IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-11-14 DOI: 10.1007/s11571-025-10376-1

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

{"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":"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.","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}

引用次数: 0

A dual brain EEG examination of the effects of direct and vicarious rewards on bilingual Language control. 直接和间接奖励对双语语言控制影响的双脑脑电图检查。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-11-12 DOI: 10.1007/s11571-025-10375-2

Junjun Huang, Shuang Liu, Mengjie Lv, John W Schwieter, Huanhuan Liu

引用次数: 0

Integrating EEG and fMRI in naturalistic paradigms: Advances in understanding mental disorders-a decade study in review (2014-2024). 在自然主义范式中整合脑电图和功能磁共振成像：理解精神障碍的进展-十年研究回顾（2014-2024）。

IF 8.9 2区医学

Dialogues in Clinical Neuroscience Pub Date : 2026-12-01 Epub Date: 2025-11-04 DOI: 10.1080/19585969.2025.2579280

Anam Mehmood, Shuyue Xu, Sultan Mehmood Siddiqi, Li Zhang, Gan Huang, Zhen Liang, Yongjie Zhou

{"title":"Integrating EEG and fMRI in naturalistic paradigms: Advances in understanding mental disorders-a decade study in review (2014-2024).","authors":"Anam Mehmood, Shuyue Xu, Sultan Mehmood Siddiqi, Li Zhang, Gan Huang, Zhen Liang, Yongjie Zhou","doi":"10.1080/19585969.2025.2579280","DOIUrl":"10.1080/19585969.2025.2579280","url":null,"abstract":"Background: Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) with naturalistic stimuli has advanced our understanding of the neural mechanisms underlying mental disorders. Naturalistic paradigms use dynamic, multimodal stimuli that capture complex emotional and cognitive processes more effectively than traditional experimental designs. Objective: This review synthesizes research from 2014 to 2024 exploring neural mechanisms of anxiety, depression, and schizophrenia within naturalistic paradigms. Methods: Recent EEG-fMRI studies employing naturalistic tasks were examined to identify common and disorder-specific neural alterations across affective and cognitive networks. Results: In anxiety, hyperactivity in the amygdala, prefrontal cortex, anterior cingulate cortex, and insula, together with changes in the dorsal attention, default mode, and frontoparietal networks, reflects excessive fear responses and impaired regulation. Depression is characterized by disruptions in default mode and frontoparietal connectivity and altered amygdala-prefrontal interactions, indicating maladaptive introspection and cognitive control. Schizophrenia shows abnormalities in motor and language processing, with aberrant activity in frontal, parietal, and temporal regions including the insula and temporoparietal junction. Conclusion: These findings highlight the shared involvement of the amygdala, prefrontal cortex, anterior cingulate cortex, and insula across disorders and demonstrate the potential of naturalistic paradigms for advancing personalized diagnostics and interventions in mental health.","PeriodicalId":54343,"journal":{"name":"Dialogues in Clinical Neuroscience","volume":"28 1","pages":"1-21"},"PeriodicalIF":8.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12590575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145446486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synaptic summation shapes information transfer in GABA-glutamate co-transmission. 突触汇总影响gaba -谷氨酸共传递的信息传递。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-11-14 DOI: 10.1007/s11571-025-10383-2

Belle Krubitski, Cesar Ceballos, Ty Roachford, Rodrigo F O Pena

{"title":"Synaptic summation shapes information transfer in GABA-glutamate co-transmission.","authors":"Belle Krubitski, Cesar Ceballos, Ty Roachford, Rodrigo F O Pena","doi":"10.1007/s11571-025-10383-2","DOIUrl":"https://doi.org/10.1007/s11571-025-10383-2","url":null,"abstract":"Co-transmission, the release of multiple neurotransmitters from a single neuron, is an increasingly recognized phenomenon in the nervous system. A particularly interesting combination of neurotransmitters exhibiting co-transmission is glutamate and GABA, which, when co-released from neurons, demonstrate complex biphasic activity patterns that vary depending on the time or amplitude differences from the excitatory (AMPA) or inhibitory (GABAA) signals. Naively, the outcome signal produced by these differences can be functionally interpreted as simple mechanisms that only add or remove spikes by excitation or inhibition. However, the complex interaction of multiple time-scales and amplitudes may deliver a more complex temporal coding, which is experimentally difficult to access and interpret. In this work, we employ an extensive computational approach to distinguish these postsynaptic co-transmission patterns and how they interact with dendritic filtering and ionic currents. We specifically focus on modeling the summation patterns and their flexible dynamics that arise from the many combinations of temporal and amplitude co-transmission differences. Our results indicate a number of summation patterns that excite, inhibit, and act transiently, which have been previously attributed to the interplay between the intrinsic active and passive electrical properties of the postsynaptic dendritic membrane. Our computational framework provides an insight into the complex interplay that arises between co-transmission and dendritic filtering, allowing for a mechanistic understanding underlying the integration and processing of co-transmitted signals in neural circuits.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10383-2.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"6"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12618799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539075","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}

