Abstract #5664

An Artificial Neural Network Framework for Early Prediction of Cognitive Deficits in Very Preterm Infants

Lili He^1,2, Hailong Li^1,2, Weihong Yuan¹, and Nehal A. Parikh^1,2

¹Pediatric Neuroimaging Research Consortium, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States, ²Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States

Annually, approximately 22,000 very preterm infants (i.e. ≤32 weeks gestational age) in the United States develop cognitive deficits. Infant brains are highly malleable, making it especially important to identify those at highest risk as early as possible to allow effective early interventions. Research supports the notion that cognitive deficits may result from a disturbance/breakdown in the connectome. We propose to develop a robust artificial neural network framework that can analyze integrated structural and functional brain connectome data obtained at term corrected age to predict long-term cognitive outcomes in very preterm infants.

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