Independent subspace analysis with prior information for fMRI data

• Published in: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 1922 - 1925
• Main Authors: Sai Ma, Xi-Lin Li, Correa, N M, Adali, T, Calhoun, V D
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2010
• Subjects: Additives; Algorithm design and analysis; fMRI; Image analysis; Independent component analysis; independent subspace analysis; Information analysis; Instruction sets; Magnetic analysis; Magnetic resonance imaging; Performance analysis; semi-blind source separation; Source separation; sparsity
• ISBN: 9781424442959; 1424442958
• ISSN: 1520-6149
• DOI: 10.1109/ICASSP.2010.5495320

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.