A Probabilistic Framework to Infer Brain Functional Connectivity from Anatomical Connections

Deligianni, F. , Varoquaux, G., Thirion, B., Robinson, E., Sharp, D. J., Edwards, A. D. and Rueckert, D. (2011) A Probabilistic Framework to Infer Brain Functional Connectivity from Anatomical Connections. In: 22nd International Information Processing in Medical Imaging Conference (IPMI 2011), Kloster Irsee, Germany, 3-8 July 2011, pp. 296-307. ISBN 9783642220913 (doi:10.1007/978-3-642-22092-0_25)

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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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Deligianni, Dr Fani
Authors: Deligianni, F., Varoquaux, G., Thirion, B., Robinson, E., Sharp, D. J., Edwards, A. D., and Rueckert, D.
College/School:College of Science and Engineering > School of Computing Science
ISSN:0302-9743
ISBN:9783642220913

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