# Accelerating connectomic proofreading for larger brains and multiple individuals

> **NIH NIH RF1** · PRINCETON UNIVERSITY · 2022 · $2,143,862

## Abstract

Technology for automating the segmentation of neurons from electron microscopic (EM) data has improved
dramatically, making it now possible to obtain accurate reconstructions of neural circuits from large EM
volumes. However, even the best automation still must be followed by human proofreading to attain high
accuracy. Hundreds of neuroscientists are already using our ChunkedGraph system for proofreading neural
circuits. The system is fully web- and cloud-ready, facilitating seamless collaboration. The software is
open-source, and at this writing is being operated by three institutions (Princeton, Harvard Medical School,
Allen Institute) to serve the proofreading of four large datasets (fly and mouse) by international communities. A
notable example is the FlyWire community, which at this writing is engaging over 160 scientists from 40 labs to
proofread a whole Drosophila brain.
The ChunkedGraph is on its way to becoming a standard and indispensable tool for connectomics. The data
structure was designed to permit scaling to arbitrarily large datasets in principle, even to the whole mouse
brain connectome project that is currently being considered by the NIH. In practice, there are deficiencies in
the current implementation that impede efficiency of proofreading of datasets on the scale of the fly brain, and
are preventing further scaling to larger volumes and brains. To remove these barriers to scaling, we will make it
possible to upgrade a ChunkedGraph system after proofreading has already started, to take advantage of new
and improved automated reconstructions made possible by advances in AI. We will make it possible to
visualize neurons in 3D with multi-resolution sharded meshes and skeletons that are rapidly updated after
every proofreading edit. We further propose to build a subsequent processing step that rapidly derives
morphological features and skeletons, an important prerequisite for downstream analysis and scientific
discovery.
One of the next frontiers in connectomics is the reconstruction of multiple brains of the same species. For
nervous systems with sufficient stereotypy, comparing reconstructions of different individuals can guide the
detection and correction of errors. We will develop software that speeds up proofreading by automatically
matching a reconstructed neuron to a reference reconstruction, and computing and suggesting candidate
corrections if necessary. We will pilot this software for the Drosophila brain, for which multiple EM datasets are
now appearing. The same software will be extendable to other model organisms with relatively stereotyped
nervous systems (e.g. bee, ant, etc.). In the long term, the approach could further be extended to mammalian
nervous systems once the field has developed sufficiently accurate morphological models of cell types.
Our proofreading software will continue to be open source and freely accessible. Proofreading generates
accurate wiring diagrams of neural circuits, which are helpful for ...

## Key facts

- **NIH application ID:** 10413515
- **Project number:** 1RF1MH129268-01
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Mala Murthy
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,143,862
- **Award type:** 1
- **Project period:** 2022-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10413515

## Citation

> US National Institutes of Health, RePORTER application 10413515, Accelerating connectomic proofreading for larger brains and multiple individuals (1RF1MH129268-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10413515. Licensed CC0.

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