI performance was similar across the scene and grid conditions, and participants rated the scene as more visually appealing.

CONCLUSION: These findings indicate that asymmetry and contextual embedding within scenes may enhance neural engagement and user experience, providing a preliminary foundation for considering photographic VSDs into pediatric BCI research.},
}

@article {pmid42896470,
year = {2026},
author = {Sun, W and Fu, S and Zhang, J and Zhu, A and Wang, J and Yin, S and Liu, G},
title = {A spatio-temporal attention convolutional network with topology-preserving channel embedding and transformer for EEG decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {206},
number = {Pt A},
pages = {109755},
doi = {10.1016/j.neunet.2026.109755},
pmid = {42896470},
issn = {1879-2782},
abstract = {EEG-based BCI has shown promising applications in motor rehabilitation and emotion recognition. Given the non-stationarity and variability of EEG signals, it is essential to develop classification models that can effectively extract spatio-temporal features while maintaining strong generalization capabilities. This paper proposes a spatio-temporal attention convolutional network for EEG decoding (E-STACNet) to encode the topological features and temporal characteristics of EEG signals within a unified network framework. It consists of channel-attention (CA) block, convolutional (CV) layers, multi-head self-attention (MSA) and classifier layer. Specifically, CA block encodes spatial topological feature via a topology-preserving channel embedding method. Then convolutional layers are employed to encode low-level temporal information and further extract spatial features, followed by MSA block to highlight the most important temporal information of EEG signals. Finally, the outputs are obtained through two fully-connected layers. We conducted experiments on three public EEG-based datasets including BCI Competition IV dataset 2a, Giga DB and DEAP. The results demonstrate E-STACNet achieves average accuracies of 88.00% and 80.62% on datasets 2a and Giga DB, respectively. On the DEAP, it reaches average accuracies of 96.49% and 96.46% for valence and arousal. The proposed model outperforms other state-of-the-art models on all three datasets. Additionally, the ablation study reveals the importance of topology-preserving channel embedding for the proposed network. Visualization analysis shows E-STACNet could obtain the importance of channels at different locations through the channel-attention mechanism, which demonstrates the interpretability of the proposed model. Overall, the accurate and generalization capabilities of E-STACNet will promote the development of EEG-based BCIs.},
}

@article {pmid42896556,
year = {2026},
author = {Kadlec, K and Gamez, J and Aflalo, T and Guan, C and Rosario, ER and Pejsa, K and Bari, A and Pouratian, N and Andersen, RA},
title = {Distinct patterns of whole-body coding in human motor cortex and posterior parietal cortex.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2026.09.030},
pmid = {42896556},
issn = {1097-4199},
abstract = {Traditional views of cortical motor areas hold that movements of different parts of the body, or effectors, are represented in distinct anatomical locations. The extent of overlap in effector representations has been a topic of debate for decades. We recorded from single neurons in hand areas of the motor cortex (MC) and posterior parietal cortex (PPC) of tetraplegic humans while they attempted movements across the body. We found a population response to every movement tested in both areas; however, the hand knob of MC more selectively responded to hand movements, whereas PPC represented all effectors strongly. Single neurons in MC responded selectively to a single effector or to two effectors that were part of the same limb. In PPC, neurons exhibited mixed selectivity, responding to combinations of effectors. These findings suggest a transition from a highly effector-general representation in PPC to regional effector specificity in MC.},
}

@article {pmid42896567,
year = {2026},
author = {Gorenshtein, A and Adiniaev, Y and Liba, T and Barash, Y and Klang, E and Daniel, O},
title = {The calibration-to-accuracy mapping in P300 spellers does not transport across cohorts and was not restored by tested alignment or local recalibration.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb62c},
pmid = {42896567},
issn = {1741-2552},
abstract = {Objective A calibration-derived score has been related to online P300-speller accuracy in the same session, always within the cohort measured. We evaluated whether a mapping from that score to expected accuracy transports to withheld cohorts. Approach Retrospective secondary analysis of BigP3BCI 1.0.0, the largest evaluation of this relationship to date: 18 of 20 studies contributed 271 participants, 739 session-condition records, and 19,611 character selections. The predictor was a decoder's cross-validated discriminability on calibration epochs, one study withheld from development. Between-cohort variation was summarised by the random-effects standard deviation tau. Main Results The association was positive in all 18 cohorts but varied widely (r 0.190 to 0.928; participant-level r = 0.714, p < 0.001), with 12 CIs excluding zero. Pooled estimation error was 0.098 (95% CI 0.091 to 0.107) against a benchmark of 0.146. Calibration did not transport: intercept tau 0.87 (unrepresented-cohort interval -1.97 to 1.85, log-odds); cohort-specific slopes varied more than tenfold (tau 0.43, interval 0.111 to 2.005). Neither alignment (signal- or score-space) nor either nonlinear boundary meaningfully reduced this heterogeneity. Local recalibration with up to 16 participants did not reliably improve on transporting; refitting both intercept and slope was significantly worse than transporting at the smallest sizes. Median time per selection, a stopping-rule proxy, accounted for 66% of between-cohort slope variance in meta-regression. The four ALS cohorts gave an uncorrected slope spread of 0.223, against 0.560 overall. Significance The evaluated score is a classifier's cross-validated discriminability on each session's calibration data rather than the source studies' undocumented online decoder. That score reproducibly signals within-session accuracy, but its mapping to accuracy is cohort-specific and supports ranking sessions after local validation rather than reporting accuracy elsewhere. The four-cohort ALS subgroup appeared more homogeneous than the full archive, illustrating how conclusions about transportability from a small cohort set can be fragile. .},
}

@article {pmid42896691,
year = {2026},
author = {Zhang, T and Bai, D and Wei, X and Zhuo, L and Zhang, Q and Zhang, J and Jin, Z and Li, L},
title = {Subjective emotional ratings dissociate brain activation and network patterns during counterfactual outcome processing.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122303},
doi = {10.1016/j.neuroimage.2026.122303},
pmid = {42896691},
issn = {1095-9572},
abstract = {Human decision-making integrates outcome evaluation, counterfactual comparison, and individual traits. We investigated the neural processing of outcome-related emotions during economic decision-making by classifying feedback according to participants' subjective emotional ratings rather than objective gain-loss categories. Participants completed a Wheel of Fortune gambling task during fMRI scanning and provided emotional ratings for feedback outcomes, enabling identification of regret, relief, disappointment, and elation. We also assessed individual differences in regret proneness, maximization tendencies, alternative search, high standards, and decision difficulty. Behaviorally, emotional ratings increased with larger obtained outcomes and decreased as reference outcomes increased, indicating robust modulation of outcome evaluation by counterfactual information Whole-brain analyses revealed greater activation for low-rating than high-rating outcomes, indicating stronger neural responses to negative than positive emotions across feedback types. Complete feedback also elicited greater activation than partial feedback within the same emotional valence. Network-level analyses revealed partially dissociable neural profiles for positive and negative emotions. Positive emotions, including elation and relief, predominantly recruited the somatomotor network, whereas negative emotions, including disappointment and regret, engaged broader distributions across the default mode, dorsal attention, and other large-scale networks. Regret elicited the largest activation extent, whereas elation elicited the smallest. Region-of-interest analyses showed that bilateral orbitofrontal cortex (OFC) responses were significantly greater for negative than positive emotions and for complete than partial feedback conditions. Across all four emotions, right OFC activation was negatively associated with maximization. For elation, relief, and disappointment, right OFC activation was also negatively associated with alternative search and decision difficulty, whereas these associations were absent for regret. Together, these findings demonstrate that subjective emotional evaluation, counterfactual context, and decision-related traits jointly shape the neural processing of decision outcomes, while suggesting relatively trait-independent neural processing of regret.},
}

@article {pmid42898475,
year = {2026},
author = {Fleeting, CR and Gunduz, A and Okun, MS and Wong, JK},
title = {Editorial: Deep brain stimulation think tank: updates in neurotechnology and neuromodulation, volume VI.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {2007514},
doi = {10.3389/fnhum.2026.2007514},
pmid = {42898475},
issn = {1662-5161},
support = {T32 GM163993/GM/NIGMS NIH HHS/United States ; },
}

@article {pmid42899673,
year = {2026},
author = {Sato, M and Hatakeyama, E and Inoue, M and Kabe, Y and Nobe, S and Yoshida, A and Yamamoto, A and Kita, Y and Shimizu, M and Tomeoka, K and Cohen, MX and Sasai, S},
title = {Delineating neural contributions to electroencephalogram-based speech decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb68d},
pmid = {42899673},
issn = {1741-2552},
abstract = {Objective. Speech Brain-computer interfaces (BCIs) have emerged as a pivotal technology in facilitating communication for individuals with speech impairments. Utilizing electroencephalography (EEG) for noninvasive speech BCIs offers an accessible and affordable solution, potentially benefiting a broader audience. However, EEG-based speech decoding remains controversial especially for overt speech, due to difficulties in separating speech-related neural activities from myoelectric potential artifacts generated during articulation. Moreover, because paradigms for collecting covert speech data are inherently less scalable than those for overt speech, whether the neural dynamics underlying EEG-based decoding of overt speech are also present during covert speech remains an open and practically important question. Approach. Here we aim to delineate the extent of the neural contributions to EEG-based speech decoding, and the degree to which the underlying neural dynamics are shared across overt and covert speech, by employing Explainable AI techniques to a convolutional neural network predicting spoken words based on signals obtained by ultra-high-density (uhd)-EEG. Main results. We found that electrode-wise contributions to the decoding cannot be explained by their mutual information with electromyography (EMG). Furthermore, contributing periods of speech to EEG-based decoding are distinct from those to decoding solely relying on EMG. In contrast, there are significant overlaps in signal timings contributing to EEG-based decoding, regardless of vocal conditions such as overt or covert speech. Notably, the denoising process successfully enhanced the decoding contribution from electrodes within speech-related brain areas for all speech conditions. Significance. Altogether, our findings support the idea that, with appropriate preprocessing, EEG becomes a valuable tool for decoding spoken words based on underlying neural activities. .},
}

@article {pmid42889466,
year = {2026},
author = {Wei, Y and Cui, H and Zhu, J and Chen, C and Qian, Z and Zhang, D and Wang, J and Zhang, T},
title = {An exploration in mental health care: The launch of China's first psychiatric noninvasive brain-computer interface (BCI) and neuromodulation clinic.},
journal = {General psychiatry},
volume = {39},
number = {5},
pages = {e70069},
pmid = {42889466},
issn = {2517-729X},
}

@article {pmid42889891,
year = {2026},
author = {Zhao, Y and Wei, LJ and Sun, XT and Liu, FR and Zhang, L and Zhu, CZ},
title = {Prior photon measurement density functions constructed in standard channel space for functional near-infrared spectroscopy.},
journal = {Biomedical optics express},
volume = {17},
number = {10},
pages = {5034-5047},
pmid = {42889891},
issn = {2156-7085},
abstract = {Photon measurement density function (PMDF) determines the measurement locations and their associated sensitivity to the hemodynamic changes for an fNIRS channel. It is therefore essential for pre-experimental optode arrangement and post-experimental signal analysis and interpretation in fNIRS studies. However, accurate PMDF estimation requires subject-specific MRI data, which are unavailable in common fNIRS studies. Previous studies often rely on PMDFs derived from a single head template (template-PMDF), ignoring inter-subject variability in anatomy and optode placement, and are limited to sparse predefined scalp locations. Here, we propose a methodological framework to construct prior PMDFs (prior-PMDF) by synthesizing PMDFs derived from multiple subjects in a structural MRI database. This approach incorporates inter-subject variability and enables estimation of PMDFs in the standard brain space for arbitrary scalp locations. We demonstrate the utility of the prior-PMDF in estimating regional sensitivities, including voxel-wise sensitivity, sensitivity to intra- and extracerebral tissues, and sensitivity to atlas-defined brain regions. The estimation accuracy of the prior-PMDF is evaluated by comparing it with subject-specific PMDFs derived from MRI data that are not included in the construction database. Furthermore, comparison with template-PMDF showed that the proposed method significantly improves accuracy, thereby supporting more reliable PMDF estimation in MRI-free fNIRS studies.},
}

@article {pmid42890584,
year = {2026},
author = {Tang, B and Wei, J and Liu, Y and He, B and Bao, W and Yang, Y and Wang, W and Hu, W and Duan, A},
title = {Unequal economic impacts of climate change via idealized carbon dioxide removal: Insights from deep learning.},
journal = {Innovation (Cambridge (Mass.))},
volume = {7},
number = {10},
pages = {101505},
pmid = {42890584},
issn = {2666-6758},
abstract = {Rapidly reducing carbon dioxide (CO2) levels is essential to meeting the Paris Agreement's temperature targets. Previous assessments of CO2 removal (CDR) have primarily focused on the hysteresis and invertibility of climate change itself, overlooking quantitative analysis of potential economic impacts of climate change via CDR. In this study, we first develop a powerful neural network model, EconClimNet, trained on decades of economic data from 1,554 sub-national regions worldwide and 111 climate indices derived from the fifth-generation ECMWF atmospheric reanalysis (ERA5). Compared to popular machine learning algorithms used in the Earth science community, EconClimNet achieves superior performance in capturing the intricate relationship between climate indices and economic outcomes. On this basis, we apply EconClimNet to the outputs from idealized CO2 ramp-up and ramp-down experiments from phase 6 of the Coupled Model Intercomparison Project (CMIP6). It is found that altering climate trajectories through idealized CDR can yield significant long-term economic benefits globally, with Oceania overall positioned to experience the greatest gains with an increasing trend of about 1.41 [0.73-8.76] million USD year[-1]. Unequal economic effects also emerge across income levels. Rich countries see more benefits (an increasing trend of about 1.56 [0.31-6.21] million USD year[-1]) via CDR, mainly contributed by services (an increasing trend of about 0.03 [0.00-0.23] million USD year[-1]). Our findings underscore the need for governments to foster the innovation, development, and prudent deployment of CDR technologies, as well as to advance global CDR strategies that prioritize vulnerable nations from a climate justice perspective.},
}

@article {pmid42882312,
year = {2026},
author = {Li, Y and Yang, R and Xu, X and Jin, L and Zhang, M and Chen, L},
title = {Lower Task-Estimated Visual Working Memory Capacity in Adults with Epilepsy: A Cross-Sectional Quantitative Assessment Using Eye Tracking.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {16},
number = {19},
pages = {},
pmid = {42882312},
issn = {2075-4418},
support = {2024ZDZX0018//Science and Technology Department of Sichuan Province/ ; },
abstract = {Background/Objectives: Cognitive dysfunction is clinically relevant in epilepsy, and objective task-based measures could complement neuropsychological assessment, but visual working memory across graded loads and task-embedded gaze remain incompletely characterized. We quantified task-estimated capacity and performance using gaze-triggered change localization. Methods: We analyzed data from 222 adults with epilepsy, including focal and generalized epilepsy, and 133 healthy controls. Participants identified the changed item in a test array by sustained gaze; set size (2-7) followed a staircase procedure. Outcomes were three task-performance measures, two capacity-related measures, and eight eye-movement measures. Repeated-measure outcomes were analyzed with mixed-effect models and the capacity-related measures by ordinary least squares, with Benjamini-Hochberg correction. Results: Adults with epilepsy had lower mean set size and K at set size 4 (K4), lower accuracy, longer response time, and greater final gaze-to-target distance than controls (all q < 0.05; absolute Cohen's d, 0.20-0.45). K4 was 2.66 versus 3.03 items. Differences persisted after adjustment for educational attainment and were directionally similar in focal and generalized epilepsy. No interaction between group and set size survived correction for task performance; group differences did not vary detectably across the observed load range. No eye-movement group difference survived correction. Conclusions: In a relatively large adult cohort, task-estimated visual working memory capacity and task performance were lower in epilepsy, without broad corrected group differences in the selected array-level eye-movement measures. By combining graded load with gaze-based response registration, the study characterizes the magnitude, load dependence, timing, and spatial precision of visual working memory performance differences within a single task.},
}

@article {pmid42884285,
year = {2026},
author = {Lee, CH and Huang, CM},
title = {Gradient-Free Filter Importance for Structured Pruning of Compact EEG Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {19},
pages = {},
pmid = {42884285},
issn = {1424-8220},
support = {NSTC-115-2221-E-324-016-.//National Science and Technology council/ ; },
mesh = {*Electroencephalography/methods ; Brain-Computer Interfaces ; Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; },
abstract = {Deploying EEG classification networks on resource-constrained brain-computer interface (BCI) hardware motivates structured filter pruning, yet which importance criterion to trust in already-compact architectures is unclear. We present a leakage-free, physically materialized comparison of five filter-importance criteria-ablation-based structural sensitivity analysis (SSA), first-order Taylor, L1-norm, geometric median, and random-across four datasets, two paradigms (motor imagery and ERP), two architectures, multiple capacities, and with and without fine-tuning. On EEGNet, after fine-tuning, SSA and Taylor are statistically equivalent: pooled over the four datasets their accuracy difference falls within a ±1 percentage-point margin (TOST, p=0.01), and on ShallowConvNet SSA is never worse than Taylor; both criteria significantly outperform the weight-based and random ones. SSA's value is therefore not a more accurate ranking but its formulation: on a weight-quantized (8-bit) EEGNet, where the loss gradient-and hence Taylor-is undefined on the quantized graph used here, SSA is the best gradient-free criterion, significantly beating L1, geometric-median, and random pruning in aggregate-extending structured pruning to the quantized, gradient-free but editable deployment regime. We further show that the pruning schedule and fine-tuning matter more than the choice between strong criteria, and that training large then pruning with fine-tuning recovers accuracy equivalent to a directly trained smaller model. The results clarify where the importance criterion does and does not matter for compact EEG decoders.},
}

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

@article {pmid42884399,
year = {2026},
author = {Habibzadeh, H and Zois, DS and Norton, JJS},
title = {MetaIDNet: A Fast Brain-Computer Interface for Color Vision Assessment.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {19},
pages = {},
pmid = {42884399},
issn = {1424-8220},
support = {1R21EB036221-02/NH/NIH HHS/United States ; 1P41EB018783-10/NH/NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Color Vision/physiology ; Evoked Potentials, Visual/physiology ; Neural Networks, Computer ; Electroencephalography ; Photic Stimulation ; Male ; Algorithms ; },
abstract = {Our laboratory has recently demonstrated objective, quantitative, and precise brain-computer interface (BCI)-based color vision (CV) assessment. During assessment, participants look at a flashing stimulus that alternates between two light sources. Light sources that appear to be different colors elicit larger steady-state visual evoked potentials (SSVEPs); those that appear to be similar colors elicit smaller SSVEPs. Thus, the stimulus-evoked responses depend on an individual's CV. BCI-based CV assessments evaluate CV by observing SSVEP sizes elicited over a color space; their accuracy depends on the sampling strategy and noise in the individual observations. Existing systems uniformly sample the color space and average multiple SSVEPs observed from the same location to reduce noise. Effectively, these systems collect additional observations to improve assessment accuracy, but at the expense of assessment speed. We present MetaIDNet, a closed-loop BCI that uses Gaussian process regression (GPR) and a neural network to improve the speed-accuracy tradeoff of BCI-based CV assessment. We perform offline and online experiments with ten and eight participants, respectively, to evaluate the speed and accuracy of MetaIDNet and compare it with two existing methods. Our offline experiments show that MetaIDNet improves CV assessment speed by 70% (while maintaining accuracy), and our online experiments show that MetaIDNet improves CV assessment accuracy by 40% (while maintaining the same speed). The results confirm that MetaIDNet significantly improves the speed and accuracy of BCIs for CV assessment.},
}

Humans
*Brain-Computer Interfaces
*Color Vision/physiology
Evoked Potentials, Visual/physiology
Neural Networks, Computer
Electroencephalography
Photic Stimulation
Male
Algorithms

@article {pmid42884552,
year = {2026},
author = {Serna, B and Salazar, R and Alonso-Silverio, GA and Baltazar, R and Ventura-Molina, E and Alarcón-Paredes, A},
title = {Fear-EEG-AI: A Conceptual and Operational Framework for Designing EEG-AI Systems for the Inference of Fear-Compatible Neurophysiological States.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {19},
pages = {},
pmid = {42884552},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Fear/physiology ; *Artificial Intelligence ; },
abstract = {Electroencephalography (EEG) and artificial intelligence (AI) are central tools in affective computing, yet fear-related inference remains constrained by a gap between experimental performance and deployable evidence: high accuracy on controlled datasets guarantees neither neurobiological specificity, nor robustness across users, nor traceable inference. EEG does not measure fear directly; it captures cortical dynamics compatible with threat processing, arousal or vigilance. This paper introduces the Fear-EEG-AIFramework, a technology-independent architecture for designing, validating and deploying EEG-AI systems targeting fear-compatible neurophysiological states. Building on a prior systematic review, it organizes development into three hierarchical levels-neurobiological foundation, neuroelectric correlates and computational modeling-governed by six cross-cutting principles, and makes them evaluable through eight observable dimensions, the Fear-EEG-AI Alignment Index (FAI) and five validation evidence levels. Applied to the eight eligible studies of that corpus by two raters who had not drafted the instrument, with adjudication by a third, the rubric reached 67.2% exact agreement (quadratic-weighted κ=0.741; ICC(2,1) =0.744). Under 10,000 admissible weightings the equal-weight ordering survived in 10.1% of draws, and one of 56 ordered pairs met a stability criterion fixed before the pairwise results were tabulated. The index is therefore reported as a descriptor of methodological documentation and not as a ranking of implementations.},
}

*Electroencephalography/methods
Humans
*Fear/physiology
*Artificial Intelligence

@article {pmid42886472,
year = {2026},
author = {Anniwa, D and Chen, Z and Chen, X and Yuan, W and Lang, H and Chen, N and Zhang, N and Qin, M},
title = {Advanced biomaterial technologies for neural regeneration in central nervous system diseases: mechanisms and applications.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d6nr02348g},
pmid = {42886472},
issn = {2040-3372},
abstract = {Effective repair of central nervous system (CNS) injuries remains highly challenging due to several formidable barriers, including severe structural disruption, hostile microenvironments, and limited intrinsic regenerative capacity. In response, biomaterials incorporated with therapeutic agents provide advanced interventional strategies for neural regeneration. This review systematically summarizes recent progress in biomaterial-based CNS repair, focusing on three core strategies: physical remodeling of the neural structure, targeted drug delivery systems for biochemical regulation, and cellular intervention. Furthermore, we critically analyze the manufacturing and regulatory bottlenecks currently impeding clinical translation. Finally, we propose the integration of neural organoids and brain-computer interface (BCI) technologies as a promising trajectory for next-generation intelligent neural repair materials.},
}

