Journal of neural engineering最新文献

{"title":"An explicit functional-map framework for structure-function correspondence in brain connectivity.","authors":"Zhengyuan Lyu, Zhiwei Song, Si Fu, Hu Yu, Xiaojuan Guo","doi":"10.1088/1741-2552/ae9acb","DOIUrl":"10.1088/1741-2552/ae9acb","url":null,"abstract":"<p><p><i>Objective</i>. Characterizing the relationship between structural connectivity (SC) and functional connectivity (FC) is a central problem in multimodal neuroimaging. SC-to-FC prediction accuracy is often used to summarize this relationship, but a high score may largely reflect population-level regularities shared across subjects and does not reveal whether regional, network-level, or subject-specific correspondence is preserved. We therefore developed a framework that makes these different levels of SC-FC correspondence explicit and directly testable.<i>Approach.</i>For each subject, we constructed separate spectral representations of SC and FC and estimated an explicit linear map between them. Signal transfer was evaluated against matched controls that disrupted node identity, within-network organization, or subject pairing, allowing aggregate transfer to be distinguished from more specific forms of correspondence. A training-derived reference map was further used to define the functional component expected under a reference SC-FC relationship and the deviation from that expectation for downstream aging and disease-related analyses.<i>Main results.</i>Across four independent datasets, all primary matched-control gaps were positive after false-discovery-rate correction, although their magnitudes varied across cohorts. Strong aggregate transfer did not necessarily imply strong subject-level correspondence. Compared with external baselines, the proposed framework more consistently preserved regional identity, within-network organization, and correct subject pairing. In aging analyses, reduced alignment with a young-reference SC-FC relationship was robust in CamCAN but not significant in NKI, whereas empirical FC magnitude showed no comparable decline. In Alzheimer's Disease Neuroimaging Initiative, reference-conditioned markers showed exploratory disease-related signals, but the associated uncertainty did not support definitive diagnostic or incremental-value claims.<i>Significance.</i>The proposed framework separates the ability to transfer SC-derived signals into FC space from the specificity of the correspondence being preserved. It provides an interpretable basis for testing population-level, network-level, and subject-sensitive SC-FC relationships and for constructing reference-conditioned individual deviation measures without treating prediction accuracy as direct evidence of biological constraint.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification.","authors":"Charlotte Genevier Wyman, Leanne Hirshfield","doi":"10.1088/1741-2552/ae9f95","DOIUrl":"10.1088/1741-2552/ae9f95","url":null,"abstract":"<p><p><i>Objective.</i>Interpretable brain-computer interface classifiers that generalize across subjects without calibration remain an open challenge. We evaluated whether prototype-based cross-attention can provide competitive, inherently interpretable event-related potential (ERP) classification across diverse paradigms under deployment-compatible conditions.<i>Approach.</i>We propose ERP-XTTN (ERP Cross-Attention), a cross-attention architecture that routes input electroencephalographic peaks to fixed difference-wave prototypes via query-key-only cross-attention with no value projection. Classification is based directly on prototype similarity and a separate measure of component amplitude, so that the prototype content contributes to every decision by construction. Prototypes are derived automatically from prominent extrema in the training-fold grand-average difference wave. We evaluated across three public sources (BNCI Horizon 2020, HRI Cursor, and ERP CORE) encompassing eight ERP components (ERN, LRP, ErrP, N170, P300, N2pc, MMN, N400). Evaluations used leave-one-subject-out (LOSO) cross-validation with causal filtering at a three-channel montage, compared against EEGNet, EEG-Deformer, ERP Prototypical Matching Net, and xDAWN with Riemannian geometry (xDAWN+RG).<i>Main results.</i>At three channels, the mean performance gap between the best baseline and ERP-XTTN was 0.025 area under the receiver operating characteristic curve. Prototype interventions confirmed that decisions depend on the physiological content of the prototypes rather than on the routing attention pattern alone. False positives morphologically resembled true positives more than true negatives did across all datasets, indicating classification errors are neurophysiologically explicable.<i>Significance.</i>ERP-XTTN generalizes across diverse ERP morphologies under causal, calibration-free conditions, while retaining competitive performance and decisions that depend directly on physiological prototype content at a three-channel montage. Unlike post-hoc explanation methods for black-box models, the basis of each decision is directly observable in the trained model itself. To our knowledge, this is the first epoch-level LOSO benchmark on ERP CORE.