Dynamic Large-Scale Functional Network Connectivity in Motor Imagery
Zhang Tao1,2#, Jiang Chenyang2, Li Mengchen2, Yao Dezhong2#, Xu Peng2#*
1(Xihua University School of Science, Chengdu 610039, China) 2(Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and TechnologyUniversity of Electronic Science and Technology of China, Chengdu 610054, China)
Abstract：Motor imagery (MI) is a multidimensional high-level cognitive ability that is widely applied to brain-computer interface control and clinical rehabilitation. However, the underlying neural mechanisms behind the application of MI are still unclear. To further understand the underlying neural mechanisms of MI, we explored the dynamic large-scale brain network functional connectivity patterns of MI. Twenty-six healthy subjects were recruited for MI functional magnetic resonance scanning experiments. Based on the task fMRI data, first, the independent component analysis was used to obtain eleven large-scale functional sub-networks, and the time series corresponding to the sub-network was extracted. Then, we evaluated the dynamic network connectivity matrixes using the sliding window analysis method. Based on these connectivity matrices, we performed the cluster analysis resulting in state-dependent dynamic connectivity. Finally, the network statistical analysis method was used to evaluate the dynamic network difference in left-hand MI and right-hand MI. Results showed that the machine learning method could obtain data features more effectively, and the optimal window length based on data-driven was 31 time points, and the classification accuracy rate for left/right hand MI was 75.6%. The large-scale network connectivity pattern of MI was a state-dependent dynamic change process, resulting in 4 dynamic reconfiguration patterns. The specificity of large-scale dynamic network connectivity pattern during left-hand/right-hand MI mainly reflected by the interactions between the frontal-parietal network (FPN) and the dorsal attention network (DAN) and other sub-networks. Our findings provided new insights into the underlying neural mechanisms of MI.
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