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Machine learning and prediction in fetal, infant, and toddler neuroimaging: a review and primer

      Abstract

      Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models are developed in samples of school-aged children or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates the risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief systematic review and primer on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline recommendations and ethical considerations for neuroimaging researchers interested in predicting health outcomes in early life, which may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning provides a foundation for accelerating the prediction of early life trajectories across the full spectrum of illness and health.
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