工程技术最新文献

{"title":"Dynamic mechanisms underlying neurotransmitter-regulated brain excitation-inhibition balance: insights from the MDMF model.","authors":"Xinxin Ren, Youyou Si, Honghui Zhang","doi":"10.1007/s11571-026-10527-y","DOIUrl":"https://doi.org/10.1007/s11571-026-10527-y","url":null,"abstract":"<p><p>Excitation-inhibition (E-I) imbalance is a core pathological mechanism underlying many psychiatric disorders. In this study, we integrate structural and functional magnetic resonance imaging data to construct a large-scale brain network model, aiming to investigate how perturbed structural connectivity drive brain network reorganization and how neurotransmitters regulate E-I balance. The results show that the multiscale dynamic mean-field (MDMF) model identifies steady-state regimes of neurotransmitter-related parameters that yield relatively high correspondence between simulated and empirical resting-state functional connectivity. Furthermore, synaptic pathology is simulated by altering structural connectivity, and the resulting functional connectivity reproduces network topological features observed in neurological and psychiatric disorders. Finally, the E-I ratio is quantified at both the neurotransmitter level and the neuronal firing-rate level. By modulating inhibitory neurotransmitter-related parameters, selected topological properties of the simulated FC networks shift closer to the empirical condition. These findings establish a link between E-I imbalance, neurotransmitter regulation, and structural abnormalities, providing new insights into the pathogenesis and potential treatment strategies for psychiatric disorders.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"157"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13469051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-accuracy EEG-based driver fatigue detection using feature-coupled tensor decomposition for multimodal brain connectivity analysis.","authors":"Tian Yan, Hechong Su, Zengyao Yang, Aimin Zhang, Yidan Ma, Xiaolong Wang, Yue Wu, Li Yan, Yichen Wang, Jianfu Cao","doi":"10.1007/s11571-026-10531-2","DOIUrl":"10.1007/s11571-026-10531-2","url":null,"abstract":"<p><p>Driver fatigue is a major threat to road safety, and electroencephalography (EEG)-based monitoring offers an effective approach for assessing a driver's cognitive state. However, existing brain network feature-extraction methods often rely on single-modality connectivity metrics, which may not fully capture the complex multidimensional reorganization of neural dynamics during the transition to fatigue. To address this methodological gap, this study proposes a Feature-Coupled Tensor Decomposition (FCTD) framework for multimodal brain network analysis. The framework constructs independent high-order tensors for three complementary connectivity metrics: signal covariance (representing amplitude coupling), phase-locking value (representing phase synchronization), and Liang-Kleeman information flow (representing directed information transfer). Using Tucker decomposition, FCTD learns spatial bases at the scalp-electrode level and employs a cross-mapping strategy to integrate latent inter-modality dependencies. The proposed framework was evaluated on two publicly available driver fatigue EEG datasets. Under a chronological block-wise 10-fold cross-validation protocol designed to reduce the risk of temporal leakage, the method achieved average accuracies of 99.42% and 93.62% on Dataset 1 and Dataset 2, respectively. Leave-one-subject-out (LOSO) cross-validation on Dataset 2 yielded an accuracy of 75.18%, indicating that cross-subject generalization remains challenging under substantial inter-subject variability. By integrating complementary connectivity views, this study provides a computationally efficient framework with feature projections for multimodal EEG feature extraction in fatigue monitoring systems.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"163"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fundamental questions on closed-loop neuromodulation: a control theory perspective.","authors":"Min Wang, Li Zhang, Zhen Liang, Gan Huang","doi":"10.1007/s11571-026-10452-0","DOIUrl":"https://doi.org/10.1007/s11571-026-10452-0","url":null,"abstract":"<p><p>Closed-loop neuromodulation aims to adjust therapeutic stimulation in real time based on ongoing neural or physiological signals. Despite growing clinical adoption, most implementations rely on heuristic rules rather than a principled systems-and-control formulation. This paper, motivated by discussions from the Brain Theory Seminar (Shanghai, March 2025), develops such a formulation around seven fundamental questions-mechanism (Q1), plant nature (Q2), state measurement (Q3), actuation (Q4), modeling (Q5), objectives (Q6), and constraints (Q7)-and, for each, provides a knowledge-based review synthesizing current