Prediction of Brain Network Age and Factors of Delayed Maturation in Very Preterm Infants

Babies born very preterm (<32 weeks postmenstral age), are at a high risk of having delayed or altered neurodevelopment. Diffusion MRI (dMRI) is a non-invasive neuroimaging modality that allows for early analysis of an infant’s brain connectivity network (i.e., structural connectome) during the critical period of development shortly after birth. In this paper we present a method to accurately assess delayed brain maturation and then use our method to study how certain anatomical and diagnostic brain injury factors are related to this delay. We first train a model to predict the age of an infant from its structural brain network. We then define the relative brain network maturation index (RBNMI) as the predicted age minus the true age of that infant. To ensure the predicted age is as accurate as possible, we examine a variety of models to predict age and use one that performs best when trained on a normative subset (77 scans) of our preterm infant cohort dataset of 168 dMRI scans. We found that a random forest regressor could predict preterm infants’ ages to within an average of \({\sim }1.6\) weeks. We validate our approach by analysing the correlation between RBNMI and a set of demographic, diagnostic and brain connectivity related variables.