Extracting Universal Representations of Cognition across Brain-Imaging Studies

Arthur Mensch 1 Julien Mairal 2 Bertrand Thirion 1 Gaël Varoquaux 1
1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
2 Thoth - Apprentissage de modèles à partir de données massives
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
Abstract : The size of publicly available data in cognitive neuro-imaging has increased a lot in recent years, thanks to strong research and community efforts. Exploiting this wealth of data demands new methods to turn the heterogeneous cognitive information held in different task-fMRI studies into common-universal-cognitive models. In this paper, we pool data from large fMRI repositories to predict psychological conditions from statistical brain maps across different studies and subjects. We leverage advances in deep learning, intermediate representations and multi-task learning to learn universal interpretable low-dimensional representations of brain images, usable for predicting psychological stimuli in all input studies. The method improves decoding performance for 80% of studies, by permitting cognitive information to flow from every study to the others: it notably gives a strong performance boost when decoding studies of small size. The trained low-dimensional representation-task-optimized networks-is interpretable as a set of basis cognitive dimensions relevant to meaningful categories of cognitive stimuli. Our approach opens new ways of extracting information from brain maps, overcoming the low power of typical fMRI studies.
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https://hal.archives-ouvertes.fr/hal-01874713
Contributeur : Arthur Mensch <>
Soumis le : vendredi 14 septembre 2018 - 15:41:54
Dernière modification le : dimanche 23 septembre 2018 - 16:11:57

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  • HAL Id : hal-01874713, version 1
  • ARXIV : 1809.06035

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Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux. Extracting Universal Representations of Cognition across Brain-Imaging Studies. 2018. 〈hal-01874713〉

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