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examination of neurocognitive processes during execution of sequential problem-solving tasks under varying demand.","authors":"Jayesh Jayashankar, Anna L Packy, Hyuk Oh, Arya Teymourlouei, James Purtilo, James A Reggia, Garrett E Katz, Rodolphe J Gentili","doi":"10.1088/1741-2552/ae900f","DOIUrl":"https://doi.org/10.1088/1741-2552/ae900f","url":null,"abstract":"<p><p><i>Objective</i>. There exists a limited body of work that has jointly assessed behavioral performance and mental workload during the execution of sequential tasks. However, none of this work has jointly assessed performance and neurocognitive dynamics during the execution of problem-solving tasks having multiple solutions similar to real-world settings across different levels of cognitive demand. Additionally, only limited efforts have quantified an individual's performance using measures which constitute distinct aspects of cognitive-motor performance.<i>Approach</i>. Therefore, we extend a prior effort to assess performance through a computational approach to obtain the optimality stratified similarity degree metric (OSSD) through two different measures of performance (i.e. optimality and similarity). These performance measures along with brain dynamics captured through electroencephalography spectral power measures were combinedly assessed to determine their impact on neural efficiency. The recorded measures allowed the stratification of performance into distinct optimality tiers within which unique differences between sequences structured by individuals were captured by the similarity metric. In addition to a traditional population-based analysis, this approach led to the clustering of the best and worst performers, thus allowing one to evaluate cortical dynamics between groups of individuals.<i>Main results</i>. The main findings of the study were that enhanced levels of task demand caused a degraded performance (i.e. decreased optimality, similarity and OSSD) along with a greater recruitment of neurocognitive processes (i.e. whole scalp increase of theta and low/high-alpha power; frontal, central, and temporal low/high-beta and gamma power increase), ultimately leading to an attenuation of neural efficiency. The cluster-based analysis between the best versus worst performers revealed that temporal low/high beta and gamma power as well as central gamma power was greater for the former relative to the latter.<i>Significance</i>. This work has the potential to inform the neurocognitive processes underlying the performance of complex sequential problem-solving tasks in various industrial, occupational, military, and healthcare applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"23 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive weighted fuzzy ILC for personalized FES trajectory reconstruction using an anatomical prior.","authors":"Shiqin Liu, Jiao Wu, Weihua He, Xueliang Bao, Yu Zhou","doi":"10.1088/1741-2552/ae9f94","DOIUrl":"10.1088/1741-2552/ae9f94","url":null,"abstract":"<p><p><i>Objective</i>. The clinical translation of closed-loop functional electrical stimulation (FES) systems is hindered by three interrelated bottlenecks: the trade-off between algorithmic complexity and embedded real-time feasibility, inefficient personalization, and a lack of empirical validation of human-machine cooperative performance in stroke patients.<i>Approach</i>. We propose a physiology-informed adaptive weighted fuzzy iterative learning control (AW-FILC) algorithm, implemented on a custom STM32F407-based embedded FES platform. The core innovation is a dual-layer adaptive mechanism that incorporates subcutaneous fat thickness, which is a key biophysical determinant of current attenuation measurable, as a feedforward prior. This prior is fused with real-time kinematic error feedback to dynamically modulate the learning gain and penalty factor.<i>Main results</i>. In simulations across two heterogeneous musculoskeletal models, AW-FILC achieves convergence within 5-7 iterations, with steady-state tracking accuracy of 10^-5for normalized torque and <for joint angles, outperforming PID-ILC and Type-1/Type-2 fuzzy ILC. In healthy participants (), AW-FILC combined with voluntary effort achieved a trajectory tracking Pearson correlation of 0.87 ± 0.04, substantially exceeding the pure-FES ceiling (0.80) and approaching the physiological gold standard of natural bilateral coordination (0.984 ± 0.014). In stroke patients (), AW-FILC elevated tracking correlation from 0.24 (pure open-loop FES) to 0.30 ± 0.02. This modest numerical gain represents a qualitative shift from pathological toward normalized movement patterns. The embedded implementation achieves per-iteration execution < 2 ms on STM32F407, and the system respects rather than quantifies patient voluntary effort, aligning with activity-dependent neuroplasticity principles.