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Poster
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
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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.

Keywords: Brain Imaging
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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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