Interpreting Anxiety Disorders From the Perspective of Interoceptive Computational Models

Area of Science:

• Computational psychiatry
• Neuroscience
• Clinical psychology

Background:

• Interoception, the sensing of internal bodily signals, is crucial for aligning internal states with the environment.
• Predictive processing theory suggests alignment arises from comparing predicted and actual sensory inputs.
• Interoceptive dysfunction, marked by persistent prediction errors, is implicated in anxiety disorders.

Purpose of the Study:

• To bridge the gap between computational models of interoceptive dysregulation and clinical interventions for anxiety.
• To propose an interoceptive computational framework for anxiety disorders.
• To integrate mechanistic insights with therapeutic innovation.

Main Methods:

• Synthesizing evidence from computational psychiatry, neurophysiology, and clinical studies.
• Modeling anxiety as a disorder of interoceptive prediction using predictive coding mechanisms.
• Exploring experimental paradigms and modulation strategies for intervention.

Main Results:

• Anxiety pathophysiology involves hyperprecise threat priors and context rigidity, amplifying interoceptive prediction errors.
• Computational failures lead to exaggerated defensive responses, cognitive biases, and maladaptive behaviors.
• Integrating computational modeling with interventions like interoceptive exposure improves outcomes.

Conclusions:

• A computational framework for interoceptive dysregulation advances a multidimensional approach to anxiety.
• Future research should focus on perturbed-prior experiments and hybrid interventions for personalized treatment.
• This integration offers potential for precision psychiatry, addressing neural computations and embodied experiences in anxiety.