脑静息功能连接自发波动的可变性分析

doi:10.3969/j.issn.0258-8021. 2017. 01.003

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Abstract The dynamics of the resting-state functional connectivity is believed to provide greater insight into fundamental properties of large-scale functional brain networks and has received increasing attention in recent years. However, few previous studies have characterized cortical distribution of resting-state connectivity fluctuation. The present work investigated the variability of resting-state brains’ low-frequency fluctuation in a cohort of young adults (n=396), using a sliding window approach and distant functional connectivity degree (dFCD). First, we used sliding windows with the size of 36 s to generate time courses of the whole brain’s dynamic connectivity. Second, taking the sphere of radius 14 mm as the neighboring region, we calculated the dFCD of each voxel within an individual window. Finally, the amplitude of low-frequency fluctuation (ALFF) was used to evaluate variability of connectivity degree at each voxel. We observed that regions within the default mode network exhibited the least variability (ALFF=402.3±79.9), implying a possible role of this network in stabilizing the spontaneous activity in the human brain. In contrast, the sensory and motor networks exhibited the greatest variability in their distant connectivity (ALFF=551.2±74.7), possibly due to subjects’ occasionally monitoring the external environment during the task-free scanning. Taken together, the present study for the first time demonstrated a significant difference (two-sample t-test: t=-6.38, P<0.0001) between the cognitive and sensorimotor networks in terms of dynamics of the spontaneous activity, providing new insights into the dynamics of resting-state connectivity and possible new means for ascertaining neuropsychiatric disorders.

Key words： non-stationary connectivity resting-state network functional connectivity degree variability

Received: 12 May 2016

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R318

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	Wang Yingjie
	Shen Hui

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Wang Yingjie,Shen Hui. Variability of Spontaneous Fluctuation in Resting-State Functional Connectivity of Human Brains[J]. Chinese Journal of Biomedical Engineering, 2017, 36(1): 20-27.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2017. 01.003 OR http://cjbme.csbme.org/EN/Y2017/V36/I1/20

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