Neural signature and symptom correlations of model-free and model-based decision making in OCD
Mon-A7-Talk II-03
Presented by: Pritha Sen
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.
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