We introduce a connectomics metric that integrates information on structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from resting-state functional MRI, at individual subject level. The metric is based on the ability of SC to broadly predict FC using a simple linear predictive model; for each connection in the brain, the metric quantifies the deviation from that model. For the metric to capture underlying physiological properties, we minimise systematic measurement errors and processing biases in both SC and FC, and address several challenges with the joint analysis. This also includes a data-driven normalisation approach. The combined metric may provide new information by indirectly assessing white matter structural properties that cannot be inferred from diffusion MRI alone, and/or complex interregional neural interactions that cannot be inferred from functional MRI alone. To demonstrate the utility of the metric, we used young adult data from the Human Connectome Project to examine all bilateral pairs of ipsilateral connections, i.e. each left-hemisphere connection in the brain was paired with its right-hemisphere homologue. We detected a minority of bilateral pairs where the metric value is significantly different across hemispheres, which we suggest reflects cases of ipsilateral connections that have distinct functional specialisation in each hemisphere. The pairs with significant effects spanned all cortical lobes, and also included several cortico-subcortical connections. Our findings highlight the potential in a joint analysis of structural and functional measures of connectivity, both for clinical applications and to help in the interpretation of results from standard functional connectivity analysis.
bioRxiv Subject Collection: Neuroscience