Extracting representations of cognition across neuroimaging studies improves brain decoding

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
2 Thoth - Apprentissage de modèles à partir de données massives

Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.

Domaines: Machine Learning [stat.ML]; Sciences cognitives; Bio-Informatique, Biologie Systémique [q-bio.QM]

Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux. Extracting representations of cognition across neuroimaging studies improves brain decoding. PLoS Computational Biology, 2021, 17 (5), pp.e1008795:1-20. ⟨10.1371/journal.pcbi.1008795⟩. ⟨hal-01874713v3⟩