Sparse dictionary learning of resting state fMRI networks

Harini Eavani, Roman Filipovych, Christos Davatzikos, Theodore D. Satterthwaite, Raquel E. Gur, Ruben C. Gur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012
Pages73-76
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012 - London, United Kingdom
Duration: Jul 2 2012Jul 4 2012

Other

Other2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012
CountryUnited Kingdom
CityLondon
Period7/2/127/4/12

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Keywords

  • functional connectivity
  • K-SVD
  • Resting state fMRI
  • sparse modeling

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Eavani, H., Filipovych, R., Davatzikos, C., Satterthwaite, T. D., Gur, R. E., & Gur, R. C. (2012). Sparse dictionary learning of resting state fMRI networks. In Proceedings - 2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012 (pp. 73-76). [6295931] https://doi.org/10.1109/PRNI.2012.25