We used Machine Learning (ML) to infer gestational age (GA) at birth, and hence, as a metric of prematurity extent, assess its effect, in 88 premature infants using T2*-w BOLD resting-state connectivity and activity, and T1-w volume in 90 brain regions. ML was able to infer GA at birth. Analysis of the spatial distribution of effects indicated that volumetric alterations, in common with BOLD activity, are partially localized to subcortical structures, but are associated with widespread alterations of connectivity. Our results suggest a potential role for ML in early prediction of neurodevelopmental outcome based on BOLD and anatomical MRI metrics.
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