Study provides insight into how OCD develops, may improve treatment

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To better understand the root of obsessive-compulsive disorder, the researchers used a behavioral model. They demonstrated that when the learning parameters for reinforcement and punishment are excessively imbalanced, the cycle between obsession and compulsion can be intensified. This research has the potential to improve mental health therapies.

Scientists from Nara Institute of Science and Technology (NAIST), Advanced Telecommunications Research Institute International and Tamagawa University have demonstrated that Obsessive-Compulsive Disorder (OCD) can be understood as the result of imbalanced learning between reinforcement and punishment. Based on empirical tests of their theoretical model, they showed that asymmetries in brain calculations that link current outcomes to past actions can lead to disordered behaviors.

Specifically, it can occur when the memory trace signal for past actions decays differently for good and bad outcomes. In this case, “good” means the result was better than expected, and “bad” means it was worse than expected. This work helps to explain how OCD develops.

OCD is a mental illness involving anxiety, characterized by intrusive and repetitive thoughts, called obsessions, associated with certain repeated actions, called compulsions. Patients with OCD often feel unable to change their behavior even when they know the obsessions or compulsions are unreasonable. In severe cases, these can render the person unable to lead a normal life. Compulsive behaviors, such as washing hands excessively or repeatedly checking to see if doors are locked before leaving the house, are attempts to temporarily relieve anxiety caused by obsessions. However, until now, the means by which the cycle of obsessions and compulsions reinforces itself has not been well understood.

Now, a team led by NAIST researchers has used reinforcement learning theory to model the disordered cycle 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 implementing reinforcement learning, it is also important to consider delays, as well as positive/negative prediction errors. In general, the result of a certain choice is available after a certain delay. Therefore, reinforcement and punishment should be attributed to recent choices within a certain time frame. This is called crediting, which is implemented as a memory trace in reinforcement learning theory.

Ideally, memory trace signals for past actions decay at an equal rate for positive and negative prediction errors. However, this cannot be completely 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 (n-) prediction errors is much smaller than that related to positive prediction errors (n+). This means that, from the opposite point of view, the view of past actions is much narrower for negative prediction errors than for positive prediction errors. “Our model, with unbalanced trace decay factors (n+ > n-) successfully represents the vicious circle of obsession and compulsion characteristic of OCD,” say co-first authors Yuki Sakai and Yutaka Sakai .

To test this prediction, the researchers asked 45 OCD patients and 168 healthy control subjects to play a computer game with monetary rewards and penalties. Patients with OCD showed significantly smaller n- compared to n+, as predicted by computational characteristics of OCD. Moreover, this unbalanced setting of trace degradation factors (n+ > n-) was normalized by serotonin activators, which are first-line drugs for the treatment of OCD. “Although we believe we always make rational decisions, our computer model proves 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 severely imbalanced decay factors may not respond to behavioral therapy alone. These results could one day be used to determine which patients are likely to be resistant to behavioral therapy before treatment begins.

(Only the title and image of this report may have been edited by Business Standard staff; the rest of the content is auto-generated from a syndicated feed.)

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