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001023459 1001_ $$0P:(DE-HGF)0$$aJiang, Lin$$b0
001023459 245__ $$aMultimodal Covariance Network Reflects Individual Cognitive Flexibility
001023459 260__ $$aSingapore [u.a.]$$bWorld Scientific Publ. Co.$$c2024
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001023459 520__ $$aCognitive 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.
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001023459 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b1
001023459 7001_ $$0P:(DE-Juel1)161225$$aGenon, Sarah$$b2
001023459 7001_ $$0P:(DE-HGF)0$$aWang, Guangying$$b3
001023459 7001_ $$0P:(DE-HGF)0$$aYi, Chanlin$$b4
001023459 7001_ $$0P:(DE-HGF)0$$aHe, Runyang$$b5
001023459 7001_ $$0P:(DE-HGF)0$$aHuang, Xunan$$b6
001023459 7001_ $$0P:(DE-HGF)0$$aYao, Dezhong$$b7
001023459 7001_ $$0P:(DE-Juel1)178872$$aDong, Debo$$b8
001023459 7001_ $$0P:(DE-HGF)0$$aLi, Fali$$b9
001023459 7001_ $$0P:(DE-HGF)0$$aXu, Peng$$b10$$eCorresponding author
001023459 773__ $$0PERI:(DE-600)1498197-X$$a10.1142/S0129065724500187$$gp. 2450018$$n4$$p2450018$$tInternational journal of neural systems$$v34$$x0129-0657$$y2024
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