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Learning Neural Representations of Human Cognition across Many fMRI Studies
Arthur Mensch · Julien Mairal · Danilo Bzdok · Bertrand Thirion · Gael Varoquaux

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #151

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.

Author Information

Arthur Mensch (Inria Parietal)
Julien Mairal (Inria)
Danilo Bzdok (RWTH Aachen University)
Bertrand Thirion (INRIA)
Gael Varoquaux (Parietal Team, INRIA)

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