Learning Neural Representations of Human Cognition across Many fMRI Studies - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Learning Neural Representations of Human Cognition across Many fMRI Studies

Résumé

Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets. It opens the door to large-scale statistical models. Finding a unified perspective for all available data calls for scalable and automated solutions to an old challenge: how to aggregate heterogeneous information on brain function into a universal cognitive system that relates mental operations/cognitive processes/psychological tasks to brain networks? We cast this challenge in a machine-learning approach to predict conditions from statistical brain maps across different studies. For this, we leverage multi-task learning and multi-scale dimension reduction to learn low-dimensional representations of brain images that carry cognitive information and can be robustly associated with psychological stimuli. Our multi-dataset classification model achieves the best prediction performance on several large reference datasets, compared to models without cognitive-aware low-dimension representations; it brings a substantial performance boost to the analysis of small datasets, and can be introspected to identify universal template cognitive concepts.
Fichier principal
Vignette du fichier
nips.pdf (1.68 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01626823 , version 1 (31-10-2017)
hal-01626823 , version 2 (31-10-2017)
hal-01626823 , version 3 (11-11-2017)

Identifiants

Citer

Arthur Mensch, Julien Mairal, Danilo Bzdok, Bertrand Thirion, Gaël Varoquaux. Learning Neural Representations of Human Cognition across Many fMRI Studies. Neural Information Processing Systems, Dec 2017, Long Beach, United States. ⟨hal-01626823v3⟩
1910 Consultations
501 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More