Schwartenbeck, P;
Friston, K;
(2016)
Computational Phenotyping in Psychiatry: A Worked Example.
eNeuro
, 3
(4)
10.1523/ENEURO.0049-16.2016.
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Abstract
Computational psychiatry is a rapidly emerging field that uses model-based quantities to infer the behavioral and neuronal abnormalities that underlie psychopathology. If successful, this approach promises key insights into (pathological) brain function as well as a more mechanistic and quantitative approach to psychiatric nosology-structuring therapeutic interventions and predicting response and relapse. The basic procedure in computational psychiatry is to build a computational model that formalizes a behavioral or neuronal process. Measured behavioral (or neuronal) responses are then used to infer the model parameters of a single subject or a group of subjects. Here, we provide an illustrative overview over this process, starting from the modeling of choice behavior in a specific task, simulating data, and then inverting that model to estimate group effects. Finally, we illustrate cross-validation to assess whether between-subject variables (e.g., diagnosis) can be recovered successfully. Our worked example uses a simple two-step maze task and a model of choice behavior based on (active) inference and Markov decision processes. The procedural steps and routines we illustrate are not restricted to a specific field of research or particular computational model but can, in principle, be applied in many domains of computational psychiatry.
| Type: | Article |
|---|---|
| Title: | Computational Phenotyping in Psychiatry: A Worked Example |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1523/ENEURO.0049-16.2016 |
| Publisher version: | http://dx.doi.org/10.1523/ENEURO.0049-16.2016 |
| Language: | English |
| Additional information: | Copyright © 2016 Schwartenbeck and Friston. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. |
| Keywords: | Markov decision process, active inference, computational psychiatry, generative model, model inversion |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1508800 |
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