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Computational psychiatry as a bridge from neuroscience to clinical applications

Nature Neuroscience volume 19, pages 404–413 (2016)Cite this article

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Abstract

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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Figure 1: The blessing and curse of dimensionality.
Figure 2: Exploiting and coping with high dimensionality in psychiatric data sets.
Figure 3: Using EEG measures for treatment selection in depression improves treatment response.
Figure 4: Networks of symptoms.
Figure 5: Theory-driven biophysical and RL approaches.
Figure 6: Mechanistic models yield parameters that can be used as features to improve ML performance.

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Acknowledgements

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.

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Author notes

  1. Quentin J M Huys and Tiago V Maia: These authors contributed equally to this work.

Authors and Affiliations

  1. Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zürich and Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland

    Quentin J M Huys

  2. Department of Psychiatry, Centre for Addictive Disorders, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zürich, Zürich, Switzerland

    Quentin J M Huys

  3. School of Medicine and Institute for Molecular Medicine, University of Lisbon, Lisbon, Portugal

    Tiago V Maia

  4. Computation in Brain and Mind, Brown Institute for Brain Science, Psychiatry and Human Behavior, Brown University, Providence, USA

    Michael J Frank

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  1. Quentin J M Huys
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Correspondence to Quentin J M Huys.

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Competing interests

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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