Sparse Dictionary Learning of Resting State fMRI Networks

• Published in: 2012 International Workshop on Pattern Recognition in NeuroImaging pp. 73 - 76
• Main Authors: Eavani, H., Filipovych, R., Davatzikos, C., Satterthwaite, T. D., Gur, R. E., Gur, R. C.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 02.07.2012
• Subjects: Correlation; Dictionaries; functional connectivity; Independent component analysis; K-SVD; Resting state fMRI; Sparse matrices; sparse modeling; Symmetric matrices; Vectors; Xenon; Resting state fMRI; sparse modeling; K-SVD; functional connectivity
• ISBN: 1467321826; 9781467321822
• ISSN: 2330-9989
• DOI: 10.1109/PRNI.2012.25

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional sub-networks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.