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Abstract #0660

Machine Learning Evaluation of the Effects of Prematurity on Regional BOLD Resting-State Activity and Connectivity, and T1-w Brain Volumes.

Antonio Maria Chiarelli1, Carlo Sestieri1, Daniele Mascali1, Richard Geoffrey Wise1, and Massimo Caulo1
1Department of Neuroscience, Imaging and Clinical Sciences, University G. D'Annunzio of Chieti Pescara, Chieti Scalo, Italy

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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