Cognitive Neurodynamics最新文献

{"title":"Real-time driver activity detection using advanced deep learning models.","authors":"Md Al Emran, Md Ariful Islam, Md Obaydullahn Khan, Md Jewel Rana, Saida Tasnim Adrita, Md Ashik Ahmed, Mahmoud M A Eid, Ahmed Nabih Zaki Rashed","doi":"10.1007/s11571-025-10376-1","DOIUrl":"https://doi.org/10.1007/s11571-025-10376-1","url":null,"abstract":"<p><p>Traffic accidents usually result from driver's inattention, sleepiness, and distraction, posing a substantial danger to worldwide road safety. Advances in computer vision and artificial intelligence (AI) have provided new prospects for designing real-time driver monitoring systems to reduce these dangers. In this paper, we assessed four known deep learning models, MobileNetV2, DenseNet201, NASNetMobile, and VGG19, and offer a unique Hybrid CNN-Transformer architecture reinforced with Efficient Channel Attention (ECA) for multi-class driver activity categorization. The framework defines seven important driving behaviors: Closed Eye, Open Eye, Dangerous Driving, Distracted Driving, Drinking, Yawning, and Safe Driving. Among the baseline models, DenseNet201 (99.40%) and MobileNetV2 (99.31%) achieved the highest validation accuracies. In contrast, the proposed Hybrid CNN-Transformer with ECA attained a near-perfect validation accuracy of 99.72% and further demonstrated flawless generalization with 100% accuracy on the independent test set. Confusion matrix studies further indicate a few misclassifications, verifying the model's high generalization capacity. By merging CNN-based local feature extraction, attention-driven feature refinement, and Transformer-based global context modeling, the system provides both robustness and efficiency. These findings show the practicality of using the suggested technology in real-time intelligent transportation applications, presenting a viable avenue toward reducing traffic accidents and boosting overall road safety.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"7"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12618750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145538985","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 dual brain EEG examination of the effects of direct and vicarious rewards on bilingual Language control.","authors":"Junjun Huang, Shuang Liu, Mengjie Lv, John W Schwieter, Huanhuan Liu","doi":"10.1007/s11571-025-10375-2","DOIUrl":"https://doi.org/10.1007/s11571-025-10375-2","url":null,"abstract":"<p><p>Little is known about whether direct and vicarious rewards affect bilingual language control in social learning. We used a dual-electroencephalogram (EEG) to simultaneously record the effects of direct and vicarious rewards on language control when bilinguals switched between their two languages. We found that both direct and vicarious rewards elicited more switch behavior. On an electrophysiological level, although both direct and vicarious rewards elicited Reward-positivity and Feedback-P3 when receiving reward outcomes, direct rewards induced greater reward effects than vicarious rewards. In addition to an N2 effect in language switching, vicarious rewards elicited more pronounced LPCs relative to direct rewards. More important, in the alpha band, there was a predictive effect of behaviors on rewards in binding vicarious rewards and language switching activities. These findings demonstrate that both direct and vicarious rewards influence language control during language selection.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"2"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12612500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539388","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":"Incorporating memristive autapse in spatio-temporal attention SNN for neuromorphic speech recognition.","authors":"Qian Cheng, Tao Chen, Xingming Tang, Shukai Duan, Lidan Wang","doi":"10.1007/s11571-025-10393-0","DOIUrl":"https://doi.org/10.1007/s11571-025-10393-0","url":null,"abstract":"<p><p>Spiking neural networks (SNNs) have gained significant attention for their biological plausibility, event-driven operation, and low power consumption, establishing them as a leading model for processing event stream data. However, current models often oversimplify neuronal dynamics to balance computational cost and performance. To address this limitation and enhance the dynamical behavior of spiking neurons, this paper introduces two key innovations. First, inspired by biological autaptic connections and memristive devices, we propose the memristive autapse (M-Autapse), a self-connection mechanism that enables adaptive modulation of a neuron's membrane potential. Second, recognizing the need for attention mechanisms that match SNNs' spatio-temporal nature, we design a spatio-temporal synergistic attention (STSA) mechanism to bolster simultaneous focus on both temporal and spatial dimensions of input data. Extensive experiments on the neuromorphic speech benchmarks SHD and SSC validate our methods. On SHD, our model demonstrates performance competitive with the state-of-the-art, while also achieving strong results on the SSC dataset.