Predictive Subnetwork Extraction with Structural Priors for Infant Connectomes

We present a new method to identify anatomical subnetworks of the human white matter connectome that are predictive of neurodevelopmental outcomes. We employ our method on a dataset of 168 preterm infant connectomes, generated from diffusion tensor images (DTI) taken shortly after birth, to discover subnetworks that predict scores of cognitive and motor development at 18 months. Predictive subnetworks are extracted via sparse linear regression with weights on each connectome edge. By enforcing novel backbone network and connectivity based priors, along with a non-negativity constraint, the learned subnetworks are simultaneously anatomically plausible, well connected, positively weighted and reasonably sparse. Compared to other state-of-theart subnetwork extraction methods, we found that our approach extracts subnetworks that are more integrated, have fewer noisy edges and that are also better predictive of neurodevelopmental outcomes.