Cognitive Neurodynamics最新文献

{"title":"Correction: Olfactory Perception and Neural Rhythms: A Simulation-Based EEG Analysis Using Power Spectral Density Features.","authors":"Aadhitya S V","doi":"10.1007/s11571-026-10511-6","DOIUrl":"https://doi.org/10.1007/s11571-026-10511-6","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1007/s11571-026-10478-4.].</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"142"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13388599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148561062","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":"Speech-based depression detection using higher-order spectral features and a multi-level transformer.","authors":"Uma Jaishankar, Jagannath H Nirmal","doi":"10.1007/s11571-026-10500-9","DOIUrl":"10.1007/s11571-026-10500-9","url":null,"abstract":"<p><p>A precise and prompt automated depression detection system utilizing auditory signals is essential, considering the scarcity of psychiatrists and the exorbitant expense of clinical diagnosis, resulting in approximately 60% of psychiatric patients globally lacking access to mental health care. This study proposes a Depression Detection model utilizing voice, which integrates hierarchical higher-order spectral distribution with deep learning models to overcome these issues. The utilized dataset is the Distress Analysis Interview Corpus, Wizard of Oz (DAIC-WOZ), comprising audio recordings of clinical interviews designed to facilitate the detection of psychological distress disorders such as depression, anxiety, and post-traumatic stress disorder. Two categories of features are extracted: statistical, handmade and bispectral features, as well as deep features utilizing the Multi-level Convolutional Transformer with attention learning (ML-CoT-AL) model. The ML-CoT-AL integrates the Convolutional Transformer (CoT), Channel and Element-wise Attention Module (CEAM), and Negotiator Modules (NM). The study presents the RIME optimization technique utilizing ML-CoT-AL for hyperparameter tuning, leading to improved accuracy in multi-level depression identification.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"130"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148419403","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":"Synergistic entrainment: a multiscale neurodynamic framework for closed-loop cross-modal neuromodulation.","authors":"Chenfei Yan, Shuo Zhang, Yingwei Li, Qianxiang Zhou, Zhongqi Liu","doi":"10.1007/s11571-026-10503-6","DOIUrl":"10.1007/s11571-026-10503-6","url":null,"abstract":"<p><p>The brain's higher cognitive functions rely on the dynamic integration of multimodal information, supported by coordinated neural oscillations. However, current neuromodulation approaches often fail to reliably control these dynamics due to high inter-individual variability and limited mechanistic grounding. To address this challenge, we introduce the Synergistic Entrainment Framework, which conceptualizes the brain as a dynamical system in which neural oscillations create periodic windows of excitability that regulate when information can be effectively processed. Within this framework, rhythmic inputs from different physical modalities (e.g., sensory streams and electromagnetic stimulation) can interact nonlinearly, leading to amplification or stabilization of neural responses, when their timing is aligned with ongoing cortical phase dynamics. We propose that this interaction operates across three coupled scales: (i) microscopic constructive interference that amplifies local neural responses, (ii) mesoscopic phase resetting that aligns regional excitability, and (iii) macroscopic network synchronization constrained by the brain's structural connectome. By explicitly linking these multiscale mechanisms, we propose the framework as a theoretical perspective and operational scaffold for formulating testable hypotheses about state-dependent, closed-loop neuromodulation. Rather than claiming empirical validation, it aims to organize operational criteria for future studies of how stimulation timing may be aligned with intrinsic brain dynamics.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"134"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430078","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":"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":"Optimized cortical EEG modeling for Parkinson disease diagnosis with snow Shepherd Stride tuning mechanism.","authors":"Morarjee Kolla, Rudra Kumar Madapuri, Prabhakar Kandukuri, Shobarani Salvadi, Satyakiaranmaie Tadepalli, Ramesh Gajula","doi":"10.1007/s11571-025-10406-y","DOIUrl":"10.1007/s11571-025-10406-y","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a neurodegenerative disease that causes extensive impacts on cognitive and motor function, hence making correct and early diagnosis essential for efficient clinical management and enhancement of the quality of life. Analysis using EEG has now become a safe and possible method for the identification of neural abnormalities in PD. Nevertheless, current models must struggle with several constraints: high false detection rates, poor generalizability across subjects, sensitivity to EEG noise pollution, and the inability to extract deep cortical representations, which have the capability to distinguish between healthy and Parkinson patterns. To alleviate these issues, the current paper proposes a novel CortiMoS-Net (Cortical Modeling with Stacked Autoencoder and MobileNet) capable of accurately detecting Parkinson's disease from EEG signals. CortiMoS-Net architecture combines deep stacked autoencoders with low-computation MobileNet convolution blocks such that low-complexity learning of complex cortical activity patterns is supplemented with computational scalability. To achieve further enhanced model convergence and optimization of learnable parameters, the present work also proposes an enhanced hybrid optimization technique named Snow Shepherd Stride Configuration Tuning (S3C-Tune). The proposed pipeline is initiated with raw EEG signal recording, preprocessing, and peak picking for the intent of artifact removal and detection of neurologically intriguing events. Model parameters are tuned by the S3C-Tune algorithm to realize maximal training accuracy. Such a pipeline hybrid enables extensive cortical modeling as well as efficient optimization and results in correct PD vs. healthy subject classification. Experimental results confirm the effectiveness of the suggested approach with better accuracy, precision, recall, and F1-score of 0.99 and minimum error rate and minimum loss of 0.01 and 0.05, respectively. The suggested model also indicates maximum prediction correctness of 0.99 and mean efficiency measure of 0.95 as compared to a large number of state-of-the-art hybrid deep learning approaches.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"47"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12881250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146141236","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 to: Optimized cortical EEG modeling for Parkinson disease diagnosis with snow Shepherd Stride tuning mechanism.","authors":"Morarjee Kolla, Rudra Kumar Madapuri, Prabhakar Kandukuri, Shobarani Salvadi, Satya Kiranmai Tadepalli, Ramesh Gajula","doi":"10.1007/s11571-026-10520-5","DOIUrl":"https://doi.org/10.1007/s11571-026-10520-5","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1007/s11571-025-10406-y.].</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"151"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148653158","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":"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":"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}