Predicting age and clinical risk from the neonatal connectome

Abstract

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.

Network controllability of structural connectomes in the neonatal brain

“…Previous studies on infants have demonstrated that fundamental network topologies of the white matter are present but continue to mature over this period 6,8,25,26 . Further, the structural connectome contains meaningful individual differences that can be used as a 'fingerprint' to identify an individual 9,27 and predict later behaviors 11,28,29 . Consistent with these previous studies, controllability rapidly changes during the newborn period.…”

The developing brain structural and functional connectome fingerprint

“…Multi-tissue CSD (Jeurissen et al , 2014) using restricted anisotropic diffusion for brain tissue and free diffusion for fluid like features (Pietsch et al , 2019) was used to estimate fibre orientation distribution (FOD) in each brain voxel. Response functions for each tissue type were generated as the average from the response functions in an independent sub-group of 20 healthy term control neonates from the dHCP (Taoudi-Benchekroun et al , 2020). Multi-tissue log-domain intensity normalisation (Raffelt, et al 2017) was applied to FODs, and normalised brain tissue like FODs were used to generate 10 million streamlines with anatomically constrained probabilistic tractography (Smith et al, 2012) with biologically accurate weights (SIFT2) (Smith, et al 2015).…”

“…Pre-processing of diffusion MRI data and structural connectome construction was performed as previously reported (Taoudi-Benchekroun et al, 2020). Briefly, after hybrid SENSE reconstruction (Zhu et al, 2016), diffusion signal was denoised (Cordero-Grande et al, 2019), and susceptibility distortions were corrected (Andersson, Skare and Ashburner, 2003).…”