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The study of functional brain connectivity (FC) is important forunderstanding the underlying mechanisms of many psychiatric disorders. Manyrecent analyses adopt graph convolutional networks, to study non-linearinteractions between functionally-correlated states. However, although patternsof brain activation are known to be hierarchically organised in both space andtime, many methods have failed to extract powerful spatio-temporal features. Toovercome those challenges, and improve understanding of long-range functionaldynamics, we translate an approach, from the domain of skeleton-based actionrecognition, designed to model interactions across space and time. We evaluatethis approach using the Human Connectome Project (HCP) dataset on sexclassification and fluid intelligence prediction. To account for subjecttopographic variability of functional organisation, we modelled functionalconnectomes using multi-resolution dual-regressed (subject-specific) ICA nodes.Results show a prediction accuracy of 94.4% for sex classification (an increaseof 6.2% compared to other methods), and an improvement of correlation withfluid intelligence of 0.325 vs 0.144, relative to a baseline model that encodesspace and time separately. Results suggest that explicit encoding ofspatio-temporal dynamics of brain functional activity may improve the precisionwith which behavioural and cognitive phenotypes may be predicted in the future.

Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity