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A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation

  • Bernard Ng
  • Gaël Varoquaux
  • Jean Baptiste Poline
  • Bertrand Thirion
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

The estimation of intra-subject functional connectivity is greatly complicated by the small sample size and complex noise structure in functional magnetic resonance imaging (fMRI) data. Pooling samples across subjects improves the conditioning of the estimation, but loses subject-specific connectivity information. In this paper, we propose a new sparse group Gaussian graphical model (SGGGM) that facilitates joint estimation of intra-subject and group-level connectivity. This is achieved by casting functional connectivity estimation as a regularized consensus optimization problem, in which information across subjects is aggregated in learning group-level connectivity and group information is propagated back in estimating intra-subject connectivity. On synthetic data, we show that incorporating group information using SGGGM significantly enhances intra-subject connectivity estimation over existing techniques. More accurate group-level connectivity is also obtained. On real data from a cohort of 60 subjects, we show that integrating intra-subject connectivity estimated with SGGGM significantly improves brain activation detection over connectivity priors derived from other graphical modeling approaches.

Keywords

brain connectivity fMRI Gaussian graphical model regularized consensus optimization sparse inverse covariance estimation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bernard Ng
    • 1
    • 2
  • Gaël Varoquaux
    • 1
  • Jean Baptiste Poline
    • 1
  • Bertrand Thirion
    • 1
  1. 1.Parietal TeamNeurospin, INRIA SaclayFrance
  2. 2.FIND LabStanford UniversityUnited States

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