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eNeuro. 2016 Aug 2;3(4). pii: ENEURO.0049-16.2016. doi: 10.1523/ENEURO.0049-16.2016. eCollection 2016 Jul-Aug.

Computational Phenotyping in Psychiatry: A Worked Example.

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The Wellcome Trust Centre for Neuroimaging, UCL, London WC1N 3BG, UK; Centre for Cognitive Neuroscience, University of Salzburg, 5020 Salzburg, Austria; Neuroscience Institute, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, A-5020 Salzburg, Austria; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK.
The Wellcome Trust Centre for Neuroimaging, UCL , London WC1N 3BG, UK.


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.


Markov decision process; active inference; computational psychiatry; generative model; model inversion

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