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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
LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
Abstract : We show in this paper how to extract shared brain representations that predict mental processes across many cognitive neuroimaging studies. Focused cognitive-neuroimaging experiments study precise mental processes with carefully-designed cognitive paradigms; however the cost of imaging limits their statistical power. On the other hand, large-scale databasing efforts increase considerably the sample sizes, but cannot ask precise cognitive questions. To address this tension, we develop new methods that turn the heterogeneous cognitive information held in different task-fMRI studies into common-universal-cognitive models. Our approach does not assume any prior knowledge of the commonalities shared by the studies in the corpus; those are inferred during model training. The method uses deep-learning techniques to extract representations - task-optimized networks - that form a set of basis cognitive dimensions relevant to the psychological manipulations. In this sense, it forms a novel kind of functional atlas, optimized to capture mental state across many functional-imaging experiments. As it bridges information on the neural support of mental processes, this representation improves decoding performance for 80% of the 35 widely-different functional imaging studies that we consider. Our approach opens new ways of extracting information from brain maps, increasing statistical power even for focused cognitive neuroimaging studies, in particular for those with few subjects.
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Contributor : Arthur Mensch <>
Submitted on : Thursday, October 18, 2018 - 10:06:17 AM
Last modification on : Friday, April 17, 2020 - 11:16:02 AM
Document(s) archivé(s) le : Saturday, January 19, 2019 - 12:30:48 PM


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  • HAL Id : hal-01874713, version 2
  • ARXIV : 1809.06035


Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux. Extracting Universal Representations of Cognition across Brain-Imaging Studies. 2018. ⟨hal-01874713v2⟩



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