基于独立成分分析的脑功能连接性分析

A Brain Functional Connectivity Analysis Based on Independent Component Analysis

静息脑功能连接性是研究脑功能的重要技术手段。我们提出了利用空域独立成分分析（Independent component analysis，ICA）来处理静息态功能磁共振（Functional magnetic resonance imaging，fMRI）数据，首次将静息态脑功能的低频振荡理论应用于ICA静态数据分析的成分选择，通过Z分数选择静息态下的活动点并去除独立噪点，然后通过频谱分析选择主要能量集中在0．01-0．1Hz的独立成分，进而采用聚类分析得出脑功能连接网络。

The resting state cortical functional connectivity is an important method in current brain researches.In this paper, we propose an approach for analyzing and manipulating the resting state functional magnetic resonance imaging （fMRI） data using spatial independent component analysis （sICA） method, and applying the low-frequency oscillations theory to the choice of component of interest （COD from the component obtained by slCA method. Firstly, we remove all the inactive voxels and independent voxels via Z value. Then, by making a spectrum analysis, we choose the COI with concentrations of energy between 0. 01 and 0. 1 Hz. And after that, we obtain the functional connectivity networks using hierarchical clustering.