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Abstract #0900

Early Prediction of Language Deficits in Very Preterm Infants Using Functional Connectome Data and Machine Learning

Lili He1,2,3, Hailong Li1,3, and Nehal Parikh1,2,3

1Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States, 2Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States, 3Pediatric Neuroimaging Research Consortium, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States

Children who are born prematurely are at an increased risk for impaired neurodevelopmental outcomes, including language deficits. Earlier identification, soon after birth, of infants who are experiencing difficulties with complex language function is urgently needed to take advantage of critical windows of brain development so that targeted delivery of Early Intervention therapies can be undertaken during this optimal period. We propose to develop a robust machine learning framework that can analyze functional brain connectome data obtained at term corrected age to make an individual-level prediction about language outcomes at two years corrected age in very preterm infants.

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