引用次数: 0

Emergence of functionally differentiated structures via mutual information minimization in recurrent neural networks. 递归神经网络中基于互信息最小化的功能分化结构的出现。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-11-14 DOI: 10.1007/s11571-025-10377-0

Yuki Tomoda, Ichiro Tsuda, Yutaka Yamaguti

{"title":"Emergence of functionally differentiated structures via mutual information minimization in recurrent neural networks.","authors":"Yuki Tomoda, Ichiro Tsuda, Yutaka Yamaguti","doi":"10.1007/s11571-025-10377-0","DOIUrl":"10.1007/s11571-025-10377-0","url":null,"abstract":"Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks with specific constraints. Here, we propose a novel approach that induces functional differentiation in recurrent neural networks by minimizing mutual information between neural subgroups via mutual information neural estimation. We apply our method to a 2-bit working memory task and a chaotic signal separation task involving Lorenz and Rössler time series. Analysis of network performance, correlation patterns, and weight matrices reveals that mutual information minimization yields high task performance alongside clear functional modularity and moderate structural modularity. Importantly, our results show that functional differentiation, which is measured through correlation structures, emerges earlier than structural modularity defined by synaptic weights. This suggests that functional specialization precedes and probably drives structural reorganization within developing neural networks. Our findings provide new insights into how information-theoretic principles may govern the emergence of specialized functions and modular structures during artificial and biological brain development.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"5"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12618794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145538935","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}

引用次数: 0

Critical behaviors of modular networks under local excitatory-inhibitory fluctuations. 局部兴奋-抑制波动下模块网络的临界行为。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-11-14 DOI: 10.1007/s11571-025-10374-3

Chuanzuo Yang, Zhao Liu, Guoming Luan, Jingli Ren

{"title":"Critical behaviors of modular networks under local excitatory-inhibitory fluctuations.","authors":"Chuanzuo Yang, Zhao Liu, Guoming Luan, Jingli Ren","doi":"10.1007/s11571-025-10374-3","DOIUrl":"https://doi.org/10.1007/s11571-025-10374-3","url":null,"abstract":"Numerous physiological observations have shown that the brain operates at the edge of a critical state between order and disorder. Meanwhile, brain structures at different scales, from cortical columns to the entire brain, are organized in a modular manner. However, whether modular brain networks represent the optimized structure shaped for criticality and in what ways, have not been fully answered. In this study, a modular network with dense intra-module links but sparse inter-module links is established, and the behavior of each neuron is governed by the Kinouchi-Copelli model. Moreover, randomized surrogate networks with identical degree distribution are introduced to illustrate the significance of modular structures for criticality. Results suggest that the modular network requires fewer synaptic resources and lower firing costs to achieve criticality. More importantly, smaller avalanches indicate that the modular structures can enhance network resilience, facilitating rapid recovery from perturbations. Furthermore, by testing the sensitivity of the network state to local excitatory-inhibitory fluctuations, it is found that the efficiency of excitatory and inhibitory regulation is closely related to the 2-level excitatory input density. In addition, inhibitory regulation targeting modules with larger maximum real eigenvalues can more effectively suppress hyperexcitatory activities to achieve balance. When local excitation is greatly enhanced, even if the modular network is adjusted to the critical state, the size-to-duration ratio of module-level avalanches can effectively capture abnormalities. The properties also manifest in clinical recordings from patients with temporal lobe epilepsy, which may provide a promising method for epileptogenic zone localization.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"4"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12618793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539317","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}

引用次数: 0

Dynamic temporal patterns of DMN connectivity in epilepsy using hidden (semi-) Markov models. 使用隐藏（半）马尔可夫模型研究癫痫患者DMN连接的动态时间模式。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-11-14 DOI: 10.1007/s11571-025-10382-3

Dimitra Amoiridou, Ioannis Kakkos, Kostakis Gkiatis, Stavros T Miloulis, Ioannis Vezakis, Kyriakos Garganis, George K Matsopoulos

