Low Rank Mixup Augmentations for Contrastive Learning of Phenotypes from Functional Connectivity

Functional magnetic resonance imaging (fMRI) and fMRI-derived metrics such as functional connectivity (FC) allow for unmatched analysis of human cognition in vivo. At the same time, contrastive learning (CL) has shown state of the art results in the computer vision domain as well as in integration of images with genomics. However, many frameworks that utilize CL depend on image augmentations, a technique that is not present in FC. In this work, we present a robust mixup-style augmentation for FC achieved by recognizing that the rank-1 approximation of patient FC derived via eigendecomposition is not effective for predicting phenotype. A mixup of this first component of FC allows for increasing the limited number of subjects found in most fMRI studies. CL using these augmentations yields a 2-10% accuracy improvement on seven phenotype prediction tasks across two datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP).