【摘要】 静息脑功能连接的动态性被认为能够反映大尺度功能脑网络的基本性质,近年来受到越来越多的关注。过去很少有研究分析静息脑连接自发波动的空间分布特点。采用远程功能连接度结合滑动窗口方法,分析一组大样本(n=396)年轻成年人脑静息功能连接低频自发波动的可变性。首先采用36 s滑动窗,计算全脑动态连接时间序列;然后在每个窗口内,以半径为14 mm的球形邻域计算每个体素的远程连接度;最后引入低频振荡幅值(ALFF)指标,评估各体素连接度波动的大小。研究结果表明:默认网络具有最小的波动性(ALFF=402.3±79.9),暗示该网络在稳定脑自发神经活动中起主要作用。与之对比,感觉运动网络相关连接波动呈现出最大的可变性(ALFF=551.2±74.7),可能与被试在无任务状态下不定期感知外界环境有关。首次揭示静息态认知网络与感觉运动网络在静息功能连接波动性方面存在显著差异(双样本t检验:t=-6.38,P<0.000 1),有助于进一步理解无任务状态下脑功能的动态组织方式,并为研究神经心理疾病提供新的方法。更多还原

【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( d FCD). 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 d FCD 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 newinsights into the dynamics of resting-state connectivity and possible new means for ascertaining neuropsychiatric disorders.更多还原