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"34"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12868354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146124064","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":"Analytical exploration of fractional neural wave structures and their stability characteristics.","authors":"Jamshad Ahmad, Khalid Masood, Hafza Javed, Nehad Ali Shah","doi":"10.1007/s11571-026-10460-0","DOIUrl":"https://doi.org/10.1007/s11571-026-10460-0","url":null,"abstract":"<p><p>The nonlinear fractional soliton neuron model is used in many complicated domains, including fluid mechanics, applied science, neuroscience, nonlinear dynamics, mathematical physics, engineering, biosciences, plasma physics, and geology. It demonstrates how nonlinear waves propagate. This article explores the the fractional nonlinear soliton neuron model, which can play a crucial role in understanding complicated phenomena in neuroscience. This model describes how axons generate and transmit action potentials using a thermodynamic theory of nerve transmission. Signals flowing through the cell membrane (CM) are believed to be single sound pulses, also known as solitons. In this paper we applied the modified Sardar sub-equation method to investigate the exact traveling wave solutions to fractional nonlinear soliton neuron model. In order to discuss the wave patterns of the model, the principal nonlinear equation is transformed to an ordinary differential equation by the usual traveling-wave transformation. The given method provides precise solutions in the form of hyperbolic, trigonometric, and exponential, which results in a well-defined system of solitary wave structures. Consequently, we obtain distinct types of solutions, containing bright solitons, dark solitons, bright-peakon mixtures, peakon and anti-peakon shapes, cuspon and compacton, compacton-kink, kink and anti-kink, dark-bright, bell-shaped solitons, singular periodic, and isolated periodic. The obtained solutions are described in terms of their physical behavior with the help of several visual tools, such as 2D and 3D surface plots, density plots, and contour plots, which have been used to display how the fractional terms reshape the wave profiles, alter their strength, and influence their overall stability. The findings provide a better understanding of the nonlinear wave propagation in neuron-inspired systems and suggest that it may be relevant in any optical media, plasma environment, and computational modeling of wave-based dynamics. Besides these analytical solutions, there is also a modulation instability study, which brings out the fact that minimal perturbation can cause instability of the underlying wave forms. This additional understanding can be used to explain the sensitivity of the model and the circumstances within which wave patterns are stable or start to increase.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"91"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13153354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147867083","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":"CrossModal-associated-SNN: multi-channel spiking neural networks with clustering and associative learning.","authors":"Lingfei Mo, Xin Liu, Mengting Tang","doi":"10.1007/s11571-026-10487-3","DOIUrl":"https://doi.org/10.1007/s11571-026-10487-3","url":null,"abstract":"<p><p>Biological sensory systems achieve remarkable robustness through efficient cross-modal integration, yet replicating this in artificial spiking neural networks (SNNs) remains a challenge. Inspired by Mayer's multi-channel learning cognitive theory, we present CrossModal-Associated-SNN, a neuro-inspired framework that synergistically integrates visual and auditory information via Spike-Timing-Dependent Plasticity (STDP) clustering and associative learning. The architecture employs a multi-channel, multi-network design for modality-specific processing, followed by a cross-channel complementary strategy that refines decision-making through associative signals. Evaluated on small-sample benchmarks (MNIST3K and Spoken-MNIST3K), the model demonstrates superior generalization and robustness in multi-modal classification. Compared to the single-channel baseline (91% accuracy), the dual-channel dual-network improved visual and auditory recognition to 93% and 84%, respectively, with a fusion accuracy of 94%. The dual-channel triple-network architecture further maximized performance, attaining 96% (visual), 90% (auditory), and a peak 97% cross-modal fusion accuracy. These results suggest that cooperative shallow micro-networks, akin to biological small-neuron ensembles in superficial brain regions, offer a potentially energy-efficient alternative for multimodal tasks processing. CrossModal-Associated-SNN represents a critical step toward mimicking human sensory cognitive integration, offering a biologically plausible solution for energy-efficient, multi-modal intelligent systems.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"110"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13237394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148197659","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":"Ordered waves in a memristive excitable media under asymmetrical diffusion.","authors":"Zixuan Zhang, Yitong Guo, Zhao Lei, Chunni Wang","doi":"10.1007/s11571-026-10471-x","DOIUrl":"https://doi.org/10.1007/s11571-026-10471-x","url":null,"abstract":"<p><p>An external electric field can induce polarization effects in the excitable media and the inner diffusion of intracellular ions is modified, accompanying by appropriate shape deformation in the media. Polarization effect supports orderliness when the medium suffers from external applied electric field, and appropriate shape deformation is induced in the flexible media, including cardiac tissue and some chemical reactions. In this article, we extend an excitable medium model by incorporating both memristive electromagnetic induction and shape deformation, and further establish an equivalent network framework to investigate the resulting collective dynamics. In one-layer excitable media, the media size (<i>L</i> _<i>x</i> ·<i>L</i> _<i>y</i> ) maintains a constant, and shape deformation seldom changes the media size, as a result, the two diffusion coefficients (<i>D</i> _<i>x</i> , <i>D</i> _<i>y</i> ) become complementary with time. The local kinetics of the memristive media is discussed, and the reaction-diffusion equations are converted into equivalent coupled neural network for exploring collective behaviors, including wave propagation and synchronization stability. The shape deformation induces changes of the diffusion coefficients in opposite way, with one direction enhanced and the other weakened, and the wave stability is modified because asymmetrical diffusion in the media. Incorporation of the memristive current and shape deformation into the excitable can better reflect the physical effect in the excitable media well.