# Harnessing biological rhythms for a resilient social motif generator

> **NIH NIH R34** · UNIVERSITY OF FLORIDA · 2024 · $337,875

## Abstract

Summary
How does the brain enable social interactions? The study of social behavior in non-human animals has long
relied on coarse behavioral metrics like time spent interacting with another animal or simply the numbers of
interactions. Although this approach has informed major insights into neural circuits which have a role in
sociability, we still do not know how these circuits orchestrate patterns of social behaviors, especially under
different social contexts where interactions have nuanced differences. Our long-term goal is to identify the
neural mechanisms supporting social behavior in affiliative vs. antagonistic social contexts. To close the
knowledge gap towards this goal, in this R34 we will build artificial intelligence (AI) tools that are capable of
integrating multivariate sources of behavior data to quantify spatiotemporal signatures or “motifs” of diverse
repertoires of social behaviors. Behavioral motifs have the potential to be captured by means of examining
concurrent autonomic rhythms, especially breathing and heart rate. Indeed, we have long known that changes
in the frequency of these rhythms coincide with specific affective and behavioral contexts. However,
spatiotemporal signatures of social behaviors have not been captured in prior studies which have considered
either breathing or heart rate in isolation. Nor have prior studies unleashed the potential to identify novel social
behavioral motifs by using these autonomic rhythms in combination with video measures. The research
objective of this Brain Initiative proposal is to develop semi-supervised artificial intelligence methods that result
in a hierarchical multi-timescale model of social behavioral motifs directly from video, breathing, heart rate, and
movement data via a head-mounted accelerometer. To accomplish this, we will use partial labels of mouse
social behaviors, as well as physiologic measurements, in order to elucidate the full range of social behavior
motifs across affiliative vs. antagonistic contexts. In Aim 1, we will define low-dimensional social behavioral
states while incorporating autonomic rhythms, while in Aim 2, we will elucidate a multi-timescale hierarchical
representation of social behavior in affiliative vs. agonistic social contexts. For both aims, we will integrate
computer vision techniques with high-dimensional video and physiological data from mice while varying their
isolation levels and who they are interacting with. The end-product will be a validated toolkit enabling the
sensitive and robust identification of behavioral motifs. The easy-to-use toolkit which we call the Social Motif
generator (So-Mo) will enable future studies to probe neural circuits during complex mouse behaviors at
unprecedented resolution.

## Key facts

- **NIH application ID:** 10797723
- **Project number:** 1R34DA059718-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Nancy Padilla Coreano
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $337,875
- **Award type:** 1
- **Project period:** 2024-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10797723

## Citation

> US National Institutes of Health, RePORTER application 10797723, Harnessing biological rhythms for a resilient social motif generator (1R34DA059718-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10797723. Licensed CC0.

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