The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. With the rise of advanced imaging methods such as diffusion MRI, the study of brain connectivity has emerged as an important tool to understand subtle alterations associated with neurodevelopmental conditions. Brain connectivity derived from diffusion MRI is complex, multi-dimensional and noisy, and hence it can be challenging to interpret on an individual basis. Machine learning methods have proven to be a powerful tool to uncover hidden patterns in such data, thus opening an opportunity for early identification of atypical development and potentially more efficient treatment. In this work, we used Deep Neural Networks and Random Forests to predict neurodevelopmental characteristics from neonatal structural connectomes, in a large sample of neonates (N = 524) derived from the developing Human Connectome Project. We achieved a highly accurate prediction of post menstrual age (PMA) at scan on term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83, p<<0.001). We also achieved good accuracy when predicting gestational age at birth on a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<<0.001). From our models of PMA at scan for infants born at term, we computed the brain maturation index (i.e. predicted minus actual age) of individual preterm neonates and found significant correlation of this index with motor outcome at 18 months corrected age. Our results suggest that the neural substrate for later neurological functioning is detectable within a few weeks after birth in the structural connectome.
bioRxiv Subject Collection: Neuroscience