  The study of functional brain connectivity (FC) is important for
understanding the underlying mechanisms of many psychiatric disorders. Many
recent analyses adopt graph convolutional networks, to study non-linear
interactions between functionally-correlated states. However, although patterns
of brain activation are known to be hierarchically organised in both space and
time, many methods have failed to extract powerful spatio-temporal features. To
overcome those challenges, and improve understanding of long-range functional
dynamics, we translate an approach, from the domain of skeleton-based action
recognition, designed to model interactions across space and time. We evaluate
this approach using the Human Connectome Project (HCP) dataset on sex
classification and fluid intelligence prediction. To account for subject
topographic variability of functional organisation, we modelled functional
connectomes using multi-resolution dual-regressed (subject-specific) ICA nodes.
Results show a prediction accuracy of 94.4% for sex classification (an increase
of 6.2% compared to other methods), and an improvement of correlation with
fluid intelligence of 0.325 vs 0.144, relative to a baseline model that encodes
space and time separately. Results suggest that explicit encoding of
spatio-temporal dynamics of brain functional activity may improve the precision
with which behavioural and cognitive phenotypes may be predicted in the future.
