# Lateral Hypothalamic Circuits for Feeding and Reinforcement

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $430,038

## Abstract

Abstract:
Motivated behaviors such as feeding and reward-seeking are critical for an organism's survival. These
processes require distributed neuronal networks and supporting cell types in multiple brain regions to be tightly
regulated and tuned in order to orchestrate behavioral output. The lateral hypothalamic area (LHA) has long
been identified as a critical neuroanatomical substrate for motivated behavior. Despite decades of research,
the molecular identity of defined LHA cell types remains poorly understood. Additionally, while LHA neurons
have been previously shown to encode appetitive and consummatory behaviors via distinct cellular ensembles,
it remains unknown how distinct LHA output neurons contribute to these processes. Here we propose to
continue to study the neural circuits of the LHA, and to identify how distinct cell types contribute to feeding and
reinforcement. Using an interdisciplinary approach to leverage cutting edge tools such as single cell
transcriptional profiling, two photon calcium imaging, and viral we aim to undercover key circuit elements, and
novel circuit specific gene expression patterns that can be leveraged for future therapeutic interventions for
addiction and other neuropsychiatric disorders.

## Key facts

- **NIH application ID:** 10798310
- **Project number:** 5R01DA038168-10
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Garret D Stuber
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $430,038
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10798310

## Citation

> US National Institutes of Health, RePORTER application 10798310, Lateral Hypothalamic Circuits for Feeding and Reinforcement (5R01DA038168-10). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10798310. Licensed CC0.

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