@article {pmid42887837,
year = {2026},
author = {Duwadi, S and Rogers, D and Boyd, AD and Carlton, LB and Zhang, Y and Gaona, AK and Pathiyaparambath, AD and Chaudhury, R and Magee, G and Zimmermann, BB and O'Brien, WJ and von Lühmann, A and Boas, DA and Yücel, MA and Sen, K},
title = {Decoding Spatial Attention in the Cocktail Party Problem Using Whole-head High-Density fNIRS.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122292},
doi = {10.1016/j.neuroimage.2026.122292},
pmid = {42887837},
issn = {1095-9572},
abstract = {Spatial attention is critical for solving the cocktail party problem, a longstanding problem in neuroscience and artificial speech recognition. The ability to decode where humans are attending in a cocktail party like scene would empower applications in brain computer interfaces and assistive devices such as hearing aids. Here we demonstrate that, in an overt attention task, the attended spatial location can be decoded robustly from single trial hemodynamic responses, using a whole head high density fNIRS system. We also identify critical brain regions that make the highest contribution to decoding accuracy. Specifically, we find that decoding based on a small fraction of channels located within the left and right inferior parietal lobule (IPL) achieves maximal decoding accuracy comparable to all channels. Decoding fell close to chance in a covert auditory condition in which participants held central fixation while attending to the same talkers, indicating that auditory attention alone without orientation is not sufficient for decoding. Finally, control experiments suggest that overt-condition decoding is influenced by sustained eye position/gaze direction, not eye movement. These results support the development of fNIRS-integrated BCIs and assistive devices controlled by overt spatial attention.},
}

@article {pmid42881397,
year = {2026},
author = {Chen, Y and Gao, Y and Zhou, J and Zhang, Z and Zong, J and Liu, Y},
title = {Brain-computer interfaces: innovations and challenges in the field of cerebral palsy rehabilitation.},
journal = {Italian journal of pediatrics},
volume = {52},
number = {1},
pages = {},
pmid = {42881397},
issn = {1824-7288},
support = {Dalian Association of Science and Technology [2020]No.42//Dalian Association of Science and Technology Expert Workstation Project/ ; },
mesh = {Humans ; *Cerebral Palsy/rehabilitation ; *Brain-Computer Interfaces ; Child ; Activities of Daily Living ; },
abstract = {Brain-computer interfaces have shown great potential in the rehabilitation of Cerebral palsy, improving the ability of children with cerebral palsy to perform activities of daily living by replacing and restoring lost neurological functions, as well as accurately evaluating cognitive functions and facilitating rehabilitation in conjunction with various rehabilitation techniques. Nonetheless, numerous challenges persist in the application of brain-computer interfaces for cerebral palsy. Future research should focus on developing enhanced and more dependable brain-computer interfaces, advancing the hardware, and establishing ethical and regulatory frameworks to facilitate their utilisation in cerebral palsy rehabilitation.},
}

Humans
*Cerebral Palsy/rehabilitation
*Brain-Computer Interfaces
Child
Activities of Daily Living

@article {pmid42877582,
year = {2025},
author = {Chen, ZS and Lu, BL and Wu, W},
title = {Representation Learning and Foundation Models for Electroencephalography Analyses: Current trends, fundamental insights, and future directions?.},
journal = {IEEE signal processing magazine},
volume = {42},
number = {5},
pages = {36-57},
pmid = {42877582},
issn = {1053-5888},
support = {UG3 NS135170/NS/NINDS NIH HHS/United States ; RF1 NS121776/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; P50 MH132642/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; R01 MH139352/MH/NIMH NIH HHS/United States ; R01 NS121776/NS/NINDS NIH HHS/United States ; R01 NS123928/NS/NINDS NIH HHS/United States ; },
abstract = {Since the first ground-breaking human electroencephalography (EEG) recordings in 1924 [1], the past century has witnessed a tremendous growth of EEG applications in cognitive neuroscience, clinical and engineering applications due to EEG's low operational cost and mobility [2]. On the one hand, advances in high-density non-invasive scalp EEG or invasive intracranial EEG (iEEG) have offered both excellent temporal resolution and increasingly improved spatial resolution to study brain functions and their link to emotions, memory, learning, and diseases. EEG-based brain-computer interface (BCI) can offer new dimensions for entertainment, virtual reality, neurofeedback and closed-loop therapy. On the other hand, recent advances in artificial intelligence (AI) and machine learning have opened new opportunities for analyses of EEG and other neural data [3]. This article aims at presenting an overview of the cutting-edge machine learning techniques for EEG analyses. By leveraging large-scale EEG data with state-of-the-art representation learning and transfer learning paradigms, we are empowered to discover latent EEG features that are proved useful for clinical care and BCI. We discuss some general principles of representation learning and show walk-through practical examples of EEG analysis. The article also aims at highlighting the effort of applying AI models to discover neuroscience insights and linking them to the fundamentals of EEG signal analyses from a signal-processing perspective. While our focus is on EEG and iEEG signals, most of the approaches discussed here are generally applicable to other brain signal modalities.},
}

@article {pmid42878278,
year = {2026},
author = {Xie, Y and Deng, J and Wei, C and Zhao, J and Lan, L and Kuang, Y and Cheng, M and Liu, C and Tian, Y and Wang, H and Yue, W and Liu, J and Ni, Z and Wang, Z and Li, M},
title = {Scalable Patterning of Organic Electrochemical Transistors for Logic Gates and Neuromorphic Computing.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e76279},
doi = {10.1002/smll.76279},
pmid = {42878278},
issn = {1613-6829},
support = {2025YFF0518300//National Key R&D Program of China/ ; //Self-deployment Project of State Key Laboratory of Fabrication Technologies for Integrated Circuits/ ; YSBR-146//CAS Project for Young Scientists in Basic Research/ ; //Fundamental Research Funds for the Central Universities/ ; JQ24030//Beijing Natural Science Foundation/ ; },
abstract = {Organic electrochemical transistors (OECTs) are promising building blocks for bioelectronics owing to their efficient ion-electron coupling, soft-material compatibility and high transconductance. However, their large off-state currents and poor scalability have so far limited high-density integration and circuit-level functionality. Here we introduce a patterning strategy suitable for solution‑processable organic semiconductors, enabling high‑fidelity OECT arrays with suppressed leakage and off‑state currents, on/off ratios up to 10[7], and good device‑to‑device uniformity. Leveraging these patterned OECTs, we demonstrate the integration of logic circuits, including complementary inverters as well as NAND and NOR gates, exhibiting high voltage gain (up to 203 V/V), large noise margins (92.17%) and stable operation at low supply voltages. Benefiting from the patterning approach and the AlOx-induced suppression of off-state current, the static power consumption of the inverter is reduced by approximately 90%. Beyond digital logic, we further show that these devices emulate key synaptic functions such as short-term plasticity and enable reservoir computing tasks, defining a manufacturable platform for integrated bioelectronic circuitry and neuromorphic architectures. This patterning strategy therefore provides a general route toward high-density biointerfaces and next-generation brain-computer interfaces.},
}

@article {pmid42879210,
year = {2026},
author = {Hu, AQ and Mao, ZX and Li, YY and Chen, GD and Chen, T and Deng, RH and Zhang, YY and Zhang, ZY and Huang, S and Dai, WJ and Xiao, ZC and Yang, X and Tao, YJ and Yi, LL and Wu, JL and Huang, YQ and Wang, HY and Li, T and Guo, WJ},
title = {Unveiling core clinical features of self-harm: A network and mediation analysis study among Chinese psychiatric adolescents.},
journal = {Journal of behavioral addictions},
volume = {},
number = {},
pages = {},
doi = {10.1556/2006.2025.00574},
pmid = {42879210},
issn = {2063-5303},
abstract = {BACKGROUND AND AIMS: Self-harm imposes a substantial burden among adolescents, yet targeted interventions remain limited. This study examined the interrelationships among clinically relevant characteristics of self-harm outcomes, non-suicidal motives (NSMs), addictive features (AF), self-injury methods (SIMs), and suicidality (SUI), to identify core clinical features for more precise intervention.

METHODS: A total of 547 adolescent patients (13-19 years) who had engaged in self-harm during the past year were recruited from two Chinese hospitals. Self-report questionnaires assessed NSMs, AF, SUI, frequencies of non-suicidal self-injury (NSSI) and suicidal self-injury (SSI), and SIMs. Exploratory factor analysis (EFA) was used as a data-driven approach to identify patterns of co-occurrence among SIMs. Network and exploratory mediation analyses were subsequently conducted to evaluate statistical interrelationships among clinical features.

RESULTS: Among participants, 69.8% engaged in both NSSI and SSI. EFA identified two empirically derived groupings of SIMs: self-injury using external tools (SIET) and self-injury involving direct bodily actions (SIDBA). Network analysis identified AF as the strongest bridge symptom and SIET with the highest node strength. Exploratory mediation analyses suggested that AF statistically accounted for substantial proportions of the associations between NSMs and multiple self-harm related outcomes, including NSSI (41.3%), SSI (75.0%), SIET (62.3%), SIDBA (45.4%), and SUI (75.7%).

DISCUSSION AND CONCLUSIONS: AF and SIET emerged as important clinically relevant features of self-harm. Greater attention to these features may help improve clinical assessment and targeted intervention for adolescents with self-harm.},
}

@article {pmid42880317,
year = {2026},
author = {Natarajan, AM and Sajda, P},
title = {Peripheral physiological decoding of arousal in a boundary-avoidance task: signal validity and individualized control.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb4ec},
pmid = {42880317},
issn = {1741-2552},
abstract = {Scoring well on a task-demand label does not show that a decoder measures arousal. We ask whether peripheral physiology decodes demand better than EEG, whether the index behaves like graded arousal rather than a clock, and what a controller could do with it. Approach. We re-analysed a public boundary-avoidance flight task in which sixteen participants received EEG neurofeedback, half-sham feedback or silence. A decoder trained on peripheral signals (cardiac, respiratory, electrodermal, pupillometric) from an open-loop calibration block was compared with a retrained EEG decoder, filter-bank common spatial patterns, and motor-only and shuffled-label controls. All analyses are offline; the decoder was never in the loop. Main results. The peripheral decoder reached 93.0% within-subject AUC against 85.2% for EEG. Ring size shrinks on a fixed schedule, so accuracy alone cannot separate arousal from trial position. Three tests did: 48.6% of hard-course trials ended below the scale midpoint; the index separated the courses at matched elapsed time (p= 1.4 ×10-31), where EEG did not; and it tracked the autonomic channels. Only the peripheral index recovered an inverted-U with performance, robust in a duration-safe hazard analysis (p= 3.5 ×10-10). Arousal rose 14.1 points in the 10 s before failure; sustained between-condition differences above 7.5 points are excluded. Subjects varied reliably in how strongly excess arousal predicted failure, predictably from pre-loop calibration. Personalised control bands gave the best held-out discrimination, though at N = 16 indistinguishable from a retuned universal band. Significance. Arousal is a state of the whole body, and here the body reads it better than the scalp. The results separate three often-conflated requirements: accuracy on a demand label, validity as an arousal signal, and usefulness for control. The signal that added information over a fixed rule was distance above a band set by each subject's recent variability.},
}

@article {pmid42880669,
year = {2026},
author = {Chrisilla, S and Selvakumari, RS},
title = {A GRAPH CONVOLUTION BASED DUAL-BRANCH NETWORK FOR DECODING OF UPPER-LIMB MOTOR IMAGERY EEG SIGNALS.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110921},
doi = {10.1016/j.jneumeth.2026.110921},
pmid = {42880669},
issn = {1872-678X},
abstract = {BACKGROUND: Analysing motor imagery EEG signals using conventional Euclidean feature computation is challenging due to low spatial resolution, non-stationarity, and complex inter-channel dependencies, which limit robust subject-independent generalization in brain-computer interface systems.

NEW METHOD: This study proposes a graph-based convolutional framework for subject-independent decoding of upper-limb motor imagery. The model integrates graph convolution with skip connections and handcrafted time-frequency features, together with parallel spatial and temporal convolutional pathways. Graph convolution captures functional connectivity between electrodes, while convolutional modules enhance spatial-temporal representation learning. Subject-independent evaluation is ensured using Stratified Group K-Fold (SGKF) and Leave-One-Subject-Out (LOSO) validation protocols.

RESULTS: The framework was evaluated on the PhysioNet, BCI IV-2a, and BCI IV-2b datasets using multiple electrode configurations. For the PhysioNet dataset, three upper-limb classes were considered, achieving SGKF accuracies of 86.42%, 91.01%, and 93.91% using 64, 21, and 5 electrodes, respectively, and LOSO accuracies of 92.25%, 95.72%, and 96.01%. On the BCI IV-2a dataset, SGKF accuracies of 86.13%, 89.01%, and 92.33% were obtained, with LOSO accuracies reaching 97.12% using five electrodes. For the BCI IV-2b dataset, accuracies of 88.60% (SGKF) and 93.35% (LOSO) were achieved using only three electrodes.

The proposed graph-based framework demonstrates competitive performance to representative graph-based methods while requiring fewer trainable parameters and EEG electrodes.

CONCLUSION: These findings demonstrate that graph-based deep learning enables accurate, subject-independent motor imagery decoding with reduced model complexity and minimal electrode usage, supporting practical and resource-efficient brain-computer interface systems.},
}

@article {pmid42585091,
year = {2026},
author = {Yang, R and Xu, S and Pang, W and Zhang, Y},
title = {Perception-Feedback Mapping Strategies for Flexible Human-Machine Interfaces.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {38},
number = {58},
pages = {e74589},
doi = {10.1002/adma.74589},
pmid = {42585091},
issn = {1521-4095},
support = {L242069//Beijing Natural Science Foundation/ ; 12225206//National Natural Science Foundation of China/ ; 12402199//National Natural Science Foundation of China/ ; 2025Z11DSB001//Tsinghua Initiative Scientific Research Program/ ; 2024YFB4707300//National Key R&D Program of China/ ; //New Cornerstone Science Foundation/ ; //Tsinghua National Laboratory for Information Science and Technology/ ; },
mesh = {Humans ; *Feedback ; *Man-Machine Systems ; *Perception ; Brain-Computer Interfaces ; },
abstract = {The field of flexible human-machine interfaces (HMIs) has been evolving rapidly with progress in materials science, structural design, device fabrication, and system integration. Developments of advanced flexible sensing and feedback devices have remarkably expanded the perceptual modalities, feedback capabilities, and functional scope of these systems. However, the broader deployment of flexible HMIs depends not only on the device performance of sensing/feedback components, but also on the control strategy that translates sensed signals into command outputs for feedback modules. This review highlights recent progress in such perception-feedback mapping strategies for flexible HMIs. Representative sensing and feedback modules, together with common system-level physical architectures, are briefly introduced as a basis for analyzing the relationship between sensed inputs and feedback outputs. A systematic framework is then proposed based on the principle of signal flow from the perception to the feedback side, allowing perception-feedback mapping strategies to be classified into command-based mapping, continuous mapping, and closed-loop control. Comprehensive discussions on these three categories of mapping strategies are presented, covering their operating principles, system implementation, and practical applications. Finally, we discuss current challenges and future directions toward more robust, generalizable, and integrated flexible HMI systems.},
}

Humans
*Feedback
*Man-Machine Systems
*Perception
Brain-Computer Interfaces

@article {pmid42869941,
year = {2026},
author = {Sun, C and Bao, K and Sun, B and Tang, B and Yang, X and Chen, Z and Tang, Y and Deng, Q and Wu, Y and Liu, L and Tang, R and Li, Z and Lei, K and Shi, Y and Shieh, W and Qiu, M and Lin, H and Li, L},
title = {Geometrical-optics resonator photonics.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {42869941},
issn = {2041-1723},
abstract = {Conventional waveguide optical resonators face inherent limitations of low damage thresholds, limited coupling tunability, and stochastic resonance. To overcome these, we introduce a geometrical-optics resonator (GOR) framework, emulating free-space reflection and thin-film coupling on-chip. Using ultra-wide waveguides with total internal reflection (TIR) mirrors and frustrated-TIR couplers, GORs eliminate conventional constraints. Fabricated devices achieve intrinsic Q-factors >1 million and enable tunable microwave photonic filters operating distortion-free at about 10× higher optical power. Design flexibility allows precisely tuned coupled resonators, facilitating cascaded networks for spectral engineering. Demonstrated GOR photonic molecules exhibit dynamically adjustable mode splitting (0.49 free spectral range), nearing theoretical limits. The platform also realizes record-large-scale Floquet topological photonic insulators with 1281 GOR lattices, supporting robust edge modes against defects and a broadband continuum. The GOR sets new performance benchmarks, enabling scalable platforms for quantum simulation and topological networks while driving integrated photonics toward higher density and system-level integration.},
}

@article {pmid42870375,
year = {2026},
author = {Cao, R and Miao, M and Gan, Y},
title = {The interplay of family support and family incivility on adolescent mental health.},
journal = {Journal of research on adolescence : the official journal of the Society for Research on Adolescence},
volume = {36},
number = {4},
pages = {e70290},
doi = {10.1111/jora.70290},
pmid = {42870375},
issn = {1532-7795},
support = {2024ZD09//Peking University Health Science Center Medical Education Research Funding Project/ ; CECA21105//Young Scholar Project of Beijing Education Sciences Planning Program during the 14th Five-Year Plan/ ; },
mesh = {Humans ; Adolescent ; Female ; Family Support ; Male ; *Mental Health ; Depression/psychology ; *Incivility ; Anxiety/psychology ; Longitudinal Studies ; China ; Personal Satisfaction ; *Family Relations/psychology ; },
abstract = {Both positive and negative family interactions are closely associated with adolescent mental health. Using an integrated point of view, this study used a longitudinal design to examine the interplay patterns of family support and family incivility on adolescent mental health. Two waves of data were collected from 999 Chinese high school students. Polynomial regressions and response surface analyses were used to examine interplay patterns of family support and incivility, specifically, their congruence effect and incongruence effect on mental health outcomes, including well-being (life satisfaction and meaning in life) and psychopathological symptoms (anxiety and depressive symptoms). Model comparison was conducted within a series of models to select the best fitting model for each mental health outcome. As for positive mental health outcomes, both life satisfaction and meaning in life had a curvilinear correlation with the incongruence of family support and family incivility. The correlations were larger when family support was higher than family incivility than vice versa. Additionally, a significant interaction effect of family support and family incivility was found on meaning in life. Specifically, the positive association between family support and meaning in life was weaker when family incivility was high. As for negative mental health outcomes, both anxiety and depressive symptoms were linearly correlated with the incongruence of family support and family incivility. Specifically, greater family support relative to family incivility was associated with lower levels of anxiety and depressive symptoms. These findings promoted understanding of the impact of family environment on adolescent mental health and highlighted the importance of considering both positive and negative forms of family interaction.},
}

Humans
Adolescent
Female
Family Support
Male
*Mental Health
Depression/psychology
*Incivility
Anxiety/psychology
Longitudinal Studies
China
Personal Satisfaction
*Family Relations/psychology

@article {pmid42870992,
year = {2026},
author = {Zaway, L and Zaway, I and Ktari, J and Amor, NB and Delahoche, L and Alaoui, LC and Jallouli, M},
title = {Optimized hybrid Transformer-based model for EEG classification: application to wheelchair control.},
journal = {AIMS neuroscience},
volume = {13},
number = {3},
pages = {452-475},
pmid = {42870992},
issn = {2373-7972},
abstract = {The purpose of this study is to improve the classification of electroencephalogram (EEG) signals for motor imagery-based brain-computer interfaces (MI-BCIs), thus enabling more reliable user interactions in assistive technologies such as autonomous wheelchairs. The study contributes by systematically investigating and comparing the performance of both simple (Transformer, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM)) and hybrid deep learning architectures (Transformer + CNN, Transformer + LSTM, CNN + LSTM) for EEG signal classification using multiple evaluation metrics, including accuracy, ROC AUC, confusion matrices, and prediction time. The findings demonstrate that the Transformer + CNN hybrid model consistently outperforms all other architectures, thereby achieving perfect classification accuracy (1.00), zero misclassifications, flawless AUC scores (1.000), and a competitive inference time (1.17 s), thus making it highly suitable for real-time MI-BCI applications. In contrast, simple models, particularly LSTM, show lower accuracies and reduced stabilities. Overall, this study highlights the advantage of combining attention mechanisms and convolutional architectures to effectively capture both spatial and contextual patterns in nonstationary EEG signals, thus establishing Transformer-based hybrid models as a promising approach for robust, real-time EEG classifications in next-generation MI-BCI systems.},
}

@article {pmid42871320,
year = {2026},
author = {Wang, S and Wu, Q and Lei, Q and Li, C and Lei, F},
title = {Brain-computer interfaces in pediatric populations: research advances, application challenges, and future directions.},
journal = {Frontiers in pediatrics},
volume = {14},
number = {},
pages = {1936801},
pmid = {42871320},
issn = {2296-2360},
abstract = {Brain-computer interfaces (BCIs) establish direct communication pathways between the brain and external devices. Leveraging the unique neuroplasticity of the developing brain, BCIs have emerged as a novel rehabilitative technology for children with neurodevelopmental disorders, including cerebral palsy, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and perinatal brain injury. This review comprehensively examines five major BCI paradigms-P300, motor imagery (MI), steady-state visual evoked potentials (SSVEP), functional near-infrared spectroscopy (fNIRS), and multimodal hybrid systems-with respect to their pediatric adaptability, technical limitations, and optimization strategies. We summarize the existing evidence across four key clinical applications: augmentative and alternative communication (AAC), motor function rehabilitation, ADHD attention training, and ASD social-emotional intervention. Key intrinsic (cognitive and physiological) and extrinsic (device and environmental) variables affecting BCI performance in children are analyzed, along with general optimization approaches including personalized parameter tuning, pre-training neurofeedback, and lightweight hardware modifications. Furthermore, we examine multilayer barriers to pediatric BCI adoption, including user experience, clinical workforce readiness, translational pathways, and child-specific ethical considerations. Preliminary evidence suggests that non-invasive BCIs can be feasibly used in children above 5 years of age, although this finding is based on short-term studies with small sample sizes and requires further validation through larger longitudinal trials. However, the immature pediatric brain, short attention spans, and low device tolerance preclude direct translation of adult BCI systems. While evidence is more established for AAC and motor rehabilitation in cerebral palsy, large-scale long-term data are still lacking for ADHD and ASD interventions. Future efforts should prioritize child-specific lightweight wearable devices, multicenter longitudinal trials, immersive integration with virtual reality (VR) and social robots, standardized clinical service frameworks, and ethical guidelines for invasive pediatric BCIs, thereby facilitating the transition of BCI technology from laboratory to clinical and home settings and maximizing its value in promoting neurodevelopmental plasticity in children with special needs.},
}

@article {pmid42874874,
year = {2026},
author = {Pan, H and Ding, P and Wang, F and Ma, Y and Gong, A and Nan, W and Li, N and Fu, Y},
title = {Post-Traumatic Stress Disorder Recognition Using the fNIRS Dual-Attention Network.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110925},
doi = {10.1016/j.jneumeth.2026.110925},
pmid = {42874874},
issn = {1872-678X},
abstract = {BACKGROUND: Post-traumatic stress disorder (PTSD) is a prevalent psychological disorder that can impair daily functioning and mental health. Current clinical assessment mainly relies on structured interviews and symptom scales, which may introduce subjectivity and inter-rater variability. Functional near-infrared spectroscopy (fNIRS) provides a noninvasive approach for measuring task-evoked hemodynamic responses and may offer objective physiological information for auxiliary PTSD recognition.

