Computationally Informed Interventions for Targeting Compulsive Behaviors.

Area of Science:

• Neuroscience
• Computational Psychiatry
• Behavioral Science

Background:

• Compulsive behaviors are key features of addiction and obsessive-compulsive disorder (OCD).
• These behaviors can be viewed as a deficit in adaptive decision-making, specifically an imbalance in behavioral control.
• Behavior is often dominated by learned associations rather than inferred outcomes, hindering flexible adaptation.

Purpose of the Study:

• To explore noninvasive neuromodulation techniques for targeting neural circuits involved in inference-based decision-making.
• To discuss the potential of transcranial magnetic stimulation and real-time neurofeedback for treating compulsive behaviors.
• To bridge understanding of neurocomputational mechanisms of inference with clinical applications.

Main Methods:

• Review of existing research on neural circuitry supporting inference.
• Discussion of network-targeted transcranial magnetic stimulation (TMS) as a noninvasive brain stimulation technique.
• Exploration of real-time neurofeedback for modulating brain activity related to inference.

Main Results:

• The orbitofrontal cortex is a key neural region implicated in inference-based decision-making and compulsive behaviors.
• Modulating orbitofrontal cortex activity can alter inference-based behaviors.
• Noninvasive techniques offer potential for targeting these circuits in humans.

Conclusions:

• Network-targeted TMS and real-time neurofeedback are promising noninvasive interventions.
• These methods can modulate the neural underpinnings of inference, potentially improving behavioral control.
• Such approaches may complement existing treatments for addiction and OCD by addressing core decision-making deficits.