Title: Learning Neural Representations of Human Cognition across Many fMRI Studies,
Abstract: 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.