# A novel approach to analyzing functional connectomics and combinatorial control in a tractable small-brain closed-loop system

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $802,851

## Abstract

SUMMARY
Adaptive behaviors emerge from neuronal networks by dynamically regulating functional connectomes. Based
on an underlying anatomical connectome, a functional connectome is the configuration of effective synaptic
connections that underlies a pattern of neuronal activity during a specific behavior. Unique combinations of
neurons activate specific functional connectomes, thereby generating a behavior (a combinatoric code). By
combining neural network and biomechanical modeling, intracellular recording, and newly developed large-scale
recording techniques, we will analyze functional connectomes and their combinatoric control of behavior, and
how local plasticity and global dynamics mediate feeding behavior, which is controlled by a small brain system.
The research will be performed by a multidisciplinary team consisting of Drs. J. Byrne (U. Texas, Houston), C.
Chestek (U. Michigan, Ann Arbor), H. Chiel (CWRU), E. Cropper (Mt. Sinai), A. Susswein (Bar Ilan U.), P.
Thomas (CWRU) and K. Weiss (Mt. Sinai). The project will: 1) develop a predictive neuromechanical model that
incorporates a biomechanical model of the feeding musculature with a computational model of the feeding neural
circuitry; 2) use large-scale and intracellular recording techniques to analyze the functional connectome and
combinatoric control for choices among different feeding behaviors in response to sensory stimuli; and 3) use
these recording techniques to analyze the ways in which the functional connectome and its combinatoric control
are reconfigured by modulatory factors, motivation, and learning. We also will examine the ways in which arousal
and satiation change the bias of the functional connectome and thus alter behavior, and the ways in which learning
may add or remove elements of the functional connectome as an animal modifies behavior to respond to changes
in the environment. The results will provide insights into how processes at multiple levels of neural organization
contribute to regulation of behavior. Such studies in a small brain model system will provide insights that will help
guide future investigations in more complex systems, such as vertebrates and humans.

## Key facts

- **NIH application ID:** 10895433
- **Project number:** 5R01NS118606-03
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** John H Byrne
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $802,851
- **Award type:** 5
- **Project period:** 2020-09-30 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10895433

## Citation

> US National Institutes of Health, RePORTER application 10895433, A novel approach to analyzing functional connectomics and combinatorial control in a tractable small-brain closed-loop system (5R01NS118606-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10895433. Licensed CC0.

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