  Neuroimaging-based prediction of neurocognitive measures is valuable for
studying how the brain's structure relates to cognitive function. However, the
accuracy of prediction using popular linear regression models is relatively
low. We propose a novel deep regression method, namely TractoSCR, that allows
full supervision for contrastive learning in regression tasks using diffusion
MRI tractography. TractoSCR performs supervised contrastive learning by using
the absolute difference between continuous regression labels (i.e.
neurocognitive scores) to determine positive and negative pairs. We apply
TractoSCR to analyze a large-scale dataset including multi-site harmonized
diffusion MRI and neurocognitive data from 8735 participants in the Adolescent
Brain Cognitive Development (ABCD) Study. We extract white matter
microstructural measures using a fine parcellation of white matter tractography
into fiber clusters. Using these measures, we predict three scores related to
domains of higher-order cognition (general cognitive ability, executive
function, and learning/memory). To identify important fiber clusters for
prediction of these neurocognitive scores, we propose a permutation feature
importance method for high-dimensional data. We find that TractoSCR improves
the accuracy of neurocognitive score prediction compared to other
state-of-the-art methods. We find that the most predictive fiber clusters are
predominantly located within the superficial white matter and projection
tracts, particularly the superficial frontal white matter and striato-frontal
connections. Overall, our results demonstrate the utility of contrastive
representation learning methods for regression, and in particular for improving
neuroimaging-based prediction of higher-order cognitive abilities.
