# Strategy to map electrical synaptic connectivity in neural networks

> **NIH NIH RF1** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2021 · $1

## Abstract

SUMMARY
Electrical synapses, also known as gap junctions, occur frequently in all nervous systems, including the human
brain. They are composed of connexins, arranged to form intercellular channels between adjacent, coupled
cells. Connexin36 (Cx36) is the predominant connexin in the CNS. In many brain and retinal circuits, gap
junctions provide direct and specific connections between cells. In addition, electrical synapses mediate
network properties such as signal averaging, noise reduction and synchronization. However, because of their
small size, gap junctions are not visible in large-scale serial EM data sets. For these reasons, gap junctions
tend to be under-reported or simply ignored.
The objective of this proposal is to develop a combined approach to image gap junction connectivity in EM
datasets and, in addition, to estimate the size, strength, and plasticity of gap junctions. We will study regions of
the retina that contain gap junctions of dramatically different sizes and shapes, to allow us to correlate
structure and function. Aim 1 will use high-resolution confocal microscopy to determine connexon number at
large and small gap junctions. Analyses will determine the number of connexons per gap junction. These
methods will provide a general-purpose tool to determine the size of gap junctions for use in all brain regions.
Aim 2 will use 3D-EM imaging to allow unambiguous identification of gap junctions in FIB-SEM images, which
will follow with first-ever immunogold quantification of a membrane-bound protein in 3D-EM structures. These
studies will allow high-resolution quantification of gap junctions and proteins in identified neurons. Aim 3 will
use electrophysiological measures to determine coupling conductance and then develop models to calculate
the maximal potential coupling conductance from the morphological data by multiplying the number of
channels/gap junction [Specific Aim 1] times the connectivity (the number of gap junctions between coupled
cells) [Specific Aim 2], times the unitary conductance of Cx36. Using paired recordings, we will obtain direct
physiological measures of the junctional conductance between coupled cells. Then, by comparison with the
potential maximum calculated from the morphological data, we can calculate the open channel probability and
place realistic limits on the operating range. These are the fundamental properties required to understand the
function of gap junctions in neuronal microcircuits.
This program is an exact match for one of the listed areas, “Tools to identify gap junctions and characterize
electrical synapses” in the Funding Opportunity Announcement, RFA-MH-20-135.

## Key facts

- **NIH application ID:** 10285599
- **Project number:** 1RF1MH127343-01
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** SUE A AICHER
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1
- **Award type:** 1
- **Project period:** 2021-07-15 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10285599

## Citation

> US National Institutes of Health, RePORTER application 10285599, Strategy to map electrical synaptic connectivity in neural networks (1RF1MH127343-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10285599. Licensed CC0.

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