Cognitive Neurodynamics最新文献

{"title":"Identification of a cognitive network with effective connectivity to post-stroke cognitive impairment","authors":"Jing Zhang, Hui Tang, Lijun Zuo, Hao Liu, Chang Liu, Zixiao Li, Jing Jing, Yongjun Wang, Tao Liu","doi":"10.1007/s11571-024-10139-4","DOIUrl":"https://doi.org/10.1007/s11571-024-10139-4","url":null,"abstract":"<p>Altered connectivity within complex functional networks has been observed in individuals with post-stroke cognitive impairment (PSCI) and during cognitive tasks. This study aimed to identify a cognitive function network that is responsive to cognitive changes during cognitive tasks and also sensitive to PSCI. To explore the network, we analyzed resting-state fMRI data from 20 PSCI patients and task-state fMRI data from 100 unrelated healthy young adults using functional connectivity analysis. We further employed spectral dynamic causal modeling to examine the effective connectivity among the pivotal regions within the network. Our findings revealed a common cognitive network that encompassed the hub regions 231 in the Subcortical network (SC), 70, 199, 242 in the Frontoparietal network (FP), 214 in the Visual II network, and 253 in the Cerebellum network (CBL). These hubs’ effective connectivity, which showed reliable but slight changes during different cognitive tasks, exhibited notable alterations when comparing post-stroke cognitive impairment and improvement statuses. Decreased coupling strengths were observed in effective connections to CBL253 and from SC231 and FP70 in the improvement status. Increased connections to SC231 and FP70, from CBL253 and FP242, as well as from FP199 and FP242 to FP242 were observed in this status. These alterations exhibited a high sensitivity to signs of recovery, ranging from 80 to 100%. The effective connectivity pattern in both post-stroke cognitive statuses also reflected the influence of the MoCA score. This research succeeded in identifying a cognitive network with sensitive effective connectivity to cognitive changes after stroke, presenting a potential neuroimaging biomarker for forthcoming interventional studies.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"3 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global synchronization of functional corticomuscular coupling under precise grip tasks using multichannel EEG and EMG signals","authors":"Xiaoling Chen, Tingting Shen, Yingying Hao, Jinyuan Zhang, Ping Xie","doi":"10.1007/s11571-024-10157-2","DOIUrl":"https://doi.org/10.1007/s11571-024-10157-2","url":null,"abstract":"<p>Functional corticomuscular coupling (FCMC), a phenomenon describing the information interaction between the cortex and muscles, plays an important role in assessing hand movements. However, related studies mainly focused on specific actions by one-to-one mapping between the brain and muscles, ignoring the global synchronization across the motor system. Little research has been done on the FCMC difference between the brain and different muscle groups in terms of precise grip tasks. This study combined the maximum information coefficient (MIC) and the S estimation method and constructed a multivariate global synchronization index (MGSI) to measure the FCMC by analyzing the multichannel electroencephalogram (EEG) and electromyogram (EMG) during precise grip tasks. Both signals were collected from 12 healthy subjects while performing different weight object tasks. Our results on Hilbert-Huang spectral entropy (HHSE) of signals showed differences in task stages in both<i> β</i> (13–30 Hz) and <i>γ</i> (31–45 Hz) bands. The weight difference was reflected in the HHSE of channel CP5 and muscles at both ends of the upper limb. The one-to-one mapping with MIC between EEG and the muscle pair AD-FDI showed larger MIC values than the muscle pair B-CED; the same trend was seen on the MGSI values. However, the difference in weight of static tasks was not significant. Both MGSI values and the connect ratio of EEG were related to HHSE values. This work investigated the changes in the cortex and muscles during precise grip tasks from different perspectives, contributing to a better understanding of human motor control.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"29 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Speed is associated with polarization during subjective evaluation: no tradeoff, but an effect of the ease of processing","authors":"Chunyu Ma, Yimeng Jin, Johan Lauwereyns","doi":"10.1007/s11571-024-10151-8","DOIUrl":"https://doi.org/10.1007/s11571-024-10151-8","url":null,"abstract":"<p>In human perceptual decision-making, the speed-accuracy tradeoff establishes a causal link between urgency and reduced accuracy. Less is known about how speed relates to the subjective evaluation of visual images. Here, we conducted a set of four experiments to tease apart two alternative hypotheses for the relation between speed and subjective evaluation. The hypothesis of “Speed-Polarization Tradeoff” implies that urgency causes more polarized evaluations. In contrast, the “Ease-of-Processing” hypothesis suggests that any association between speed and polarization is due to the salience of