PyReconstruct2: Easier, faster 3D brain wiring mapping tools
Official title: PyReconstruct2: Enhanced Modular, High-throughput, Interoperable, Connectomics Level Annotation Software
Funded at University of Texas at Austin · NIH-11442554 · Data updated 2026-11-12
A new, open-source software update aims to make high-resolution brain wiring maps easier to create by improving how researchers annotate and process huge 3D brain images.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas at Austin NIH-funded |
| Lab location | 1 site (Austin, United States) |
| Project start | September 1, 2026 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $80,000 |
| NIH project number | 1R03EB040282-01 |
| Project ID | NIH-11442554 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not applicable to directly enrolled patients; the work targets researchers studying brain circuits and neuroanatomy.
✕Less likely to help
No direct patient involvement or benefit in this grant; it is a tool-development project for researchers.
What this research studies
This project will upgrade PyReconstruct to handle very large 3D brain datasets and to work smoothly with other AI tools. It will introduce a chunked data architecture, spatial indexing, lazy loading, and multi-scale representations to speed up annotations and segmentations. A server-based system will support collaborative, real-time editing with conflict resolution to reduce bottlenecks in neural circuit reconstruction. The goal is to empower researchers with accessible, high-performance tools for connectomics without requiring deep technical expertise.
Why it matters
Potential benefit: If successful, patients could benefit indirectly through faster advances in understanding brain networks and potential future therapies for neurological conditions.
How similar studies have gone: Open-source, community-driven tools have accelerated connectomics, though integrating large datasets remains technically challenging and this project aims to simplify that workflow.
Where this research is happening
-
University of Texas at Austin
Austin, United States Active
Researchers
- Principal investigator: Harris, Kristen M — University of Texas at Austin
- Study coordinator: Harris, Kristen M
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.