Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterm Birth

Morphometric similarity networks (MSNs) have been recently proposed as a novel, robust, and biologically plausible approach to generate structural connectomes from neuroimaging data. In this work, we apply this method to multi-centre neonatal data (postmenstrual age range: 37–45 weeks) to predict brain dysmaturation in preterm infants. To achieve this goal, we combined different imaging sequences (diffusion and structural MRI) to extract a set of metrics from cortical and subcortical brain regions (e.g. regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging features) which were used to construct a similarity network. A regression model was then trained to predict postmenstrual age at the time of scanning from inter-regional connections. Finally, to quantify brain maturation, the Relative Brain Network Maturation Index (RBNMI) was computed as the difference between predicted and actual age. The model predicted chronological age with a mean absolute error of 0.88 (±0.63) weeks, and it consistently predicted preterm infants to have a lower RBNMI than term infants. We conclude that MSNs derived from multimodal imaging predict chronological brain development accurately, and provide a data-driven approach for defining cerebral dysmaturation associated with preterm birth.