Cognitive Neurodynamics最新文献

{"title":"A stacking classifier for distinguishing stages of Alzheimer's disease from a subnetwork perspective.","authors":"Gaoxuan Li, Bo Chen, Weigang Sun, Zhenbing Liu","doi":"10.1007/s11571-025-10221-5","DOIUrl":"10.1007/s11571-025-10221-5","url":null,"abstract":"<p><p>Accurately distinguishing stages of Alzheimer's disease (AD) is crucial for diagnosis and treatment. In this paper, we introduce a stacking classifier method that combines six single classifiers into a stacking classifier. Using brain network models and network metrics, we employ <i>t</i>-tests to identify abnormal brain regions, from which we construct a subnetwork and extract its features to form the training dataset. Our method is then applied to the ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets, categorizing the stages into four categories: Alzheimer's disease, mild cognitive impairment (MCI), mixed Alzheimer's mild cognitive impairment (ADMCI), and healthy controls (HCs). We investigate four classification groups: AD-HCs, AD-MCI, HCs-ADMCI, and HCs-MCI. Finally, we compare the classification accuracy between a single classifier and our stacking classifier, demonstrating superior accuracy with our stacking classifier from a subnetwork-based viewpoint.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"38"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381814","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":"Neural dynamics of deception: insights from fMRI studies of brain states.","authors":"Weixiong Jiang, Lin Li, Yulong Xia, Sajid Farooq, Gang Li, Shuaiqi Li, Jinhua Xu, Sailing He, Xiangyu Wu, Shoujun Huang, Jing Yuan, Dexing Kong","doi":"10.1007/s11571-025-10222-4","DOIUrl":"10.1007/s11571-025-10222-4","url":null,"abstract":"<p><p>Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain's temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states-termed <i>truth-preferred</i> and <i>lie-preferred</i>-exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10222-4.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"42"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482401","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":"Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.","authors":"B Nageswara Rao, U Rajendra Acharya, Ru-San Tan, Pratyusa Dash, Manoranjan Mohapatra, Sukanta Sabut","doi":"10.1007/s11571-025-10259-5","DOIUrl":"10.1007/s11571-025-10259-5","url":null,"abstract":"<p><p>Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of mortality and morbidity. Accurate diagnosis using brain non-contrast computed tomography (NCCT) is crucial for decision-making on potentially life-saving surgery. Limited access to expert readers and inter-observer variability imposes barriers to timeous and accurate ICH diagnosis. We proposed a hybrid deep learning model for automated ICH diagnosis using NCCT images, which comprises a convolutional autoencoder (CAE) to extract features with reduced data dimensionality and a dense neural network (DNN) for classification. In order to ensure that the model generalizes to new data, we trained it using tenfold cross-validation and holdout methods. Principal component analysis (PCA) based dimensionality reduction and classification is systematically implemented for comparison. The study dataset comprises 1645 (\"ICH\" class) and 1648 (\"Normal\" class belongs to patients with non-hemorrhagic stroke) labelled images obtained from 108 patients, who had undergone CT examination on a 64-slice computed tomography scanner at Kalinga Institute of Medical Sciences between 2020 and 2023. Our developed CAE-DNN hybrid model attained 99.84% accuracy, 99.69% sensitivity, 100% specificity, 100% precision, and 99.84% F1-score, which outperformed the comparator PCA-DNN model as well as the published results in the literature. In addition, using saliency maps, our CAE-DNN model can highlight areas on the images that are closely correlated with regions of ICH, which have been manually contoured by expert readers. The CAE-DNN model demonstrates the proof-of-concept for accurate ICH detection and localization, which can potentially be implemented to prioritize the treatment using NCCT images in clinical settings.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"77"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119113","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 new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.","authors":"Ilknur Sercek, Niranjana Sampathila, Irem Tasci, Tuba Ekmekyapar, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Ru-San Tan, U R Acharya","doi":"10.1007/s11571-025-10249-7","DOIUrl":"https://doi.org/10.1007/s11571-025-10249-7","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"71"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981499","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":"TCANet: a temporal convolutional attention network for motor imagery EEG decoding.","authors":"Wei Zhao, Haodong Lu, Baocan Zhang, Xinwang Zheng, Wenfeng Wang, Haifeng Zhou","doi":"10.1007/s11571-025-10275-5","DOIUrl":"10.1007/s11571-025-10275-5","url":null,"abstract":"<p><p>Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features. Specifically, TCANet employs a multi-scale convolutional module to extract local spatiotemporal representations across multiple temporal resolutions. A temporal