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

TL;DR

This paper introduces a novel approach inspired by skeleton-based action recognition to model long-range spatio-temporal dynamics in brain functional connectivity, significantly enhancing phenotype prediction accuracy.

Contribution

It adapts a spatio-temporal modeling technique from action recognition to functional connectivity analysis, improving phenotype prediction over existing methods.

Findings

94.4% accuracy in sex classification

Increased correlation with fluid intelligence (0.325 vs 0.144)

Better modeling of brain dynamics improves behavioral predictions

Abstract

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.