understanding together with a prospective scientific opinion on unresolved issues. Five recurring themes unify the seven questions: (i) nonstationarity as the default operating condition, (ii) structural partial observability and under-actuation, (iii) closed-loop confounding between stimulation and measurement, (iv) the primacy of hard constraints over unconstrained optimization, and (v) the necessity of layered governance separating performance seeking from safety enforcement. We argue that the neural plant is fundamentally different from classical engineered systems in ways that reshape what can be sensed, modeled, actuated, and verified; accordingly, we reframe therapeutic goals from setpoint tracking toward set-based regulation within a therapeutic window, and we treat safety, ethics, and accountability not as external add-ons but as architectural primitives that define the admissible design space. We close with a discussion synthesizing system-level barriers and near-term architectural directions, including bidirectional brain-computer interfaces, hybrid learning-and-control pipelines with independent safety supervision, and digital twins as regulated test harnesses.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"88"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13153344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147867236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring electromagnetic induction and astrocyte influence in excitatory-inhibitory coupling neuron network.","authors":"Zhongquan Gao, Peihua Feng","doi":"10.1007/s11571-026-10451-1","DOIUrl":"https://doi.org/10.1007/s11571-026-10451-1","url":null,"abstract":"<p><p>This study investigates how electromagnetic induction and astrocytic modulation jointly influence firing dynamics and synchronization in excitatory-inhibitory neuronal networks. Using a computational model that integrates pyramidal neurons, interneurons, and astrocytes with memristor-based electromagnetic feedback and calcium-dependent signaling, we demonstrate that electromagnetic induction generally suppresses neuronal firing, but this effect can be bidirectionally modulated by astrocytic feedback depending on intracellular calcium levels. Key findings reveal the non-linear dependence of neuronal responses on astrocytic calcium thresholds and [Formula: see text] dynamics; the distinct roles of excitatory, inhibitory, and astrocytic coupling in regulating network synchrony and chimera states; and the existence of parameter regimes where astrocytic feedback is overridden under strong electromagnetic induction. These results highlight the critical interplay between electromagnetic and glial mechanisms in shaping network activity, offering insights for models of neural synchronization and potential therapeutic strategies for epilepsy and related disorders.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"84"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in aperiodic EEG activity following acupuncture stimulation in stroke.","authors":"Zhicai Hu, Zhen Zhang, Fan Xu, Sixian Wu, Jiang Wang, Guiping Li, Haitao Yu","doi":"10.1007/s11571-026-10466-8","DOIUrl":"https://doi.org/10.1007/s11571-026-10466-8","url":null,"abstract":"<p><p>Electroencephalography (EEG) enables the direct measurement of stroke-related brain activity, which is crucial for objectively evaluating therapeutic effects of neuromodulatory interventions in stroke. Numerous studies indicate that EEG spectra comprise a mixture of periodic oscillations and aperiodic background; however, how aperiodic activity, distinct from oscillatory components, characterizes and quantifies external stimulation remains unclear. In this paper, we parameterized the power spectra of EEG recorded from 30 stroke subjects with acupuncture stimulation and investigated the changes of oscillatory and aperiodic brain activity following acupuncture stimulation. For the oscillatory components, we found that acupuncture increased both the power and center frequency of alpha oscillations in the left fronto-parietal cortex, indicating a shift toward faster and more strongly synchronized activity in the lesioned regions. Next, analysis of aperiodic brain activity demonstrated widespread decreases in the aperiodic exponent and offset, most prominently in the ipsilesional hemisphere. These globally and spatially coherent changes suggest that aperiodic dynamics are highly sensitive to acupuncture, reflecting coordinated changes in the background spectral organization. Furthermore, analyses of both periodic and aperiodic features revealed that acupuncture improved interhemispheric symmetry, with particularly pronounced effects observed in alpha center frequency, alpha power, and the aperiodic exponent. Collectively, this study extracted oscillatory and aperiodic spectral features to characterize EEG activity, identified alpha center frequency, alpha power, and the aperiodic exponent as potential biomarkers of acupuncture-induced changes in stroke, and provided evidence for the modulatory effects of peripheral acupuncture stimulation on brain activity.