Cognitive Neurodynamics最新文献_第10页

An effective ECG signal classification method based on a minimalistic memristive reservoir computing system. 一种基于极简记忆库计算系统的心电信号分类方法。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-18 DOI: 10.1007/s11571-025-10295-1

Xiaoyuan Wang, Meng Yang, Yuji Zeng, Zhuosheng Lin, Herbert H C Iu

{"title":"An effective ECG signal classification method based on a minimalistic memristive reservoir computing system.","authors":"Xiaoyuan Wang, Meng Yang, Yuji Zeng, Zhuosheng Lin, Herbert H C Iu","doi":"10.1007/s11571-025-10295-1","DOIUrl":"10.1007/s11571-025-10295-1","url":null,"abstract":"The advantage of reservoir computing (RC) is that only the connection weight between reservoir layer and output layer needs to be trained, and the rest of the connection weights are randomly generated and fixed, which is especially suitable for time series data processing. However, the hardware implementation of high-dimensional random connection of neurons in reservoir layer is still a challenge. The memristors are emerging components with unique nonlinear and memory characteristics. Particularly, memristors allow nonlinear mapping of input time series into high-dimensional feature space, which meets the requirements of the reservoir layer. The reservoir layers of current memristor-based RC systems are based on the dynamics of volatile memristors. In this paper, a non-volatile memristor-based reservoir layer is designed for constructing RC system, in which the voltages across the two memristors are utilized to calculate the reservoir states. In this design, one-dimensional voltage input signal can be easily nonlinearly mapped to a two-dimensional space, which significantly simplifies the complexity of data analysis and enhances the separability of signal features, satisfying the requirement of RC for the high-dimensional feature. The experimental results of the proposed RC system for electrocardiogram (ECG) signal classification task achieve high accuracies of 98.3% and 100% for QRS complexes with and without shift, respectively, which validates the effectiveness of the proposed RC system.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"97"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12176724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144368700","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

Individuals with high autistic traits exhibit altered interhemispheric brain functional connectivity patterns. 具有高自闭症特征的个体表现出半球间脑功能连接模式的改变。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10213-x

Junling Wang, Ludan Zhang, Sitong Chen, Huiqin Xue, Minghao Du, Yunuo Xu, Shuang Liu, Dong Ming

{"title":"Individuals with high autistic traits exhibit altered interhemispheric brain functional connectivity patterns.","authors":"Junling Wang, Ludan Zhang, Sitong Chen, Huiqin Xue, Minghao Du, Yunuo Xu, Shuang Liu, Dong Ming","doi":"10.1007/s11571-024-10213-x","DOIUrl":"10.1007/s11571-024-10213-x","url":null,"abstract":"Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"9"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969955","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

Brain-region specific epileptic seizure detection through EEG dynamics: integrating spectral features, SMOTE and long short-term memory networks. 基于脑电动力学的脑区特异性癫痫发作检测：整合频谱特征、SMOTE和长短期记忆网络。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-03 DOI: 10.1007/s11571-025-10250-0

Indu Dokare, Sudha Gupta

{"title":"Brain-region specific epileptic seizure detection through EEG dynamics: integrating spectral features, SMOTE and long short-term memory networks.","authors":"Indu Dokare, Sudha Gupta","doi":"10.1007/s11571-025-10250-0","DOIUrl":"https://doi.org/10.1007/s11571-025-10250-0","url":null,"abstract":"Investigating neural dynamics through EEG signals offers valuable insights into brain activity, especially for automated seizure detection. The identification of epileptogenic zones is crucial for effective epilepsy treatment, particularly in surgical planning. This work introduces a novel method for seizure detection using EEG signals, designed to benefit clinicians by integrating spectral features with Long Short-Term Memory (LSTM) networks, enhanced by brain region-specific analysis. This research work captures critical frequency domain characteristics by extracting pivotal spectral features from EEG data, thereby improving the signal representation for LSTM networks. Additionally, this proposed work has employed the Synthetic Minority Over-sampling Technique (SMOTE) to handle the class imbalance problem. Furthermore, a comprehensive spatial analysis of EEG signals is performed to evaluate performance variations across distinct brain regions, enabling targeted region-wise analysis. This strategy effectively reduces the number of channels required, minimizing the need to process all 22 channels specified in the CHB-MIT dataset, thus significantly decreasing computational complexity while preserving high seizure detection performance. This work has obtained a mean value of accuracy of 95.43%, precision of 95.46%, sensitivity of 95.59%, F1-score of 95.48%, and specificity of 95.25% for the brain region providing the best performance for seizure discrimination. The results demonstrate that integrating spectral features and LSTM, augmented by spatial insights, enhances seizure detection performance and hence assists in identifying epileptogenic regions. This tool enhances clinical applications by improving diagnostic precision, personalized treatment strategies, and supporting precise surgical planning for epilepsy, ensuring safer resection and better outcomes.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"67"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143983265","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

