title: Detecting directional influence in fMRI connectivity analysis using PCA based Granger causality.
abstract: An fMRI connectivity analysis approach combining both principal component analysis (PCA) and Granger causality method (GCM) is proposed to study directional influence between functional brain regions. Both simulated data and human fMRI data obtained during behavioral tasks were used to validate this method. If PCA is first used to reduce number of fMRI time series, then more energy and information features in the signal can be preserved than using averaged values from brain regions of interest. Subsequently, GCM can be applied to principal components extracted in order to further investigate effective connectivity. The simulation demonstrated that by using GCM with PCA, between-region causalities were better represented than using GCM with average values. Furthermore, after localizing an emotion task-induced activation in the anterior cingulate cortex, inferior frontal sulcus and amygdala, the directional influences among these brain regions were resolved using our new approach. These results indicate that using PCA may improve upon application of existing GCMs in study of human brain effective connectivity.
authors: Pediatric Brain Imaging Laboratory, Department of Psychiatry, Columbia University, New York, NY 10032, USA. zhouz@childpsych.columbia.edu
authors: Zhenyu
authors: Z
authors: Zhou
authors: Mingzhou
authors: M
authors: Ding
authors: Yonghong
authors: Y
authors: Chen
authors: Paul
authors: P
authors: Wright
authors: Zuhong
authors: Z
authors: Lu
authors: Yijun
authors: Y
authors: Liu
mesh_terms: D000293:Adolescent; D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D004644:Emotions / Q000502:physiology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology*; D025341:Principal Component Analysis / Q000379:methods*; D011597:Psychomotor Performance