Mapping infant neurodevelopmental precursors of mental disorders: How synthetic cohorts & computational approaches can be used to enhance prediction of early childhood psychopathology

Abstract

Bridging advances in neurodevelopmental assessment and the established onset of common psychopathologies in early childhood with epidemiological data science and computational methods holds much promise for identifying risk for mental disorders as early as infancy. In particular, we propose the development of a mental health risk algorithm for the early detection of mental disorders with the potential for high public health impact that applies and adapts methods innovated in and successfully applied to early detection of cardiovascular risk. Specifically, we propose methods to advance risk prediction of early developmental psychopathology by creating synthetic cohorts that contain complete behavioral and neural data in the first years of life, as the basis for a robust and generalizable risk algorithm. The application of computational approaches within synthetic cohorts, an approach increasingly applied in psychiatry, may be particularly well suited to advancing risk prediction in early childhood mental health. We propose new research directions using these methods to generate an early childhood mental health risk calculator that could significantly advance early mental health risk detection to direct preventive intervention and/or need for more intensive assessment within a pragmatic framework for maximal clinical utility. The availability of such a tool in early childhood, a period of high neuroplasticity, holds promise to reduce the burden of mental disorder by identifying risk early in the clinical sequence and delivering prevention that targets the neurodevelopmental vulnerability phase.