Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.
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Q.J.M.H. was supported by a project grant from the Swiss National Science Foundation (320030L_153449/1) and M.F. by NSF grant 1460604 and NIMH R01 MH080066-01.
M.F. receives consulting fees from Hoffman La Roche Pharmaceuticals for using computational psychiatry approaches.
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Huys, Q., Maia, T. & Frank, M. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci 19, 404–413 (2016). https://doi.org/10.1038/nn.4238
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