Semiblind spatial ICA of fMRI using spatial constraints

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.