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Conference papers

Connectivity-informed Sparse Classifiers for fMRI Brain Decoding

Abstract : In recent years, sparse regularization has become a dominant means for handling the curse of dimensionality in functional magnetic resonance imaging (fMRI) based brain decoding problems. Enforcing sparsity alone, however, neglects the interactions between connected brain areas. Methods that additionally impose spatial smoothness would account for local but not long-range interactions. In this paper, we propose incorporating connectivity into sparse classifier learning so that both local and long-range connections can be jointly modeled. On real data, we demonstrate that integrating connectivity information inferred from diffusion tensor imaging (DTI) data provides higher classification accuracy and more interpretable classifier weight patterns than standard classifiers. Our results thus illustrate the benefits of adding neurologically-relevant priors in fMRI brain decoding.
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Contributor : Bernard Ng Connect in order to contact the contributor
Submitted on : Friday, August 31, 2012 - 6:32:41 PM
Last modification on : Monday, December 13, 2021 - 9:16:02 AM
Long-term archiving on: : Friday, December 16, 2016 - 8:53:29 AM


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  • HAL Id : hal-00726656, version 1



Bernard Ng, Viviana Siless, Gaël Varoquaux, Jean-Baptiste Poline, Bertrand Thirion, et al.. Connectivity-informed Sparse Classifiers for fMRI Brain Decoding. Pattern Recognition in Neuroimaging, Christos Davatzikos, Moritz Grosse-Wentrup, Janaina Mourao-Miranda, Dimitri Van De Ville, Jul 2012, London, United Kingdom. ⟨hal-00726656⟩



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