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"94"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13180013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147970827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel motor imagery brain-computer interface classification framework based on augmented covariance matrix: deep Riemannian geometry learning with self-attention mechanism.","authors":"Tian Yan, Aimin Zhang, Zengyao Yang, Hechong Su, Yidan Ma, Yue Wu, Li Yan, Yichen Wang, Fei Guo, Jianfu Cao","doi":"10.1007/s11571-026-10548-7","DOIUrl":"10.1007/s11571-026-10548-7","url":null,"abstract":"<p><p>The rapid development of motor imagery brain-computer interface (MI-BCI) technology has introduced a novel mode of human-machine interaction, drawing significant interest. However, due to the nonlinearity and nonstationarity of motor imagery signals, this communication method faces challenges such as low recognition accuracy and poor stability, which severely hinder the practical application of MI-BCIs. Riemannian geometry techniques, leveraging their non-Euclidean properties, have shown promise in addressing motor imagery signal pattern recognition. Traditional Riemannian geometry methods, however, still suffer from limitations, such as requiring manual feature extraction and resulting in suboptimal classification performance. This paper proposes Aug-SPD-AttentionNet, a deep Riemannian geometry learning framework for MI-BCI classification that combines lagged augmented covariance representations, a learnable high-dimensional embedding layer, and self-attention operations defined on the SPD manifold. The proposed framework employs multiple deep Riemannian learning blocks to extract manifold-aware features and uses a Riemannian self-attention mechanism to capture long-range dependencies among different SPD representations. Aug-SPD-AttentionNet was evaluated on BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, achieving mean classification accuracies of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Ablation experiments further demonstrate that the proposed components collectively improve classification performance, increasing the mean accuracy across the three evaluated subjects from 70.60 to 82.62%, corresponding to a 12.03-percentage-point improvement over the baseline SPDNet.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"180"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149024037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A balanced multimodal decoding framework of EEG and fNIRS for motor imagery.","authors":"Jiaru Dai, Li Zhu, Fabio Babiloni, Wanzeng Kong","doi":"10.1007/s11571-026-10530-3","DOIUrl":"10.1007/s11571-026-10530-3","url":null,"abstract":"<p><p>Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) exhibit complementary advantages in temporal and spatial resolution for brain activity monitoring, and their integration has the potential to improve motor imagery (MI) decoding performance. However, in EEG-fNIRS multimodal MI decoding, modality heterogeneity and differences in signal-to-noise ratio and convergence speed often cause joint training to bias toward the modality that is easier to learn, leading to modality dominance, degraded fused representations, and reduced generalization. To address this issue, we propose a dynamic re-initialization framework for EEG-fNIRS multimodal decoding, which achieves cross-modal balanced learning through a diagnosis-adjustment-re-initialization mechanism. The proposed method uses the separability difference of unimodal features between the training and validation sets as a diagnostic signal, and integrates network hierarchical priors with modality-specific gradient statistics to derive adjustment factors. Guided by these signals, the framework periodically performs soft re-initialization of the EEG and fNIRS encoder parameters during training, promoting re-learning of weaker modalities, suppressing single-modality dominance, and preserving the stability of converged representations. As a result, multimodal imbalance is alleviated and the discriminative capability of fused representations is enhanced. Experiments on a publicly available EEG-fNIRS motor imagery dataset demonstrate that the proposed method achieves an average classification accuracy of 92.42 ± 4.54%, significantly outperforming both unimodal and conventional fusion baselines and showing strong cross-subject consistency.