RT Journal Article
SR Electronic
T1 Computational Phenotyping in Psychiatry: A Worked Example
JF eneuro
JO eneuro
FD Society for Neuroscience
SP ENEURO.0049-16.2016
DO 10.1523/ENEURO.0049-16.2016
VO 3
IS 4
A1 Philipp Schwartenbeck
A1 Karl Friston
YR 2016
UL http://www.eneuro.org/content/3/4/ENEURO.0049-16.2016.abstract
AB 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.