Independent subspace analysis with prior information for fMRI data
Sai Ma, Xi-Lin Li, Nicolle M. Correa, Tülay Adalı, Vince D. Calhoun · 2010
Independent component analysis (ICA) has been successfully applied for the analysis of functional magnetic resonance imaging (fMRI) data. However, independence might be too strong a constraint for certain sources. In this paper, we present an independent subspace analysis (ISA) framework that forms independent subspaces among the estimated sources having dependencies by a hierarchial clustering approach and subsequently separates the dependent sources in the task-related subspace using prior information. We study the incorporation of two types of prior information to transform the sources within the task-related subspace: sparsity and task-related time courses. We demonstrate the effectiveness of our proposed method for source separation of multi-subject fMRI data from a visuomotor task. Our results show that physiologically meaningful dependencies among sources can be identified using our subspace approach and the dependent estimated components can be further separated effectively using a subsequent transformation.