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"165"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: A brain-constrained neural model of cognition and language withNEST: transitioning from the Felix framework.","authors":"Maxime Carriere, Fynn Dobler, Hans Ekkehard Plesser, Agata Feledyn, Rosario Tomasello, Thomas Wennekers, Friedemann Pulvermüller","doi":"10.1007/s11571-026-10524-1","DOIUrl":"https://doi.org/10.1007/s11571-026-10524-1","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1007/s11571-026-10415-5.].</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"164"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13518705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148839543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discrete second-order locally active memristor-based neuron map with grid-like attractor coexistence.","authors":"Xiongjian Chen, Kehui Sun, Mingzhen Mao, Xu Sun, Huihai Wang","doi":"10.1007/s11571-026-10539-8","DOIUrl":"10.1007/s11571-026-10539-8","url":null,"abstract":"<p><p>Higher-order memristors offer a compact approach to representing multiple internal memory states in neuronal models. In this work, a discrete second-order locally active memristor (SO-LAM), featuring two internal states and a bounded periodic memductance, is integrated into the Aihara neuron map, yielding a memristive neuron with two history-dependent internal states. The membrane potential drives the evolution of both memristive states, while the two states jointly determine the memductance and feedback current. The discrete SO-LAM exhibits memristive characteristics, local activity, and edge-of-chaos regions. Through the combined effects of neuronal nonlinearity and memristive coupling, the SO-LAM-based neuron map generates regular and complex firing dynamics. The periodicity of the two memristive states gives rise to two-dimensional translational equivariance, indicating that the coexisting attractors in the grid-like array are spatially shifted copies of the same fundamental attractor rather than dynamically independent states. Representative hardware trajectories corresponding to the numerically identified hyperchaotic regime and symmetry-related attractor copies are reproduced using a field-programmable gate array (FPGA), demonstrating the feasibility of digital implementation. These results provide a compact framework for studying multistate history-dependent modulation and symmetry-induced coexistence in discrete memristive neurons.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"168"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review.","authors":"Andreas Miltiadous, Aimilia Ntetska, Vasileios Aspiotis, Efthalia Moustakli, Markos G Tsipouras, Alexandros T Tzallas, Nikolaos Giannakeas, Euripidis Glavas, Pantelis Angelidis, Katerina D Tzimourta","doi":"10.1007/s11571-026-10464-w","DOIUrl":"10.1007/s11571-026-10464-w","url":null,"abstract":"<p><p>Accurate and reproducible electroencephalography (EEG)-based classification of dementia remains a key challenge in computational neurodiagnostics. The open-access AHEPA dataset has become the most commonly used benchmark for Alzheimer's disease (AD) and Frontotemporal dementia (FTD) classification, yet reported results vary widely due to methodological inconsistencies. This study presents the first systematic and quantitative benchmark review of all published machine learning approaches applied to the AHEPA dataset. Forty-six studies were reviewed and stratified into three validity tiers, with Validity 1 representing the highest methodological rigor and Validity 3 the lowest.According to their evaluation rigor: (1) subject-level validation (e.g., Leave-One-Subject-Out cross-validation, LOSO-CV), (2) subject-level train/test splits, and (3) epoch-level k-fold cross-validation. Performance metrics were normalized across classification problems. The analysis revealed that methodological rigor is inversely correlated with reported accuracy: for AD versus Cognitively Normal controls, mean accuracy decreased from 90.81% overall to 82.11% in Validity-1 studies; for FTD versus controls, accuracy dropped from 86.53% to 75.18%. Linear regression analyses demonstrated that weaker validation protocols were associated with systematic increases of 7-10% points in reported accuracy, explaining more than half of the observed performance variance. Deep and hybrid models reported the highest nominal accuracies, but under proper validation, traditional algorithms performed comparably, indicating that data leakage often drives apparent improvements. The review also highlights the lack of cross-configuration generalization and the urgent need for adaptive, montage-independent methodologies. Overall, this benchmark establishes the first reproducible reference framework for EEG-based dementia classification on the AHEPA dataset, providing quantitative baselines and validity criteria against which all future studies should be evaluated.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"95"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147981037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}