Multi-subject fMRI connectivity analysis using sparse dictionary learning and multiset canonical correlation analysis

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 683 - 686
• Main Authors: Khalid, Muhammad Usman, Seghouane, Abd-Krim
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2015
• Subjects: Australia; Blind source separation; Correlation; Data models; Dictionaries; Encoding; fMRI; functional connectivity; K-SVD; MCCA; Principal component analysis
• DOI: 10.1109/ISBI.2015.7163965
• ISSN: 1945-7928

In this paper, we propose an effective technique to analyze task-based functional connectivity across multiple subjects for functional magnetic resonance imaging (fMRI) data. Instead of applying the assumption of group-independence or multiset correlation maximization, an alternative approach is adopted based on a combined framework of sparse dictionary learning (SDL) and multi-set canonical correlation analysis (MCCA) to obtain connectivity maps. The proposed technique encapsulates commonality and uniqueness solely based on sparsity of cross dataset corresponding components. It is validated using real fMRI data and its superior performance is illustrated using a simulation study, which shows its better capability in obtaining connectivity maps that are more specific.