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"108"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13237415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148197722","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":"Computational modeling of tyrosine hydroxylase pathway for dopamine synthesis in nerve cells: effect of tetrahydrobiopterin deficiency and oxidative stress.","authors":"Malak Damdoum, Saptarshi Kar, Mahendra Kavdia","doi":"10.1007/s11571-026-10486-4","DOIUrl":"https://doi.org/10.1007/s11571-026-10486-4","url":null,"abstract":"<p><p>In neuronal dysfunction, there is a disruption of synthesis and release of neurotransmitters including dopamine. Oxidative stress is a major contributor to neuronal dysfunction. Oxidative stress affects tetrahydrobiopterin (BH₄), an essential cofactor in the synthesis of several neurotransmitters such as dopamine and nitric oxide. BH₄ supports the activity of enzyme tyrosine hydroxylase (TH) and initiates dopamine synthesis. Studies have reported contradictory results on neuronal dysfunction and disease progression following treatments to reduce oxidative stress and BH₄ supplementation. In this study, we developed a computational model of TH biochemical pathway in dopaminergic nerve cells. Using this model, we quantitatively analyzed the impact of reduced BH₄ synthesis and oxidative stress on L-dopa and dopamine synthesis. The base case concentration of L-dopa was 382.5 nM and cytosolic dopamine was 12.4 nM. The results showed a significant decrease in the concentrations of L-dopa and dopamine with reduction in BH₄ synthesis. The presence of oxidative stress further exacerbated these decreases in concentration. The mechanistic analysis suggests that this computational model provides an initial framework for evaluating how BH₄-related perturbations influence species concentrations in the TH biochemical pathway in dopaminergic nerve cells. Our findings indicate that under the simulated conditions, BH₄ supplementation produced short-term changes in species concentrations and that sustained improvement in nerve cell dysfunction likely requires a multi-target approach that simultaneously enhances de novo BH₄ synthesis and reduces cellular oxidative stress.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-026-10486-4.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"111"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13243162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148210421","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 write-cost bottleneck: energy constraints on continual learning in brains and machines.","authors":"Bingru Xu, Hao Shen, Yuguo Yu","doi":"10.1007/s11571-026-10508-1","DOIUrl":"10.1007/s11571-026-10508-1","url":null,"abstract":"&lt;p&gt;&lt;p&gt;In recent decades, neural energy-budget research has extensively quantified signaling costs-the \"read\" side of computation-including action potentials, synaptic transmission, and ionic homeostasis. Yet the metabolic cost of learning itself remains far less studied. The \"write\" side-encompassing synaptic plasticity (LTP, LTD, and homeostatic scaling), receptor trafficking, protein synthesis, spine remodeling, and long-term consolidation-is still poorly understood. We argue that these write costs, although smaller than signaling costs in their time-averaged share of the mature brain's energy budget, constitute a real peak-throughput bottleneck for any intelligent system that must learn continuously within fixed energy constraints. A durable synaptic update aggregates an order-of-magnitude estimate of ~ 10⁴-10⁶ ATP per synapse across multiple coincident processes (receptor trafficking, protein turnover, local translation, actin remodeling, Ca²⁺ handling, and concurrent ionic recovery), with the range reflecting both natural variability and a 1-2 order-of-magnitude uncertainty across independent estimates (Karbowski, 2019). This per-synapse multi-process sum is larger than the protein-synthesis component alone-a term that standard energy budgets typically fold into housekeeping rather than treat as a distinct plasticity cost. Dense simultaneous writes are therefore unlikely to be sustainable at scale, because active cortical and hippocampal tissue can operate close to its oxidative ceiling during high-demand states, suggesting that many mature cortical circuits operate with limited additional metabolic headroom during high-demand states, plausibly on the order of tens of percent rather than several-fold (Hyder et al., 2013; Yu et al., 2023; Watts et al., 2018). The brain manages this constraint across multiple regimes: awake activity is characterized by sparse population coding and asynchronous irregular firing that preserves metabolic headroom; durable plasticity also occurs during waking but is sparse, neuromodulator- and attention-gated, and supplemented by brief