Header

UZH-Logo

Maintenance Infos

Computational psychiatry as a bridge from neuroscience to clinical applications


Huys, Quentin J M; Maia, Tiago V; Frank, Michael J (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3):404-413.

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.

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.

Statistics

Citations

60 citations in Web of Science®
59 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

0 downloads since deposited on 29 Apr 2016
0 downloads since 12 months

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Language:English
Date:February 2016
Deposited On:29 Apr 2016 16:35
Last Modified:08 Dec 2017 19:25
Publisher:Nature Publishing Group
ISSN:1097-6256
Publisher DOI:https://doi.org/10.1038/nn.4238
Official URL:http://www.nature.com/neuro/journal/v19/n3/full/nn.4238.html
PubMed ID:26906507

Download