Characteristics analysis of a single electromechanical arm driven by a functional neural circuit. 功能神经回路驱动单机电臂的特性分析。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-04-22 DOI: 10.1007/s11571-025-10218-0

Xinlin Song, Ya Wang, Zhenhua Yu, Feifei Yang

引用次数: 0

Classification for Alzheimer's disease and frontotemporal dementia via resting-state electroencephalography-based coherence and convolutional neural network. 基于静息状态脑电图的相干性和卷积神经网络对阿尔茨海默病和额颞叶痴呆的分类。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-03-04 DOI: 10.1007/s11571-025-10232-2

Rundong Jiang, Xiaowei Zheng, Jiamin Sun, Lei Chen, Guanghua Xu, Rui Zhang

{"title":"Classification for Alzheimer's disease and frontotemporal dementia via resting-state electroencephalography-based coherence and convolutional neural network.","authors":"Rundong Jiang, Xiaowei Zheng, Jiamin Sun, Lei Chen, Guanghua Xu, Rui Zhang","doi":"10.1007/s11571-025-10232-2","DOIUrl":"10.1007/s11571-025-10232-2","url":null,"abstract":"The study aimed to diagnose of Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) based on brain functional connectivity features extracted via resting-state Electroencephalographic (EEG) signals, and subsequently developed a convolutional neural network (CNN) model, Coherence-CNN, for classification. First, a publicly available dataset of EEG resting state-closed eye recordings containing 36 AD subjects, 23 FTD subjects, and 29 cognitively normal (CN) subjects was used. Then, coherence metrics were utilized to quantify brain functional connectivity, and the differences in coherence between groups across various frequency bands were investigated. Next, spectral clustering was used to analyze variations and differences in brain functional connectivity related to disease states, revealing distinct connectivity patterns in brain electrode position maps. The results demonstrated that brain functional connectivity between different regions was more robust in the CN group, while the AD and FTD groups exhibited various degrees of connectivity decline, reflecting the pronounced differences in connectivity patterns associated with each condition. Furthermore, Coherence-CNN was developed based on CNN and the feature of coherence for three-class classification, achieving a commendable accuracy of 94.32% through leave-one-out cross-validation. This study revealed that Coherence-CNN demonstrated significant performance for distinguishing AD, FTD, and CN groups, supporting the disorder of brain functional connectivity in AD and FTD.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"46"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572409","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 multidimensional adaptive transformer network for fatigue detection. 一种多维自适应变压器网络疲劳检测方法。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-02-20 DOI: 10.1007/s11571-025-10224-2

Dingming Wu, Liu Deng, Quanping Lu, Shihong Liu

{"title":"A multidimensional adaptive transformer network for fatigue detection.","authors":"Dingming Wu, Liu Deng, Quanping Lu, Shihong Liu","doi":"10.1007/s11571-025-10224-2","DOIUrl":"10.1007/s11571-025-10224-2","url":null,"abstract":"Variations in information processing patterns induced by operational directives under varying fatigue conditions within the cerebral cortex can be identified and analyzed through electroencephalogram (EEG) signals. The inherent complexity of EEG signals poses significant challenges in the effective detection of driver fatigue across diverse task scenarios. Recent advancements in deep learning, particularly the Transformer architecture, have shown substantial benefits in the retrieval and integration of multi-dimensional information. Nevertheless, the majority of current research primarily focuses on the application of Transformers for temporal information extraction, often overlooking other dimensions of EEG data. In response to this gap, the present study introduces a Multidimensional Adaptive Transformer Recognition Network specifically tailored for the identification of driving fatigue states. This network features a multidimensional Transformer architecture for feature extraction that adaptively assigns weights to various information dimensions, thereby facilitating feature compression and the effective extraction of structural information. This methodology ultimately enhances the model's accuracy and generalization capabilities. The experimental results indicate that the proposed methodology outperforms existing research methods when utilized with the SEED-VIG and SFDE datasets. Additionally, the analysis of multidimensional and frequency band features highlights the ability of the proposed network framework to elucidate differences in various multidimensional features during the identification of fatigue states.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"43"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482399","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

