# Computational and neural signatures of interoceptive learning in anorexia nervosa > **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $40,415 ## Abstract PROJECT SUMMARY Anorexia nervosa (AN) is a highly impairing, chronic, and often fatal disorder, however its etiology remains poorly understood. Aberrant aversive learning, particularly in relation to internal bodily signals (i.e., aversive interoceptive learning), may be a critical feature of eating disorder pathology, as interoceptive domains are linked to greater body image disturbance, distorted hunger/satiety cues, and dysregulated affective processing in AN. Aversive interoceptive learning is driven by discrepancies between anticipated and observed sensory states (i.e., prediction errors), brain-based computations associated with networks consisting of the insula, striatum, prefrontal cortex, and amygdala. Individuals with AN demonstrate difficulties distinguishing between expected and experienced sensations, suggesting their ability to successfully learn from body sensations is compromised, which may maintain disordered eating. Despite this, aversive interoceptive learning is considerably understudied in eating disorders. This is the first study to examine 1) how individuals with AN learn from aversive interoceptive outcomes, 2) whether neuroanatomical regions supporting aversive interoceptive learning display altered functional connectivity in AN, and 3) how behavioral and neural signatures of aversive interoceptive learning are linked. Thirty-two adult women diagnosed with AN and 32 demographically matched healthy controls will complete an associative learning paradigm utilizing aversive breathing restrictions and will undergo resting-state functional magnetic resonance imaging. Interoceptive learning will be operationalized using computational models that track trial-by-trial prediction errors (PE) and stimulus value estimates. Aim 1 will examine model- generated latent behavioral differences in aversive interoceptive learning (e.g., learning rates) between AN participants and healthy controls, as well as associations with clinical eating disorder measures. Aim 2 will assess group differences in insula functional connectivity with regions linked to aversive learning and interoceptive processing (i.e., amygdala, striatum, prefrontal cortex). Aim 3 will explore associations between insular connectivity and learning rates. Uncovering behavioral and neural signatures of aversive interoceptive learning will not only inform etiological models of risk and maintenance in AN, but will also signify an imperative next step in the development of novel treatments that target both cognitive and sensory processes contributing to eating disorder pathology. Moreover, this project will provide invaluable training in computational and neuroimaging methodology, skills critically needed to enhance eating disorder research and treatment development. ## Key facts - **NIH application ID:** 10824044 - **Project number:** 1F31MH133362-01A1 - **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO - **Principal Investigator:** Carina Samantha Brown - **Activity code:** F31 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $40,415 - **Award type:** 1 - **Project period:** 2024-01-01 → 2026-12-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10824044 ## Citation > US National Institutes of Health, RePORTER application 10824044, Computational and neural signatures of interoceptive learning in anorexia nervosa (1F31MH133362-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10824044. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*