convolutional module then fuses and compresses these multi-scale features while modeling both short- and long-term dependencies. Subsequently, a stacked multi-head self-attention mechanism refines the global representations, followed by a fully connected layer that performs MI-EEG classification. The proposed model was systematically evaluated on the BCI IV-2a and IV-2b datasets under both subject-dependent and subject-independent settings. In subject-dependent classification, TCANet achieved accuracies of 83.06% and 88.52% on BCI IV-2a and IV-2b respectively, with corresponding Kappa values of 0.7742 and 0.7703, outperforming multiple representative baselines. In the more challenging subject-independent setting, TCANet achieved competitive performance on IV-2a and demonstrated potential for improvement on IV-2b. The code is available at https://github.com/snailpt/TCANet.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"91"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12167204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309661","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":"Astrocytic signatures in neuronal activity: a machine learning-based identification approach.","authors":"João Pedro Pirola, Paige DeForest, Paulo R Protachevicz, Laura Fontenas, Ricardo F Ferreira, Rodrigo F O Pena","doi":"10.1007/s11571-025-10276-4","DOIUrl":"10.1007/s11571-025-10276-4","url":null,"abstract":"<p><p>This study investigates the expanding role of astrocytes, the predominant glial cells, in brain function, focusing on whether and how their presence influences neuronal network activity. We focus on particular network activities identified as synchronous and asynchronous. Using computational modeling to generate synthetic data, we examine these network states and find that astrocytes significantly affect synaptic communication, mainly in synchronous states. We use different methods of extracting data from a network and compare which is best for identifying glial cells, with mean firing rate emerging with higher accuracy. To reach the aforementioned conclusions, we applied various machine learning techniques, including Decision Trees, Random Forests, Bagging, Gradient Boosting, and Feedforward Neural Networks, the latter outperforming other models. Our findings reveal that glial cells play a crucial role in modulating synaptic activity, especially in synchronous networks, highlighting potential avenues for their detection with machine learning models through experimental accessible measures.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10276-4.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"89"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144301260","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":"Visual statistical learning based on a coupled shape-position recurrent neural network model.","authors":"Baolong Sun, Yihong Wang, Xuying Xu, Xiaochuan Pan","doi":"10.1007/s11571-025-10285-3","DOIUrl":"10.1007/s11571-025-10285-3","url":null,"abstract":"<p><p>The visual system has the ability to learn the statistical regularities (temporal and/or spatial) that characterize the visual scene automatically and implicitly. This ability is referred to as the visual statistical learning (VSL). The VSL could group several objects that have fixed statistical properties into a chunk. This complex process relies on the collaborative involvement of multiple brain regions that work together to learn the chunk. Although behavioral experiments have explored cognitive functions of the VSL, its computational mechanisms remain poorly understood. To address this issue, this study proposes a coupled shape-position recurrent neural network model based on the anatomical structure of the visual system to explain how chunk information is learned and represented in neural networks. The model comprises three core modules: the position network, which encodes object position information; the shape network, which encodes object shape information; and the decision network, which integrates the neuronal activity in the position and shape networks to make decisions. The model successfully simulates the results of a classic spatial VSL experiment. The distribution of neural firing rates in the decision network shows a significant difference between chunk and non-chunk conditions. Specifically, these neurons in the chunk condition exhibit stronger firing rates than those in the non-chunk condition. Furthermore, after the model learns a scene containing both chunk and non-chunk stimuli, neurons in the position network selectively encode far and near stimuli, respectively. In contrast, neurons in the shape network distinguish between chunk and non-chunk. The chunk encoding neurons selectively respond to specific chunks. These results indicate that the proposed model is able to learn spatial regularities of the stimuli to discriminate chunks from non-chunks, and neurons in the shape network selectively respond to chuck and non-chunk information. These findings offer important theoretical insights into the representation mechanisms of chunk information in neural networks and propose a new framework for modeling spatial VSL.