NEW METHOD: This study proposes an end-to-end deep learning framework, the fNIRS Dual Attention Network (FDANet), for PTSD recognition. Ten PTSD patients and ten healthy controls (HC) were recruited, and prefrontal hemodynamic signals, including oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT), were recorded during fear and neutral facial-expression stimuli. The three hemodynamic signals were concatenated along the channel dimension to form the model input. FDANet combines Transformer-based temporal modeling, one-dimensional temporal-channel attention, and convolutional feature extraction to learn discriminative representations from multichannel fNIRS signals.

RESULTS: The model was evaluated using subject-wise nested cross-validation with five outer folds. At the trial level, FDANet achieved an accuracy of 79.4%. At the subject level, FDANet achieved an accuracy of 95.0%.

Compared with the evaluated traditional machine-learning and deep-learning baseline methods, FDANet achieved higher classification accuracy at both trial and subject levels.

CONCLUSIONS: These findings indicate that task-evoked prefrontal fNIRS signals contain discriminative information for PTSD recognition. Given the portability and noninvasive measurement characteristics of fNIRS, FDANet may support the future development of objective auxiliary screening tools for PTSD, although further validation with larger and independent samples is required.},
}

@article {pmid42876598,
year = {2026},
author = {Schilling, A and Krauss, P},
title = {Can artificial systems become conscious?-Recursive embodied closure as an integrative framework.},
journal = {Frontiers in psychology},
volume = {17},
number = {},
pages = {1950309},
pmid = {42876598},
issn = {1664-1078},
abstract = {Recent advances in artificial intelligence, connectomics, and computational neuroscience increasingly raise the question of whether artificial systems-or even pure computer simulations-could become conscious. Contemporary theories emphasize embodiment, predictive processing, homeostatic regulation, and continuous interaction between cognition, body, and environment rather than isolated computation. We propose recursive embodied closure as an integrative framework linking embodied, predictive, functionalist, and dynamical approaches to consciousness. Rather than replacing existing theories, the framework identifies a shared organizational principle: the continuous recursive coupling of cognition, bodily regulation, action, and environmental feedback. We argue that consciousness may, in principle, emerge in artificial systems, including computational simulations, for three reasons. First, consciousness may depend on recursively closed loops linking cognition, body, and environment, and such loops could also be realized in robotic, neuromorphic, or virtual embodiments. Second, evidence from comparative neuroscience, convergent evolution, neurotechnology, and philosophy of mind suggests that the mechanisms relevant for consciousness are largely substrate-independent and may therefore be implemented in both biological and artificial systems. Third, although unknown physical mechanisms cannot be excluded, there is currently no empirical evidence for biological processes that are uniquely necessary for consciousness. Taken together, these considerations support the view that consciousness is best understood as an emergent property of recursively embodied, environmentally embedded cognitive systems rather than of biological matter itself. If so, biological brains, artificial neural networks, neuromorphic hardware, and even virtual agents may constitute plausible candidates for future artificial consciousness.},
}

@article {pmid42877105,
year = {2026},
author = {Park, W and Mansour, S and Kim, L and Eid, M},
title = {Decoding user satisfaction during reading of pre-defined question-answer pairs: BCI approach using delta and gamma EEG.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1934150},
pmid = {42877105},
issn = {1662-4548},
abstract = {INTRODUCTION: The rapid proliferation of large language models (LLMs) has intensified the need for robust evaluation frameworks to align artificial intelligence (AI) behavior with human expectations. While current reinforcement learning from human feedback (RLHF) strategies heavily rely on explicit, post-hoc self-reports, these metrics are fundamentally limited by subjective biases and user non-compliance.

METHODS: To address this, the current study presents a novel brain-computer interface (BCI) framework that systematically characterizes the continuous, implicit neural correlates of user satisfaction and dissatisfaction during a reading-based evaluative task involving pre-validated LLM-generated question-answer pairs. Sixty-four-channel electroencephalography (EEG) data were recorded from 30 participants while they read and evaluated various LLM-generated responses. To capture genuine subjective experiences, individual trials were relabeled based on post-trial 5-point Likert scale ratings into five distinct evaluative states, and spatiotemporal spectral analyses were performed. A ShallowFBCSPNet architecture was further used to evaluate single-trial classification between the very satisfied and very dissatisfied conditions under a subject-mixed cross-validation scheme.

RESULTS: Spatiotemporal spectral analyses revealed distinct neural oscillatory dynamics that temporally dissociate satisfaction and dissatisfaction. When reading highly satisfactory responses, a pronounced, sustained enhancement of low-frequency delta power was observed within the bilateral frontotemporal areas during the middle (4-8 s) and late (8-12 s) reading intervals, indexing high-order cognitive consolidation and reward processing. Conversely, processing highly dissatisfactory responses led to a localized suppression of high-frequency gamma power in the right frontotemporal region during the early interval (0-4 s), reflecting an immediate affective appraisal and expectation violation. Utilizing the ShallowFBCSPNet architecture, the system successfully discriminated between the "very satisfied" and "very dissatisfied" states, achieving an average accuracy of 81% under a subject-mixed cross-validation scheme.

DISCUSSION: These findings demonstrate that user evaluative states are encoded via distinct frequency-specific cortical networks over differing temporal windows. By bridging cognitive neuroscience and artificial intelligence, this study establishes the conceptual and empirical foundation for passive BCI-driven implicit feedback channels, laying groundwork toward future real-time paradigms for next-generation LLM alignment; the classification result should be interpreted as a feasibility demonstration rather than an estimate of real-world decoding accuracy.},
}

@article {pmid42863800,
year = {2026},
author = {Tang, E and Zhang, S and Qin, W and Xu, Y and Zheng, J and Chen, X and Wu, R and Zhao, H and Fu, Y and Zhao, C and Song, W and Li, J and Liu, C and Liu, Y and Yin, J and Huang, M and Chen, H},
title = {Attenuated attention engagement and sensorimotor resonance to social events in first-episode adolescent depression: Evidence from EEG oscillations.},
journal = {Journal of affective disorders},
volume = {417},
number = {},
pages = {122620},
doi = {10.1016/j.jad.2026.122620},
pmid = {42863800},
issn = {1573-2517},
abstract = {BACKGROUND: Depressive symptoms may disrupt social brain development during adolescence, resulting in adverse long-term quality of life. Yet, little is known regarding the cognitive and neural changes during the processing of social interactions in adolescents with first-episode depression, particularly from an allocentric perspective.

METHODS: We used electroencephalography (EEG) to assess two neural indicators during the observation of others' interactions, including sensorimotor mu suppression and occipital alpha-band activity, in 22 adolescents with first-episode major depressive disorder (MDD) and 23 healthy counterparts. Participants observed animated motions wherein actions were directed toward animate (human-like) or inanimate (rock) targets, comprising social (giving) and kinematically matched control events (disposing, taking and acquiring).

RESULTS: Healthy controls (HCs) exhibited stronger central-parietal mu suppression in the lower sub-band during observation of socially interactive giving events than disposing events. In contrast, adolescents with MDD manifested no corresponding difference in mu suppression. Additionally, depressive adolescents demonstrated significantly higher occipital alpha power when viewing social (patient) than non-social (rock) targets, and this alpha power was significantly higher than that observed in HCs. Exploratory correlation analyses revealed that attenuated sensorimotor resonance of social interactions was significantly associated with increased severity of depressive symptoms in the MDD group.

CONCLUSIONS: Adolescents with first-episode depression showed difficulties in efficiently encoding social information and implicitly extracting the social-relational meaning of observed social interactions. These findings critically extend current understanding of neurocognitive mechanisms underlying social cognitive alterations in this population, and provide potential avenues for early screening and targeted interventions of social functioning impairment in adolescence.},
}

@article {pmid42864430,
year = {2026},
author = {Chen, F and Wang, Q and Wang, L and Bhatia, S},
title = {Simulated responses from large language models are weak proxies for human economic preferences across 74 countries.},
journal = {Nature human behaviour},
volume = {},
number = {},
pages = {},
pmid = {42864430},
issn = {2397-3374},
support = {72631009, 72322009, 72173113//National Natural Science Foundation of China (National Science Foundation of China)/ ; 72371226//National Natural Science Foundation of China (National Science Foundation of China)/ ; Grant SES-1847794//National Science Foundation (NSF)/ ; },
abstract = {Large-scale surveys are central to investigating economic preferences but are costly and slow. Can large language model (LLM)-simulated responses substitute for human survey data? We conducted a virtual Global Preference Survey, simulating six experimentally validated economic preferences for 67,201 matched human respondents across 74 countries. Across 20 LLM configurations with diverse prompts and robustness checks, we found systematic but modest individual-level alignment (87.5% of Spearman correlations have 95% confidence intervals (CIs) above zero), with LLMs recovering the direction of associations in 77.8% (95% CI, (71.6%, 83.1%); CI-based consistency ratio) of gender, age and cognitive-ability dimensions, but only 48.1% (95% CI, (45.1%, 51.2%)) of cultural dimensions. Ablation and error decompositions suggest weaker alignment when removing demographic information than when removing country labels, and in many models alignment is weaker in economically undeveloped and less digitally connected countries. LLM-simulated responses may recover some individual- and group-level patterns but are not yet reliable proxies for cross-national representative surveys in macro-level inference or policy evaluation.},
}

@article {pmid42865765,
year = {2026},
author = {Lee, AH and Lee, J and Leung, V and Larson, L and Nurmikko, A},
title = {Distributed implantable microchips as wireless networks for neural sensing and stimulation.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1855768},
pmid = {42865765},
issn = {1662-4548},
abstract = {We examine ongoing research for implantable sensors and stimulators for future brain-computer applications and give a review on the concept where grain-of-sand size silicon microchips form the individual nodes in a multichannel wireless implant. In such an approach, each chip can perform the function of a stand-alone independent sensor if equipped with active microelectronic circuits designed to detect or stimulate target populations of neurons and capability for wireless communication. Delivering power to individual as well as spatially distributed ensembles of microchips requires a method for efficient transcutaneous/transcranial wireless energy transfer approach. Here we offer examples from authors' experience and comparisons to contemporary approaches as well as the challenges ahead in developing clinically viable wireless cortical chip-in-the-brain interfaces.},
}

@article {pmid42866011,
year = {2026},
author = {Trocellier, D and Kojima, S and N'kaoua, B and Lotte, F},
title = {Visual cues in MI-BCI induce biases in EEG classification models with deep learning but not with standard machine learning.},
journal = {Frontiers in neuroergonomics},
volume = {7},
number = {},
pages = {1804143},
pmid = {42866011},
issn = {2673-6195},
abstract = {OBJECTIVE: Deep learning classification methods are increasingly popular in the brain-computer interface (BCI) field due to their ability to learn complex, non-linear representations from EEG data. Unlike traditional machine learning approaches, which typically rely on hand-crafted features, deep learning models autonomously extract spatio-temporal features directly from the raw or minimally preprocessed signal. However, this flexibility may come at the cost of reduced interpretability and increased susceptibility to confounding factors. This study aims to investigate whether the inclusion of task-related visual cues in the input EEG window biases deep learning models by leading them to exploit irrelevant features, thereby compromising their neurophysiological validity.

APPROACH: This study investigates how including EEG signals measured during visual cue presentation on classification accuracy and feature extraction in motor imagery (MI)-based BCIs. Three classification pipelines were evaluated: CSP+LDA, a traditional machine learning approach; DeepConvNet, a convolutional neural network for EEG classification; and REVE, a Transformer-based EEG foundation model. All methods are trained and tested on two large scale open-access EEG datasets using two epoching strategies, with or without including the visual cue onset, to assess their impact on model performance and neurophysiological relevance. Attribution maps (e.g., Saliency maps) and CSP patterns are used to interpret the features learned by each model.

MAIN RESULTS: Including the visual cue onset significantly improved the classification accuracy of DeepConvNet and REVE. However, attribution analysis showed that the model mainly relied on early parietal activations, most likely cue-related visual evoked potentials, rather than MI-related sensorimotor rhythms (ERD/ERS), which were more prominent when the cue onset was excluded. In contrast, CSP+LDA showed minimal accuracy changes across conditions, with consistent motor cortex activation patterns.

SIGNIFICANCE: These findings demonstrate that higher classification performance does not necessarily imply neurophysiological validity. In particular, deep learning models may exploit confounding signals, such as visual cue processing, which undermines their interpretability and applicability in asynchronous or real-world BCIs, in which there is no such cue. We advocate for systematically applying explainability tools in BCI research to ensure classifiers rely on task-relevant brain activity. Additionally, we emphasize the importance of excluding visual cue periods from EEG segments containing reposenses evoked by the visual cue onset from the input to deep learning models.},
}

@article {pmid42866037,
year = {2026},
author = {Zhou, C and Ding, X and Zhao, J and Gu, X and Xu, Z},
title = {Terahertz liquid networks: a mesoscopic passive architecture for circumventing physical deadlocks in brain-computer interfaces.},
journal = {Nanoscale advances},
volume = {},
number = {},
pages = {},
pmid = {42866037},
issn = {2516-0230},
abstract = {Brain-computer interfaces (BCIs) for major neurological disorders face physical and physiological limitations. Mechanical mismatch in rigid microelectrode arrays precipitates chronic immune rejection and irreversible central nervous system damage, whereas bandwidth scaling in traditional wireless links inevitably breaches the 1 °C tissue thermal safety threshold. To circumvent this deadlock among bandwidth, heat dissipation, and biocompatibility, this review proposes the "terahertz liquid network" architecture. Utilizing injectable plasmonic nanoparticles as free-floating relay nodes, this architecture fundamentally abolishes the mechanical shear stresses characteristic of conventional implant interfaces. Exploiting broadband and dielectric properties of terahertz (THz) waves, the network achieves cross-scale bioelectronic integration across both physical and physiological boundaries. For communication and decoding, the system bypasses the strong THz absorption barrier of brain tissue via non-line-of-sight multipath scattering and THz-photoacoustic cross-modal transduction. Integrating these mechanisms with passive backscattering fulfills the terabits-per-second (Tbps) absolute bandwidth requirements dictated by full-cortical, high-fidelity motor decoding. For neuromodulation, specific THz photon energies are directed at voltage-gated ion channels to induce targeted non-thermal resonance, mediating precise, subcellular-level interventions devoid of macroscopic current overflow. Through an analysis of typical clinical scenarios-namely, audiovisual perception reconstruction and physical neural injury rehabilitation-this review demonstrates the feasibility of this architecture in resolving the conflict between high-throughput concurrency and stringent physiological constraints. Ultimately, the terahertz liquid network reconfigures the neural interface paradigm from a macroscopic active solid-state to a mesoscopic passive liquid-state. This transition grants the system superior biocompatibility and endows it with disease-modifying potential targeting underlying pathologies, establishing a trajectory for next-generation BCIs.},
}

@article {pmid42866729,
year = {2026},
author = {Hassan, ST and Faheem, MSB and Yaqubi, AJ and Brhaish, MS},
title = {Brain-computer interfaces and neuroprostheses for severe speech impairment in amyotrophic lateral sclerosis.},
journal = {Annals of medicine and surgery (2012)},
volume = {88},
number = {10},
pages = {6221-6224},
pmid = {42866729},
issn = {2049-0801},
abstract = {Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disorder that frequently culminates in severe dysarthria and anarthria, profoundly isolating patients and diminishing their quality of life. Traditional speech therapy and conventional augmentative and alternative communication devices rely on residual motor control and become ineffective in late-stage ALS. In response, brain-computer interfaces and speech neuroprostheses have emerged as transformative solutions. By decoding neural signals directly from the motor cortex and translating them into synthesized speech, these devices bypass compromised neuromuscular pathways. Recent high-impact clinical trials have demonstrated unprecedented success, achieving high-speed decoding and integrating artificial intelligence-driven voice banking to preserve a patient's unique vocal identity. However, significant translational challenges remain, including signal degradation, surgical risks, high socioeconomic costs, and ethical dilemmas regarding patient autonomy and data privacy. This perspective reviews the current state-of-the-art in speech neuroprosthetics, analyzes the barriers to widespread clinical implementation, and advocates for increased interdisciplinary collaboration and funding to establish these technologies as a standard of care for patients with severe speech impairments.},
}

@article {pmid42869466,
year = {2026},
author = {Hamard, R and Hug, F and Dick, TJM},
title = {Passive ankle exoskeleton assistance selectively modulates triceps surae neural control during balance.},
journal = {Journal of the Royal Society, Interface},
volume = {23},
number = {243},
pages = {},
doi = {10.1098/rsif.2025.1305},
pmid = {42869466},
issn = {1742-5662},
support = {//ISB International Travel Grant/ ; //The Company of Biologists Travelling Fellowship/ ; },
mesh = {Humans ; *Muscle, Skeletal/physiology/innervation ; *Exoskeleton Device ; *Postural Balance/physiology ; Male ; Female ; Adult ; *Ankle/physiology ; Electromyography ; Biomechanical Phenomena ; },
abstract = {Wearable assistive devices have potential to improve human balance, but their effectiveness remains limited by an incomplete understanding of how mechanical assistance influences neuromuscular control. This study aimed to determine the neural adaptations in the control of lower leg muscles in response to passive ankle exoskeleton assistance during standing balance tasks with varying levels of difficulty. Lower limb kinematics and kinetics were recorded to assess changes in joint dynamics and balance performance. Neural drive to the muscles was assessed by decomposing high-density electromyography signals into motor unit spike trains. Ankle exoskeleton assistance, provided by a simple spring-loaded passive device, did not alter or improve balance performance and had minimal effects on lower limb kinematics. However, exoskeleton assistance affected triceps surae neural control, but only in the soleus, by reducing neural drive, while the gastrocnemii showed no detectable changes in neural control. Together, these results demonstrate that passive exoskeleton assistance alters neural control of the triceps surae in a muscle-specific manner during balance. Understanding muscle-specific adaptations can support more effective exoskeleton design and the development of their control strategies informed by myoelectrical signals.},
}

Humans
*Muscle, Skeletal/physiology/innervation
*Exoskeleton Device
*Postural Balance/physiology
Male
Female
Adult
*Ankle/physiology
Electromyography
Biomechanical Phenomena

@article {pmid42861763,
year = {2026},
author = {Palthe, S and van Merode, NAM and Kolenburg, J and Marcelissen, T and Steffens, MG and van Koeveringe, GA and Heesakkers, JP and Witte, LPW},
title = {Bladder Contractility Classification Before Bladder Outlet Surgery: Do BCI, Wmax and the Maastricht-Hannover Nomogram Agree and Does It Matter for Outcome?.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.70489},
pmid = {42861763},
issn = {1520-6777},
abstract = {OBJECTIVES: The aim of this study was to assess the concordance between three classification systems (bladder contractility index, Maximum Watt's factor and Maastricht-Hannover nomogram) in allocating men to bladder contractility subgroups. Secondary aim was to explore whether preoperative contractility classification was associated with short-term flowmetry outcomes after BOP.

MATERIALS AND METHODS: This multicenter retrospective cohort study included men who underwent BOP (2010-2016) for lower urinary tract symptoms with available baseline urodynamic study and 6-week postoperative flowmetry. Patients were classified according to BCI, Wmax and MH-nomogram into very weak (BCI < 50, Wmax < 5, MH-nomogram < 10th percentile), weak (BCI: 50-100, Wmax: 5-10, MH-nomogram 10-25th percentile) and normal detrusor contractility (BCI > 100, Wmax > 10, MH-nomogram > 25th percentile) subgroups. Concordance was quantified using percentage agreement and Cohen's linear weighted kappa (κ). Postoperative flowmetry outcomes were compared between subgroups within each classification and pre-postoperative differences were determined for each subgroup.

RESULTS: One hundred and thirty three patients were included (median age 69 years). Agreement was highest between BCI and Wmax (78.9%, κ = 0.61, substantial), while agreement was weak between Wmax and MH-nomogram (48.9%, κ = 0.31) and negligible between BCI and MH-nomogram (37.6%, κ = 0.05). All subgroups showed improvement Qmax and PVR parameters after BOP, with no significant association found between preoperative contractility grade and postoperative outcomes.

