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Unsupervised Learning of Functional Network Dynamics in Resting State fMRI

  • Harini Eavani
  • Theodore D. Satterthwaite
  • Raquel E. Gur
  • Ruben C. Gur
  • Christos Davatzikos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

Research in recent years has provided some evidence of temporal non-stationarity of functional connectivity in resting state fMRI. In this paper, we present a novel methodology that can decode connectivity dynamics into a temporal sequence of hidden network “states” for each subject, using a Hidden Markov Modeling (HMM) framework. Each state is characterized by a unique covariance matrix or whole-brain network. Our model generates these covariance matrices from a common but unknown set of sparse basis networks, which capture the range of functional activity co-variations of regions of interest (ROIs). Distinct hidden states arise due to a variation in the strengths of these basis networks. Thus, our generative model combines a HMM framework with sparse basis learning of positive definite matrices. Results on simulated fMRI data show that our method can effectively recover underlying basis networks as well as hidden states. We apply this method on a normative dataset of resting state fMRI scans. Results indicate that the functional activity of a subject at any point during the scan is composed of combinations of overlapping task-positive/negative pairs of networks as revealed by our basis. Distinct hidden temporal states are produced due to a different set of basis networks dominating the covariance pattern in each state.

Keywords

resting state fMRI functional connectivity temporal network dynamics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Harini Eavani
    • 1
  • Theodore D. Satterthwaite
    • 2
  • Raquel E. Gur
    • 2
  • Ruben C. Gur
    • 2
  • Christos Davatzikos
    • 1
  1. 1.Section of Biomedical Image Analysis, Department of RadiologyUniversity of PennsylvaniaUSA
  2. 2.Brain Behavior Laboratory, Department of PsychiatryUniversity of PennsylvaniaUSA

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