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.
Bidirectional dopaminergic intervention reduces exaggerated cingulate prediction error signal in OCD
Mon-A7-Talk II-05
Presented by: Franziska Knolle
Franziska Knolle
Diagnostische und Interventionelle Neuroradiologie, Technische Universität München
Patients with obsessive-compulsive disorder (OCD) show exaggerated error responses and prediction error learning signals, with data converging on the anterior cingulate cortex as a key locus of dysfunction. Considerable evidence has linked prediction error processing to dopaminergic function. We therefore investigated potential dopaminergic dysfunction during reward processing in OCD.

During a fMRI-task, OCD patients (n=18) and controls (n=18) learned probabilistic associations between abstract stimuli and monetary rewards. On separate visits, participants were administered a dopamine receptor agonist, pramipexole 0.5mg; a dopamine receptor antagonist, amisulpride 400mg; and a placebo, while completing the task. We fitted a Q-learning computational model to fMRI prediction error responses; with regions of interest in the anterior cingulate and nucleus accumbens.

There were no effects in the number of correct choices; but computational modelling suggested marginal difference in learning rates between groups. The imaging results revealed that OCD patients showed abnormally strong cingulate signalling of prediction errors during omission of an expected reward, with unexpected reduction by both pramipexole and amisulpride. Furthermore, exaggerated cingulate prediction error signalling to omitted reward in placebo was related to trait subjective difficulty in self-regulating behaviour in OCD.

Our data support cingulate dysfunction during reward processing in OCD, and bidirectional remediation by dopaminergic modulation, suggesting that exaggerated cingulate error signals in OCD may be of dopaminergic origin. The results help to illuminate the mechanisms through which dopamine receptor antagonists achieve therapeutic benefit in OCD. Further research is needed to disentangle the different functions of dopamine receptor agonists and antagonists during cingulate activation.
Keywords: Anterior cingulate, Computational model, Nucleus accumbens, Obsessive-compulsive disorder, Prediction error, Reward learning