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A Probabilistic Framework to Infer Brain Functional Connectivity from Anatomical Connections

  • Fani Deligianni
  • Gael Varoquaux
  • Bertrand Thirion
  • Emma Robinson
  • David J. Sharp
  • A. David Edwards
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6801)

Abstract

We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.

Keywords

Functional Connectivity Default Mode Network Structural Connectivity Probabilistic Framework Sparsity Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fani Deligianni
    • 1
  • Gael Varoquaux
    • 2
    • 3
  • Bertrand Thirion
    • 3
  • Emma Robinson
    • 1
  • David J. Sharp
    • 4
  • A. David Edwards
    • 5
  • Daniel Rueckert
    • 1
  1. 1.Department of ComputingImperial College LondonUK
  2. 2.INSERM U922, Neurospin, CEASaclayFrance
  3. 3.Parietal project teamINRIASaclayFrance
  4. 4.C3NL, The Division of Experimental MedicineImperial CollegeLondonUK
  5. 5.Institute of Clinical SciencesImperial College LondonUK

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