Mapping structure to function and behavior with individual-level connectome embedding

Abstract

Connectome embedding (CE) are compact vectorized representations of brain nodes capturing their context in the global network topology. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n=542; Cam-CAN: n=601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that CE substantially improves structural to functional connectivity mapping in individuals. Furthermore, age-related differences in this structure-function mapping are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.