An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID
NIMH - National Institute of Mental Health
About This Grant
PROJECT SUMMARY/ABSTRACT The K23 application proposes to examine learning and neurocognitive mechanisms underlying food approach in avoidant/restrictive food intake disorder (ARFID) using computational methods and will position the applicant, Marita Cooper, Ph.D., to transition to research independence with expertise using computational modeling to examine mechanisms of restrictive eating disorders (ED) in youth. ARFID is the most prevalent ED in childhood, with sequelae including malnutrition, delayed growth, cardiac complications, and death. Early data suggest youth with ARFID exhibit executive functioning deficits, including weak central coherence and poor response inhibition. These data underly the hypothesis that youth with ARFID may be slow to learn food approach, impacting food intake, and requiring more exposure to novel foods for learning to occur. Knowledge of neurocognition and learning in ARFID is in its infancy, yet computational modeling offers an innovative approach to probe underlying processes and identify target mechanisms related to aberrant food approach. Two approaches with utility in other EDs, active inference and reinforcement learning, have not been applied to ARFID. The study will examine learning mechanisms and neurocognition of aberrant food approach in ARFID. We will recruit 99 youth (66 with ARFID, 33 controls) ages 8-18, matched on age and sex. Participants will complete a three-armed bandit task, assessing learning mechanisms (via food and neutral stimuli), and a meal-based buffet task assessing food approach (macronutrient and caloric intake). We will assess neurocognition, ED symptoms, and approach/ avoidance. Aim 1 hypothesizes that youth with ARFID will exhibit poorer performance (under both neutral and food conditions) than healthy controls and that worse performance will relate to overall intake during the buffet task. Aim 2 follows participants naturalistically, repeating assessments at 6- and 12-month follow-up. We will examine whether baseline performance predicts improvement in ARFID symptoms at follow-up. Aim 3 will compare whether active inference or reinforcement learning models best fit participant learning behavior. The project will be an important major step in developing a data-driven model of ARFID, providing critical information about potential drivers of aberrant food approach. The proposed project will support expert mentorship and training for Dr. Cooper including 1) learning and neurocognitive development in youth; 2) conducting and managing longitudinal research in clinical samples; and 3) practical skills in computational modeling transferrable to future research. The resources of Children’s Hospital of Philadelphia and University of Pennsylvania and an expert team of mentors (with expertise in mechanisms of ARFID/restrictive ED, development, clinical research, and computational modeling) provide an outstanding context to launch Dr. Cooper’s career. Project findings are consistent with the NIMH strategic goal to identify validated targets for intervention and will inform a competitive R01 application examining computational learning mechanisms in a transdiagnostic sample of youth with restrictive ED.
Grant Summary
An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID is a NIMH - National Institute of Mental Health grant providing up to $170K for university, nonprofit, healthcare org. Applications are due 2031-04-30 (open). Check eligibility and apply with FindGrants.
Focus Areas
Eligibility
How to Apply
Up to $170K
2031-04-30
- 1Confirm your organization is eligible for An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID from NIMH - National Institute of Mental Health, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIMH - National Institute of Mental Health before the deadline.
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An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID: Frequently Asked Questions
Who is eligible for the An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID?
An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID is offered by NIMH - National Institute of Mental Health and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID provide?
An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID provides up to $170K per award from NIMH - National Institute of Mental Health. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID deadline?
Applications for An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID are due 2031-04-30 (open). Because deadlines can change, verify the date with the funder, NIMH - National Institute of Mental Health, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID?
To apply for An Examination of Computational Learning Mechanisms Underlying Aberrant Food Approach in Youth with ARFID, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIMH - National Institute of Mental Health.