Principal Component Regression predicts functional responses across individuals

Abstract : Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes.
Type de document :
Communication dans un congrès
MICCAI, Sep 2014, Boston, United States. Springer, 2014
Liste complète des métadonnées

Littérature citée [12 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01015173
Contributeur : Bertrand Thirion <>
Soumis le : mercredi 25 juin 2014 - 23:32:14
Dernière modification le : vendredi 22 juin 2018 - 01:20:40
Document(s) archivé(s) le : jeudi 25 septembre 2014 - 11:49:27

Fichier

paper_869.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01015173, version 1

Collections

Citation

Bertrand Thirion, Gaël Varoquaux, Olivier Grisel, Cyril Poupon, Philippe Pinel. Principal Component Regression predicts functional responses across individuals. MICCAI, Sep 2014, Boston, United States. Springer, 2014. 〈hal-01015173〉

Partager

Métriques

Consultations de la notice

11316

Téléchargements de fichiers

424