Multimodal Covariance Network Reflects Individual Cognitive Flexibility.

Cognitive flexibility; EEG-MRI; multimodal covariance network; response prediction; trail-making test; Humans; Electroencephalography; Neural Pathways/diagnostic imaging; Cognition; Brain/diagnostic imaging; Brain Mapping; Executive Function; Magnetic Resonance Imaging; Covariance networks; Electroencephalograph-magnetic resonance imaging; Multi-modal; Performance; Resting state; Structural-functional; Trail making tests; Brain; Neural Pathways; Computer Networks and Communications

Abstract :

[en] Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.

Disciplines :

Neurosciences & behavior

Author, co-author :

Jiang, Lin ; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China ; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China

Eickhoff, Simon B ; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany ; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany

Genon, Sarah ; Université de Liège - ULiège > Département des sciences cliniques ; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany ; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany

Wang, Guangying ; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China ; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China

Yi, Chanlin ; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China ; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China

He, Runyang ; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China ; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China

Huang, Xunan ; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China ; School of Foreign Languages, University of Electronic Science and Technology of China, Sichuan, Chengdu 611731, P. R. China

Yao, Dezhong ; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China ; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China ; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China ; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China

Dong, Debo ; Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany ; Faculty of Psychology, Southwest University, Chongqing 400715, P. R. China

Li, Fali ; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China ; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China ; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China ; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, P. R. China

Xu, Peng ; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China ; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China ; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China ; Radiation Oncology Key Laboratory of Sichuan Province, ChengDu 610041, P. R. China ; Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, P. R. China

Language :

English

Title :

Multimodal Covariance Network Reflects Individual Cognitive Flexibility.

Publication date :

April 2024

Journal title :

International Journal of Neural Systems

ISSN :

0129-0657

Publisher :

World Scientific, Singapore

Volume :

Issue :

Pages :

2450018

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.worldscientific.com/doi/pdf/10.1142/S0129065724500187

Funders :

NSCF - National Natural Science Foundation of China [CN]

Funding text :

This work was supported by the National Natural Science Foundation of China (Nos. 62103085, 62076209, and U19A2082), the Key R&D projects of Science and Technology Department of Sichuan Province (No. 2023YFS0324), and the STI 2030 - Major Projects (Nos. 2022ZD0208500 and 2022ZD0211400).

Available on ORBi :

since 10 September 2024

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