基于独立成分分析和时间相关分析的脑功能连通研究方法[英文]

目的:利用静息状态功能磁共振成像(functional magnetic resonance imaging,fMRI)技术,提出联合独立成分分析(independent component analysis,ICA)和时间相关分析的人脑功能连通性研究方法。方法:首先采用空间ICA定位任务激活的脑区;然后选择一个激活区作为感兴趣区域(region of interest,ROI),采用时间相关分析方法检测静息状态大脑特定皮层的功能连通性,并通过检测人脑运动皮层的功能连通性验证方法的有效性。结果:大脑运动皮层功能连通网络包括初级运动区、辅助运动区、初级感觉皮层、背侧前运动区和后顶骨体觉区。实验结果表明,静息状态下,时间相关分析检测到的运动皮层的功能连通网络与已知的解剖连通相一致。结论:利用静息fMRI,结合空间ICA和时间相关分析方法,检测了静息时人脑运动皮层的功能连通网络,为脑区间功能连通的研究提供了一种简便的、无损的、有效的研究方法。

Research of Functional Connectivity Based on Independent Component Analysis and Temporal Correlation Analysis

Objective: Combining spatial independent component analysis (sICA) with temporal correlation analysis to investigate the functional connectivity of human brain using resting state fMRI. Methods: First, activated area was localized by performing sICA on the data from block design run , then one of the activated brain areas was chosen as a region of interest (ROI) and low frequency correlations between ROI and other regions were calculated in resting state to detect the functional connectivity networks. To validate the method, neural connectivity to primary motor cortex was assessed using this method during a resting state. Results: Functional connectivity network of motor cortex was detected, including primary motor cortex (M1), supplementary motor area (SMA), primary sensory cortex (S1), dorsal premotor cortex (PMd) and posterior parietal somatosensory association area (PSAAp). The connectivity implied by the resting state correlation was far more similar to the connectivity established by non-imaging methods. Conclusion: Functional connectivity of human motor primary cortex was investigated by combining sICA with temporal correlation using resting fMRI data. It provided a simple and noninvasive method for the research of brain functional connectivity.