<i>Significance</i>. This work provides a computationally efficient, physiologically adaptive control core for embedded FES devices and empirically validates that AW-FILC-driven active rehabilitation bridges the gap between pathological and physiological coordination.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust time delay estimation for measurement of ictal EEG traveling waves.","authors":"Trisha Mendoza, Marco Antonio Pinto-Orellana, Joffre E Olaya, Hernando Ombao, Daniel W Shrey, Beth A Lopour","doi":"10.1088/1741-2552/ae9bed","DOIUrl":"10.1088/1741-2552/ae9bed","url":null,"abstract":"<p><p><i>Objective.</i>Seizures are often characterized by epileptiform discharges that appear to be highly synchronous across electroencephalogram (EEG) channels, yet closer analysis reveals small time delays indicative of fast traveling waves. Microelectrode studies suggest that a possible source of these waves is the ictal wavefront, a slowly advancing boundary emitting opposing waves; attempts to extend this analysis to macroelectrodes produced conflicting theories, suggesting both static and moving radial sources. Because variability in EEG measurement and analysis may contribute to conflicting results, we systematically evaluated how signal characteristics and methodological choices affect wave direction estimates. We applied the resulting framework to human intracranial EEG (iEEG) to validate the results.<i>Approach.</i>We simulated iEEG data using a second-order autoregressive model, modeling coherence as a distance-weighted sum of signals. We then applied one of eight different time-delay propagation patterns at a range of wave speeds, and accuracy was assessed for multiple referencing schemes, sampling rates, and spatial resolutions. Human iEEG recordings were then analyzed to validate methodological recommendations and characterize ictal waves.<i>Main Results.</i>Simulations revealed that wave patterns could be measured most accurately for wave speeds <1000 mm s^-1and average iEEG coherence levels >0.4. Under these conditions, a corner electrode reference outperformed other referencing schemes. Wave estimation accuracy was highest for high sampling rates and low electrode spacing. Accurate results were obtained at 3 mm electrode spacing, but not at 9 mm spacing, which approximates standard clinical subdural grids (<i>p</i>< 0.01). Human iEEG exhibited a wide range of propagation patterns, including spirals, sources, and sinks which have not been previously reported.<i>Significance.</i>Detailed analyses of simulated and human iEEG data highlight critical methodological choices that must be considered when characterizing complex seizure wave patterns. Our findings provide a validated framework to increase the rigor of future studies.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-sleep central-occipital alpha-theta dynamics predict NREM2 onset in adult.","authors":"Cheng-Hua Su, Li-Wei Ko, Tzyy-Ping Jung, Shey-Cherng Tzou, Jia-Chi Juang, Chung-Yao Hsu","doi":"10.1088/1741-2552/ae9226","DOIUrl":"10.1088/1741-2552/ae9226","url":null,"abstract":"<p><p><i>Objective.</i>Sleep onset (SO) marks the transition from wakefulness to a restorative state of sleep in the human brain. Recent studies suggest that the sleep onset process (SOP) is a back-and-forth progression that occurs before the brain reaches Non-Rapid Eye Movement Sleep Stage 2 (NREM2) and that its duration is highly variable. Very short sleep onset latencies may increase the risk of accidents, while long SO may indicate poor sleep quality and insomnia.<i>Approach.</i>In this study, we investigated electroencephalography power dynamics before the first NREM2 sleep epoch to identify potential predictors of SO ending and factors that differentiate fast from slow sleepers. The study took a dual-dataset approach, where the main dataset with 18 adults were used to extract potential markers, while a secondary dataset derived from publicly available data to verify the pattern.<i>Main results</i>. We found that both groups exhibited an initial rise followed by a decline in alpha rhythms, resembling an inverted-<i>U</i>curve, in central and occipital regions. Compared to slow sleepers, fast sleepers showed a narrower, steeper alpha-power curve and an earlier rise in theta power. When examining the alpha-theta ratio, we found that, while slow sleepers followed an inverted-<i>U</i>-shaped progression in central and occipital regions, fast sleepers' ratio decreased monotonically toward NREM2. Finally, we combined the alpha-theta ratio and the temporal rate of decline in the ratio to predict NREM2 onset using supervised learning. A fuzzy C-means (FCM) classifier achieved the highest accuracy (0.65).