Resting-state EEG network variability predicts individual working memory behavior
Even during periods of rest, the brain exhibits spontaneous activity that dynamically fluctuates across spatially distributed regions in a globally coordinated manner, which has significant cognitive implications. However, the relationship between the temporal variability of resting-state networks a...
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| Language: | English |
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Elsevier
2025-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925001223 |
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| author | Chunli Chen Shiyun Xu Jixuan Zhou Chanlin Yi Liang Yu Dezhong Yao Yangsong Zhang Fali Li Peng Xu |
| author_facet | Chunli Chen Shiyun Xu Jixuan Zhou Chanlin Yi Liang Yu Dezhong Yao Yangsong Zhang Fali Li Peng Xu |
| author_sort | Chunli Chen |
| collection | DOAJ |
| description | Even during periods of rest, the brain exhibits spontaneous activity that dynamically fluctuates across spatially distributed regions in a globally coordinated manner, which has significant cognitive implications. However, the relationship between the temporal variability of resting-state networks and working memory (WM) remains largely unexplored. This study aims to address this gap by employing an EEG-based protocol combined with fuzzy entropy. First, we identified both flexible and robust patterns of dynamic resting-state networks. Subsequently, we observed a significant positive correlation between WM performance and network variability, particularly in connections associated with the frontal, right central, and right parietal lobes. Moreover, we found that the temporal variability of network properties was positively and significantly associated with WM performance. Additionally, distinct patterns of network variability were delineated, contributing to inter-individual differences in WM abilities, with these distinctions becoming more pronounced as task demands increased. Finally, using a multivariable predictive model based on these variability metrics, we effectively predicted individual WM performances. Notably, analogous analyses conducted in the source space validated the reproducibility of the temporal variability of resting-state networks in predicting individual WM behavior at higher spatial resolution, providing more precise anatomical localization of key brain regions. These results suggest that the temporal variability of resting-state networks reflects intrinsic dynamic changes in brain organization supporting WM and can serve as an objective predictor for individual WM behaviors. |
| format | Article |
| id | doaj-art-ead5dc9f82de4c3e9e2ce3d4c9147cb7 |
| institution | DOAJ |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-ead5dc9f82de4c3e9e2ce3d4c9147cb72025-08-20T03:17:35ZengElsevierNeuroImage1095-95722025-04-0131012112010.1016/j.neuroimage.2025.121120Resting-state EEG network variability predicts individual working memory behaviorChunli Chen0Shiyun Xu1Jixuan Zhou2Chanlin Yi3Liang Yu4Dezhong Yao5Yangsong Zhang6Fali Li7Peng Xu8The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, ChinaThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China; Corresponding authors.The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China; Corresponding authors.The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China; Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China; Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, China; Corresponding authors.Even during periods of rest, the brain exhibits spontaneous activity that dynamically fluctuates across spatially distributed regions in a globally coordinated manner, which has significant cognitive implications. However, the relationship between the temporal variability of resting-state networks and working memory (WM) remains largely unexplored. This study aims to address this gap by employing an EEG-based protocol combined with fuzzy entropy. First, we identified both flexible and robust patterns of dynamic resting-state networks. Subsequently, we observed a significant positive correlation between WM performance and network variability, particularly in connections associated with the frontal, right central, and right parietal lobes. Moreover, we found that the temporal variability of network properties was positively and significantly associated with WM performance. Additionally, distinct patterns of network variability were delineated, contributing to inter-individual differences in WM abilities, with these distinctions becoming more pronounced as task demands increased. Finally, using a multivariable predictive model based on these variability metrics, we effectively predicted individual WM performances. Notably, analogous analyses conducted in the source space validated the reproducibility of the temporal variability of resting-state networks in predicting individual WM behavior at higher spatial resolution, providing more precise anatomical localization of key brain regions. These results suggest that the temporal variability of resting-state networks reflects intrinsic dynamic changes in brain organization supporting WM and can serve as an objective predictor for individual WM behaviors.http://www.sciencedirect.com/science/article/pii/S1053811925001223Temporal variabilityResting-state networksFuzzy entropyBehavior predictionWorking memory |
| spellingShingle | Chunli Chen Shiyun Xu Jixuan Zhou Chanlin Yi Liang Yu Dezhong Yao Yangsong Zhang Fali Li Peng Xu Resting-state EEG network variability predicts individual working memory behavior NeuroImage Temporal variability Resting-state networks Fuzzy entropy Behavior prediction Working memory |
| title | Resting-state EEG network variability predicts individual working memory behavior |
| title_full | Resting-state EEG network variability predicts individual working memory behavior |
| title_fullStr | Resting-state EEG network variability predicts individual working memory behavior |
| title_full_unstemmed | Resting-state EEG network variability predicts individual working memory behavior |
| title_short | Resting-state EEG network variability predicts individual working memory behavior |
| title_sort | resting state eeg network variability predicts individual working memory behavior |
| topic | Temporal variability Resting-state networks Fuzzy entropy Behavior prediction Working memory |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925001223 |
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