evaluation-relevant image content. The more salient the content, the easier to process, and therefore the faster and more extreme the evaluation. In each experiment, we asked participants to evaluate images on a continuous scale from − 10 to + 10 and measured their response times; in Experiments 1–3, the participants rated real-world images in terms of morality (from “very immoral,” -10, to “very moral,” +10); in Experiment 4, the participants rated food images in terms of appetitiveness (from “very disgusting,” -10, to “very attractive,” +10). In Experiments 1, 3, and 4, we used a cueing procedure to inform the participants on a trial-by-trial basis whether they could make a self-paced (SP) evaluation or whether they had to perform a time-limited (TL) evaluation within 2 s. In Experiment 2, we asked participants to rate the easiness of their SP moral evaluations. Compared to the SP conditions, the responses in the TL condition were consistently much faster, indicating that our urgency manipulation was successful. However, comparing the SP versus TL conditions, we found no significant differences in any of the evaluations. Yet, the reported ease of processing of moral evaluation covaried strongly with both the response speed and the polarization of evaluation. The overall pattern of data indicated that, while speed is associated with polarization, urgency does not cause participants to make more extreme evaluations. Instead, the association between speed and polarization reflects the ease of processing. Images that are easy to evaluate evoke faster and more extreme scores than images for which the interpretation is uncertain.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synaptic plasticity: from chimera states to synchronicity oscillations in multilayer neural networks","authors":"Peihua Feng, Luoqi Ye","doi":"10.1007/s11571-024-10158-1","DOIUrl":"https://doi.org/10.1007/s11571-024-10158-1","url":null,"abstract":"<p>This research scrutinizes the simultaneous evolution of each layer within a multilayered complex neural network and elucidates the effect of synaptic plasticity on inter-layer dynamics. In the absence of synaptic plasticity, a predominant feedforward effect is observed, resulting in the manifestation of complete synchrony in deep networks, with each layer assuming a chimera state. A significant increase in the number of synchronized neurons is observed as the layers augment, culminating in complete synchronization in the deeper sections. The study categorizes the layers into three distinct parts: the initial layers (1–4) demonstrate the emergence of non-uniformity in the random firing of neurons; the middle layers (5–7) exhibit an amplification of this non-uniformity, forming a higher degree of synchronization; and the final layers (8–10) display a completely synchronized process. The introduction of synaptic plasticity disrupts this synchrony, inducing periodic oscillation characteristics across layers. The specificity of these oscillations is notably accentuated with increasing network depth. These insights shed light on the interplay between neural network complexity and synaptic plasticity in influencing synchronization dynamics, presenting avenues for enhanced neural network architectures and refined neuroscientific models. The findings underscore the imperative to delve deeper into the implications of synaptic plasticity on the structure and function of intricate multi-layer neural networks.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"151 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electroencephalogram criticality in cognitive impairment: a monitoring biomarker?","authors":"Vasilis-Spyridon Tseriotis, George Vavougios, Magdalini Tsolaki, Martha Spilioti, Efstratios K. Kosmidis","doi":"10.1007/s11571-024-10155-4","DOIUrl":"https://doi.org/10.1007/s11571-024-10155-4","url":null,"abstract":"<p>Critical states present scale-free dynamics, optimizing neuronal complexity and serving as a potential biomarker in cognitively impaired patients. We explored electroencephalogram (EEG) criticality in amnesic Mild Cognitive Impairment patients with clinical improvement in working memory, verbal memory, verbal fluency and overall executive functions after the completion of a 6-month prospective memory training. We compared “before” and “after” stationary resting-state EEG records of right-handed MCI patients (n = 17; 11 females), using the method of critical fluctuations and Haar wavelet analysis. Improvement of criticality indices was observed in most electrodes, with mean values being higher after prospective memory training. Significant criticality enhancement was found in the subgroup analysis of frontotemporal electrodes [mean dif: 0.10; Z = 7, <i>p</i> = 0.019]. In the isolated electrode signal analysis, significant post-intervention improvement was noted in pooled criticality indices of electrodes T6 [mean dif: 0.204; t(10) = −2.3, <i>p</i> = 0.044] and F4 [mean dif: 0.0194; t(10) = −2.82; <i>p</i> = 0.018]. EEG criticality agreed with clinical improvement, consisting a possible quantifiable and easy-to-obtain biomarker in MCI and Alzheimer’s disease (AD), especially in patients under cognitive training/rehabilitation. We highlight the role of EEG in prognostication, monitoring and potentially early treatment optimization in MCI or AD patients. Further standardization of the methodology in larger patient cohorts could be valuable for AD theragnostics in patients receiving disease-modifying treatments by providing insights regarding synaptic brain plasticity.