awake replay events; and sleep provides additional write windows-slow-wave sleep offers a low-cost offline window, while REM sleep provides a selective plasticity window in which restricted circuits sustain patterned activity supporting durable plasticity. We recast learning as the selective allocation of scarce writes-metabolic investments that reorganize circuit structure and can reduce future signaling costs at the population level. Gated plasticity, synaptic caching, and sleep-dependent consolidation are, under this view, write-management strategies. At the systems level, selective attention allows the brain to concentrate plasticity on the circuits engaged in a current task while other regions idle, further constraining writes in space as well as time. By contrast, backpropagation in modern machine learning tightly couples reads and writes, which increases update traffic, ampli","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"139"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13373113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148469377","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 bi-domain discriminator adversarial network for EEG emotion recognition.","authors":"Leyang Yu, Qingshan She, Zizhuo Wu, Zhaowen Xiao, Ming Meng, Kun Luo","doi":"10.1007/s11571-026-10497-1","DOIUrl":"10.1007/s11571-026-10497-1","url":null,"abstract":"<p><p>Emotions are constantly generated in daily activities. They not only control people's behavioral patterns and thinking decisions, but also affect physical and mental health. Therefore, the emotion recognition technology based on electroencephalogram (EEG) signals has broad application prospects in multiple fields such as human-computer interaction, medical health, and intelligent driving. To address the issues of insufficient labeled data in EEG and significant differences in EEG data among different subjects and at different time periods, this paper introduces the domain adaptation (DA) technique to solve the task of cross-domain EEG emotion recognition under unsupervised conditions. Aiming at the problem that the existing domain adaptation methods ignore the different weights of the global domain and subdomains when reducing domain differences, this paper proposes a dynamic bi-domain discriminator adversarial network (DBDAN). A feature extractor is constructed to extract the low-level domain invariant features of the EEG signals, and a multi-branch domain-specific feature extractor is used to generate domain-specific features. In order to reduce domain differences, adversarial learning for the global domain and subdomains is achieved through a dual-domain discriminator. Meanwhile, dynamic factors are introduced to dynamically adjust adversarial learning, enabling the model to strike a balance between coarse-grained adversarial and fine-grained adversarial and achieve domain adaptation more precisely. The cross-subject accuracy rates on SEED, SEED-IV and DEAP were 89.43%, 75.09% and 63.42%, respectively. These results indicate that our method achieves competitive performance among the evaluated EEG transfer-learning baselines and suggest that dynamic global-subdomain alignment is beneficial for cross-domain EEG emotion recognition.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"126"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13332908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148390075","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":"EEG brain network analysis of mind-wandering characteristics and its modulation by shift work in high-risk industries.","authors":"Qing Xin, Su Hao, Cai Hongbin, Wang Xiaoqin, Wang Jian, Liu Lu, Shen Liaoyuan","doi":"10.1007/s11571-026-10488-2","DOIUrl":"10.1007/s11571-026-10488-2","url":null,"abstract":"<p><p>Mind-wandering (MW), a form of attentional disengagement during operational tasks, has emerged as a critical cognitive risk factor for unsafe behavior in high-risk industries. Moreover, the disruption of circadian rhythms induced by shift work further amplifies the risks associated with MW in operational contexts. However, existing research has largely relied on short duration experiments conducted under controlled laboratory conditions, leaving the neural mechanisms of MW in authentic high risk operational contexts insufficiently understood. Even fewer studies have empirically examined how shift work influences MW from the perspective of brain network organization. In response to this research gap, the present study employs Lab-in-the-Field experiments to analyze the brain network characteristics of workers in high-risk industries, aiming to elucidate the potential mechanisms by which MW and shift work jointly impact operational safety. The results demonstrated that MW significantly prolonged response times, and that shift work further exacerbated this negative effect. EEG based brain network analysis further showed that MW was accompanied by changes in brain network resource allocation, with a particularly evident decline in hub efficiency in the left prefrontal cortex. Moreover, shift work was found to affect cognitive states predominantly by altering the structural topology of brain networks, rather than by simply modulating the overall strength of functional connectivity. This study validates the brain network reorganization underlying MW and provides scientific evidence for developing cognitive state monitoring tools and targeted intervention strategies based on brain network topological features.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"131"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148419465","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}