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Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify

Abstract : The prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accu- racy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected rele- vant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment.
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Contributor : Alexandre Gramfort Connect in order to contact the contributor
Submitted on : Wednesday, June 6, 2012 - 2:17:00 PM
Last modification on : Saturday, June 25, 2022 - 8:27:17 PM
Long-term archiving on: : Thursday, December 15, 2016 - 12:36:59 PM


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Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion. Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify. NIPS 2011 MLINI Workshop, Dec 2011, Granada, Spain. pp.9-16, ⟨10.1007/978-3-642-34713-9_2⟩. ⟨hal-00704875⟩



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