Grid cell modules coordination improves accuracy and reliability for spatial navigation. 网格单元模块的协调提高了空间导航的精度和可靠性。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-19 DOI: 10.1007/s11571-025-10263-9

Luca Sarramone, Jose A Fernandez-Leon

{"title":"Grid cell modules coordination improves accuracy and reliability for spatial navigation.","authors":"Luca Sarramone, Jose A Fernandez-Leon","doi":"10.1007/s11571-025-10263-9","DOIUrl":"10.1007/s11571-025-10263-9","url":null,"abstract":"Most mammals efficiently overcome self-localization deviations by coordinating grid and place cells in their brain's navigation system. However, the coordination of grid cell modules during spatial navigation and its impact on position estimation are poorly understood. This study addresses this issue by introducing a system that decodes grid-cell module activity and integrates networks of multiple grid-cell modules for self-position estimation in a mobile robot. Our results show that even when individual grid module estimates deviated substantially from the robot's actual location, the modules remained tightly coordinated. Corrections of these deviations were studied based on anchoring the activity of grid cells to spatial landmarks. Detailed numerical investigations indicate that path integration is critically dependent on the intrinsic coordination between grid cell modules which enhances the accuracy and reliability of spatial navigation. Furthermore, we show that this coordination enables effective vector navigation, even when the overall position estimation is inaccurate. These insights advance our understanding of grid-cell module coordination in location estimation during path integration and offer potential applications in robotics.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"76"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119120","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

Multiple-timescale dynamics, mixed mode oscillations and mixed affective states in a model of bipolar disorder. 双相情感障碍模型中的多重时间尺度动力学、混合模式振荡和混合情感状态。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2022-10-26 DOI: 10.1007/s11571-022-09900-4

Efstathios Pavlidis, Fabien Campillo, Albert Goldbeter, Mathieu Desroches

{"title":"Multiple-timescale dynamics, mixed mode oscillations and mixed affective states in a model of bipolar disorder.","authors":"Efstathios Pavlidis, Fabien Campillo, Albert Goldbeter, Mathieu Desroches","doi":"10.1007/s11571-022-09900-4","DOIUrl":"10.1007/s11571-022-09900-4","url":null,"abstract":"Mixed affective states in bipolar disorder (BD) is a common psychiatric condition that occurs when symptoms of the two opposite poles coexist during an episode of mania or depression. A four-dimensional model by Goldbeter (Progr Biophys Mol Biol 105:119-127, 2011; Pharmacopsychiatry 46:S44-S52, 2013) rests upon the notion that manic and depressive symptoms are produced by two competing and auto-inhibited neural networks. Some of the rich dynamics that this model can produce, include complex rhythms formed by both small-amplitude (subthreshold) and large-amplitude (suprathreshold) oscillations and could correspond to mixed bipolar states. These rhythms are commonly referred to as mixed mode oscillations (MMOs) and they have already been studied in many different contexts by Bertram (Mathematical analysis of complex cellular activity, Springer, Cham, 2015), (Petrov et al. in J Chem Phys 97:6191-6198, 1992). In order to accurately explain these dynamics one has to apply a mathematical apparatus that makes full use of the timescale separation between variables. Here we apply the framework of multiple-timescale dynamics to the model of BD in order to understand the mathematical mechanisms underpinning the observed dynamics of changing mood. We show that the observed complex oscillations can be understood as MMOs due to a so-called folded-node singularity. Moreover, we explore the bifurcation structure of the system and we provide possible biological interpretations of our findings. Finally, we show the robustness of the MMOs regime to stochastic noise and we propose a minimal three-dimensional model which, with the addition of noise, exhibits similar yet purely noise-driven dynamics. The broader significance of this work is to introduce mathematical tools that could be used to analyse and potentially control future, more biologically grounded models of BD.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"3239-3257"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876472","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

