The high risk of neurodevelopmental impairments is a major concern for parents and clinicians caring for premature babies. Annually, approximately 22,000 very preterm infants (i.e. ?32 weeks gestational age) in the United States develop cognitive deficits. Efforts to target interventions to prevent neurodevelopmental sequelae are hampered by our current inability to diagnose disabilities before the age of 3 to 5 years. Research supports the notion that cognitive deficits may result from a perturbation of neural connection and communication. Recent brain connectome studies in adults and older children show that abnormal network properties are useful as prognostic biomarkers. Many of these studies have exploited machine learning models based on brain connectome data for the prediction of a variety of neurological conditions, however this progress has not been fully extended to the preterm population. Our preliminary studies suggest that early and accurate prediction of cognitive deficits at an individual level is possible using machine learning models based on brain connectome features at term corrected age (CA). We have correctly classified 91.3% of very preterm infants at high risk of cognitive deficits with 90% specificity and 92.3% sensitivity. Our overall objective is to develop a robust machine learning model that can analyze integrated structural and functional brain connectome data obtained at term CA to make a prediction of later cognitive deficits in very preterm infants. Our central hypothesis is that machine learning techniques analyzing integrated structural and functional brain connectome features at birth can predict cognitive deficits at 2 years CA at an individual level in very preterm infants with accuracy of greater than 90%, exceeding the performance of current classical multivariate analyses. The two specific aims to test the central hypothesis are: 1) Develop and implement a machine learning model to extract high-level brain connectome features and 2) Develop and validate a machine learning framework to predict cognitive deficits. On completion of the first aim, we will explicate the brain connectome, and extract high-dimensional connectome features that best represent the brain connectome. In the second aim, the machine learning model we proposed will be applied in predicting both cognitive deficit (i.e. 2-class classification) and cognitive scores on a continuous scale (i.e., regression) at 2 years CA. To quantify the model's discrimination, we will also validate its performance in data that are not used for the model development, and compare with the current conventional multivariate approach. The proposed research is significant because it will increase scientific knowledge about the developing brain connectome in very preterm infants and facilitate earlier identification of babies at high risk of neurodevelopmental deficits, allowing timely clinical interventions for optimal cognitive outcome.
Babies born before 32 weeks' gestational age are at a very high risk of developing cognitive deficits, which may impact their brain function in many ways, including life-long learning difficulties. Fortunately, infant brains are highly malleable, so it is important to identify those at highest risk as early as possible to allow effective early interventions. We propose to apply a reliable, accurate and sensitive artificial intelligence approach based on state-of-the-art brain MRI to allow identification of babies at highest risk of learning difficulties.