运动想象的大尺度动态功能网络连接

doi:10.3969/j.issn.0258-8021.2019.04.4

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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.

Key words： motor imagery dynamic brain network machine learning sliding window

Received: 24 August 2018

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R318

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	Articles by authors
	Zhang Tao
	Jiang Chenyang
	Li Mengchen
	Yao Dezhong
	Xu Peng

Cite this article:

Zhang Tao,Jiang Chenyang,Li Mengchen, et al. Dynamic Large-Scale Functional Network Connectivity in Motor Imagery[J]. Chinese Journal of Biomedical Engineering, 2019, 38(4): 409-416.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.04.4 OR http://cjbme.csbme.org/EN/Y2019/V38/I4/409

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