# Accelerating discovery of the human foveal microconnectome with deep learning

> **NIH NIH RF1** · UNIVERSITY OF WASHINGTON · 2022 · $1,099,863

## Abstract

Project Summary
The human retina is one of the most complex microcircuits of the central nervous system (CNS) and is a model
of CNS neurodegenerative disease with unique advantages for microconnectomics technology advancement.
The central retina or fovea mediates high acuity vision, drives activity in half of the brain, and is a critical locus
for prevalent blinding disease. The fovea is small (<1 mm), accessible, and relevant to CNS disease diagnosis
through advanced cellular-level clinical imaging. The full foveal microconnectome comprises both the diverse
neural circuits that create parallel visual pathways as well as complex microconnectivity with two specialized
cell types of neuroectodermal origin, the retinal pigment epithelium (RPE) and the Müller glia. Our group has
pioneered ultra-short recovery times of eyes from organ donors, to create exquisitely preserved retinal tissue
volumes suitable for the first microconnectomic analysis of an intensively investigated human CNS structure.
The goal of this proposal is to accelerate the human foveal microconnectome by refining and augmenting a
highly successful and professionally supported software platform, Dragonfly by Object Research Systems
(ORS), an industry leader in implementation of deep learning methods for auto-segmentation of complex
structure. Our collaboration with ORS will target development of deep learning (DL) models as well as
annotation and proofreading tools that will have broad applicability to neuroscience microconnectomics. In
preliminary studies we discovered that RPE cells give rise to extremely dense neural-like projections to
photoreceptor cells and that foveal Müller glia similarly have a specialized and complex relationship to foveal
microcircuits. Moreover, single foveal cone photoreceptors were presynaptic to dozens of parallel visual
circuits of extreme complexity. To advance understanding of these complex microconnectomes ORS will
augment fast auto-segmentation using newly developed convolutional neural networks and refine sophisticated
tools for rapid annotation, proofreading, data visualization, and quantitative analysis. In Aims 1 and 2 we will
develop complete deep learning models of the human RPE cell-neuronal microconnectome and the Müller cell-
neuronal microconnectome respectively that will transform our understanding of the critical roles these cell
types play in foveal function and disease. In Aim 3 we will develop a deep learning model of the multiple neural
cell types and microconnectome of parallel visual pathways for form, color, and motion vision. The major
outcome will be the transformation of a powerful, widely used, professionally supported, DL-based platform for
broad application to neuroscience microconnectomics, free for academic research via a no-cost license. The
ORS-Dragonfly platform will accelerate microconnectomics of complex CNS circuitry and impact systems
neuroscience, human neuro-pathophysiology, and interpretation of cellular-level ...

## Key facts

- **NIH application ID:** 10411154
- **Project number:** 1RF1MH129260-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** DENNIS MICHAEL DACEY
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,099,863
- **Award type:** 1
- **Project period:** 2022-08-15 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10411154

## Citation

> US National Institutes of Health, RePORTER application 10411154, Accelerating discovery of the human foveal microconnectome with deep learning (1RF1MH129260-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10411154. Licensed CC0.

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