<i>Significance</i>. These findings indicate that neurodynamics during SOP are directly related to its duration. The alpha-theta ratio in central and occipital channels, together with its rate of decline, provides a compact and physiologically interpretable predictor of NREM2 onset. This predictive capability supports applications in both occupational safety and sleep health by enabling early detection of unintended SO and allowing individuals to adopt proactive interventions when a prolonged SOP is anticipated.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148623369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TriGA-Net: a graph attention network for brain-controlled speaker extraction.","authors":"Youhao Si, Yuan Liao, Qiushi Han, Rui Dai, Yuhang Yang, Liya Huang","doi":"10.1088/1741-2552/ae9e8f","DOIUrl":"10.1088/1741-2552/ae9e8f","url":null,"abstract":"<p><p><i>Objective.</i>Electroencephalography (EEG)-guided target speaker extraction aims to recover the listener's attended speech from mixed speech. However, effectively representing and integrating the target-related information shared between EEG and speech remains challenging. This study focuses on this challenge.<i>Approach.</i>We propose TriGA-Net, a Graph Attention Network for Brain-Controlled Speaker Extraction, in which EEG recorded from the listener is used to guide target speech extraction. The EEG encoder combines multi-scale temporal features and frequency-domain features with graph convolution to model dependencies among electrodes, while self-attention captures interactions across the full set of EEG channels. The resulting EEG representation is fused with encoded speech features and passed to a MossFormer2 separator. By combining MossFormer with a recurrent module that does not rely on recurrent neural networks, the separator models long-range context together with the rhythmic and prosodic structure of speech.<i>Main results.</i>Experiments on the public Cocktail Party and KU Leuven (KUL) datasets yielded scale-invariant signal-to-distortion ratio (SI-SDR) values of 15.91 and 16.90 dB, respectively. Compared with the strongest baseline on each dataset, TriGA-Net improved SI-SDR by 1.96 dB on the Cocktail Party dataset and by 2.30 dB on the KUL dataset. Additional improvements were observed in short-time objective intelligibility (STOI) and extended STOI (ESTOI) on both datasets and in perceptual evaluation of speech quality on the KUL dataset.<i>Significance.</i>These results suggest that jointly modeling temporal-frequency EEG information, inter-electrode relationships, and long-range speech structure is effective for EEG-guided target speaker extraction.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aligned fibrous scaffolds containing glycosaminoglycan mimetics enhance Schwann cell-supported axon growth and myelination.","authors":"Sharareh Hashemi, Sumayia Saif Jaima Chowdhury, Anna Kylat, Mary B Bunge, Martin Oudega, Treena L Arinzeh","doi":"10.1088/1741-2552/ae98b7","DOIUrl":"10.1088/1741-2552/ae98b7","url":null,"abstract":"<p><p><i>Objective.</i>Traumatic spinal cord injury causes functional impairments in large part due to the limited ability of damaged axons to regenerate. During development, the extracellular matrix (ECM) plays a critical role in providing molecular cues that promote and direct axon growth. Biomaterials that mimic the physicochemical properties of the ECM may be promising for promoting axon growth in the injured spinal cord. Glycosaminoglycans (GAGs) in the ECM during development are known to direct axon growth and guidance. We previously reported that GAG-mimetic cellulose sulfate, which can be synthesized with varying degrees of sulfation, partially and fully sulfated cellulose (pCelS and fCelS, respectively), promoted neurite extension over native GAGs.<i>Approach.</i>The present study investigated the use of GAG-mimetic containing scaffolds as a platform for Schwann cell transplants to promote axon growth. The aligned fibrous scaffolds consisted of gelatin or gelatin blended with polycaprolactone (PCL), in order to improve hydrolytic stability, with 0.25 wt.% GAG-mimetics.<i>Main results.</i>Our results demonstrated that the fCelS-containing scaffolds with SCs promoted more axon growth than the pCelS-containing scaffolds and the greatest myelination over all other scaffolds. Also, in the presence of astrocytes, fCelS-containing scaffolds supported axon growth. Furthermore, the fCelS-containing scaffolds were capable of binding and retaining a greater amount of the neurotrophins brain-derived neurotrophic factor and neurotrophin-3 as compared to all other scaffolds.<i>Significance.</i>Aligned fibrous GAG-mimetic scaffolds containing fully sulfated cellulose may hold promise to repair damaged spinal cord tissue by providing physiochemical axon growth-promoting cues and a transplantation platform for SCs to support axon growth and myelination.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13641151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148727505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motor unit decomposition detects altered trunk neural output during seated postural maintenance after stroke.","authors":"Hiroki Hanawa, Keisuke Hirata, Taku Miyazawa","doi":"10.1088/1741-2552/ae9db9","DOIUrl":"10.1088/1741-2552/ae9db9","url":null,"abstract":"<p><p><i>Objective.