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"96"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332590","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 role of beta band phase resetting in audio-visual temporal order judgment.","authors":"Yueying Li, Yasuki Noguchi","doi":"10.1007/s11571-024-10183-0","DOIUrl":"10.1007/s11571-024-10183-0","url":null,"abstract":"<p><p>The integration of auditory and visual stimuli is essential for effective language processing and social perception. The present study aimed to elucidate the mechanisms underlying audio-visual (A-V) integration by investigating the temporal dynamics of multisensory regions in the human brain. Specifically, we evaluated inter-trial coherence (ITC), a neural index indicative of phase resetting, through scalp electroencephalography (EEG) while participants performed a temporal-order judgment task that involved auditory (beep, A) and visual (flash, V) stimuli. The results indicated that ITC phase resetting was greater for bimodal (A + V) stimuli compared to unimodal (A or V) stimuli in the posterior temporal region, which resembled the responses of A-V multisensory neurons reported in animal studies. Furthermore, the ITC got lager as the stimulus-onset asynchrony (SOA) between beep and flash approached 0 ms. This enhancement in ITC was most clearly seen in the beta band (13-30 Hz). Overall, these findings highlight the importance of beta rhythm activity in the posterior temporal cortex for the detection of synchronous audiovisual stimuli, as assessed through temporal order judgment tasks.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-024-10183-0.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"28"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001022","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":"Neural oscillations predict flow experience.","authors":"Bingxin Lin, Baoshun Guo, Lingyun Zhuang, Dan Zhang, Fei Wang","doi":"10.1007/s11571-024-10205-x","DOIUrl":"10.1007/s11571-024-10205-x","url":null,"abstract":"<p><p>Flow experience, characterized by immersion in the activity at hand, provides a motivational boost and promotes positive behaviors. However, the oscillatory representations of flow experience are still poorly understood. In this study, the difficulty of the video game was adjusted to manipulate the individual's personalized flow or non-flow state, and EEG data was recorded throughout. Our results show that, compared to non-flow tasks, flow tasks exhibit higher theta power, moderate alpha power, and lower beta power, providing evidence for a focused yet effortless brain pattern during flow. Additionally, we employed Lasso regression to predict individual subjective flow scores based on neural data, achieving a correlation coefficient of 0.571 (<i>p</i> < 0.01) between the EEG-predicted scores and the actual self-reported scores. Our findings offer new insights into the oscillatory representation of flow and emphasize that flow, as a measure of individual experience quality, can be objectively and quantitatively predicted through neural oscillations.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"1"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920787","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":"Aberrant regional neural fluctuations and functional connectivity in insomnia comorbid depression revealed by resting-state functional magnetic resonance imaging.","authors":"Shuang Wang, Bo Li, Minghe Xu, Chunlian Chen, Zhe Liu, Yuqing Ji, Shaowen Qian, Kai Liu, Gang Sun","doi":"10.1007/s11571-024-10206-w","DOIUrl":"https://doi.org/10.1007/s11571-024-10206-w","url":null,"abstract":"<p><p>Insomnia is a common mental illness seriously affecting people lives, that might progress to major depression. However, the neural mechanism of patients with CID comorbid MDD remain unclear. Combining fractional amplitude of low-frequency fluctuation (fALFF) and seed-based functional connectivity (FC), this study investigated abnormality in local and long-range neural activity of patients with CID comorbid MDD. Here, we acquired resting-state blood oxygenation level dependent (BOLD) data from 57 patients with CID comorbid MDD and 57 healthy controls (HC). Compared with the controls, patients with CID comorbid MDD exhibited abnormal functional activity in posterior cerebral cortex related to the visual cortex, including the middle occipital gyrus (MOG), the cuneus and the lingual gyrus, specifically, lower fALFF values in the right MOG, left cuneus, and right postcentral gyrus, increased FC between the right MOG and the left cerebellum, and decreased FC between the right MOG and the right lingual gyrus. Neuropsychological correlation analysis revealed that the decreased fALFF in the right MOG was negatively correlated with all the neuropsychological scores of insomnia and depression, reflecting common relationships with symptoms of CID and MDD. While the decreased fALFF of the left cuneus was distinctly correlated with the scores of depression related scales. The decreased FC between the right MOG and the right lingual gyrus was distinctly correlated with the scores of insomnia related scales. This study not only widened neuroimaging evidence that associated with insomnia and depressive symptoms of patients with CID comorbid MDD, but also provided new potential targets for clinical treatment.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"8"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945837","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}