# Developing deep learning algorithms for studying infant brain and behavior relationships

> **NIH NIH DP2** · STANFORD UNIVERSITY · 2021 · $86,547

## Abstract

Project Summary
Infants communicate to their caregivers that they need food by crying. This represents our very first social
interaction that lays the foundation for a healthy life by acquiring nutrition for growth and establishing a strong
social bond with caregivers. Infants that cannot regulate their nutrition are at risk for malnourishment or obesity,
whose deleterious effects will negatively impact the wellness of these individuals for their lifetime. Abnormalities
in social recognition and communication, like those found in autism spectrum disorders, also become apparent
during infancy. Despite the critical importance of infants communicating nutritional need to caregivers, the
neuronal basis remains unknown. To address this deficit, I propose to study social tadpoles that beg their parents
for food by dancing. Tadpoles use this begging display to encode nutritional state, enabling us to quantify hunger-
based communication. These tadpoles are translucent, allowing us to visualize the development and activity of
neurons in the brain. I am combining this novel model system and behavioral paradigm with advanced
neurogenetic tools to interrogate the neuronal substrates of hunger-based communication. I will examine
whether nutritional quality influences the development of neurons that regulating feeding and communication
with in vivo brain imaging. I will also test for a functional role of these neuronal cell-types in begging behavior
using a high throughput behavior assay, whole brain clearing and immunohistochemistry, and cell-specific
manipulations of neuronal activity. As social recognition is important for establishing parent-offspring bonds, I
will then use in vivo neural activity imaging to determine how tadpoles recognize their parents using multi-modal
sensory integration. Completion of these experiments will transform our understanding of a social behavior
critical for infant survival and life-long wellbeing. There is a pressing need for this research because there are
currently no established models for studying the neural mechanisms of infant communication of hunger. This
work is important to public health because some of the most prevalent disorders afflicting children in the United
States are eating related disorders and conditions involving abnormalities in social recognition and
communication, such as autism spectrum disorders. More research on infant feeding and communication is
needed to better understand these pathologies in the youngest members of our society.

## Key facts

- **NIH application ID:** 10263607
- **Project number:** 3DP2HD102042-01S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Lauren A O'Connell
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $86,547
- **Award type:** 3
- **Project period:** 2021-01-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10263607

## Citation

> US National Institutes of Health, RePORTER application 10263607, Developing deep learning algorithms for studying infant brain and behavior relationships (3DP2HD102042-01S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10263607. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*