Multimodal covariance network reflects individual cognitive flexibility
Poster No:
953
Submission Type:
Abstract Submission
Authors:
Lin Jiang1, Guangying Wang1, Runyang He1, Dezhong Yao1, Fali Li1, Peng Xu1
Institutions:
1School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
First Author:
Lin Jiang
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
Co-Author(s):
Guangying Wang
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
Runyang He
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
Dezhong Yao
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
Fali Li
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
Peng Xu
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, China
Introduction:
Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional neural activity supported by inherent anatomical structures. However, the intrinsic structural-functional coordination subserving cognitive flexibility remains unrevealed. Herein, we quantitatively evaluate the structural-functional interactions within and across brain subsystems by multimodal covariance network (Jiang, et al. 2023), to uncover the coordinated structural-functional substrates supporting human cognitive flexibility at a large-scale level.
Methods:
A total of 182 unmedicated healthy participants (63 females, aged 20 - 80 years) were enrolled with the approval of the ethics committee at the medical faculty of the University of Leipzig. All participants were instructed to perform the trail-making test, and the trail-making test B-A score was calculated to measure individual cognitive flexibility. Then, the large-scale multimodal covariance networks were constructed by combining electroencephalograph, structural, functional, and diffusion magnetic resonance imaging features that were divided into regions using the Desikan-Killiany atlas (Fig. 1). The relationships between multimodal covariance network and individual cognitive flexibility were probed by Pearson's correlation from three levels (i.e., network topology, whole-brain network properties, and subsystem properties). Finally, representative features were uncovered, with a prediction model being built by the stepwise multivariable linear regression, to help predict individual cognitive flexibility performance.
Results:
Results show that the intra-subsystem covariation of the somatomotor and visual network and inter-subsystem couplings spanning the somatomotor and visual/frontoparietal/default mode network are significantly related to individual cognitive flexibility (pFDR < 0.01, Fig. 2a). Meanwhile, significant correlations were observed between the cognitive flexibility scores and both the whole-brain network properties (Fig. 2b) and subsystem properties (particularly the somatomotor and visual network; Fig. 2c). Based on these (sub)network properties, a stepwise multivariable linear regression model with the leave-one-out cross-validation approach was employed to achieve the prediction of the cognitive flexibility performance. We found that network properties significantly contribute to predicting cognitive flexibility (r = 0.44, p < 0.001). Additionally, the root mean square error was calculated as 19.18, indicating a satisfactory level of prediction accuracy.
Conclusions:
Collectively, current discoveries enhanced our understanding of cognitive flexibility from the viewpoint of structural-functional interaction and will further derive useful physiological markers of cognitive functioning.
Higher Cognitive Functions:
Higher Cognitive Functions Other 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI
Diffusion MRI
Keywords:
Other - Cognitive flexibility; Multimodal covariance network; EEG-MRI; Response prediction
1|2Indicates the priority used for review