中国科学院心理健康重点实验室知识库

已有研究表明,任务诱发的局部脑区激活可通过全脑功能网络中脑区间的神经活动信息流(activity flow,AF)预测,然而静息态下自发脑神经振荡与AF的关系仍不清楚。本文旨在研究静息态自发神经活动是否也反映脑区间在功能连接(functional connectivity,FC)路径上的信息传播。用来自千人脑功能连接组计划中的197名健康被试的静息态功能磁共振成像(RS-fMRI)数据,计算全脑160个感兴趣区的自发性神经振荡低频振幅(amplitude of low-frequency fluctuation,ALFF),采用相关性分析和多元回归分析2种方法计算脑区间FC;基于AF模型,通过ALFF与FC的加权和估计汇聚到目标脑区的AF,利用Pearson相关性分析在全脑层面和默认网络脑区层面ALFF与AF之间的空间相关性。结果表明,在全脑层面和默认网络(default-mode network,DMN)脑区层面,AF与ALFF的分布模式显著相关;用多元回归改进FC估计可改善预测效果。低频振幅不仅体现脑区局部的神经振荡和功能情况,同时也反映自发性脑神经活动通过FC通道在脑区间进行信息交互。

Previous studies have revealed that the task-evoked regional activations can be predicted by interareal activity flow (AF) in brain-wide functional connections. However，little is known about the relationship between regional spontaneous activity and the AF during the resting-state. Here，we aim to investigate if the resting-state reflects communications between brain regions via intrinsic functional connectivity (FC) pathways. The resting-stage functional magnetic resonance imaging (RS一fMRI) data of 197 participants from 1 000 Functional Connectomes Project was used to calculate the amplitude of low一frequency fluctuation (ALFF) for 160 regions of interests，and the FC between these regions was explored using Pearson correlations and multiple regression methods. Based on the AF model，the AF aggregated to each region was then calculated as the FC-weighted sums of the ALFF of all other regions. Pearson correlation analysis was finally performed to examine the spatial correlation between the distribution of ALFF and AF across all 160 regions and default-mode network (DMN) regions. The results show that the ALFF pattern is significantly correlated with the spatial distribution of the AF across both whole brain and the DMN;the FC calculated by multiple regression can improve the predictive effect.0ur findings suggest that the ALFF reflects not only the neural oscillations and functions regions ma resting-state in local regions but also the transmission and stream of spontaneous activity among distributed FC pathways.

国家自然科学基金资助项目（61673374）;; 国家重点研发计划项目（2018YFC2000303）