Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models

Generating models from functional connectivity data that predict behavioral measures holds great clinical potential. While the majority of the literature has focused on using only connectivity data from a single source, there is ample evidence that different cognitive conditions amplify individual differences in functional connectivity in a distinct, complementary manner. In this work, we introduce a computational model, labeled multidimensional Connectome-based Predictive Modeling (mCPM), that combines connectivity matrices collected from different task conditions in order to improve behavioral prediction by using complementary information found in different cognitive tasks. We apply our algorithm to data from the Human Connectome Project and UCLA Consortium for Neuropsychiatric Phenomics (CNP) LA5c Study. Using data from multiple tasks, mCPM generated models that better predicted IQ than models generated from any single task. Our results suggest that prediction of behavior can be improved by including multiple task conditions in computational models, that different tasks provide complementary information for prediction, and that mCPM provides a principled method for modeling such data.