TY  - JOUR
AU  - Jiang, Lin
AU  - Eickhoff, Simon B.
AU  - Genon, Sarah
AU  - Wang, Guangying
AU  - Yi, Chanlin
AU  - He, Runyang
AU  - Huang, Xunan
AU  - Yao, Dezhong
AU  - Dong, Debo
AU  - Li, Fali
AU  - Xu, Peng
TI  - Multimodal Covariance Network Reflects Individual Cognitive Flexibility
JO  - International journal of neural systems
VL  - 34
IS  - 4
SN  - 0129-0657
CY  - Singapore [u.a.]
PB  - World Scientific Publ. Co.
M1  - FZJ-2024-01698
SP  - 2450018
PY  - 2024
AB  - 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.Keywords: Cognitive flexibility; EEG-MRI; multimodal covariance network; response prediction; trail-making test.
LB  - PUB:(DE-HGF)16
C6  - 38372035
UR  - <Go to ISI:>//WOS:001164162800001
DO  - DOI:10.1142/S0129065724500187
UR  - https://juser.fz-juelich.de/record/1023459
ER  -