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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