Computational psychiatry as a bridge from neuroscience to clinical applications

By Quentin J. M. Huys, Tiago V. Maia, Michael J Frank. Nature Neuroscience, 2016

873 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/42abde7800c25b90ec1bb9c88dfbe2ab37e5ae16

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Citations in the corpus

Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package (2016, positive): “As has been summarized in recent review articles (Ahn & Busemeyer, 2016; Friston, Stephan, Montague, & Dolan, 2014; Huys, Maia, & Frank, 2016; Montague, Dolan, Friston, & Dayan, 2012; Stephan, Bach, et al., 2016; Stephan, Binder, et al., 2016; Stephan, Iglesias, Heinzle, & Diaconescu, 2015; Wang &…”
Supervised machine learning: A brief primer (2020, methodology): “Alternatively, if correlations among predictors are due to substantive, conceptual overlap, unsupervised machine learning methods first may be used to operationalize a smaller set of underlying dimensions, components, or classes (Huys et al., 2016).”
Machine Learning for Precision Psychiatry: Opportunities and Challenges. (2017, methodology): “Learning algorithms can be directly applied in single patients to predict inherently valid and immediately useful clinical objects (5), such as choosing drug dosage.”