Pupil dilation and behavior as complementary measures of fear response in Mice. 瞳孔扩张和行为作为小鼠恐惧反应的互补测量。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2024-10-21 DOI: 10.1007/s11571-024-10180-3

Jing Sun, Lin Zhu, Xiaojing Fang, Yong Tang, Yuci Xiao, Shaolei Jiang, Jianbang Lin, Yuantao Li

{"title":"Pupil dilation and behavior as complementary measures of fear response in Mice.","authors":"Jing Sun, Lin Zhu, Xiaojing Fang, Yong Tang, Yuci Xiao, Shaolei Jiang, Jianbang Lin, Yuantao Li","doi":"10.1007/s11571-024-10180-3","DOIUrl":"10.1007/s11571-024-10180-3","url":null,"abstract":"The precise assessment of emotional states in animals under the combined influence of multiple stimuli remains a challenge in neuroscience research. In this study, multi-dimensional assessments, including high-precision pupil tracking and behavioral analysis, were conducted to investigate the combined effects of fear stimuli and drug manipulation on emotional responses in mice. Mice exposed to foot shocks showed typical freezing and flight behaviors, but neither of these measures could effectively distinguish between dexmedetomidine, isoflurane, and saline groups. In contrast, the change in pupil diameter clearly distinguished the groups. Our results showed that fear stimulation could induce significant pupil dilation, and dexmedetomidine-isoflurane combined stimulation could significantly inhibit this response, but isoflurane anesthesia alone could not achieve good inhibitory effect. This further demonstrates the superiority of pupil data in resolving the effects of combined stimuli on emotional states and the potential of multidimensional assessments to refine animal disease models and drug evaluations.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4047-4054"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876517","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

Resting state EEG delta-beta amplitude-amplitude coupling: a neural predictor of cortisol response under stress. 静息状态脑电图δ - β振幅-振幅耦合：应激下皮质醇反应的神经预测因子。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2024-10-03 DOI: 10.1007/s11571-024-10174-1

Xiaoyu Wang, Li Lin, Lei Zhan, Xianghong Sun, Zheng Huang, Liang Zhang

{"title":"Resting state EEG delta-beta amplitude-amplitude coupling: a neural predictor of cortisol response under stress.","authors":"Xiaoyu Wang, Li Lin, Lei Zhan, Xianghong Sun, Zheng Huang, Liang Zhang","doi":"10.1007/s11571-024-10174-1","DOIUrl":"10.1007/s11571-024-10174-1","url":null,"abstract":"Stress is ubiquitous in daily life. Subcortical and cortical regions closely interact to respond to stress. Delta-beta cross-frequency coupling (CFC), believed to signify communication between different brain areas, can serve as a neural signature underlying the heterogeneity in stress responses. Nevertheless, the role of cross-frequency coupling in stress prediction has not received sufficient attention. To examine the predictive role of resting state delta-beta CFC across the whole scalp, we obtained amplitude-amplitude coupling (AAC) and phase-amplitude coupling (PAC) from 4-minute resting state EEG of seventy-three healthy participants. The Trier Social Stress Test (TSST) was administered on a separate day to induce stress. Salivary cortisol and heart rate were recorded to measure stress responses. Utilizing cluster-based permutation analysis, the results showed that delta-beta AAC was positively correlated with cortisol increase magnitude (cluster t = 26.012, p = .020) and cortisol AUCi (cluster t = 23.039, p = .022) over parietal-occipital areas, which means that individuals with a stronger within-subject AAC demonstrated a greater cortisol response. These results suggest that AAC could be a valuable biomarker for predicting neuroendocrine activity under stress. However, no association between PAC and stress responses was found. Additionally, we did not detect the predictive effect of power in the delta or beta frequency bands on stress responses, suggesting that delta-beta AAC provides unique insights beyond single-band power. These findings enhance our understanding of the neurophysiological mechanism underpinning individual differences in stress responses and offer promising biomarkers for stress assessment and the detection of stress-related disorders.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-024-10174-1.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"3995-4007"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876522","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