{"title":"Dynamic temporal patterns of DMN connectivity in epilepsy using hidden (semi-) Markov models.","authors":"Dimitra Amoiridou, Ioannis Kakkos, Kostakis Gkiatis, Stavros T Miloulis, Ioannis Vezakis, Kyriakos Garganis, George K Matsopoulos","doi":"10.1007/s11571-025-10382-3","DOIUrl":"https://doi.org/10.1007/s11571-025-10382-3","url":null,"abstract":"Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures. Altered connectivity within the default mode network (DMN) has been associated with epilepsy, highlighting its role in seizure propagation. In this study, we investigate the temporal patterns of DMN connectivity in epilepsy patients compared to healthy controls using data-driven models of dynamic functional connectivity (dFC). Specifically, we employ one Hidden Markov Model (HMM) and two Hidden Semi-Markov Models (HSMMs) with Gamma and Poisson sojourn distributions to capture latent brain state transitions, as well as hidden connectivity states and their temporal properties. Dynamic metrics (i.e., fractional occupancy, switching rate, and mean lifetime) were derived for each subject, revealing prolonged dwell times in low-connectivity states and reduced flexibility in state transitions, particularly in low-connectivity DMN states. HSMMs, especially the Gamma variant, demonstrated superior sensitivity in capturing these alterations compared to the standard HMM, highlighting the importance of flexible sojourn modeling in dynamic functional connectivity analysis. Additionally, group-specific transition patterns suggested disrupted temporal progression of DMN state transitions. Our findings highlight the potential of HSMMs in capturing alterations in functional brain states and provide new insights into the dynamic reorganization of the DMN in epilepsy.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10382-3.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"3"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12618792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145538501","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}

引用次数: 0

Research on the classification of EEG signals for dementia and its interpretability using the GWOCS agorithm. 基于GWOCS算法的痴呆脑电信号分类及其可解释性研究。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-11-10 DOI: 10.1007/s11571-025-10348-5

Ruofan Wang, Haojie Xu, Yijia Ma, Yanqiu Che

{"title":"Research on the classification of EEG signals for dementia and its interpretability using the GWOCS agorithm.","authors":"Ruofan Wang, Haojie Xu, Yijia Ma, Yanqiu Che","doi":"10.1007/s11571-025-10348-5","DOIUrl":"10.1007/s11571-025-10348-5","url":null,"abstract":"Alzheimer's disease (AD) and frontotemporal dementia (FTD) have insidious, similar and ambiguous clinical symptoms, which make their diagnosis difficult. Currently, in the field of EEG signal analysis, there are relatively few studies on the interpretability analysis of feature selection using intelligent optimization algorithms. To analyze the EEG signals of AD and FTD patients more comprehensively, first, 16 features in three dimensions of entropy, time-frequency domain and SODP were extracted in this paper. Secondly, Pearson correlation analysis, importance ranking and SHAP interpretability analysis methods were adopted to select SE, SW, ZCR, STA, CTM2 and CTM5 as the best discriminative features, and the Relief algorithm was used for fusion and dimension reduction based on weights. Thirdly, GWOCS was used for channel screening to determine the optimal channel combination of Fz, F7, Fp1, Fp2, F3, T3, P4 and C3, achieving the three-classification identification of the two patient groups and the normal control group, with the classification accuracy reaching 89.35[Formula: see text] and 81.12[Formula: see text] in cross-validation and LOSO validation, respectively. Finally, the SHAP method was used to prove that for the diagnosis of dementia, the prefrontal and temporal lobe brain regions play a decisive role, verifying the effectiveness of this framework in rapid channel selection and improving the efficiency of disease detection.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"1"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12597862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145494651","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}

引用次数: 0

Decompression sickness type II and patent foramen ovale: when a common congenital anomaly becomes a life-threatening risk. 减压病II型和卵圆孔未闭：当一个常见的先天性异常成为危及生命的风险。

IF 2.9

Medical Gas Research Pub Date : 2026-06-01 Epub Date: 2025-08-18 DOI: 10.4103/mgr.MEDGASRES-D-25-00065

Alejandro Fernandez-Cisneros

引用次数: 0

Old theory, new evidence: inhalational anesthetics disrupt a collective quantum state of intraneuronal microtubules to cause unconsciousness. 旧理论，新证据：吸入麻醉剂破坏了神经元内微管的集体量子态，从而导致无意识。

IF 2.9

Medical Gas Research Pub Date : 2026-06-01 Epub Date: 2025-08-18 DOI: 10.4103/mgr.MEDGASRES-D-25-00111

Michael C Wiest

引用次数: 0

全部最新文献