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3><p> Critical states’ scale-free dynamics optimize neuronal complexity, emerging as biomarkers in cognitive neuroscience. Applying the method of critical fluctuations and Haar wavelet analysis in stationary EEG time-series, we demonstrate criticality enhancement in the frontotemporal electroencephalographic (EEG) recordings of mild cognitive impairment (MCI) patients after a 6-month prospective memory training, suggesting EEG criticality as a possible monitoring biomarker in MCI and Alzheimer’s disease.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"27 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141786085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-based classification of Alzheimer’s disease and frontotemporal dementia: a comprehensive analysis of discriminative features","authors":"Mehran Rostamikia, Yashar Sarbaz, Somaye Makouei","doi":"10.1007/s11571-024-10152-7","DOIUrl":"https://doi.org/10.1007/s11571-024-10152-7","url":null,"abstract":"<p>Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are two main types of dementia. These diseases have similar symptoms, and they both may be considered as AD. Early detection of dementia and differential diagnosis between AD and FTD can lead to more effective management of the disease and contributes to the advancement of knowledge and potential treatments. In this approach, several features were extracted from electroencephalogram (EEG) signals of 36 subjects diagnosed with AD, 23 FTD subjects, and 29 healthy controls (HC). Mann–Whitney U-test and t-test methods were employed for the selection of the best discriminative features. The Fp1 channel for FTD patients exhibited the most significant differences compared to AD. In addition, connectivity features in the delta and alpha subbands indicated promising discrimination among these two groups. Moreover, for dementia diagnosis (AD + FTD vs. HC), central brain regions including Cz and Pz channels proved to be determining for the extracted features. Finally, four machine learning (ML) algorithms were utilized for the classification purpose. For differentiating between AD and FTD, and dementia diagnosis, an accuracy of 87.8% and 93.5% were achieved respectively, using the tenfold cross-validation technique and employing support vector machines (SVM) as the classifier.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"25 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI","authors":"Ming Meng, Bin Xu, Yuliang Ma, Yunyuan Gao, Zhizeng Luo","doi":"10.1007/s11571-024-10154-5","DOIUrl":"https://doi.org/10.1007/s11571-024-10154-5","url":null,"abstract":"<p>Brain-computer interface (BCI) based on the motor imagery paradigm typically utilizes multi-channel electroencephalogram (EEG) to ensure accurate capture of physiological phenomena. However, excessive channels often contain redundant information and noise, which can significantly degrade BCI performance. Although there have been numerous studies on EEG channel selection, most of them require manual feature extraction, and the extracted features are difficult to fully represent the effective information of EEG signals. In this paper, we propose a spatio-temporal-graph attention network for channel selection (STGAT-CS) of EEG signals. We consider the EEG channels and their inter-channel connectivity as a graph and treat the channel selection problem as a node classification problem on the graph. We leverage the multi-head attention mechanism of graph attention network to dynamically capture topological relationships between nodes and update node features accordingly. Additionally, we introduce one-dimensional convolution to automatically extract temporal features from each channel in the original EEG signal, thereby obtaining more comprehensive spatiotemporal characteristics. In the classification tasks of the BCI Competition III Dataset IVa and BCI Competition IV Dataset I, STGAT-CS achieved average accuracies of 91.5% and 85.4% respectively, demonstrating the effectiveness of the proposed method.