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.
Type de document :
Communication dans un congrès
Pattern Recognition in Neuroimaging, Jul 2012, London, United Kingdom. 2012
Liste complète des métadonnées

Littérature citée [18 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00726656
Contributeur : Bernard Ng <>
Soumis le : vendredi 31 août 2012 - 18:32:41
Dernière modification le : vendredi 22 juin 2018 - 01:20:34
Document(s) archivé(s) le : vendredi 16 décembre 2016 - 08:53:29

Fichier

connClassifierPRNI2012.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00726656, version 1

Collections

Citation

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, Jul 2012, London, United Kingdom. 2012. 〈hal-00726656〉

Partager

Métriques

Consultations de la notice

397

Téléchargements de fichiers

334