A probabilistic framework to infer brain functional connectivity from anatomical connections

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.
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Communication dans un congrès
Information Processing in Medical Imaging, Jul 2011, Kaufbeuren, Germany. Springer, 6801, pp.296-307, 2011, Information Processing in Medical Imaging. 〈10.1007/978-3-642-22092-0_25〉
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Dernière modification le : vendredi 22 juin 2018 - 01:20:24
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Fani Deligianni, Gaël Varoquaux, Bertrand Thirion, Emma Robinson, David Sharp, et al.. A probabilistic framework to infer brain functional connectivity from anatomical connections. Information Processing in Medical Imaging, Jul 2011, Kaufbeuren, Germany. Springer, 6801, pp.296-307, 2011, Information Processing in Medical Imaging. 〈10.1007/978-3-642-22092-0_25〉. 〈inria-00627914〉

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