CONCLUSIONS: BCI, Wmax and the MH-nomogram show only limited concordance, particularly involving the MH-nomogram, and cannot be regarded as interchangeable. Contractility classification was not associated with postoperative outcomes, and all subgroups improved after BOP. These findings underscore the need for a clinically relevant urodynamic consensus definition of DU and support shared decision-making over classification-based patient selection alone.},
}

@article {pmid42863207,
year = {2026},
author = {Ma, Y and Li, Y and Zhang, S and Liang, L and Li, G},
title = {Emerging promise in artificial vision research: Unveiling transformative potential through brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {206},
number = {Pt A},
pages = {109693},
doi = {10.1016/j.neunet.2026.109693},
pmid = {42863207},
issn = {1879-2782},
abstract = {Brain-computer interface (BCI) technology has driven revolutionary advancements in the field of artificial vision, presenting new opportunities for individuals with visual impairments. This review summarizes the key technological developments, clinical applications, and future challenges of BCI in artificial vision. In the domain of signal acquisition, both invasive techniques (such as MEA, ECoG, and DBS) and non-invasive techniques (including EEG, MEG, fMRI, and fNIRS) have significantly improved signal resolution and stability. Advances in visual encoding have facilitated the more efficient transmission of visual information. Input BCIs, including technologies based on SSVEP and P300, as well as output BCIs involving motor and speech decoding, are progressing rapidly. Clinically, third-generation retinal prostheses (such as Argus II and PRIMA) have helped patients with retinal degenerative diseases regain partial light perception. Based on closed-loop neural feedback, it is expected to help patients with cortical blindness achieve the remodeling of the optic nerve. Nevertheless, technical challenges remain, particularly regarding the long-term stability of neural signals and biocompatibility issues. Emerging solutions such as anti-scarring nanocoatings, self-powered wireless systems, neuromorphic spike encoding, and multimodal sensory integration are addressing several of these challenges. Looking ahead, advancements in neuro-augmented reality are expected to further propel progress in the field. The future of BCI development will depends on continued interdisciplinary collaboration and global cooperation to drive innovation and provide more efficient, accessible visual restoration solutions for patients.},
}

@article {pmid42863253,
year = {2026},
author = {Wang, C and Ding, J and Qi, X and Jialin, A and Xue, C and Hu, X and Shao, Y and Cheng, Y and Deng, W},
title = {Medial prefrontal cortex circuitry in inflammation-associated major depressive disorder: Brain-immune interactions and translational rationale for noninvasive neuromodulation.},
journal = {Asian journal of psychiatry},
volume = {125},
number = {},
pages = {105181},
doi = {10.1016/j.ajp.2026.105181},
pmid = {42863253},
issn = {1876-2026},
abstract = {Major depressive disorder (MDD) is a leading cause of disability worldwide, characterized by marked clinical and biological heterogeneity. Inflammation represents one key mechanism contributing to MDD, with a substantial proportion of patients exhibiting elevated inflammatory biomarkers. Inflammatory signaling affects neurotransmitter systems and neural circuits involved in reward, motivation, arousal, and threat processing, thereby linking to neurovegetative and motivational symptoms and reduced treatment responsiveness. The medial prefrontal cortex (mPFC) is a recurring node across inflammation-related symptom dimensions and provides a tractable focus for examining how inflammatory burden intersects with prefrontal, striatal, and limbic dysfunction. Sex may further moderate this brain-immune relationship through differences in stress susceptibility, brain structure and function, and inflammatory activity. Given the inconsistent effects of anti-inflammatory agents, noninvasive neuromodulation offers a targeted strategy for inflammation-associated MDD. Bottom-up transcutaneous auricular vagus nerve stimulation may regulate autonomic-immune signaling and ascending network pathways, whereas top-down high-definition transcranial direct current stimulation may modulate prefrontal plasticity and engage mPFC-centered circuitry. In this review, we synthesize evidence on peripheral and central inflammatory involvement in MDD, with emphasis on mPFC-centered circuit vulnerability and sex as a moderator of brain-immune interactions, and evaluate the translational rationale for complementary bottom-up and top-down stimulation in patients with elevated inflammatory burden. We also highlight key priorities for future research, including patient enrichment, outcome alignment, target engagement, parameter optimization, and sex-related variables to advance precision and stratified therapeutics in depression.},
}

@article {pmid42863491,
year = {2026},
author = {Chen, B and Deng, C and Ge, H and Su, X and Shen, Y},
title = {MTDANet: lightweight multi-scale temporal hierarchy and distance-regularized linear attention network for motor imagery brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb330},
pmid = {42863491},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor-imagery (MI) electroencephalography (EEG) offers a non-invasive route to brain-computer interaction, yet reliable single-trial decoding remains difficult because task-related patterns are weak, nonstationary, and distributed over multiple temporal scales. This study investigates whether a staged local-to-global model can integrate fine temporal-spatial structure with longer-range context without relying on unrestricted self-attention.

APPROACH: We propose MTDANet, an architecture that combines multi-scale temporal-spatial feature extraction, factorized temporal consolidation, and distance-regularized recurrent attention. The model was evaluated on three public MI datasets under subject-specific and leave-one-subject-out (LOSO) protocols. Separately initialized models were also trained on P300 and steady-state visual evoked potential (SSVEP) datasets to examine broader architectural applicability. Participant-level paired analyses used two-sided paired t tests with within-setting Holm correction.

MAIN RESULTS: MTDANet achieved subject-specific accuracies of 85.61 ± 8.32%, 90.85 ± 7.62%, and 87.79 ± 13.93% on BCI Competition IV 2a, BCI Competition IV 2b, and BNCI2014_002, respectively. Relative to the highest-mean baseline, the paired improvements were supported after within-setting Holm correction on BCI Competition IV 2a (3.56 percentage points, adjusted p = 0.0097) and 2b (1.62 points, adjusted p = 0.0168). Under LOSO evaluation, the corresponding accuracies were 63.31 ± 15.74%, 79.77 ± 7.57%, and 79.96 ± 11.95%. The 2.08-point BNCI2014_002 difference had a 95% confidence interval of 0.42-3.73 points and an adjusted p value of 0.0387. These three comparisons did not remain significant in a broader 56-comparison Holm sensitivity analysis. The P300 and SSVEP models attained the highest observed mean accuracies.

SIGNIFICANCE: The results provide within-setting evidence for staged local-to-global temporal modeling in offline MI-EEG evaluations.},
}

@article {pmid42863775,
year = {2026},
author = {Lin, YG and Ni, J and Yu, H and Li, T},
title = {Increased Morning Cortisol Levels across Different Mood States in Bipolar Disorder: A Systematic Review and Meta-Analysis.},
journal = {Neuroscience and biobehavioral reviews},
volume = {},
number = {},
pages = {107027},
doi = {10.1016/j.neubiorev.2026.107027},
pmid = {42863775},
issn = {1873-7528},
abstract = {OBJECTIVE: Bipolar disorder (BD) is associated with hypothalamic-pituitary-adrenal (HPA) axis dysregulation. However, findings regarding morning cortisol levels across different mood states remain inconsistent. This systematic review and meta-analysis aimed to synthesize evidence on morning cortisol levels in BD across different mood states, while considering the effects of various clinical moderators.

METHODS: A systematic search of seven databases was conducted for articles published up to August 31, 2026. Studies were included if they reported cortisol levels measured under eligible morning sampling protocols in BD patients and healthy controls. A total of 39 studies (n = 3,611) were included. A multilevel mixed-effects meta-analysis was applied to account for the dependence of mood-state subgroups within studies. Moderator analyses were conducted via meta-regression.

RESULTS: Compared with healthy controls, BD patients exhibited significantly elevated morning cortisol levels, both overall and across specific mood states. In exploratory moderator analyses, earlier age at onset was associated with higher morning-cortisol, whereas a greater proportion of participants receiving medication was associated with lower morning cortisol after adjustment for mood state.

CONCLUSION: BD is associated with a significant elevation in morning cortisol across mood states. This pattern is consistent with a potential trait-related component of HPA-axis dysregulation, although its expression may vary with clinical state and sampling conditions.},
}

@article {pmid42855851,
year = {2026},
author = {Heyat, MBB and Sultana, A and Rehman, AU and Begum, A and Sawan, M},
title = {Ensemble-Based Machine Learning Models for Classifying Intervention-Related Psychological Outcome Patterns in Menopausal Women Treated With Pimpinella anisum.},
journal = {Brain and behavior},
volume = {16},
number = {10},
pages = {e71787},
pmid = {42855851},
issn = {2162-3279},
support = {10318A992001//Westlake University/ ; //Hangzhou Postdoctoral Research/ ; 10318A992001//Westlake University/ ; 10318A992001//Westlake University/ ; },
mesh = {Humans ; Female ; *Menopause/psychology ; *Machine Learning ; Middle Aged ; Random Forest ; Boosting Machine Learning Algorithms ; Predictive Learning Models ; Classification Algorithms ; Depression ; Anxiety ; Prediction Algorithms ; },
abstract = {BACKGROUND: Menopause, a natural biological transition in the life of women, is often associated with significant psychological disturbances such as depression, anxiety, and stress. However, early identification and management of psychological disorders in menopausal women remain under-addressed due to limited specialist access and a lack of systematic screening approaches.

PURPOSE: This study aimed to develop ensemble-based machine learning (ML) models to classify intervention-related clinical and psychological outcome patterns in menopausal women using clinical and questionnaire-based data collected during a controlled intervention study.

METHOD: Psychological and menopausal symptoms were assessed using standardized tools, including the Depression Anxiety Stress Scale-21 (DASS-21), modified Kupperman index (MKI), vaginal health index (VHI), and menopause symptoms treatment satisfaction questionnaire (MS-TSQ). Statistical analyzes were performed using chi-square tests and independent t-tests to evaluate group differences. Three ensemble classifiers, random forest (RF), gradient boosting (GB), and AdaBoost (AB), were evaluated using repeated cross-validation.

RESULT: Among these, the RF classifier achieved the best performance with a sensitivity of 93.3%, a specificity of 90.0%, an accuracy of 91.7%, a precision of 90.3%, and an F1-score of 91.8%.

CONCLUSION: These results indicate that ensemble ML models, particularly RF, can serve as effective supplementary tools for psychological disorder risk stratification in menopausal care, aiding clinicians in decision-making. However, these models are intended to complement, not replace, clinical assessments.},
}

Humans
Female
*Menopause/psychology
*Machine Learning
Middle Aged
Random Forest
Boosting Machine Learning Algorithms
Predictive Learning Models
Classification Algorithms
Depression
Anxiety
Prediction Algorithms

@article {pmid42856243,
year = {2026},
author = {Conlan, EC and Foli, C and Memberg, WD and Herring, EZ and Sweet, JA and Ajiboye, AB},
title = {Assessing the Relative Impact of Grasp and Object on Inferior Frontal Gyrus Activity during a Grasping Task.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.06.30.735663},
pmid = {42856243},
issn = {2692-8205},
abstract = {The lateral grasp network, responsible for translating visual properties of an object to execution of a motor act, is comprised of the anterior intraparietal area (AIP), area F5, and the primary motor cortex (M1). Non-human primate studies of F5 have shown that it encodes a wide range of hand positions and object properties. Human studies in F5's human homologue, the inferior frontal gyrus (IFG), have leveraged this area's ability to encode grasp-object pairs for the purposes of Brain Machine Interface (BMI) control. However, whether modulation is driven by grasp, object, or the interaction between grasp and object is unclear. In the present study, intracortical IFG signals were recorded from human participants with tetraplegia during a motor visualization task where grasp and object were varied. Grasp was found to be the predominant factor driving modulation of IFG signals. Object was found to only be weakly represented in neural data. However, object contribution peaked earlier than grasp contribution, indicating early integration of object information. Grasp-object interactions were also found to have a significant impact. Cortical separation between object conditions varied based on the grasp used. In addition, subspace analysis showed that the underlying neural population structure associated with each grasp type was significantly different from one another. Despite the impact of grasp-object interactions, the present study demonstrates that grasp is the primary driver of inferior frontal gyrus neural modulation.},
}

@article {pmid42856741,
year = {2026},
author = {Settgast, T and Shriki, O and Northoff, G and Zilio, F and Borgheai, SB and Shahriari, Y and Kübler, A},
title = {All Roads Lead to Rome: Resting-State Criticality as a Neurophysiological Marker of Brain-Computer Interface (BCI) Performance in People with Amyotrophic Lateral Sclerosis (ALS) and Healthy Participants.},
journal = {Research square},
volume = {},
number = {},
pages = {},
doi = {10.21203/rs.3.rs-10422321/v1},
pmid = {42856741},
issn = {2693-5015},
abstract = {This exploratory study investigates the relationship between resting-state brain dynamics (EEG) and subsequent brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis (ALS) and healthy participants. Grounded in the critical brain hypothesis, we examined whether neurophysiological markers of criticality in resting-state EEG correlate with subsequent BCI performance. We analyzed neuronal avalanche dynamics, multiscale criticality measures, the spectral slope, and Lempel-Ziv complexity across two datasets: a longitudinal single-case study of a person in the locked-in state after a disease history with ALS (Dataset 1) and longitudinal data from 5 people with ALS and single sessions from 6 healthy participants (Dataset 2). Neuronal avalanche dynamics, the spectral slope, and Lempel-Ziv complexity consistently correlated with subsequent BCI performance in people with ALS. Conversely, healthy participants showed inverted correlation patterns for the neuronal avalanche dynamics. While group comparisons were not statistically significant, effect size analyses indicated supercritical brain dynamics in people with ALS. We integrate these findings into the B-ARC (BCI-Aptitude through Resting-State Criticality) framework proposing an inverted U-shaped relationship between the proximity of resting-state brain activity to criticality and BCI control. In this framework, healthy participants approach the critical optimum from a subcritical regime, while people with ALS appear to approach it from a putatively supercritical regime characterized by cortical hyperexcitability. These findings and the proposed framework provide potential resting-state predictors of BCI aptitude in people with ALS and may inform the detection of so-called Windows of Consciousness .},
}

@article {pmid42858309,
year = {2026},
author = {Li, Y and Feng, J and Hu, W and Zeng, Z and Pan, Q and Wang, J and Li, M and Elbert, T and Karl, A and Zang, Y},
title = {Dismantling Psychological Interventions for Youth with Complex Trauma: A Systematic Review and Component Network Meta-Analysis.},
journal = {Psychotherapy and psychosomatics},
volume = {},
number = {},
pages = {1},
doi = {10.1159/pps/aemag009},
pmid = {42858309},
issn = {1423-0348},
abstract = {INTRODUCTION: Complex trauma-related posttraumatic stress disorder (PTSD) and comorbidities pose a significant mental health burden in children. However, the comparative effectiveness of psychological interventions and shared therapeutic components remains unclear.

METHODS: We conducted a systematic review, network, and component network meta-analysis of randomized controlled trials of psychological interventions for children and adolescents with complex trauma. PubMed, Embase, Web of Science, PsycINFO, and Cochrane Library were searched up to September 2024. PTSD was the primary outcome; retention and comorbid symptoms (depression, anxiety, dissociation, externalizing problems) were secondary outcomes.

RESULTS: Forty-eight studies (6,017 participants) were included in this review. Multimodal group interventions (SMD -1.68, 95% confidence interval [CI] -3.04 to -0.32), phase-based trauma-focused cognitive behavioral therapy (TF-CBT) with parental involvement (-1.12, -1.53 to -0.70), TF-CBT (-1.06, -1.59 to -0.52), non-TF-CBT with parental involvement (-0.93, -1.70 to -0.17), eye movement desensitization and reprocessing (-0.85, -1.60 to -0.10), and phase-based TF-CBT (-0.79, -1.20 to -0.39) significantly reduced PTSD. Exposure emerged as the only significantly effective component for PTSD (-0.85, 95% credible interval [CrI] -1.60 to -0.06). Efficient component combinations included affective modulation with a therapeutic foundation of psychoeducation and nonspecific elements for PTSD (-0.97, 95% CrI -1.92 to -0.01) and exposure plus parental involvement with the same foundation for depression (-2.11, 95% CrI -4.06 to -0.20).

CONCLUSION: Trauma-focused approaches were the most effective treatments for pediatric PTSD and emotional comorbidities related to complex trauma. A more cost-effective intervention approach may prioritize exposure techniques, incorporate affective modulation, and include parental involvement when feasible.},
}

@article {pmid42858322,
year = {2026},
author = {Yang, Y and Zhang, X and Zan, X and Niu, C and Wei, X and Feng, P and Li, G and Gao, M},
title = {A Clearance-Repair-Activation Framework for Alzheimer's Disease: A Perspective and Validation Roadmap.},
journal = {Aging and disease},
volume = {},
number = {},
pages = {},
doi = {10.14336/AD.2026.1040},
pmid = {42858322},
issn = {2152-5250},
abstract = {Alzheimer's disease (AD) is a heterogeneous clinicobiological syndrome involving impaired protein clearance, neuroinflammation, synaptic and cellular injury, network dysfunction, and progressive functional decline. Amyloid-directed antibodies provide an evidence-based treatment backbone for selected patients with early symptomatic AD, but adjunctive strategies may be needed to address other disease mechanisms. This Perspective proposes a clearance-repair-activation framework for hypothesis-driven research. Clearance includes established pharmacological treatment and risk management as well as investigational drainage approaches; lymphaticovenous anastomosis (LVA) and deep cervical lymphaticovenous anastomosis (dcLVA) are considered only as research options within this broader domain. Repair includes neural stem-cell (NSC), induced pluripotent stem-cell-derived, engineered-cell, and secretome-based strategies, for which AD evidence remains predominantly preclinical. Activation encompasses invasive and non-invasive stimulation, sensory entrainment, neurofeedback, brain-computer interface (BCI) monitoring, and cognitive rehabilitation; BCI is positioned mainly as an experimental monitoring and adaptive rehabilitation platform. We define falsifiable links between clearance and repair readiness and between repair markers and activation responsiveness, consider adaptive and parallel alternatives to fixed sequencing, and integrate disease stage, heterogeneity, regulation, and combination-trial design. Informatics is limited to trial infrastructure and safety governance. The validation roadmap specifies success criteria, stop rules, interaction testing, clinically meaningful outcomes, and long-term surveillance.},
}

@article {pmid42860745,
year = {2026},
author = {Lee, C and Park, S and Lee, C and Lee, D and Ha, H and Shin, H and Kim, T and Cho, C and Byun, K and Lee, S},
title = {Associations of adiposity and physical fitness with hippocampaldependent mnemonic discrimination in postmenopausal women.},
journal = {Physical activity and nutrition},
volume = {30},
number = {3},
pages = {162-172},
pmid = {42860745},
issn = {2733-7545},
support = {//Ministry of Education/ ; 2023S1A5A2A21084054//National Research Foundation of Korea/ ; },
abstract = {PURPOSE: This study examined the associations of adiposity indices and physical fitness components with domain-specific cognitive function in postmenopausal women, focusing on hippocampus-dependent mnemonic discrimination.

METHODS: The study involved 68 postmenopausal women aged ≥ 40 years who were at least one year postmenopausal. Body composition was assessed using dual-energy X-ray absorptiometry, and adiposity indices comprised body fat mass, body fat percentage, fat mass index, fat-to-lean mass ratio, and android-to-gynoid fat ratio. Cognitive function was assessed using the color-word Stroop task and the object and spatial versions of the Mnemonic Discrimination Task. Physical fitness was evaluated based on grip strength, sit-and-reach flexibility, and peak oxygen uptake. Correlation and hierarchical multiple linear regression analyses were performed (n = 61).

RESULTS: Adiposity indices were not significantly associated with Stroop performance. Body fat mass and fat mass index were negatively associated with object-version recognition score and spatial-version lure discrimination index, and body fat mass was also negatively associated with spatial-version recognition score. Spatial-version lure discrimination index was positively correlated with flexibility and peak oxygen uptake. In hierarchical regression, adding fat mass index and peak oxygen uptake to age significantly improved the model predicting spatial-version lure discrimination index; peak oxygen uptake was significant at this step. However, flexibility provided no significant additional explanatory power, and no predictor remained significant in the final model.

