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Abstract

Psychiatry faces numerous challenges: the reconceptualization of symptoms and diagnoses, disease prevention, treatment development and monitoring of its effects, and the provision of individualized, precision medicine. To confront the complexity and heterogeneity intrinsic to brain disorders, psychiatry needs better biological, quantitative, and theoretical grounding. This chapter seeks to identify the sources of complexity and heterogeneity, which include the interplay between genetic and epigenetic factors with the environment and their impact on neural circuits. Computational approaches provide a framework to address complexity and heterogeneity, which cannot be seen as noise to be eliminated from diagnosis and treatment of disorders. Complexity and heterogeneity arise from intrinsic features of brain function, and thus present opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. Challenges to be addressed by a computational framework include: (a) improving the search for risk factors and biomarkers, which can be used toward primary prevention of disease; (b) representing the biological ground truth of psychiatric disorders, which will improve the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine; (c) representing how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.