13:30 - 15:00
Mon-A7-Talk II-
Mon-Talk II-
Room: A7
Chair/s:
Franziska Knolle
Computational psychiatry provides a direct approach for the investigation and development of mechanistic explanations for psychiatric illnesses, through the mathematical description of processes underlying behaviour. Alterations in decision-making, a cognitive process relevant for the successful interaction with the environment, have been reported in disorders including psychosis, obsessive-compulsive disorder (OCD), and eating disorders, and have been linked to the development of symptoms. Importantly, we describe a transdiagnostic approach using similar tasks and models to show that specific alterations in mathematical parameters express disease-specific dysfunction and symptom associations. Kelly Diederen (King’s College London) used a novel gamified task in conjunction with computational modelling to demonstrate that decision-confidence and belief-updating can be measured at scale using online assessment, and that these processes are altered in people at increased risk of psychosis. Elisabeth Sterner (LMU/TUM) will show that deficits in punishment learning in early psychosis are linked to increased forgetting and reduced confidence in policy selection using an Active Inference model of the Go/NoGo-Task. Pritha Sen (LMU/TUM) will present first-time imaging data investigating model-based (MB) and model-free (MF) decision making in OCD using hierarchical Bayesian modelling of the Two-Step task. Computational result show differences between patients and controls with links to symptom strength. Margaret Westwater (Oxford/Yale) will report data from both laboratory-based and online assessments of learning under uncertainty, which used computational modelling to
demonstrate that impaired learning in individuals with eating disorders is linked to altered reward sensitivity. Franziska Knolle (TUM/Cambridge) will discuss the effect of dopaminergic treatment on probabilistic reward learning in OCD using Rescorla-Wagner-modelling, showing that exaggerated cingulate reward prediction errors in patients are remediated by dopaminergic modulation. This state-of-the-art symposium demonstrates that computational models of decision making provide mechanistic explanations for dysfunctions underlying three common psychiatric conditions: psychosis, OCD, and eating disorders, and may provide starting points for treatment development.
Neural signature and symptom correlations of model-free and model-based decision making in OCD
Mon-A7-Talk II-03
Presented by: Pritha Sen
Pritha Sen
Technische Universität München
Decision-making in humans is considered to be based on two parallel systems of habitual model-free (MF) learning and goal-directed model-based (MB) learning. Healthy individuals show parallel engagement of both systems, whereas obsessive-compulsive disorder (OCD) patients appear to be biased towards a MF pattern. This tendency may promote obsessive and compulsive decision behaviours relating to clinical symptoms. Computational modelling of decision-making has been integrated into the analysis of neural data to explain dysfunctional underlying mechanisms. The neural signatures of these processes are still unclear in OCD.

Here, we combined computational modelling with fMRI to investigate the underlying mechanisms of potentially altered decision-making patterns in 22 OCD patients compared to 22 controls.
Using hierarchical Bayesian modelling in the two-step Markov decision task, we explored MB and MF decision-making behaviours based on four model parameters: model-weights representing MF vs. MB decisions, learning-rate, choice-randomness and perseverance.

Patients demonstrated higher choice-randomness than controls. While the behavioural results suggested a MF decision-making behaviour in both groups, model-weights indicated that controls chose a more MF approach, and patients chose a mixed approach.

In OCD, anterior cingulate cortex was associated with MB, and nucleus accumbens with MF decisions. Furthermore, we found that elevated activation in the orbito-frontal cortex was linked with lower learning-rate in OCD.

To our knowledge, this is the first fMRI study exploring decision-making in OCD with this task using computational modelling. Our results show great potential for this approach to identify underlying neural mechanisms of OCD, and hence, aid in developing targeted treatments and interventions.
Keywords: OCD, decision-making, model-free, model-based, computational modelling, fMRI, computational psychiatry