dc.contributor.author | Browning, Michael | |
dc.date.accessioned | 2023-02-02T16:25:35Z | |
dc.date.available | 2023-02-02T16:25:35Z | |
dc.date.issued | 2022-09 | |
dc.identifier.citation | Michael Browning Martin Paulus Quentin J.M. Huys. What is Computational Psychiatry Good For? Biological Psychiatry September 2022 | en |
dc.identifier.uri | https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/1173 | |
dc.description | Available with an NHS OpenAthens log in for eligible users | en |
dc.description.abstract | It is rare in the field of biological psychiatry for hypotheses to be definitively refuted. Rather, topics
of investigation drift into and out of fashion, often driven by the initial excitement of technological
innovation, followed by the necessary corrective of nuanced or underwhelming clinical results. A
well-known example of this is the association between depression and abnormal function of the HPA
axis, as measured using the dexamethasone suppression test (DSST; 1). This observation led to a
great deal of work investigating whether the association might help us identify useful subtypes of
depression (2) or predict treatment response (3). As it turned out, the specificity and predictive
value of the DSST was not thought to be of a level that would be useful clinically and the topic has
gradually moved out of the spotlight. We are left in the familiar position of knowing that nonsuppression of the DSST is associated with depression, but not being sure how we can use this
knowledge to help patients. | en |
dc.description.uri | https://doi.org/10.1016/j.biopsych.2022.08.030 | en |
dc.language.iso | en | en |
dc.subject | Computational Psychiatry | en |
dc.title | What is Computational Psychiatry Good For? | en |
dc.type | Article | en |