Communication Dans Un Congrès Année : 2012

Statistical Learning for Resting-State fMRI: Successes and Challenges

Résumé

In the absence of external stimuli, fluctuations in cerebral activity can be used to reveal intrinsic structures. Well-conditioned probabilistic models of this so-called resting-state activity are needed to support neuroscientific hypotheses. Exploring two specific descriptions of resting-state fMRI, namely spatial analysis and connectivity graphs, we discuss the progress brought by statistical learning techniques, but also the neuroscientific picture that they paint, and possible modeling pitfalls.

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hal-00753132 , version 1 (17-11-2012)

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Gaël Varoquaux, Bertrand Thirion. Statistical Learning for Resting-State fMRI: Successes and Challenges. Machine Learning and Interpretation in Neuroimaging, NIPS workshop, Dec 2011, Sierra Nevada, Spain. pp.172-177, ⟨10.1007/978-3-642-34713-9_22⟩. ⟨hal-00753132⟩
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