CONCLUSION: Hippocampus-dependent mnemonic discrimination in postmenopausal women may be related to adiposity and overall fitness status at the level of simple associations; however, the independent contribution of specific fitness components requires further investigation.},
}

@article {pmid42852247,
year = {2026},
author = {Guan, Y and Gao, Y},
title = {Neurorehabilitation for cognitive and motor recovery: mechanisms, interventions, and translational advances.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1828447},
pmid = {42852247},
issn = {1664-2295},
mesh = {Humans ; *Recovery of Function/physiology ; *Neurological Rehabilitation/methods/trends ; Neuronal Plasticity/physiology ; Translational Research, Biomedical ; *Stroke Rehabilitation/methods ; Cognitive Training ; Stroke/physiopathology/complications ; Animals ; },
abstract = {This narrative review synthesizes recent advances in neurorehabilitation, focusing on mechanisms of neural plasticity, glial cell function, and interventions for cognitive and motor recovery. We explore multidisciplinary approaches, including cognitive training, motor rehabilitation, neurostimulation, and regenerative therapies, highlighting their translational potential. Special attention is given to clinically relevant rehabilitation modifiers and complications, including spasticity, frailty, and post-stroke epilepsy, alongside strategies for enhancing quality of life and social reintegration. Emerging technologies such as brain-computer interfaces, virtual reality, and robotics are discussed for their role in personalized rehabilitation. This review emphasizes evidence-based, patient-centered interventions to enhance functional recovery and quality of life, identifying future directions for personalized neurorehabilitation across stroke survivors, elderly, and frail populations.},
}

Humans
*Recovery of Function/physiology
*Neurological Rehabilitation/methods/trends
Neuronal Plasticity/physiology
Translational Research, Biomedical
*Stroke Rehabilitation/methods
Cognitive Training
Stroke/physiopathology/complications
Animals

@article {pmid42852356,
year = {2026},
author = {Song, W and Si, S and Wang, Y and Chang, H and Tang, X},
title = {Application of Artificial Intelligence in the Prediction and Management of Stroke Rehabilitation.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0702},
pmid = {42852356},
issn = {2692-7632},
abstract = {Artificial intelligence is being studied across the stroke care pathway, but evidence from acute diagnosis, early prognosis, rehabilitation-outcome prediction, and long-term management is often discussed without clearly separating these clinical tasks. This structured narrative review adds an integrated framework that maps each application to its decision point, relevance to rehabilitation, validation level, and readiness for clinical use. Unlike previous technology-centered reviews, it explicitly separates acute prognostic evidence from rehabilitation-specific evidence and distinguishes technical performance from transportability, clinical impact, and rehabilitation benefit. Acute imaging and prognostic models may provide baseline information for later rehabilitation planning, but their diagnostic or prognostic performance does not establish rehabilitation efficacy. Most rehabilitation models and robotic, virtual-reality, brain-computer interface, wearable, and home-monitoring applications remain supported mainly by internal validation or early exploratory studies. Small or selected datasets, limited external and prospective validation, uncertain workflow effects, and sparse patient-centered outcomes continue to constrain clinical interpretation.},
}

@article {pmid42854016,
year = {2026},
author = {Zhu, SY and Xiao, QX and Duan, ZY and Ma, YY and Zhao, PC and Chen, HX and Bao, YJ and Lei, JM and Wang, L and Zhou, H and Ma, RX and Pu, HL and Wu, KF and Xiong, LL and Du, RL},
title = {Bionic sleep therapy combined with multimodal management for chronic insomnia with alcohol withdrawal-related symptoms: A case report.},
journal = {Medicine},
volume = {105},
number = {41},
pages = {e50991},
doi = {10.1097/MD.0000000000050991},
pmid = {42854016},
issn = {1536-5964},
support = {No. GZWJWPF2025021//Guizhou Provincial Health Commission Clinical Key Specialty Construction “Peak Climbing Plan†Project/ ; Not applicable//Young Elite Scientists Sponsorship Program by the Guizhou Association for Science and Technology/ ; QNB(2026)001//Youth Science Fund Project (Category B) of the Basic Research Program of the Science and Technology Plan Project of Guizhou Province/ ; },
mesh = {Humans ; Male ; Aged ; *Sleep Initiation and Maintenance Disorders/therapy ; *Substance Withdrawal Syndrome/therapy ; Hypnotics and Sedatives/administration & dosage/therapeutic use ; Combined Modality Therapy ; Dexmedetomidine/administration & dosage/therapeutic use ; Propofol/administration & dosage/therapeutic use ; Cognitive Behavioral Therapy/methods ; *Alcoholism/complications ; },
abstract = {RATIONALE: Patients who use alcohol as a sleep aid may experience withdrawal-related insomnia and hyperarousal, which can reinforce continued drinking. Bionic sleep therapy (BST) is an exploratory intervention using intravenous anesthetic and sedative agents under close monitoring to induce a brief, controllable, sleep-like state. It may provide short-term support for sleep initiation during early alcohol withdrawal.

PATIENT CONCERNS: A 71-year-old man had chronic insomnia for over 15 years and consumed 100 to 200 mL of Chinese baijiu nightly to initiate sleep, with additional alcohol after nocturnal awakenings. Repeated attempts to stop drinking were followed by worsening insomnia, restlessness, palpitations, diaphoresis, and intense alcohol craving, which improved after drinking.

DIAGNOSES: Chronic insomnia with recurrent mild alcohol withdrawal-related symptoms and a high risk of complicated alcohol withdrawal.

INTERVENTIONS: The patient underwent 2 monitored BST sessions on hospital days 1 and 2 under continuous cardiorespiratory monitoring. Propofol (1 mg/kg) was administered intravenously for rapid induction, followed by a 60-minute dexmedetomidine infusion via an IV pump at 0.2 to 0.4 μg/kg/h. Multimodal treatment was intensified on hospital day 2, including cognitive behavioral therapy for insomnia, medication adjustment, and adjunctive neuromodulation.

OUTCOMES: During the overall multimodal treatment course, on the night of the first BST session, the patient initiated sleep without alcohol and returned to sleep after a nocturnal awakening without further drinking. Polysomnography showed a total sleep time of 373 minutes and sleep efficiency of 85.6%. Following the second session, he consumed approximately 20 mL of alcohol after a nocturnal awakening before returning to sleep; sleep diary-reported sleep efficiency was 90.6%. Vital signs remained stable during both sessions, and no apparent anesthesia- or sedation-related adverse events occurred. No further alcohol intake was recorded from hospital day 3 to day 7. At discharge, subjective sleep efficiency was 87.7%, with improvements in sleep initiation, maintenance, and some daytime symptoms. Postdischarge follow-up data were unavailable.

LESSONS: BST using propofol and dexmedetomidine may warrant further investigation as an adjunct to multimodal management in patients with chronic insomnia, long-term alcohol use as a sleep aid, and withdrawal-related symptoms. However, improvements cannot be attributed specifically to BST, and its contribution and durability require prospective evaluation.},
}

Humans
Male
Aged
*Sleep Initiation and Maintenance Disorders/therapy
*Substance Withdrawal Syndrome/therapy
Hypnotics and Sedatives/administration & dosage/therapeutic use
Combined Modality Therapy
Dexmedetomidine/administration & dosage/therapeutic use
Propofol/administration & dosage/therapeutic use
Cognitive Behavioral Therapy/methods
*Alcoholism/complications

@article {pmid42855049,
year = {2026},
author = {Shaukat, W and Nobrega, DB and Rehman, A and van Heumen, J and Roos, R and de Pater, M and Liaqat, C and Hall, DC and Lhermie, G and Kastelic, JP and Kelton, DK and De Buck, J and Barkema, HW},
title = {Bayesian estimation of true herd-level prevalence and spatial distribution of Salmonella Dublin-seropositive dairy herds in Alberta, Canada.},
journal = {Journal of dairy science},
volume = {},
number = {},
pages = {},
doi = {10.3168/jds.2026-29221},
pmid = {42855049},
issn = {1525-3198},
abstract = {Salmonella Dublin, a cattle-adapted serovar of Salmonella enterica ssp. enterica with zoonotic potential, has become a pathogen of increasing concern for the dairy industry in Canada and beyond. A comprehensive understanding of its epidemiology is crucial to guide effective surveillance and control. The objectives of this serial cross-sectional study were to estimate the true herd-level seroprevalence of Salmonella Dublin and to characterize the spatial distribution of Salmonella Dublin-seropositive dairy herds in Alberta, Canada. Bulk tank milk (BTM) samples were collected from all active dairy herds in Alberta, Canada, in June (n = 462) and December (n = 451) 2024, and PrioCHECK ELISA was used to detect antibodies against Salmonella Dublin. Herds were classified as positive if ≥ 1 sample had a percent positivity ≥ 35. True seroprevalence was estimated using a Bayesian latent class model accounting for imperfect sensitivity and specificity. Spatial patterns were evaluated using a Bayesian geostatistical model, and a spatial scan statistic was used to detect localized clusters. The estimated true prevalence of Salmonella Dublin-seropositive herds in Alberta was 14.9% (95% Bayesian credible interval: 7.3-28.2%), with regional estimates of 8.0% (95% BCI = 1.6-22.6%), 13.9% (95% BCI = 4.2-31.8%), and 20.9% (95% BCI = 8.5-42.5%) for the north, central, and south regions of Alberta, respectively. The continuous spatial risk surface revealed that the true herd seropositivity varied markedly across Alberta, with some areas having much higher predicted seropositivity probabilities than others. Furthermore, 2 localized clusters of higher seropositivity were detected: a prominent cluster (95-km radius) in southwestern Alberta and a relatively smaller cluster (19-km radius) in a high-density dairy corridor in the central region. These findings indicate that Salmonella Dublin exposure is moderate in Alberta, with marked regional heterogeneity and pronounced spatial gradients in herd-level seropositivity. Incorporating spatial context into surveillance and control may improve targeting of interventions aimed at reducing within-region transmission. These findings also illustrate the value of Bayesian latent class modeling for generating bias-adjusted true seroprevalence estimates in the absence of a gold-standard diagnostic test.},
}

@article {pmid42855606,
year = {2026},
author = {Jahanian Najafabadi, A and Longo, MR and Liepelt, R},
title = {The dynamic bodily self across lifespan: Agency, ownership, and the plasticity of internal body models.},
journal = {Psychonomic bulletin & review},
volume = {33},
number = {8},
pages = {},
pmid = {42855606},
issn = {1531-5320},
mesh = {Humans ; *Sense of Agency ; *Body Image ; *Illusions/physiology ; *Aging/physiology ; },
abstract = {Two fundamental aspects of research on bodily self-consciousness, agency and body ownership, have recently seen exponential growth. The seminal paper of Botvinick and Cohen in 1998 provided a vital psychophysical tool for studying the bodily self - the rubber hand illusion, in which one feels as if an external object becomes a part of one's body. Recent motor control views of ownership highlight the influence of agency, the sense that one is in control of one's movements. Technological advancements have facilitated more ecologically valid experimental paradigms, such as virtual versions of the rubber hand illusion, furthering our understanding of the mechanisms through which the brain generates agency and ownership sensations. Recent findings suggest that agency and ownership can interact while remaining dissociable, as one may experience agency over an object that is not embodied or ownership of an object that cannot be controlled. Understanding the neurocognitive mechanisms underlying agency and ownership is critical for addressing the challenges of healthy aging. As sensory-motor precision declines across the lifespan, maintaining a coherent sense of body and control becomes essential for mobility, the successful adoption of assistive technologies, and overall cognitive resilience. This review synthesizes current findings on the agency-ownership interaction, discusses its neurobiological substrates, and explores its transformative potential for the design of "self-inclusive" prosthetics and brain-machine interfaces. We conclude by outlining a framework showing how optimization of these processes can support functional independence and psychological well-being in an aging population.},
}

Humans
*Sense of Agency
*Body Image
*Illusions/physiology
*Aging/physiology

@article {pmid42855610,
year = {2026},
author = {Shibuya, J and Kyoda, Y and Hashimoto, K and Kobayashi, K and Tanaka, T and Masumori, N},
title = {Changes in Bladder Outlet Obstruction After Photoselective Vaporization of the Prostate in Men With and Without Detrusor Underactivity: A Retrospective Propensity Score-Matched Study.},
journal = {Lower urinary tract symptoms},
volume = {18},
number = {6},
pages = {e70100},
doi = {10.1111/luts.70100},
pmid = {42855610},
issn = {1757-5672},
mesh = {Humans ; *Urinary Bladder Neck Obstruction/surgery/physiopathology/etiology ; Male ; Retrospective Studies ; *Prostatic Hyperplasia/surgery/complications ; *Urinary Bladder, Underactive/complications/physiopathology ; Aged ; Urodynamics ; Propensity Score ; *Laser Therapy/methods ; Treatment Outcome ; Middle Aged ; },
abstract = {OBJECTIVES: To compare symptomatic improvement after photoselective vaporization of the prostate (PVP) between patients with and without detrusor underactivity (DU), together with objective changes in urodynamic parameters assessed by pressure-flow study (PFS).

METHODS: This single-center retrospective study evaluated 136 benign prostatic hyperplasia (BPH) patients treated with a 180 W PVP-XPS system from 2020 through 2025. The patients were divided into DU and non-DU groups based on the bladder contractility index (BCI), with DU defined as BCI < 100. We retrospectively analyzed selected patients with BPH and clinically or urodynamically relevant bladder outlet obstruction (BOO) who underwent PVP and completed PFS before and 6 months after surgery. Propensity score matching (PSM) was applied to adjust for age and the baseline BOO index (BOOI).

RESULTS: A total of 105 patients were included in the final analysis and 53 were in the DU group. The DU group had a significantly lower BOOI (48.6 ± 17.5 vs. 71.4 ± 32.3, p < 0.01) than the non-DU group. After PSM, age and baseline BOOI were well balanced, but BCI remained significantly different. No significant differences between the two groups were observed in questionnaire scores or uroflowmetry findings at 6 months after surgery. The BOOI at 6 months after surgery was not significantly different between the DU group (13.4 ± 13.7) and the non-DU group (13.7 ± 19.3) (p = 0.93). BOOI reduction was also not significantly different between the DU group (-37.0 ± 24.1) and the non-DU group (-36.2 ± 24.5) (p = 0.90).

CONCLUSION: In selected patients with clinically or urodynamically relevant BOO, significant improvements in BOOI and LUTS were observed in patients with DU after PVP, with no statistically significant between-group differences detected at 6 months.},
}

Humans
*Urinary Bladder Neck Obstruction/surgery/physiopathology/etiology
Male
Retrospective Studies
*Prostatic Hyperplasia/surgery/complications
*Urinary Bladder, Underactive/complications/physiopathology
Aged
Urodynamics
Propensity Score
*Laser Therapy/methods
Treatment Outcome
Middle Aged

@article {pmid42855737,
year = {2026},
author = {La, Y and Yuan, J and Chen, J and Lv, K and Wang, X and Gao, Q and Wang, Y and Wang, C and Xu, B and Yang, Y and Zhang, J},
title = {The glymphatic-lymphatic axis: known pathways, open questions, and emerging roles in neurodegeneration.},
journal = {Translational neurodegeneration},
volume = {15},
number = {1},
pages = {},
pmid = {42855737},
issn = {2047-9158},
support = {LY24H090006//Natural Science Foundation of Zhejiang Province/ ; 82571418//National Natural Science Foundation of China/ ; 32530027//National Natural Science Foundation of China/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; },
mesh = {Humans ; *Glymphatic System/metabolism/pathology ; *Neurodegenerative Diseases/pathology/metabolism/physiopathology ; Animals ; *Lymphatic Vessels/metabolism/pathology ; *Brain/metabolism ; Lymphatic System ; Cerebrospinal Fluid/metabolism ; },
abstract = {The discovery of the glymphatic system and meningeal lymphatic vessels has transformed our understanding of brain fluid homeostasis and central-peripheral communication, highlighting the critical role of cerebrospinal fluid in brain clearance. While substantial progress has been made in defining the structural components and physiological regulators of brain clearance, major uncertainties remain regarding (1) how these systems interact as an integrated physiological network, (2) how they fail in disease, and (3) whether they can be therapeutically manipulated. In this review, we first summarize the anatomical and physiological organization of the glymphatic-lymphatic axis, with particular emphasis on its regulatory mechanisms in light of rapid advances in this field. We then discuss how the glymphatic-lymphatic axis functionally integrates with peripheral immune and metabolic systems and describe how impairment of these clearance pathways is associated with the accumulation of pathological proteins, neuroinflammation, and vascular dysfunction and may contribute to the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Finally, we discuss emerging therapeutic strategies and biomarkers targeting the glymphatic-lymphatic axis. By redefining the central-peripheral crosstalk, the glymphatic-lymphatic axis provides a unifying framework for understanding brain clearance, neurodegeneration, and future therapeutic interventions.},
}

Humans
*Glymphatic System/metabolism/pathology
*Neurodegenerative Diseases/pathology/metabolism/physiopathology
Animals
*Lymphatic Vessels/metabolism/pathology
*Brain/metabolism
Lymphatic System
Cerebrospinal Fluid/metabolism

@article {pmid42850796,
year = {2026},
author = {Zheng, X and Araújo, J and Busson, Q and Goswami, U},
title = {Towards a Brain-Computer Interface (BCI) for Improving Phonological Processing in Developmental Dyslexia: An Exploratory Study.},
journal = {The European journal of neuroscience},
volume = {64},
number = {7},
pages = {e70709},
pmid = {42850796},
issn = {1460-9568},
support = {//Yidan Prize Foundation/ ; },
mesh = {Humans ; *Dyslexia/physiopathology ; *Brain-Computer Interfaces ; Female ; Electroencephalography/methods ; Male ; Reading ; Adult ; *Speech Perception/physiology ; *Brain/physiopathology ; Young Adult ; Phonetics ; },
abstract = {Brain-computer interfaces (BCIs) have immense potential regarding the provision of therapies for disorders of development, but to date have typically been created for non-linguistic disorders such as ADHD (attention deficit hyperactivity disorder). Here, we present a BCI designed to improve linguistic phonological processing during natural speech listening, aimed at participants with developmental dyslexia. Phonological 'deficits' are considered a core feature of dyslexia across languages and are present from infancy in those later diagnosed with dyslexia. Here, a non-invasive EEG-BCI relying on auditory inputs and visual feedback was developed to optimise brain patterns related to phonological development in infants and children. The BCI focused on neural patterns identified using Temporal Sampling theory, which has found the theta/delta ratio during natural speech listening to be a key neural associate of impaired phonology. As a first step, the BCI was tested with adults. Adults with and without dyslexia played the BCI for 16 sessions and received pre- and post-testing regarding phonological awareness and single word and non-word reading skills. Learning was assessed both online, as in most BCI studies, and offline, enabling removal of a greater number of potential EEG artefacts. Significant associations between offline BCI scores (a measure of BCI learning) and improvements in non-word reading were found for all participants who learned the BCI, both dyslexic and control. BCI learning also showed significant associations with improvements in real word reading and amplitude rise time discrimination. The data are interpreted with respect to Temporal Sampling theory.},
}

Humans
*Dyslexia/physiopathology
*Brain-Computer Interfaces
Female
Electroencephalography/methods
Male
Reading
Adult
*Speech Perception/physiology
*Brain/physiopathology
Young Adult
Phonetics

@article {pmid42850844,
year = {2026},
author = {Annikov, YG and Chekhonatskiy, AA and Komleva, NE and Chekhonatskiy, VA and Urzaleeva, IA and Annikova, OV and Mazilov, SI},
title = {[THE NEUROPROSTHETICS AND NEUROSTIMULATION: HISTORY OF DEVELOPMENT, KEY ACHIEVEMENTS AND PROSPECTS (THE REVIEW)].},
journal = {Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny},
volume = {34},
number = {4},
pages = {825-833},
doi = {10.32687/0869-866X-2026-34-4-825-833},
pmid = {42850844},
issn = {0869-866X},
mesh = {Humans ; *Brain-Computer Interfaces/history ; History, 20th Century ; History, 21st Century ; },
abstract = {The purpose of the study is to systematize data from National and foreign publications on history of development, key achievements and prospects of application of neuroprosthetics and neurostimulation. The analysis was carried out concerning databases of PubMed, eLibrary, Cohrane Library, MEDLINE over 2000-2025 according keywords neuroprosthetics, neuroengineering, cognitive neuroprosthesis, brain-computer interface, neurostimulation. The review includes 68 full-text sources.},
}

Humans
*Brain-Computer Interfaces/history
History, 20th Century
History, 21st Century

@article {pmid42851647,
year = {2026},
author = {Li, J and Sha, L and Yan, X and Wang, C and Shi, J and Qiao, H and Wang, C and Wei, Y and Gong, C},
title = {PADA: perturbation-aware differential Riemannian attention for robust motor imagery decoding.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1943934},
pmid = {42851647},
issn = {1662-4548},
abstract = {INTRODUCTION: Riemannian geometry models have significantly improved the robustness and performance of EEG-based motor imagery decoding and are better suited to small sample sizes; however, their ability to extract nonlinear and spatiotemporal variability features often falls short of Euclidean-based deep learning methods. To address these limitations, we introduce a perturbation-aware differential Riemannian attention mechanism (PADA).

METHODS: PADA develops a window-based learnable Riemannian module that maps informative segments onto the manifold to characterize nonlinear features while preserving local structure, together with a two-stream differential manifold attention mechanism. Tangent-space perturbations are used as a probe for feature instability, and attention weights that are hypersensitive to noise are differentially suppressed to distill robust geometric dependencies.

RESULTS: On the BCI Competition IV-2a and Zhou2016 datasets, PADA achieved competitive clean-condition accuracies of 81.44% and 92.79%, respectively. Under 50% noise, its accuracy dropped by only 12%, achieving the best classification performance among the compared models. On a self-constructed dataset involving 227 subjects, PADA achieved up to 70.46% binary classification accuracy on the classic hand motor imagery task using only 12 training samples.

DISCUSSION: These results indicate that PADA provides robust and effective feature learning for noisy and data-limited brain-computer interface applications.},
}

@article {pmid42848345,
year = {2026},
author = {Lu, M and Di, J and Deng, M and Tang, Z and He, J and Lu, Z and Liu, B},
title = {Single-cell and Spatial Omics Reveal Spinal Cord Injury Microenvironmental Heterogeneity Relevant to Neural Interfaces and Spinal Neuromodulation.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neurom.2026.08.010},
pmid = {42848345},
issn = {1525-1403},
abstract = {OBJECTIVE: Spinal cord injury (SCI) interrupts brain-spinal communication and causes persistent motor, sensory, and autonomic deficits.

MATERIALS AND METHODS: We summarize the current clinical and translational evidence on brain-computer interface (BCI)/epidural electrical stimulation (EES)/brain-spinal interface (BSI) for locomotor recovery after traumatic SCI and outline the major patterns of microenvironmental heterogeneity revealed by single-cell and spatial omics. We then link these patterns to clinically relevant axes-acute vs subacute versus chronic phases, complete vs incomplete injury, and peri-lesional vs scar-dominated regions-and discuss how they may influence circuit excitability, plasticity, and responsiveness to neuromodulation.

RESULTS: BCIs/EESs/BSIs have enabled standing, stepping, or walking-related functions, but clinical outcomes remain highly variable. Emerging single-cell and spatial omics studies indicate that the injured spinal cord is not a uniform lesion but consists of distinct microenvironments that differ across species, anatomical regions, cell states, and stages after injury. We further propose a staged, microenvironment-aware framework linking injury stage, tissue state, and neuromodulation response through testable hypotheses. Representative clinical scenarios are presented as conceptual models rather than as prescriptive algorithms.

