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Nat Neurosci. 2016 Mar;19(3):404-13. doi: 10.1038/nn.4238.

Computational psychiatry as a bridge from neuroscience to clinical applications.

Author information

  • 1Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zürich and Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.
  • 2Centre for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zürich, Zürich, Switzerland.
  • 3School of Medicine and Institute for Molecular Medicine, University of Lisbon, Lisbon, Portugal.
  • 4Computation in Brain and Mind, Brown Institute for Brain Science, Psychiatry and Human Behavior, Brown University, Providence, USA.

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.

PMID:
26906507
DOI:
10.1038/nn.4238
[PubMed - indexed for MEDLINE]
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