</i>Trunk control after stroke is clinically important, yet the neural output supporting seated postural maintenance remains poorly characterized. This limits not only the physiological interpretation of trunk dysfunction but also the development of neuromodulation technologies. We therefore examined post-stroke trunk muscle control across three levels: motor unit firing behavior, the frequency distribution of surface electromyography (sEMG) envelope power, and low-frequency sEMG-kinematic coherence.<i>Approach.</i>Twenty individuals with subacute stroke and 20 elderly controls performed a seated postural maintenance task with bilateral arm elevation. High-density sEMG was recorded from the lumbar longissimus, and pelvic motion was measured using an inertial measurement unit. Motor unit discharge metrics, EMG envelope power, and sEMG-kinematic coherence were quantified.<i>Main results.</i>Twenty-two motor units from 12 participants with stroke and 14 motor units from 10 elderly participants were analyzed. Mixed-effects models showed higher 95th percentile discharge rate after stroke (beta = 4.70 Hz,<i>p</i>= 0.037; cluster-bootstrap<i>p</i>= 0.0098), whereas mean discharge rate and inter-spike interval variability were not different. EMG envelope power showed frequency-band and group-by-band effects, with the clearest difference at 0.5-5 Hz. sEMG-kinematic coherence showed no significant effects.<i>Significance.</i>Post-stroke trunk motor output was most clearly detected as an upper-tail motor unit discharge-rate difference rather than altered mean firing. EMG envelope power provided secondary evidence of low-frequency modulation, whereas muscle-motion coupling showed no stroke-specific modulation. Motor unit decomposition may therefore serve as a useful physiological reference for trunk motor control after stroke.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping neural representations of fine and gross upper-limb movements across dorsoventral subthalamic nucleus subregions in Parkinson's disease.","authors":"Erin M Radcliffe, Morgan K Hampton, Hayley Granberg, Karen Gerecht, Ross Moseley, Steven Ojemann, Alexander J Baumgartner, Drew S Kern, Daniel R Kramer, John A Thompson","doi":"10.1088/1741-2552/ae863f","DOIUrl":"10.1088/1741-2552/ae863f","url":null,"abstract":"<p><p><i>Objective</i>. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an established therapy for Parkinson's disease (PD), yet optimizing outcomes remains challenging due to the complexity of STN functional architecture and the subjectivity of intraoperative assessments. Furthermore, the encoding of fine versus gross movements across STN dorsoventral subregions remains poorly understood. This study aimed to characterize region- and movement-specific modulation of STN neuronal activity using synchronized electrophysiology and markerless motion tracking-derived kinematics.<i>Approach</i>. We recorded intraoperative electrophysiological and videographic data from 11 subjects with PD (15 hemispheres) undergoing awake STN-DBS implantation surgery. Microelectrode recordings were synchronized with high-resolution kinematics during repeated trials of continuous upper-limb motor tasks involving both distal and proximal muscle groups. Instantaneous firing rate (IFR) and neuronal responsiveness metrics were quantified using a parameter-free Zenith of Event-based Time-locked Anomalies (ZETA) statistical framework and analyzed with linear mixed-effects models.<i>Main results</i>. STN single-unit activity (SUA) and multi-unit activity (MUA) features exhibited robust region- and movement-specific modulation. Firing rates for both SUA and MUA were significantly higher in the dorsal STN during active movements, particularly distal fine motor tasks, compared to ventral STN and baseline activity. ZETA-test analyses revealed significant movement-specific responsiveness across STN subregions, with more dextrous, distal movements eliciting stronger responses than more coordinated, proximal movements. Temporal dynamics of neuronal responsiveness also varied with STN depth and movement type, indicating hierarchical recruitment of STN subregions in the encoding of movement-specific temporal motifs.<i>Significance</i>. These findings demonstrate that STN motor representations are context-dependent, anatomically organized, and temporally distinct, reinforcing the STN's dynamic engagement during motor execution. By identifying objective electrophysiological features linked to movement context and STN region, this work provides a quantitative foundation for reducing reliance on subjective assessments to inform data-driven DBS targeting, programming, and closed-loop control strategies.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148383388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}