Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques.
Spatial Independent component analysis (sICA) has been widely used to analyze functional magnetic resonance imaging (fMRI) data. The well accepted implicit assumption is the spatially statistical independency of intrinsic sources identified by sICA, making the sICA applications difficult for data in which there exist interdependent sources and confounding factors. This interdependency can arise, for instance, from fMRI studies investigating two tasks in a single session. In this study, we introduced a linear projection approach and considered its utilization as a tool to separate task-related components from two-task fMRI data. The robustness and feasibility of the method are substantiated through simulation on computer data and fMRI real rest data. Both simulated and real two-task fMRI experiments demonstrated that sICA in combination with the projection method succeeded in separating spatially dependent components and had better detection power than pure model-based method when estimating activation induced by each task as well as both tasks.
Long
Zhiying
Z
State Key Lab of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Chen
Kewei
K
Wu
Xia
X
Reiman
Eric
E
Peng
Danling
D
Yao
Li
L
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002985:Clinical Protocols; D003198:Computer Simulation; D001330:Electronic Data Processing / Q000379:methods; D005260:Female; D020342:Finite Element Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D011597:Psychomotor Performance / Q000502:physiology; D012984:Software; D014796:Visual Perception / Q000502:physiology; D055815:Young Adult