title: Semiblind spatial ICA of fMRI using spatial constraints.
abstract: Independent component analysis (ICA) utilizing prior information, also called semiblind ICA, has demonstrated considerable promise in the analysis of functional magnetic resonance imaging (fMRI). So far, temporal information about fMRI has been used in temporal ICA or spatial ICA as additional constraints to improve estimation of task-related components. Considering that prior information about spatial patterns is also available, a semiblind spatial ICA algorithm utilizing the spatial information was proposed within the framework of constrained ICA with fixed-point learning. The proposed approach was first tested with synthetic fMRI-like data, and then was applied to real fMRI data from 11 subjects performing a visuomotor task. Three components of interest including two task-related components and the "default mode" component were automatically extracted, and atlas-defined masks were used as the spatial constraints. The default mode network, a set of regions that appear correlated in particular in the absence of tasks or external stimuli and is of increasing interest in fMRI studies, was found to be greatly improved when incorporating spatial prior information. Results from simulation and real fMRI data demonstrate that the proposed algorithm can improve ICA performance compared to a different semiblind ICA algorithm and a standard blind ICA algorithm.
authors.affiliation: School of Electronic and Information Engineering, Dalian University of Technology, Dalian, People's Republic of China. qhlin@dlut.edu.cn
authors.forename: Qiu-Hua
authors.initials: QH
authors.lastname: Lin
authors.forename: Jingyu
authors.initials: J
authors.lastname: Liu
authors.forename: Yong-Rui
authors.initials: YR
authors.lastname: Zheng
authors.forename: Hualou
authors.initials: H
authors.lastname: Liang
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001271:Atlases as Topic; D001331:Automation; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D007839:Functional Laterality; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009043:Motor Activity / Q000502:physiology; D009483:Neuropsychological Tests; D012815:Signal Processing, Computer-Assisted; D014796:Visual Perception / Q000502:physiology