Conference Proceedings

DICTIONARY LEARNING ALGORITHM FOR MULTI-SUBJECT FMRI ANALYSIS VIA TEMPORAL AND SPATIAL CONCATENATION

Asif Iqbal, Abd-Krim Seghouane

2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | IEEE | Published : 2018

Abstract

In recent history, dictionary learning (DL) methods have been successfully used for analyzing multi-subject functional magnetic resonance imaging. These algorithms try to learn group-level spatial activation maps (SM) or voxel time courses (TC) from temporally or spatially concatenated fMRI datasets respectively. However, in multi-subject fMRI studies, we are interested in both group-level TCs as well as SMs. In this paper, we propose a DL algorithm which combines temporally and spatially concatenated fMRI datasets to learn not only the shared TC/SM pairs but also the subject-specific ones. We do this by separating group-level information and sub-specific information from each subject fMRI d..

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University of Melbourne Researchers