CONCLUSIONS: Although these relationships remain hypothesis-generating, integrating neuromodulation with omics-based profiling may provide a foundation for developing more biologically informed and individually adapted strategies for SCI rehabilitation.},
}

@article {pmid42849154,
year = {2026},
author = {Zhang, Y and Piao, Q and Yang, Y and Boland, JE and Du, Y and Wang, Y and Han, M},
title = {Corrigendum to "When Chinese verb transitivity meets wrong syntactic category" [Brain Lang. 281 (2026) 105820].},
journal = {Brain and language},
volume = {283},
number = {},
pages = {105849},
doi = {10.1016/j.bandl.2026.105849},
pmid = {42849154},
issn = {1090-2155},
}

@article {pmid42849701,
year = {2026},
author = {Sharma, N and Kumar, S and Prakash, C},
title = {A comparative study and analysis of recent EEG-based brain-computer interface approaches and their applications.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2026.10.006},
pmid = {42849701},
issn = {1873-7544},
abstract = {Brain-Computer Interface (BCI) is an innovative tool that interprets brain instructions and enables humans for using their mental instructions to operate computers, robots, and other external equipment. People with motor disabilities may now utilize external devices much more easily because to recent advancements in BCI technology. This study analyzes newly developed models and provides a comprehensive literature overview of current developments for the field of completely EEG-based BCI systems. In this paper, the basic concepts of EEG-based BCIs are presented, and the key constituents of the system, such as electrophysiological control signals, feature extraction, classification, and performance measurements in different BCI applications, are discussed. The paper considers the brain's functional connectivity, which is studied through relations among different brain areas as connectivity patterns that reflect information exchange between them at rest and during certain tasks. A comparison among various deep learning models for EEG-Based BCI with subject dependent mode is also presented. The result shows that ATCNet model gives highest accuracy with 86.46% and the lowest accuracy was given by EEG-TCNet with 50.69.},
}

@article {pmid42850277,
year = {2026},
author = {Wang, C and Liu, Z and Zhang, W and Yang, R and Li, Y and Wang, Z and Chu, D and Zhou, X and Hong, W and Wang, S and Wang, X and Wang, J and Li, X and Zhang, W and Wang, G and Lu, S and Yin, J and Wu, M and Liu, J},
title = {A freestanding submicron electrocorticography electrode array.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {42850277},
issn = {2041-1723},
mesh = {Animals ; *Electrocorticography/instrumentation/methods ; Male ; Electrodes, Implanted ; Rats ; Epilepsy/physiopathology ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Signal-To-Noise Ratio ; Electrodes ; Odonata ; },
abstract = {Electrocorticography electrode arrays (EEAs) are key brain-computer interfaces. To be freestanding, which lays the foundation of fabrication and implantation, EEAs need to maintain sufficient rigidity. However, to form seamless and damage-free contact on the cerebral cortex, EEAs should be super-flexible, raising a seemingly unsolvable dilemma. Here, inspired by dragonfly wings, we present a freestanding submicron EEA (fs-EEA) that combines rigidity and flexibility, which are typically mutually exclusive characteristics. A wing vein-like frame is introduced to enable the freestanding capability, while the wing membrane-like functional region of fs-EEA is reduced to 850-nm-thickness that boosts its softness. This fs-EEA ensures excellent cortical conformality and biocompatibility on male epileptic rat models, improves electrocorticographic amplitude and signal-to-noise ratio. Our vein-membrane hybrid structure strategy thus resolves the inherent contradiction of EEAs, and has the potential to support advances in brain science and related fields.},
}

Animals
*Electrocorticography/instrumentation/methods
Male
Electrodes, Implanted
Rats
Epilepsy/physiopathology
*Brain-Computer Interfaces
Cerebral Cortex/physiology
Signal-To-Noise Ratio
Electrodes
Odonata

@article {pmid42843403,
year = {2026},
author = {Lee, S},
title = {Ordered onset of regional desynchronization and a transition in movement-class discriminability during cue-paced motor imagery.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb161},
pmid = {42843403},
issn = {1741-2552},
abstract = {Motor imagery recruits a distributed sensorimotor network, yet the order in which its regions are engaged has not been tested statistically within the healthy brain, and the interval in which information about the imagined movement appears remains unidentified. Early post-cue classification peaks have been attributed to visually induced orienting rather than to imagery, an inference drawn from a condition contrast rather than direct measurement.Two independent public datasets were analysed under a matched pipeline of causal filtering, ocular regression and current source density: Weibo2014 as the primary dataset and BCI Competition IV dataset 2a for validation. Mu and beta desynchronization onset was ordered across cortex, from occipital through sensorimotor and midline pre-supplementary motor area to lateral prefrontal. The ordering held under all nine onset criteria examined, under channel-count matching and within-trial pairing, and after removal of the sensorimotor component. Class-discriminative information rose most steeply between 0.60 and 0.75 s and saturated within a further 0.3 s. This transition lay closer to sensorimotor onset than to cue presentation in every participant and fell at or after that onset in nearly all of them, although the two were not shown equivalent at the resolution of the decoding grid. Windows centred on each region's own onset classified equivalently to reassigned windows, so the ordering serves as a temporal landmark rather than a decodable feature, locating the interval in which information about the imagined movement becomes available.},
}

@article {pmid42845593,
year = {2026},
author = {Yi, J and Patel, KK and Miller, RJH and Marcinkiewicz, AM and Kamagate, A and Shanbhag, A and Hijazi, W and Lemley, M and Zhou, J and Liang, JX and Ramirez, G and Mostafavi, S and Urs, M and Spielvogel, CP and Slipczuk, L and Travin, M and Alexanderson, E and Caraval-Juarez, I and Packard, RRS and Al-Mallah, M and Ruddy, TD and Einstein, AJ and Feher, A and Miller, EJ and Acampa, W and Knight, S and Le, VT and Mason, S and Calsavara, VF and Chareonthaitawee, P and Wopperer, S and Kwan, AC and Wang, L and Li, D and Fishman, EK and Lopez-Ramirez, F and Berman, DS and Kwiecinski, J and Dey, D and Carli, MD and Slomka, PJ},
title = {Beyond BMI: an interpretable integrated body composition index from low-dose chest CT for all-cause mortality risk stratification: a multicentre study.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.08.05.26359437},
pmid = {42845593},
abstract = {BACKGROUND: Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues.

METHODS: We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients' CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction.

FINDINGS: During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell's concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction.

INTERPRETATION: An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies.

FUNDING: National Institute of Health.

RESEARCH IN CONTEXT: Evidence before this study: We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms ("mortality prediction" OR "death prediction") AND ("integrated body composition analysis" OR "body composition metrics integration") AND ("predictive model" OR "model development"), and identified 41 publications describing predictive models incorporating body composition metrics.Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations.Added value of this study: In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments - bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests.In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction.Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice.Implications of all the available evidence: BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.},
}

@article {pmid42845720,
year = {2026},
author = {Olivares, CY and Schettini, N},
title = {A high-precision synchronization and centroid-based framework for single-trial decoding of perceived natural speech.},
journal = {Frontiers in computational neuroscience},
volume = {20},
number = {},
pages = {1947069},
pmid = {42845720},
issn = {1662-5188},
abstract = {Decoding natural speech from EEG is constrained by low signal-to-noise ratios, commonly addressed through event-related potential (ERP) grand averaging. However, this approach acts as a low-pass filter, obscuring trial-to-trial variability and high-frequency neural dynamics. To overcome these limitations, we propose an open-box, FPGA-synchronized framework for single-trial EEG analysis of Spanish vowels (/a/, /i/, /u/). The proposed architecture enforces sub-millisecond hardware determinism, eliminating software-induced temporal jitter and enabling robust extraction of high-frequency oscillatory features, including the Gamma band, without relying on averaged ERPs. This synchronization framework facilitates the separation of early exogenous sensory responses from late endogenous cognitive processing, while comprehensive ablation studies were performed to evaluate the effect of software-induced jitter on decoding performance. Results showed that introducing simulated jitter degraded classification performance to theoretical chance levels (33.20%), confirming hardware synchronization as a strict prerequisite for reliable single-trial decoding. The framework was further validated using a Centroid-PCA-SVM pipeline operating on a 16-channel spatial subset. Using the full proposed architecture, a grand mean classification accuracy of 67.70% was achieved, substantially exceeding the theoretical chance level of 33.33%. The across-participant mean of the maximum fold accuracies reached 83.00%, with individual validation folds achieving up to 90.00%. In contrast, zero-calibration leave-one-subject-out (LOSO) generalization remained at chance level (33.08%; κpooled = -0.0036), indicating limited cross-participant transferability and highlighting the current need for participant-specific calibration or alignment. Overall, these findings demonstrate that hardware-deterministic EEG acquisition enables the recovery of robust trial-by-trial brain dynamics that are typically obscured by conventional averaging approaches. By combining precise synchronization with low-latency algorithmic inference, the proposed framework establishes a foundation for real-time neural decoding and represents a stepping stone for practical brain-computer interfaces and advanced clinical monitoring applications.},
}

@article {pmid42845760,
year = {2026},
author = {Hai, X and Shen, R and Wang, X and Yang, GM},
title = {Noninvasive Intelligent Brain-Metasurface System for Real-Time Electromagnetic Wave Manipulation.},
journal = {Nanophotonics (Berlin, Germany)},
volume = {15},
number = {13},
pages = {e70194},
pmid = {42845760},
issn = {2192-8614},
abstract = {A noninvasive intelligent brain-computer interface (BCI) is integrated with a reconfigurable metasurface to establish a direct neural-to-electromagnetic transduction pathway for real-time, multimodal wave control. By combining convolutional-neural-network-based decoding of motor-imagery electroencephalography (EEG) signals with threshold-based recognition of electrooculography (EOG) commands, the proposed system converts neural and ocular activities into high-speed control commands, which are subsequently implemented through an FPGA-driven dual-polarized reconfigurable metasurface for real-time electromagnetic-wave manipulation. This architecture concurrently enables beam steering, polarization conversion, and radar cross-section (RCS) reduction within a unified platform. Three distinct operational modes are demonstrated: polarization-invariant beam deflection, joint steering and polarization conversion, and broadband RCS reduction via random phase coding. Experimental results reveal a peak gain of 18.54-19.29 dBi with a 3 dB beamwidth of 12°-15° at a +20° steering angle, and an RCS reduction exceeding 10 dB across 3.7-3.85 GHz. The proposed intelligent BCI-metasurface paradigm validates the feasibility of brain-controlled electromagnetic manipulation, opening new avenues for adaptive wireless communications, immersive virtual environments, and next-generation assistive technologies.},
}

@article {pmid42846039,
year = {2026},
author = {Alanoğlu, M and Khudhur, RS and Demirtaş, Z and Uslukaya, A},
title = {Distributed leadership and teachers' work engagement: subjective well-being as a mediator and perceived collective teacher efficacy as a moderator.},
journal = {Frontiers in psychology},
volume = {17},
number = {},
pages = {1887759},
pmid = {42846039},
issn = {1664-1078},
abstract = {INTRODUCTION: This study examined the association between distributed leadership and teachers' work engagement, with teacher subjective well-being specified as a mediator and perceived collective teacher efficacy as a moderator.

METHODS: Drawing on the Job Demands-Resources model and social cognitive theory, a moderated mediation model was tested using Bayesian structural equation modeling. Data were collected from 598 teachers working in 60 public schools in Erbil, Kurdistan Region of Iraq.

RESULTS: Distributed leadership was positively associated with teacher subjective well-being [β = 0.233, 95% BCI (0.077, 0.381)] and work engagement [β = 0.234, 95% BCI (0.135, 0.333)], while subjective well-being was positively associated with work engagement [β = 0.172, 95% BCI (0.112, 0.232)]. The indirect association through subjective well-being was supported [B = 0.039, 95% BCI (0.012, 0.074)]. Perceived collective teacher efficacy moderated the association between distributed leadership and subjective well-being [β = 0.099, 95% BCI (0.003, 0.192)]. The conditional indirect association was supported at the mean and higher levels of perceived collective teacher efficacy but not at lower levels, and the index of moderated mediation excluded zero [B = 0.019, 95% BCI (0.001, 0.043)]. These conclusions remained stable under alternative prior specifications.

DISCUSSION: The findings indicate an indirect statistical association between distributed leadership and work engagement through teacher subjective well-being, which varied according to perceived collective teacher efficacy. Given the cross-sectional, self-report design, this mediation represents a statistical decomposition of covariance rather than evidence of a causal mechanism or temporal sequence.},
}

@article {pmid42846040,
year = {2026},
author = {Alba Diaz, PA and Kwon, J and Berstis, K and Weed, M and Ferrari, P and Wang, J},
title = {Optimal sensor set for MEG-based spontaneous and intended speech decoding toward practical communication.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1925703},
pmid = {42846040},
issn = {1662-5161},
abstract = {INTRODUCTION: Neural speech decoding has demonstrated great potential as an alternative to current brain-computer interface (BCI) spellers, enabling a faster, more natural way of communication for patients who have lost voluntary abilities, including speech. Most neural speech decoding studies have focused on decoding cued speech, in which participants are presented with visual or auditory signals before producing imagined or overt speech. Only a few recent studies have investigated the decoding of spontaneous and intended speech without external cues using neural activity acquired from a full-scalp clinical magnetoencephalography (MEG) system. With recent advances in MEG technology, including optically pumped magnetometers (OPMs) and nitrogen-vacancy (NV) center (NV-diamond) magnetometers, MEG is emerging as a next-generation wearable system for practical speech BCIs. However, it remains unclear how many sensors are needed, where to place them, and what sampling frequency is optimal for wearable MEG devices that require cost-effective, computationally efficient operation.

METHODS: In this study, we addressed these gaps by using a forward selection algorithm to find an optimal subset of sensors for spontaneous intended and overt speech decoding and by evaluating decoding performance across different frequency bands. Support vector machine and random forest were used as the classifiers in these experiments.

RESULTS: Our results indicate that only a small number of sensors (7-8 on average, with a maximum of fewer than 24) are sufficient to achieve decoding performance comparable to that obtained using the whole scalp (more than 200) sensors. In addition, we observed that the Lower High-Gamma (62-125 Hz) band yielded the best performance in some settings, although not statistically significantly higher than other bands in all settings.

DISCUSSION: These findings suggest that future wearable MEG devices may not need the whole-scalp sensor set and should support a sampling rate of at least 250 Hz, while 500 Hz is also encouraged, if possible. In summary, these findings provide practical insights for MEG-based BCIs for communication.},
}

@article {pmid42846320,
year = {2026},
author = {Landeira-Suquia, L and Lee, A and Cuttaz, EA and Goding, J and John, SE and Green, RA},
title = {Endovascular approaches to neural interfacing: lessons from cardiovascular technologies.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1933040},
pmid = {42846320},
issn = {1662-4548},
abstract = {Endovascular neural interfaces (ENIs) have emerged as a minimally invasive alternative to traditional neural interfaces. These devices are implanted within blood vessels adjacent to neural tissue to record or stimulate neural activity across the vessel wall. This enables minimally invasive implantation through angiographic procedures and reduces the risk of neural tissue damage. As they are deployed in the intravascular space, ENIs have a similar technological evolution to cardiovascular devices and share many features and constraints. First-generation ENIs borrowed guide wires and catheters employed in cardiovascular procedures for endovascular neural recording but were limited to acute settings. Concurrently, the cardiovascular field explored the possibility of integrating electronics onto endovascular devices. Catheters have been used for endovascular stimulation as a treatment for cardiovascular conditions. Cardiovascular stents have been integrated with wireless sensors, enabling self-reporting stents to monitor implant health. In 2016, a breakthrough in the field of ENIs was achieved by leveraging the chronic stability of stents to support an electrode array. Exemplified by Synchron's Stentrode™, stent electrode arrays have been successfully employed as a brain-computer interface for chronic motor cortex recording. Subsequent research in the field has focused on expanding applications and improving spatial resolution. Incorporating valuable lessons from the field of interventional cardiology will be key to accelerate ENI development, especially in the areas of device miniaturisation, enhancement of stimulation capabilities, and wireless communication. Addressing these limitations could revolutionise the treatment of neurological disorders, enabling high spatial resolution recording and stimulation possible through minimally invasive, accessible procedures.},
}

@article {pmid42847828,
year = {2026},
author = {Li, J and Wen, S and Hu, J and Wang, Q and Zhang, B and Zhou, Q and Zhao, J},
title = {Exploiting Phase-Sliding Oscillations for Robust Asynchronous Brain-Computer Interface Control.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2750022},
doi = {10.1142/S0129065727500225},
pmid = {42847828},
issn = {1793-6462},
abstract = {Asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) show great potential for real-world control, yet their performance is often impaired by phase shifts between ongoing electroencephalogram (EEG) signals and fixed-phase templates. In this study, we observed and validated a phase-sliding oscillation (PSO) phenomenon: when a fixed-phase template is used, phase-sliding trajectories derived from sliding-window sequences with different initial phases exhibit stable periodic oscillations locked to the stimulus frequency. Based on this finding, we propose a PSO-based asynchronous classification (PSO-AC) method. Specifically, EEG signals are reconstructed into sequences with varying initial phases and transformed into stable time-frequency representations via the wavelet synchrosqueezing transform to extract phase-decoupled features. A probabilistic temporal model is then used to characterize temporal dynamics and distinguish control from noncontrol states. Offline experiments (n = 22) showed that PSO-AC achieved a mean control/noncontrol accuracy of 94.2% with a 2[Formula: see text]s data length, outperforming a state-of-the-art baseline. It also maintained superior performance in nine-class classification under different time delays. In online robotic-arm experiments, PSO-AC enabled more stable command triggering and higher control efficiency, reducing the command cost per successful trial from 4.30 to 1.14.},
}

@article {pmid42834306,
year = {2026},
author = {Mhande, Z and Mihale, M and Hellar-Kihampa, H and Brion, N and Baeyens, W},
title = {Isotopic fingerprinting of sediment organic matter reveals pervasive sewage influence in urbanised tropical rivers.},
journal = {Environmental science and pollution research international},
volume = {},
number = {},
pages = {},
pmid = {42834306},
issn = {1614-7499},
support = {296 (ZEIN2014Z163)//VLIR-UOS/ ; },
abstract = {Rapid urbanisation in tropical coastal regions is altering riverine biogeochemistry, yet specific drivers of sediment organic matter (OM) dynamics remain understudied in Sub-Saharan Africa. This study assessed spatial and seasonal variation in sediment total nitrogen (TN), total organic carbon (TOC), molar C/N ratio, and stable isotopes (δ[13]C and δ[15]N) in three coastal rivers draining Dar es Salaam, Tanzania (Kizinga, Mbezi, and Msimbazi). Across all rivers, TN (2.18-5.18 mg/g) and TOC (24.47-50.31 mg/g) concentrations showed no statistically significant seasonal differences (p > 0.05), indicating persistent anthropogenic loading across contrasting hydrological seasons. Msimbazi River exhibited the highest organic enrichment and lowest molar C/N ratios (9.5 ± 1.5-10.5 ± 1.0), consistent with N-rich organic inputs. In contrast, the Kizinga River displayed higher molar C/N ratios but the most enriched δ[15]N values (up to 10.3 ± 1.0‰), consistent with wastewater-derived nitrogen subjected to fractionating transformations such as ammonia volatilisation and denitrification. Bayesian source apportionment using the Food Reconstruction using Isotope Transferred Signals (FRUITS) model estimated that sewage-derived OM contributes a posterior median of 41% (95% Bayesian credible interval (BCI) 28-52%) in Msimbazi and 43% (95% BCI 30-54%) in Kizinga sediments. These findings demonstrate a state of biogeochemical stationarity in which chronic urban wastewater inputs dominate sediment composition across contrasting hydrological seasons. The results highlight that seasonal flushing is insufficient to remove accumulated anthropogenic signatures from riverbeds and underscore the need for structural interventions to reduce sewage inputs to urban tropical rivers.},
}

@article {pmid42835206,
year = {2026},
author = {Li, M and Pan, J and Han, J and Bao, X and Wu, H and Wang, F and Yu, Y and Li, A and Liu, Y and Chen, D and Yang, X and He, Y and Qin, P and Li, Y},
title = {Detecting EEG index of arousal in sleeping patients with unresponsive wakefulness syndrome.},
journal = {iScience},
volume = {29},
number = {10},
pages = {117448},
pmid = {42835206},
issn = {2589-0042},
abstract = {Consciousness is assumed to be defined by two components: awareness and arousal. However, fewer electroencephalographic (EEG) indices have been identified for arousal than for awareness. Since patients clinically diagnosed with unresponsive wakefulness syndrome (UWS) show preserved sleep-wake cycles but markedly reduced reproducible behavioral evidence of awareness, their sleep-wake patterns may provide a useful model for examining arousal-related EEG fluctuations. This study recorded nighttime polysomnography in patients with UWS, patients in a minimally conscious state, and healthy controls. The EEG signal showed that spectral slope could index arousal, fluctuating among sleep stages in all three groups. Both spectral entropy and Lempel-Ziv complexity indexed awareness, showing a significant difference between conscious and unconscious states. These findings provide fundamental evidence for the two-component hypothesis of consciousness.},
}

@article {pmid42835420,
year = {2026},
author = {Liu, Y and Lu, DL and Hong, Y and Hu, L and Tang, H and Cheng, Y and Yu, Y and Yin, X and Xia, H and Lu, L},
title = {The application of AI-driven digital phenotyping and interventions in neuropsychiatric disorders.},
journal = {Frontiers in psychiatry},
volume = {17},
number = {},
pages = {1907262},
pmid = {42835420},
issn = {1664-0640},
abstract = {Artificial intelligence (AI) is increasingly influencing the management of neuropsychiatric disorders, extending its role beyond assistive diagnosis toward adaptive intervention and individualized disease management. Digital phenotyping enables continuous assessment of multimodal behavioral and biological signals, allowing neuropsychiatric disorders to be modeled as dynamic rather than static processes. This review summarizes current AI-driven approaches for neuropsychiatric assessment and intervention, including computer vision-based behavioral analysis, wearable sensing, neuroimaging biomarkers, digital therapeutics, virtual reality, social robotics, and brain-computer interfaces. We further discuss emerging closed-loop frameworks that integrate real-time state estimation with adaptive intervention strategies, shifting neuropsychiatric AI from passive observation toward continuous therapeutic optimization. Despite rapid progress, integration between phenotyping and intervention remains limited, and most closed-loop systems are still confined to experimental settings. Key challenges include multimodal representation, interpretability, generalizability across populations and devices, and translation into real-world clinical workflows. We argue that the primary bottleneck in AI-driven neuropsychiatry is no longer prediction accuracy alone, but the ability to connect continuous patient-state estimation with clinically meaningful adaptive action. Finally, we outline future directions toward clinically grounded, human-AI collaborative neuropsychiatric systems.},
}

@article {pmid42835676,
year = {2026},
author = {Chen, Y and Zheng, Z and Liu, F and Cai, H and Chu, T and Luo, S and Li, Z and Zhu, C},
title = {Intrinsic functional architecture predicts remote motor cortical excitability changes induced by intermittent theta-burst stimulation.},
journal = {iScience},
volume = {29},
number = {10},
pages = {117633},
pmid = {42835676},
issn = {2589-0042},
abstract = {Repetitive transcranial magnetic stimulation (rTMS) modulates cortical excitability both locally and remotely through connected regions. However, how intrinsic functional architecture influences these remote effects remains unclear. Leveraging multi-site stimulation (targeting the left primary motor cortex [M1], dorsal premotor cortex, and caudal inferior parietal lobule) combined with resting-state functional magnetic resonance imaging, we investigate whether intrinsic functional architecture predicts rTMS-induced changes in contralateral M1 excitability. While group-level effects were absent, individual variability in excitability changes was significantly predicted by functional characteristics. When the engaged network of stimulated region is sensorimotor network, resting-state functional connectivity between the stimulated region and the right M1 significantly predicts excitability changes, moderated by within-network interactions. Otherwise, exploratory analysis found associations with network topological properties rather than direct coupling. These findings provide preliminary insights into rTMS neuromodulation and may inform future development of personalized treatments (e.g., for post-stroke motor rehabilitation), yet require further investigation in clinical populations.},
}

@article {pmid42837245,
year = {2026},
author = {Zhang, J and Li, F and Fu, B and Cai, X and Zhao, Y and Li, Y and Zhao, Z and Dong, M and Chen, Y},
title = {Improving RSVP EEG Decoding Performance under Auditory Perturbation: A Dual-View Model with Robust Distillation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3739839},
pmid = {42837245},
issn = {1558-2531},
abstract = {OBJECTIVE: Decoding event-related potentials (ERPs) from Rapid Serial Visual Presentation (RSVP) EEG offers a promising approach for target recognition in brain-computer interfaces (BCIs). However, auditory noise in real-world environments degrades target-ERP decoding, substantially reducing accuracy and robustness.

