Researchers shed light on OCD cycle for better understanding of mental behaviour

ANI |
Updated: September 01, 2022 14:25 IST

Washington [US], September 1 (ANI): Researchers used a behavioral model to better understand the basis of obsessive-compulsive disorder. They showed that when the learning parameters for reinforcement and punishment are grossly out of balance, the cycle between obsession and compulsion can become stronger. This work may help improve mental health treatments.
Scientists from the Nara Institute of Science and Technology (NAIST), the International Research Institute for Advanced Telecommunications, and Tamagawa University have shown that obsessive-compulsive disorder (OCD) can be understood as a result of imbalanced learning between reinforcement and punishment. Building on empirical tests of their theoretical model, they showed that asymmetries in brain computations linking current outcomes to past actions can lead to disordered behavior. Specifically, this can happen when the memory tracking signal for past actions decays differently for good and bad outcomes. In this case, “good” means that the result was better than expected and “bad” means that it was worse than expected. This work helps explain how OCD develops.
OCD is a mental illness involving anxiety, characterized by repetitive, intrusive thoughts, called obsessions, along with certain repeated actions, known as compulsions. OCD sufferers often feel unable to change behavior even when they know the obsessions or compulsions are unreasonable. In severe cases, these can make the person unable to lead a normal life. Compulsive behaviors, such as excessive handwashing or repeatedly checking to see if doors are locked before leaving the house, are attempts to temporarily relieve the anxiety caused by the obsessions. However, until now, it was not well understood how the cycle of obsessions and compulsions was strengthened.

Now, a team led by NAIST researchers has used reinforcement learning theory to model the disordered cycling associated with OCD. In this framework, a better-than-expected outcome becomes more likely (positive prediction error), while a worse-than-expected outcome is suppressed (negative prediction error). In the implementation of reinforcement learning, it is also important to take into account delays as well as positive/negative prediction errors. In general, the result of a certain election is available after a certain delay. Therefore, reinforcement and punishment must be assigned to recent choices within a given time frame. This is called credit assignment, which is implemented as a memory trace in reinforcement learning theory. Ideally, memory trace signals for past actions decay at the same rate for both positive and negative prediction errors. However, this cannot be fully realized in discrete neural systems. Using simulations, NAIST scientists found that agents implicitly learn obsessive-compulsive behavior when the decay factor of memory traces of past actions related to negative prediction errors (n-) is much smaller than that related to negative prediction errors. positive prediction (n+). This means that, from the opposite perspective, the view of past actions is much narrower for negative forecast errors than for positive forecast errors. “Our model, with unbalanced trace decomposition factors (n+ > n-), successfully represents the vicious cycle of obsession and compulsion characteristic of OCD,” say co-authors Yuki Sakai and Yutaka Sakai.
To test this prediction, the researchers had 45 OCD patients and 168 healthy control subjects play a computer-based game with monetary rewards and penalties. OCD patients showed a much smaller n- compared to n+, as predicted by computational features of OCD. Furthermore, this unbalanced configuration of trace breakdown factors (n+ > n-) was normalized by serotonin enhancers, which are first-line medications for the treatment of OCD. “Although we think we always make rational decisions, our computational model shows that we sometimes implicitly reinforce maladaptive behaviors,” says corresponding author Saori C. Tanaka.
Although it is currently difficult to identify treatment-resistant patients based on their clinical symptoms, this computational model suggests that patients with highly imbalanced trace breakdown factors may not respond to behavioral therapy alone. These findings may one day be used to determine which patients are likely to be resistant to behavioral therapy before treatment begins. (AND ME)