# The Role of Negative Affect in the Cognitive-Behavioral Model of Avoidant/Restrictive Food Intake Disorder: An Integrated Analysis of Neural Circuitry, Hormones, and Momentary Negative Emotions

> **NIH NIH K23** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $199,453

## Abstract

PROJECT SUMMARY/ABSTRACT
Avoidant/restrictive food intake disorder (ARFID) is identified as a restrictive eating disorder in DSM-5 and is
associated with substantial medical morbidity, psychiatric comorbidity, and high treatment costs. Maintenance
mechanisms for ARFID are unknown. However, the cognitive behavioral model of ARFID suggests that
negative reinforcement, via reductions in negative affect, may be key in understanding these highly persistent
and medically compromising behaviors. This 5-year K23-Patient-Oriented Research Career Development
award application addresses this hypothesis using an innovative, multi-disciplinary approach to examine
differences in neural circuitry and hormone functioning in adults with ARFID compared to healthy controls (HC)
and to explore the relationships between real-time emotions and behaviors with brain responsivity and
endocrine signaling to food stimuli. Specifically, this proposal leverages ongoing data collection from a funded
R01 (MH108595) investigating the neurobiology of ARFID in youth and extends these methods by focusing on
adults and including one week of ecological momentary assessment (EMA). Activation in the amygdala,
hippocampus, anterior insula, anterior cingulate cortex, and medial prefrontal cortex (brain regions in the limbic
and paralimbic circuits governing emotional processing) as well as cortisol and oxytocin levels around a
standardized meal (hormones associated with mood and anxiety) will be compared between adults with ARIFD
and HC. EMA ratings of negative emotions and eating behaviors will be used to test whether negative affect is
reduced following common problematic eating behaviors (i.e., food refusal) in ARFID. As a final aim, this study
will explore if activity in limbic/paralimbic circuitry and aberrant cortisol and oxytocin correlate with negative
affect levels as well as reports of avoidant/restrictive eating during the one-week EMA period. This project
represents the first exploration of ARFID in adults, the first examination of emotional functioning in ARFID, and
a rigorous first test of the cognitive-behavioral model of ARFID. The training plan corresponding to this project
will support Dr. Kendra R. Becker in becoming an independent clinical scientist with a program of research
examining neurobiological underpinnings of affect and reward maintenance mechanisms in feeding/eating
disorders to better understand illness trajectory and inform personalized formulations of pathology. Each aim of
the study corresponds to a specific training goal, which will map onto four main areas of competency for Dr.
Becker: (1) reproducible fMRI methodology, (2) study design and analysis/interpretation of endocrine data, (3),
EMA methodology including advanced longitudinal data analysis integrating neurobiological variables, and (4)
career development. Training goals will be implemented under the guidance of Dr. Jennifer J. Thomas (primary
mentor), Drs. Elizabeth A. Lawson and Laura M. Hol...

## Key facts

- **NIH application ID:** 10774194
- **Project number:** 5K23MH125143-04
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Kendra R Becker
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $199,453
- **Award type:** 5
- **Project period:** 2021-02-01 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10774194

## Citation

> US National Institutes of Health, RePORTER application 10774194, The Role of Negative Affect in the Cognitive-Behavioral Model of Avoidant/Restrictive Food Intake Disorder: An Integrated Analysis of Neural Circuitry, Hormones, and Momentary Negative Emotions (5K23MH125143-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10774194. Licensed CC0.

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