METHODS: We propose a novel time-frequency dual-view network (TFDV-Net) with temporal and time-frequency branches. Cross-view global features guide local feature extraction to fully exploit complementary information in both views and obtain a more complete representation of task-relevant ERP information from auditory-perturbed EEG. Additionally, dual-stream knowledge distillation transfers feature representations and inter-layer processing patterns from a clean-EEG-trained teacher through feature-distribution alignment and inter-layer relational constraints, guiding the student preserve task-relevant information under auditory perturbations.

RESULTS: Experiments used an RSVP-EEG dataset collected from nine participants under clean, urban-noise, and conversational-noise conditions. The proposed method, comprising the TFDV-Net backbone and the proposed dual-stream knowledge-distillation strategy, achieved balanced accuracies of 83.02% and 80.78% under the two noise conditions, respectively, approaching the 84.54% achieved by the TFDV-Net backbone under the clean condition. With the same dual-stream knowledge-distillation strategy, TFDV-Net outperformed the state-of-the-art baseline, TSformer, by 3.27% and 4.36% in BA under the two noise conditions, respectively.

CONCLUSION: The proposed method combines dual-view feature extraction with dual-stream knowledge distillation, effectively improving RSVP-EEG decoding accuracy and robustness under auditory perturbations.

SIGNIFICANCE: This work provides a robust RSVP-EEG decoding approach for noisy environments, showing potential for application in nonlaboratory environments and offering insights into noise-robust decoding in other BCI paradigms.},
}

@article {pmid42838213,
year = {2026},
author = {Wang, J and Zhang, X and Shi, H and Guo, W and Liu, S and Ming, D},
title = {40-Hz chirp auditory stimulation alleviates chronic restraint stress-induced depression-like behaviors and restores disrupted gamma oscillations.},
journal = {Progress in neuro-psychopharmacology & biological psychiatry},
volume = {150},
number = {},
pages = {111969},
doi = {10.1016/j.pnpbp.2026.111969},
pmid = {42838213},
issn = {1878-4216},
abstract = {Psychiatric disorders are increasingly treated using frequency-specific neuromodulation approaches. Our previous studies demonstrated impaired 40-Hz gamma oscillations in the temporal lobe of both patients with depression and depression-like animal models. Given that 40-Hz auditory stimulation can elicit robust frequency-specific neural responses in the auditory cortex, it may provide a contact-free means of modulating temporal-lobe gamma oscillations. Accordingly, this study aimed to establish a 40-Hz chirp auditory stimulation protocol and examine its effects on depression-like behaviors and gamma-band neural activity in a chronic restraint stress (CRS) rat model, providing a convenient intervention strategy for depression. Twenty-four rats were randomly assigned to a control group (n = 8) or a CRS group (n = 16), with CRS rats further receiving either 40-Hz chirp stimulation or sham stimulation for five consecutive days (20 min/day). Behavioral assessments were performed, followed by bilateral local field potential (LFP) recordings in the primary auditory cortex (A1). Results showed that 40-Hz chirp stimulation effectively alleviated CRS-induced depression- and anxiety-like behaviors, as reflected by increases in the sucrose preference index (SPI) and open-arm time. It selectively restored CRS-induced reductions in right A1 gamma activity, including task-rest gamma modulation, task-related narrowband gamma power and theta-gamma coupling. Moreover, right A1 gamma measures were significantly correlated with SPI, suggesting that the restoration of gamma oscillatory activity may underlie improvements in depression-like behavior. In summary, 40-Hz chirp stimulation alleviates depression-like behavior in CRS rats and modulates gamma-related neural activity, supporting its potential as a non-invasive neuromodulation strategy for depression.},
}

@article {pmid42838379,
year = {2026},
author = {Zhang, X and Wu, Y and Ming, X and Bai, R},
title = {Non-invasive brain stimulation and glymphatic function: an aquaporin-4 perspective.},
journal = {Experimental neurology},
volume = {},
number = {},
pages = {116052},
doi = {10.1016/j.expneurol.2026.116052},
pmid = {42838379},
issn = {1090-2430},
abstract = {The glymphatic system is a brain-wide fluid transport network in which cerebrospinal fluid enters along periarterial spaces, exchanges with interstitial fluid within the parenchyma, supports the redistribution and clearance of interstitial solutes, and drains toward meningeal and extracranial lymphatic pathways. Aquaporin-4 (AQP4), a water channel highly enriched at perivascular astrocytic endfeet, forms a polarized astroglial membrane interface that facilitates water exchange across the gliovascular compartment and critically shapes glymphatic transport. While non-invasive brain stimulation has been widely studied for its effects on neural circuits and network activity, its potential role in regulating brain fluid homeostasis and waste clearance has only recently gained attention. Accumulating evidence suggests that rhythmic sensory stimulation, transcranial ultrasound stimulation, and transcranial magnetic stimulation may influence glymphatic transport through effects on neural oscillations, neurovascular coupling, vascular pulsatility, vasomotion, meningeal lymphatic drainage, and astrocytic endfoot organization. This review summarizes the anatomical and functional basis of the glymphatic system, the membrane targeting and polarization of AQP4, and current evidence on non-invasive brain stimulation-mediated glymphatic modulation from an AQP4-centered perspective.},
}

@article {pmid42838386,
year = {2026},
author = {Smith, TJ and Wang, J and Khajehzadeh, M and Wu, Y and Haghighi, P and Srinivasan, H and Khan, AA and Cheon, C and Bendea, M and Kaul, M and Faruk, F and Vargas, SL and Villafranca, LR and Kumara, ST and Ogundipe, A and Capadona, JR and Cogan, SF and Hernandez-Reynoso, AG and Pancrazio, JJ},
title = {Demonstration of a Methodology for Long-Term Tracking of Intracortical Microstimulation-Evoked Perception Thresholds in Rat Somatosensory Cortex.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110918},
doi = {10.1016/j.jneumeth.2026.110918},
pmid = {42838386},
issn = {1872-678X},
abstract = {BACKGROUND: Intracortical microstimulation (ICMS) offers a promising approach for enabling sensory feedback by directly activating neurons within the somatosensory cortex to more fully realize closed-loop neuroprosthetic systems. Despite this potential, questions persist regarding how stable ICMS-evoked responses remain over extended implantation periods.

NEW METHOD: Given the paucity of preclinical tools for examining these temporal patterns, we extended a previously developed rodent nose-poke go/no-go behavioral framework to assess the reliability of ICMS perception thresholds across a 30-week implantation window. Rats implanted with microwire arrays underwent weekly threshold and behavioral-performance assessment, alongside recordings of electrophysiological and electrochemical data to assess correlates of paradigm reliability.

RESULTS: Threshold values initially fell in an exponential-like manner from weeks 5-12 post-implantation before approaching a fitted asymptote of ~1.1 nC/ph per electrode across 10 stimulating electrodes. Thereafter, thresholds remained stable through week 30 in animals retaining detectable percepts. Over this period, behavioral performance stabilized, with animals correctly responding to the conditioned stimulus ~90% of the time. Notably, neither neural recording nor electrochemical properties of the arrays provided a robust, cross-animal explanation for perceptual threshold trends, leaving the underlying mechanism unresolved. Compared with naïve cortical tissue, gene expression profiling of the implanted tissue expressed elevated Gfap and Cd68 alongside reduced NeuN and Bdnf-findings common to prior neuroinflammation studies.

This positive-reinforcement rodent paradigm offers a dependable preclinical alternative for long-term ICMS-threshold assessment.

CONCLUSION: Despite sensation loss in two animals, this behavioral framework offers investigational value for chronic ICMS threshold tracking while sensory percepts remain detectable.},
}

@article {pmid42839015,
year = {2026},
author = {Shen, H and Zhao, S and Marin-Llobet, A and Qin, S and Jiang, S and Tang, X and Lee, M and Zhang, X and Lee, J and Chen, J and Liu, J},
title = {Realignment of representational drift in mouse visual cortex via flexible electrode arrays.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {42839015},
issn = {2157-846X},
abstract = {The ability to stably decode brain activity is crucial for brain-computer interfaces, which are often compromised by recording instability due to immune responses and probe drifting. In addition, many brain regions undergo intrinsic dynamics such as 'representational drift', in which neural activities associated with stable sensation and action continually change over time. Here we demonstrate that tissue-like flexible electrode arrays enable stable, months-long tracking of the same neurons in the mouse visual cortex and hippocampus during visual stimulation. Using these arrays, we recorded single-unit action potentials over extended periods and characterized representational drift in neuron activities and population-level latent dynamics to repeated stimuli. We further modelled these latent dynamics using geometric transformation to capture shifts in neural representations across sessions. Using this approach, we built a decoder that maintains high performance across long time spans and demonstrates the potential to generalize across different animals when applied to the aligned latent dynamics. These findings suggest a path towards neural recording and decoding frameworks that remain reliable despite continuous changes in neural activity.},
}

@article {pmid42839593,
year = {2026},
author = {Hou, X and Jing, J and Shi, Z and Sun, L},
title = {Targeted Mechanogenetics to Modulate Specific Deep Neural Circuits.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e78177},
pmid = {42839593},
issn = {2198-3844},
support = {C5053-22 GF//Hong Kong Research Grants Council Collaborative Research Fund/ ; 15224323//General Research Fund/ ; 15104520//General Research Fund/ ; 2023YFC2410900//National Key Research and Development Program of Ministry of Science and Technology of China/ ; 1-YWDQ//Hong Kong Polytechnic University/ ; //Research Center for Non-invasive Brain Computer Interface/ ; //Research Institute of Smart Ageing/ ; },
abstract = {Sonogenetics utilizes ultrasound for targeted, cell-type-specific modulation of neuronal activity. However, manipulating specific circuits within complex brain areas poses significant challenges due to heterogenous cell types with varying inherent sonosensitivity. Building on previous gas-vesicle-assisted ultrasound neuromodulation studies, we investigate an in vivo nanobubble-enhanced sonogenetic strategy, termed mechanogenetics, for circuit-level neural modulation. Application of this approach to distinct brain regions increased electromyography responses following motor-cortex stimulation and enhanced neural activity in deep-brain regions. Importantly, mechanogenetics enables us to modulate neural circuits controlling feeding behavior by specifically stimulating the mouse lateral hypothalamus (LH), with minimal disturbance in adjacent areas. The observed mechanogenetically-induced decreases in feeding behavior were further found to be mediated by LH-glutamatergic inputs to the ventral tegmental area. Furthermore, assessments of neural integrity, inflammation, and apoptosis support biosafety under the tested conditions. These findings demonstrate the feasibility of applying mechanogenetics to a behaviorally relevant deep neural circuit and provide a framework for localized, genetically targeted ultrasound neuromodulation.},
}

@article {pmid42832453,
year = {2026},
author = {Jafari Malali, M and Zolfaghari, S and Davoodi, R},
title = {EEG emotion recognition via multilevel entropy analysis: The power of entropy of entropy on phase dynamics.},
journal = {PloS one},
volume = {21},
number = {10},
pages = {e0329598},
pmid = {42832453},
issn = {1932-6203},
mesh = {Humans ; *Electroencephalography/methods ; Entropy ; *Emotions/physiology ; *Brain/physiology ; Signal Processing, Computer-Assisted ; Female ; Male ; },
abstract = {Emotion recognition from EEG signals has become a central task in affective computing, offering non-invasive insight into human emotional states. Due to the nonlinear and nonstationary nature of EEG, entropy-based features have gained prominence as reliable indicators of brain complexity. However, traditional entropy metrics-such as Shannon entropy (ShanEn) and its variants-often lack sensitivity to transient dynamics and multiscale irregularities in the brain's electrical activity. This study introduces a novel application of Entropy of Entropy (EoE) to EEG phase series (EoE - phase), aiming to enhance the detection of emotional states by capturing second-order variability in signal complexity. Unlike conventional measures, EoE quantifies not just signal unpredictability, but also the fluctuation of that unpredictability over time, offering a more dynamic representation of neural information flow. Using the GAMEEMO dataset, which includes EEG recordings from 28 participants during four emotion-evoking game scenarios (funny, boring, horror, calm), we computed and compared four feature sets: Phase Entropy, EoE - phase, ShanEn, and EoE applied to EEG amplitude time series. These features were classified using six machine learning models, including AdaBoost, SVM, and Random Forest. Our results demonstrate that EoE applied to phase dynamics significantly outperforms other entropy-based features, achieving a classification accuracy of 71.7% and AUC of 0.869 using the RF classifier with mixed PhaseEn and EoE-phase features, while traditional Shannon-based features reached only ~59% accuracy. This study provides the first systematic evaluation of EoE on EEG phase signals for emotion recognition, revealing its strong discriminative power and potential for integration into next-generation brain-computer interfaces.},
}

Humans
*Electroencephalography/methods
Entropy
*Emotions/physiology
*Brain/physiology
Signal Processing, Computer-Assisted
Female
Male

@article {pmid42832897,
year = {2026},
author = {Haupt, T and Straetmans, L and Adiloglu, K and Bleichner, MG and Debener, S},
title = {Exploring Auditory Attention Decoding to go with mobile and portable hardware.},
journal = {Hearing research},
volume = {482},
number = {},
pages = {109817},
doi = {10.1016/j.heares.2026.109817},
pmid = {42832897},
issn = {1878-5891},
abstract = {Auditory attention decoding (AAD) has emerged as a promising approach for neuroadaptive hearing technology. However, its feasibility in naturalistic, mobile settings remains underexplored. In this study, we investigated AAD using a minimal and wearable setup comprising of around-the-ear EEG and portable hearing aid research hardware. Participants performed an auditory attention task under both controlled (seated) and naturalistic (walking) conditions with varying auditory scene complexity (single- and dual-speaker conditions, cafeteria background noise). We applied both backward (decoding) and forward (encoding) models to assess attention-dependent neural tracking of continuous speech. Decoding results replicated established trends, with the highest reconstruction accuracy for the single speaker, followed by dual-speaker attend, and then ignored. These trends were extended to the seated and walking contexts, initially demonstrating the potential of cEEGrids for mobile AAD. Post hoc forward modeling revealed an artifactual response in the walking condition, biasing the reconstruction and prediction accuracy. Further analysis indicated that this effect could reflect hardware-related interference, possibly from contact between hearing aid cables and electrodes. Importantly, the artifact, while speech-locked, was detectable only through the forward model. Despite these constraints, our results demonstrate the feasibility of decoding auditory attention using a lightweight, unobtrusive EEG system in stationary scenarios. This work emphasizes the need for robust hardware integration, real-time validation, and user-centered design in future AAD applications. Our findings add a critical step towards practical brain-computer interface solutions for hearing support in everyday environments and highlight the importance of interpretable methods in application-driven research.},
}

@article {pmid42833259,
year = {2026},
author = {Tang, Y and Corballis, PM and Hallum, L},
title = {Involuntary facial muscle activity during imagined vocalisation contaminates EEG and enables emotion decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aeb073},
pmid = {42833259},
issn = {1741-2552},
abstract = {OBJECTIVE: Decoding imagined speech from electroencephalography (EEG) recordings is potentially useful for brain-computer interfaces. Previous studies have focused on decoding semantic information from EEG, leaving the decoding of emotion - an important component of human communication - largely unexplored.

APPROACH: Here, we report two experiments involving participants tasked with overt (n = 14) or imagined (n = 21) emotional vocalisation in five different categories: anger, happiness, neutral, sadness, and pleasure. Throughout, we recorded 64-channel EEG; we computed time-frequency features and used a logistic-regression classifier to evaluate emotion decoding accuracy. In five participants, we also recorded facial surface electromyography (sEMG) during imagined vocalisation, and studied the contamination of EEG by sEMG.

MAIN RESULTS: Our results show that emotion can be decoded from single-trial EEG recordings of both overt (78.1%, chance = 20%) and imagined vocalisation (36.4%). The high-gamma band (50 to 100 Hz) and lateral EEG channels (T7, T8, and proximal) were important for decoding. sEMG analysis indicated that involuntary facial muscle activity contributed to these spectral and spatial patterns during imagined vocalisation. We identified a prominent muscle-induced pattern over lateral EEG channels - the railroad cross-tie pattern - showing greater activation during imagined happy vocalisations. We conclude that involuntary facial muscle activity is associated with certain emotion categories (i.e., happiness) and contributes to the decoding of emotion from single-trial EEG recordings.

SIGNIFICANCE: This study provides converging EEG and sEMG evidence demonstrating that involuntary facial muscle activity occurs during imagined vocalisation and is modulated by emotion, producing emotion-specific EEG contamination patterns. Although we focused on imagined vocalisations, the EMG patterns we observed closely resembled EEG patterns commonly reported in emotion decoding studies, raising concerns about involuntary facial muscle activity in such work.},
}

@article {pmid42824274,
year = {2026},
author = {Wu, X and Allison, BZ and Chen, W and He, X and Wang, C and Wang, X and Cichocki, A and Lu, Y and Jin, J},
title = {A spatial-channel reconstruction guided frequency-time-space deep neural framework for SSVEP-based brain-computer interface EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {190},
pmid = {42824274},
issn = {1871-4080},
abstract = {In recent years, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have attracted extensive attention owing to their high information transfer rate (ITR) and multi-target recognition capability. Deep learning (DL) methods have shown strong potential in SSVEP feature extraction and target frequency classification. However, their relatively high model complexity and dependence on large-scale calibration data increase the burden of data acquisition and may induce subject fatigue, thereby limiting user experience and practical application. To address these issues, this study proposes a Spatial and Channel Reconstruction-based Frequency-Time-Space Deep Convolutional Neural Network (SCR-FTSDNN) for efficient and accurate SSVEP target recognition. Specifically, the raw EEG signals are first transformed into the frequency domain using Fourier transform to extract preliminary frequency-related representations. Then, temporal convolution is employed to further capture dynamic temporal characteristics. In the network architecture, spatial and channel reconstruction convolution modules are introduced to enhance the extraction of discriminative features and improve feature representation capability. Moreover, dilated convolution is adopted to enlarge the receptive fields of frequency, spatial, and temporal features, suppress redundant information, and assign higher weights to key discriminative components. To evaluate the effectiveness of SCR-FTSDNN, systematic experiments are conducted on two public SSVEP datasets, namely Benchmark and BETA, and comparisons are made with several existing methods. The experimental results demonstrate that SCR-FTSDNN achieves superior recognition accuracy and robustness, particularly under limited calibration data, indicating its strong adaptability and potential for practical SSVEP-BCI applications. The reproducibility code is available at: https://github.com/Walek669/SSVEPclassification.},
}

@article {pmid42824656,
year = {2026},
author = {Zhao, Y and Xu, W and Luo, X and Gao, Y},
title = {Tsmd-net: two-stage multi-domain distillation network for cross-subject MI-EEG domain generalization.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {192},
pmid = {42824656},
issn = {1871-4080},
abstract = {Motor imagery electroencephalography (MI-EEG) decoding enables brain-computer interface (BCI) systems to convert self-generated motor intentions into external control commands. However, inter-subject variability and EEG non-stationarity cause distribution shifts that limit the cross-subject generalization of existing decoding models. Although existing domain generalization methods can learn shared representations without target-subject data during training, they mostly rely on feature alignment or data augmentation and insufficiently exploit complementary discriminative knowledge among source subjects. In response to this problem, this study proposes TSMD-Net, a Two-Stage Multi-Domain Distillation Network for cross-subject MI-EEG domain generalization. TSMD-Net distills knowledge from multiple domain-specific heads into a unified leader head. The proposed method adopts a progressive two-stage optimization strategy, where feature covariance alignment first establishes stable cross-domain representations, and multi-domain collaborative distillation with hyperbolic supervised contrastive learning then enhances cross-domain prediction consistency and class discriminability. Experiments were conducted on two motor imagery benchmarks, namely BCI Competition IV 2a and 2b, and on the larger-scale High Gamma Dataset for motor execution. TSMD-Net achieved mean accuracies of 70.21%, 77.82%, and 79.53%, respectively. These results indicate that TSMD-Net provides effective and generalizable cross-subject motor decoding across the three evaluated EEG datasets.},
}

