Computational Phenotyping in Psychiatry: A Worked Example

TLDR

Computational psychiatry is a rapidly emerging field that uses model‑based quantities to infer behavioral and neuronal abnormalities underlying psychopathology. The study demonstrates how computational psychiatry can provide mechanistic insights into brain function, refine psychiatric nosology, and guide interventions by illustrating the full modeling pipeline from choice‑behavior modeling to group‑level inference. The authors build a computational model of choice behavior, infer individual and group parameters from observed responses, simulate data, invert the model to estimate group effects, and use cross‑validation to assess recovery of between‑subject variables, exemplified with a two‑step maze task and a Markov decision process based on active inference.

Abstract

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.