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Updated: Oct 8, 2025

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

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Published on: May 23, 2017

FlyWire: online community for whole-brain connectomics.

Sven Dorkenwald^1,2, Claire E McKellar¹, Thomas Macrina^1,2

¹Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.

|December 24, 2021

Summary

FlyWire is an online platform that uses collaborative editing to speed up the proofreading of large neural circuits for whole-brain connectomics. This community-driven approach accelerates the analysis of brain connectivity in species like Drosophila melanogaster.

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Published on: July 21, 2021

Area of Science:

Neuroscience
Computational Biology
Bioinformatics

Background:

Automated image acquisition and analysis are advancing rapidly, making whole-brain connectomes with over 100,000 neurons feasible.
Proofreading these massive datasets is a significant bottleneck, requiring extensive human effort.

Purpose of the Study:

To introduce FlyWire, an online community designed for efficient, scalable proofreading of neural circuits.
To detail the computational and social structures enabling large-scale connectomics.

Main Methods:

Browser-based 3D interactive segmentation using collaborative editing of a supervoxel graph.
Programmatic access to edit history for accuracy assessment and incentive systems.
Distributed proofreading model engaging a global community.

Main Results:

Demonstrated scalability of the FlyWire platform for whole-brain connectomics.
Successfully reconstructed and analyzed the connectome of mechanosensory neurons in Drosophila melanogaster.
Established a framework for community-driven scientific discovery through open information sharing.

Conclusions:

FlyWire significantly accelerates the proofreading process for large neural datasets.
The platform's open community model enhances both the speed of data processing and the pace of scientific discovery.
FlyWire provides a viable solution for analyzing complex neural circuits at the whole-brain scale.