@article {pmid42825748,
year = {2026},
author = {Kong, XZ and Duan, Y and Pu, Y},
title = {The scale mismatch problem in metabolic cognitive modeling.},
journal = {The Behavioral and brain sciences},
volume = {49},
number = {},
pages = {e468},
doi = {10.1017/S0140525X26106165},
pmid = {42825748},
issn = {1469-1825},
mesh = {Humans ; *Cognition/physiology ; Animals ; *Models, Neurological ; },
abstract = {Haueis and Colaço compellingly argue for integrating metabolic considerations into cognitive modeling. We suggest that current discussions suffer from a scale mismatch: metabolic constraints are typically framed at global or microscopic levels, whereas cognitive models may operate at a systems level. We propose mesoscale metabolic properties of functional neural networks as a more promising interface for constraining cognitive models.},
}

Humans
*Cognition/physiology
Animals
*Models, Neurological

@article {pmid42825777,
year = {2026},
author = {Beierholm, UR and Colonius, H and Shams, L},
title = {Viewpoint on Computational Modeling of Multisensory Integration.},
journal = {Multisensory research},
volume = {},
number = {},
pages = {1-10},
doi = {10.1163/22134808-bja10212},
pmid = {42825777},
issn = {2213-4808},
abstract = {This article presents a compiled interview with three prominent figures in multisensory research: Ulrik Ravnsborg Beierholm, Hans Colonius, and Ladan Shams. Through structured dialogue, the authors reflect on the evolution of computational modeling, from early mathematical psychology to the current dominance of Bayesian causal inference (BCI). The discussion highlights key paradigm shifts, specifically the transition from descriptive studies of illusions to normative, mechanistic explanations of perception. The authors identify the 2002 demonstration by Ernst and Banks of statistically optimal integration as a pivotal milestone in establishing how the brain weighs sensory reliability. Crucially, the authors discuss the subsequent development of BCI as the first framework to formally address the 'binding problem' - the computational challenge of determining whether sensory signals originate from a common source (integration) or distinct sources (segregation). Current trends and controversies are explored, including the application of computational phenotypes to clinical populations, the debate over the neural implementation of Bayesian computations, and the rising influence of deep neural networks. While artificial intelligence (AI) offers new avenues for analyzing high-dimensional data, the authors emphasize the need for biological realism and explainability. The dialogue also addresses the importance of open science, the challenge of accounting for individual differences, and the necessity of moving toward naturalistic environments. The article concludes with professional advice for early-career scientists, emphasizing interdisciplinary training in mathematics and statistics. Collectively, the authors argue that multisensory processing is not a 'special case' of perception but the default mode of brain operation, providing essential principles for understanding learning, plasticity, and memory.},
}

@article {pmid42826206,
year = {2026},
author = {Hsieh, JC and Yao, M and Alawieh, H and Koptelova, V and Kumar, S and Tang, KWK and Wang, W and Jeong, J and Ding, H and Ahmad, Z and Wang, D and Engrav, T and Wang, R and Gupta, A and He, W and Moscoso-Barrera, WD and Grimes, A and Millán, JDR and Wang, H},
title = {Highly stretchable, hair-compatible multichannel neural interface with time-stable hydrogel for high-fidelity long-term EEG sensing.},
journal = {Science advances},
volume = {12},
number = {40},
pages = {eaee1540},
pmid = {42826206},
issn = {2375-2548},
support = {R35 GM147408/GM/NIGMS NIH HHS/United States ; },
mesh = {*Electroencephalography/methods/instrumentation ; Humans ; *Brain-Computer Interfaces ; Electrodes ; *Hair ; *Hydrogels/chemistry ; },
abstract = {Reliable brain-computer interfaces (BCIs) require electroencephalography (EEG) sensors that maintain conformal, low-impedance contact through hair, motion, and repeated use. Here, we present MindStretcH, a stretchable and hair-compatible EEG platform integrating a kirigami-patterned elastomeric mesh, deformable eutectic gallium-indium (EGaIn) interconnects, and detachable hydrogel-based conical electrodes. The system adapts to variations in head geometry, preserves electrical performance under substantial mechanical deformation, and maintains stable electrode-scalp contact over prolonged use. Longitudinal motor-imagery recordings and repeated online BCI experiments showed consistent task-related EEG activity without measurable deterioration in recording quality. These results establish MindStretcH as a reusable platform for long-term EEG monitoring and BCI applications in laboratory, clinical, and home settings.},
}

*Electroencephalography/methods/instrumentation
Humans
*Brain-Computer Interfaces
Electrodes
*Hair
*Hydrogels/chemistry

@article {pmid42826852,
year = {2026},
author = {Yang, L and Wang, Y and Yin, W and Xu, X and Yuan, X and Yang, H and Zhang, S and Jiang, S and Guo, Y and Wang, Y and Yang, C and Zhou, Y and Li, D and Song, Y and Dang, CP},
title = {Altered neural prioritization of auditory information during cross-modal attention in children with ADHD.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122278},
doi = {10.1016/j.neuroimage.2026.122278},
pmid = {42826852},
issn = {1095-9572},
abstract = {Efficient auditory attention requires prioritizing target over nontarget information. Although attentional deficits are a core feature of attention-deficit/hyperactivity disorder (ADHD), the neural mechanisms underlying auditory prioritization during cross-modal attention remain unclear. We examined neural differentiation between target and nontarget auditory information in children with ADHD. Ninety-five school-aged children (mean age = 10.81 years), including 46 children with ADHD and 49 typically developing (TD) children, performed a visually cued auditory spatial attention task during electroencephalography (EEG) recording. Auditory selective attention was assessed using the N2-anterior-contralateral (N2ac) component and multivariate pattern analysis (MVPA). TD children showed enhanced MVPA decoding of target locations and reduced decoding of nontarget locations. Greater target-nontarget neural differentiation was associated with larger N2ac amplitudes. In contrast, children with ADHD showed reduced target lateralization, smaller N2ac amplitudes, weaker target decoding, and enhanced nontarget decoding, resulting in diminished neural differentiation between relevant and irrelevant auditory information. Reduced target-related lateralization was associated with greater response-time variability, whereas reduced target decoding accuracy was linked to more severe hyperactivity-impulsivity symptoms. Together, these findings indicate that children with ADHD show altered neural prioritization of auditory information within a visually cued attention context. Altered neural prioritization was associated with behavioral variability and symptom severity, providing new insight into the neurocognitive mechanisms underlying attentional dysfunction in ADHD.},
}

@article {pmid42829016,
year = {2026},
author = {Hu, J and Mou, C and Jiang, X and Zhang, L and Ling, Y and Zhao, J and Meng, F and He, F and Wang, H and Peng, F and Ning, J and Gao, J and Xu, C and Xiao, J and Zhao, S and Yu, J and Pan, G and Luo, B},
title = {HD-tACS at 40 Hz improves consciousness and brain connectivity in prolonged disorders of consciousness: a randomized sham-controlled clinical trial.},
journal = {NeuroImage},
volume = {341},
number = {},
pages = {122274},
doi = {10.1016/j.neuroimage.2026.122274},
pmid = {42829016},
issn = {1095-9572},
abstract = {BACKGROUND: Frequency-specific high-definition transcranial alternating current stimulation (HD-tACS) may facilitate recovery in patients with prolonged disorders of consciousness (pDoC), but predictive biomarkers and mechanistic insights are limited.

OBJECTIVE: To investigate the therapeutic efficacy of 40 Hz or 4 Hz HD-tACS in pDoC.

METHODS: Sixty pDoC patients were randomized into four groups receiving 40 Hz or 4 Hz HD-tACS, each with a corresponding sham condition. Behavioral recovery was assessed via Coma Recovery Scale-Revised (CRS-R), network connectivity was analyzed with electroencephalography (EEG), and baseline 40 Hz auditory steady-state response (ASSR) was explored as a predictive biomarker.

RESULTS: Five days of 40 Hz HD-tACS yielded greater improvements of total CRS-R scores than 4 Hz or sham, particularly in visual and arousal subscales. EEG revealed strengthened short- and long-range connectivity quantified by phase-locking value (PLV), correlating with behavioral gains. At six months, 37.5% of 40 Hz patients got a good outcome with Glasgow Outcome Scale-Extended (GOS-E) score ≥ 4, which was significantly higher than that observed in the sham group (P = 0.019). Higher baseline ASSR predicted stronger network enhancement and clinical improvement.

CONCLUSIONS: 40 Hz HD-tACS promotes recovery and network reorganization in pDoC. Baseline ASSR may provide complementary information for understanding individual variability in responses to frequency-specific neuromodulation.},
}

@article {pmid42830114,
year = {2026},
author = {Wang, Q and Li, Y and Liang, Y and Hoi, SP and Yi, L},
title = {The Impact of Infectious Disease Severity on Stigma Associated With Affected Regions: A Facial Representation Perspective.},
journal = {International journal of psychology : Journal international de psychologie},
volume = {61},
number = {6},
pages = {e70276},
doi = {10.1002/ijop.70276},
pmid = {42830114},
issn = {1464-066X},
support = {22YJC190022//Humanities and Social Sciences Youth Foundation of the Ministry of Education of China/ ; },
mesh = {Humans ; Female ; Male ; Adult ; *Social Stigma ; *COVID-19/psychology ; Severity of Illness Index ; China ; Young Adult ; *Facial Recognition ; Disease Outbreaks ; },
abstract = {Individuals from regions affected by infectious disease outbreaks may be stigmatized because others hold negative mental images-or mental representations-of them. This study investigated whether such outbreaks lead to the formation of negative mental representations of residents in affected areas and explored the role of disease severity in this process. Study 1 revealed that during the early stages of the COVID-19 epidemic, the public's mental representations of residents from Wuhan were perceived as less attractive than those of Chengdu residents and associated with disgust and behavioural avoidance. However, differences disappeared after the restrictions of the epidemic were lifted. The results indicate that the COVID-19 outbreak triggered stigmatization of residents in Wuhan. In Study 2, disease severity and perceived threat were manipulated, and results show that participants formed less attractive face representations of residents from areas where severe infectious diseases broke out than those from areas affected by mild infectious diseases. This proved that the severity of infectious diseases affected the extent of stigmatization towards residents in outbreak areas. These results further our understanding of the mechanisms and factors underlying stigmatization associated with affected regions and may inform effective public health strategies to mitigate stigma and support disease control efforts.},
}

Humans
Female
Male
Adult
*Social Stigma
*COVID-19/psychology
Severity of Illness Index
China
Young Adult
*Facial Recognition
Disease Outbreaks

@article {pmid42822187,
year = {2026},
author = {Long, H and Wu, H and Guan, Q and Fan, M and Yang, X and Xiao, J and Ma, G and Zhang, Y and Liu, S},
title = {Hierarchical multi-atlas fusion network for robust and interpretable cross-site analysis of functional connectomes in major depressive disorder.},
journal = {Medical image analysis},
volume = {115},
number = {},
pages = {104346},
doi = {10.1016/j.media.2026.104346},
pmid = {42822187},
issn = {1361-8423},
abstract = {Major depressive disorder (MDD) is associated with distributed disruptions in functional brain networks, yet current connectome-based biomarkers often rely on a single parcellation and show limited cross-site robustness and biological interpretability. We propose a Hierarchical Multi-Atlas Fusion Network (HMAF-Net) for robust and interpretable cross-site analysis of functional connectomes in MDD. HMAF-Net reconciles heterogeneous anatomical and functional atlases by learning a small set of shared fusion nodes that serve as latent anchors for cross-atlas semantic alignment. For each atlas, it extracts node-wise descriptors and uses Transformer-based sparse soft assignment with a structure-aware contrastive objective to align atlas nodes in a common semantic space while retaining atlas-specific information. The resulting fusion-node embeddings support subject-level classification and spatial mapping for biomarker localization and imaging-transcriptomic association. We evaluated HMAF-Net on the multi-site REST-meta-MDD cohort (N = 1,611). Under 10-fold cross-validation, HMAF-Net achieved 72.28% accuracy and 71.49% AUC, showing the highest mean accuracy among representative atlas-fusion and graph-based baselines under identical splits. Under leave-one-site-out evaluation, it reached 77.48% mean accuracy, with site-wise analyses indicating residual variability associated with cohort size and head motion. External validation on two independent MDD datasets further supported cross-dataset generalizability. Fusion-node involvement shifts revealed reproducible fronto-limbic and default-mode-salience alterations. These spatial profiles were associated with Allen Human Brain Atlas expression gradients and enriched for synaptic and neurotransmission pathways, providing a molecularly grounded axis for interpretation. By integrating cross-atlas alignment, cross-site evaluation, and transcriptomic interpretation, HMAF-Net yields compact connectomic embeddings for reproducible and mechanistically informed MDD biomarker development.},
}

@article {pmid42822747,
year = {2026},
author = {Liu, H and Guo, X and Li, Z and Li, T and Shi, W and Li, X and Xiao, Y and Li, Z and Dai, X and Yang, B and Chai, S and Ren, S and Cai, Y and Pei, L and Xiong, N and Jiang, X},
title = {Thermosensitive hydroxypropyl chitin-based hydrogel for trigeminal neuralgia: Rapid analgesia, myelin repair, and attenuation of gamma-band hyperactivity in the spinal trigeminal nucleus caudalis.},
journal = {Acta biomaterialia},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.actbio.2026.09.051},
pmid = {42822747},
issn = {1878-7568},
abstract = {Trigeminal neuralgia is a debilitating neuropathic disorder characterized by the complex interplay of peripheral demyelination and central sensitization. Current pharmacotherapies remain palliative, limited by systemic toxicity and an inability to reverse pathological microenvironments. Herein, an injectable hydrogel integrating analgesic and nerve-repair functions is engineered using thermosensitive hydroxypropyl chitin (HPCH) and oxidized chondroitin sulfate (OCS) to encapsulate microcrystalline carbamazepine (CBZ) for targeted neuro-restoration. Upon perineural injection, the hydrogel precursor undergoes a swift in situ sol-gel transition via dual-crosslinking network formation, intimately enveloping injured nerve fibers to create a stable, bioactive depot. This system executes a spatiotemporally coordinated biphasic intervention: rapid CBZ release provides immediate analgesia by blocking voltage-gated sodium channels, while the bioactive OCS matrix subsequently remodels the local inflammatory microenvironment, downregulating pro-inflammatory cytokines, and guides prolonged regeneration of compact myelin sheaths, which may help restore peripheral nerve insulation and reduce ephaptic transmission. Crucially, in vivo local field potential recordings in the spinal trigeminal nucleus caudalis demonstrate that CBZ@HPCH/OCS treatment attenuated injury-associated gamma-band (30-80 Hz) hyperactivity. This multimodal paradigm integrates rapid pharmacological inhibition with sustained biological repair to effectively alleviate neuropathic pain with no obvious treatment-associated histopathological abnormalities observed in the examined major organs at 14 days post-treatment. STATEMENT OF SIGNIFICANCE: Trigeminal neuralgia causes excruciating facial pain, yet current oral treatments often provide only temporary relief while causing significant systemic toxicity. This study introduces an injectable hydrogel integrating analgesic and nerve-repair functions that transitions from liquid to solid at body temperature for targeted therapy. Unlike traditional drugs that merely mask symptoms, this dual-action system delivers immediate analgesia while actively repairing damaged myelin sheaths. This structural restoration effectively attenuated injury-associated gamma-band hyperactivity and alleviated neuropathic pain. By integrating rapid pharmacological action with long-term biological repair, this work offers a localized, low-toxicity paradigm for chronic pain management, representing a significant advance for researchers in biomaterials, neuroscience, and clinical pain therapy.},
}

@article {pmid42823305,
year = {2026},
author = {Seymour, K and Ienca, M and Koenig-Robert, R},
title = {When minds become data: brain-computer interfaces and the future of mental privacy.},
journal = {Trends in neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tins.2026.09.003},
pmid = {42823305},
issn = {1878-108X},
abstract = {The rapid advancement of brain-computer interfaces (BCIs), particularly adaptive closed-loop systems, presents unprecedented opportunities for medical intervention and human augmentation. At the same time, the growing adoption of applications outside clinical settings also poses significant risks to mental privacy. In this article, we review the current state of neural decoding advancements and their projected trajectory to assess what current and foreseeable technologies can reveal about mental states and the implications for mental privacy. Drawing on a neuroscientific perspective, we clarify the scope and limits of emerging mental privacy risks and argue for a shift in responsibility from reactive regulation to proactive, privacy-embedded design.},
}

@article {pmid42822044,
year = {2026},
author = {Sarikonda, A and Quraishi, D and Garcia, J and Ceccon, L and Khoury, E and Basavalingiah, A and McGowan, K and Patel, S and Momin, A and Lan, M and Self, DM and Kabani, AS and Prasad, SK and Heller, J and Sharan, A and Vaccaro, AR and Harrop, J and Jallo, J and Sivaganesan, A},
title = {Transforaminal endoscopic lumbar discectomy: an economically viable option for lumbar disc herniations? A hierarchical Bayesian analysis using time-driven activity-based costing.},
journal = {Neurosurgical focus},
volume = {61},
number = {4},
pages = {E13},
doi = {10.3171/2026.7.FOCUS26310},
pmid = {42822044},
issn = {1092-0684},
mesh = {Humans ; Female ; *Intervertebral Disc Displacement/surgery/economics ; Retrospective Studies ; Bayes Theorem ; Male ; *Lumbar Vertebrae/surgery ; *Endoscopy/economics/methods ; *Diskectomy/economics/methods ; Middle Aged ; Adult ; Cohort Studies ; },
abstract = {OBJECTIVE: Transforaminal endoscopic lumbar discectomy (TELD) has yet to achieve widespread adoption, in large part due to an unclear understanding of cost drivers. Therefore, using time-driven activity-based costing (TDABC), the authors sought to 1) compare risk-adjusted differences in the true costs (not charges or reimbursement) of TELD versus open and tubular microdiscectomy, and 2) identify independent predictors of cost for TELD.

METHODS: A retrospective cohort study (2020-2024) was conducted of all cases of TELD and microdiscectomy performed at a single institution. The primary exposure was procedure: TELD, open microdiscectomy, or tubular microdiscectomy. The primary outcome was cost, calculated using TDABC. Bayesian hierarchical regression was performed to 1) estimate risk-adjusted cost differences between procedures and 2) identify independent predictors of cost for endoscopy. Covariates included age, sex, American Society of Anesthesiologists (ASA) class, use of navigation, disc herniation (central/paracentral, foraminal/extraforaminal, or lateral recess/subarticular), number of decompressed levels, and surgeon.

RESULTS: A total of 503 patients underwent TELD (113, 22.5%) or microdiscectomy (390, 77.5%) for lumbar disc herniation. After adjusting for confounders, TELD was significantly more expensive than both open microdiscectomy (mean difference [MD] $1105, 95% Bayesian credible interval [BCI] $846-$1392) and tubular microdiscectomy (MD $1012, 95% BCI $616-$1432), driven by greater cost of consumables, particularly access/navigation kits (i.e., spinal access kits, endoscopic portals) and specialized instruments (i.e., high-speed drills, endoscopic forceps). Personnel costs did not differ significantly across procedures. Factors independently associated with greater cost for endoscopy included use of intraoperative navigation, patient ASA class, and disc herniation location. Specifically, navigation increased total cost by approximately 29% (estimate 0.258, 95% BCI 0.158-0.358; > 99.9% posterior probability). Likewise, each 1-standard deviation increase in ASA class increased cost by 4.2% (estimate 0.042, 95% BCI 0.004-0.080; 98.4% posterior probability). Compared to foraminal/extraforaminal herniations, central/paracentral herniations increased cost by 7.4% (estimate 0.071, 95% BCI 0.000-0.140; 97.6% posterior probability).

CONCLUSIONS: TELD was more expensive than both open and tubular microdiscectomy, largely driven by more expensive consumables. Moreover, both modifiable and nonmodifiable factors were identified as independent drivers of cost for endoscopy. Continued refinement of technique and patient selection, along with targeted vendor negotiations to reduce consumable costs, may improve both the economic viability and overall utilization of endoscopy.},
}

Humans
Female
*Intervertebral Disc Displacement/surgery/economics
Retrospective Studies
Bayes Theorem
Male
*Lumbar Vertebrae/surgery
*Endoscopy/economics/methods
*Diskectomy/economics/methods
Middle Aged
Adult
Cohort Studies

@article {pmid42812150,
year = {2026},
author = {Lu, R and Davidson, E and Miller-Smith, J and Lyu, D and Lusk, Z and Kundu, P and Doyon, J and Chai, XJ and Bernhardt, BC and Turner, GR and Spreng, RN},
title = {Thalamocortical orchestration of human theory of mind.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {42812150},
issn = {2692-8205},
abstract = {Reasoning about others' thoughts or beliefs is central to human social behavior. This ability, known as theory of mind (ToM), has been primarily attributed to cortical regions of the default network (DN). However, whether and how subcortical structures, particularly the thalamus, contribute to this high-level computation remains unknown. Here, we investigated human thalamocortical dynamics during a naturalistic ToM movie watching condition, taking a rare dual-modality approach by combining high-field 7T fMRI and intracranial stereoelectroencephalography (sEEG). Across both modalities, ToM events reliably activated the DN, whereas the thalamus lacked canonical local activation. Despite this absence of local activation, the thalamus shared ToM-related representational structure and exhibited enhanced bidirectional interactions with the DN during mentalizing across methods. Crucially, sEEG revealed that the thalamus coordinated DN activity via cross-frequency phase-amplitude coupling (PAC), whereby thalamic low-frequency phase unidirectionally modulated DN high-frequency activity. Furthermore, the strength of thalamic-DN PAC predicted both the activity magnitude and the representational quality of ToM-related information within the dorsomedial DN subsystem. Together, these findings identify the human thalamus as a regulatory hub that gates DN computations during ToM without exhibiting observable localized activity, revealing a previously unrecognized mechanism by which thalamic dynamics coordinate high-level social cognition in humans.},
}