Computational approaches have the potential to transform mental health research and care. Biophysical modeling of brain mechanisms can inform how neural activity produces behavior. Computational phenotyping can dissect behavior into its components, helping identify their neural bases. Finally, data-driven approaches can lead us to novel diagnostic systems and biomarkers, as well as better individual-level predictors that can guide care with precision.

There is early, suggestive evidence of the promise of such approaches. The B-SNIP study [1] used a data-driven approach to stratify a transdiagnostic sample of 3000 individuals with psychosis into three biotypes with differences in cognitive and social processing, sensorimotor reactivity, and psychosis symptoms. Similarly, a clustering analysis of functional connectivity patterns in ~1000 subjects with depression revealed four biotypes that differ by symptomatology and treatment responses [2]. These studies, based on cross-sectional snapshots of primarily neurobiological data, represent important first steps toward harnessing computational approaches.

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