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"46 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network","authors":"Pramod H. Kachare, Sandeep B. Sangle, Digambar V. Puri, Mousa Mohammed Khubrani, Ibrahim Al-Shourbaji","doi":"10.1007/s11571-024-10153-6","DOIUrl":"https://doi.org/10.1007/s11571-024-10153-6","url":null,"abstract":"<p>Dementia is a neuro-degenerative disorder with a high death rate, mainly due to high human error, time, and cost of the current clinical diagnostic techniques. The existing dementia detection methods using hand-crafted electroencephalogram (EEG) signal features are unreliable. A convolution neural network using spatiotemporal EEG signals (STEADYNet) is presented to improve the dementia detection. The STEADYNet uses a multichannel temporal EEG signal as input. The network is grouped into feature extraction and classification components. The feature extraction comprises two convolution layers to generate complex features, a max-pooling layer to reduce the EEG signal’s spatiotemporal redundancy, and a dropout layer to improve the network’s generalization. The classification processes the feature extraction output nonlinearly using two fully-connected layers to generate salient features and a softmax layer to generate disease probabilities. Two publicly available multiclass datasets of dementia are used for evaluation. The STEADYNet outperforms existing automatic dementia detection methods with accuracies of <span>(99.29%)</span>, <span>(99.65%)</span>, and <span>(92.25%)</span> for Alzheimer's disease, mild cognitive impairment, and frontotemporal dementia, respectively. The STEADYNet has a low inference time and floating point operations, suitable for real-time applications. It may aid neurologists in efficient detection and treatment. A Python implementation of the STEADYNet is available at https://github.com/SandeepSangle12/STEADYNet.git</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"25 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-based cross-frequency graph convolutional network for driver fatigue estimation","authors":"Jianpeng An, Qing Cai, Xinlin Sun, Mengyu Li, Chao Ma, Zhongke Gao","doi":"10.1007/s11571-024-10141-w","DOIUrl":"https://doi.org/10.1007/s11571-024-10141-w","url":null,"abstract":"<p>Fatigue driving significantly contributes to global vehicle accidents and fatalities, making driver fatigue level estimation crucial. Electroencephalography (EEG) is a proven reliable predictor of brain states. With Deep Learning (DL) advancements, brain state estimation algorithms have improved significantly. Nonetheless, EEG’s multi-domain nature and the intricate spatial-temporal-frequency correlations among EEG channels present challenges in developing precise DL models. In this work, we introduce an innovative Attention-based Cross-Frequency Graph Convolutional Network (ACF-GCN) for estimating drivers’ reaction times using EEG signals from theta, alpha, and beta bands. This method utilizes a multi-head attention mechanism to detect long-range dependencies between EEG channels across frequencies. Concurrently, the transformer’s encoder module learns node-level feature maps from the attention-score matrix. Subsequently, the Graph Convolutional Network (GCN) integrates this matrix with feature maps to estimate driver reaction time. Our validation on a publicly available dataset shows that ACF-GCN outperforms several state-of-the-art methods. We also explore the brain dynamics within the cross-frequency attention-score matrix, identifying theta and alpha bands as key influencers in fatigue estimating performance. The ACF-GCN method advances brain state estimation and provides insights into the brain dynamics underlying multi-channel EEG signals.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"9 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controlling Alzheimer’s disease by deep brain stimulation based on a data-driven cortical network model","authors":"SiLu Yan, XiaoLi Yang, ZhiXi Duan","doi":"10.1007/s11571-024-10148-3","DOIUrl":"https://doi.org/10.1007/s11571-024-10148-3","url":null,"abstract":"<p>This work aims to explore the control effect of DBS on Alzheimer's disease (AD) from a neurocomputational perspective. Firstly, a data-driven cortical network model is constructed using the Diffusion Tensor Imaging data. Then, a typical electrophysiological feature of EEG slowing in AD is reproduced by reducing the synaptic connectivity parameters. The corresponding changes in kinetic behavior mainly include an oscillation decrease in the amplitude and frequency of the pyramidal neuron population. Subsequently, DBS current with specific parameters is introduced into three potential targets of the hippocampus, the nucleus accumbens and the olfactory tubercle, respectively. The results indicate that applying DBS to simulated mild AD patients induces an increase in relative alpha power, a decrease in relative theta power, and a significant rightward shift of the dominant frequency. This is consistent with the EEG reversal in pharmacological treatments for AD. Further, the optimal stimulation strategy of DBS is investigated through spectral and statistical analyses. Specifically, the pathological symptoms of AD could be alleviated by adjusting the critical parameters of DBS, and the control effect of DBS on various targets is that the hippocampus is superior to the olfactory tubercle and nucleus accumbens. Finally, using correlation analysis between the power increments and the nodal degrees, it is concluded that the control effect of DBS is related to the importance of the nodes in the brain network. This study provides a theoretical guidance for determining DBS targets and parameters, which may have a substantial impact on the development of DBS treatment for AD.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"22 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}