Computational Psychiatry is a rapidly emerging field that uses model-based quantities to infer the behavioural 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 formalises a behavioural or neuronal process. Measured behavioural (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 modelling of choice behaviour 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 behaviour 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.
Significance Statement: We provide an overview over the process of using formal models to understand psychiatric conditions, which is central in the emerging research field of ‘Computational Psychiatry’. This approach promises key insights into both healthy and pathological brain function as well as a more mechanistic understanding of psychiatric nosology, which may have important consequences for therapeutic interventions or predicting response and relapse. In a worked example, we discuss the generic aspects of using a computational model to formalise a task, simulating data and estimating parameters as well as inferring group effects between patients and healthy controls. We also provide routines that can be used for these steps and are freely available in the academic software SPM.
The authors report no conflict of interest.
The Wellcome Trust Centre for Neuroimaging is supported by core funding from the Wellcome Trust 091593/Z/10/Z; Austrian Academy of Sciences (Österreichischen Akademie der Wissenschaften) 501100001822.