An Algorithm for Multi Subject Fmri Analysis Based on the SVD and Penalized Rank-1 Matrix Approximation

Abstract

In recent years, data driven methods have been successfully used for analyzing multi-subject functional magnetic resonance imaging (fMRI) datasets. These methods attempt to learn shared spatial activation maps (SM) or voxel time courses (TC) from temporally or spatially concatenated fMRI datasets respectively. Most of the methods proposed so far do not distinguish whether a particular SM/TC is a group level component or only present in a certain subject dataset. In this paper we present a new two stage algorithm which aims to separate the joint and sub-specific information from the temporally concatenated multi-subject datasets. The proposed method is based on the singular value decompositio..