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FlyWire: online community for whole-brain connectomics

Authors :
Sven Dorkenwald
Claire E. McKellar
Thomas Macrina
Nico Kemnitz
Kisuk Lee
Ran Lu
Jingpeng Wu
Sergiy Popovych
Eric Mitchell
Barak Nehoran
Zhen Jia
J. Alexander Bae
Shang Mu
Dodam Ih
Manuel Castro
Oluwaseun Ogedengbe
Akhilesh Halageri
Kai Kuehner
Amy R. Sterling
Zoe Ashwood
Jonathan Zung
Derrick Brittain
Forrest Collman
Casey Schneider-Mizell
Chris Jordan
William Silversmith
Christa Baker
David Deutsch
Lucas Encarnacion-Rivera
Sandeep Kumar
Austin Burke
Doug Bland
Jay Gager
James Hebditch
Selden Koolman
Merlin Moore
Sarah Morejohn
Ben Silverman
Kyle Willie
Ryan Willie
Szi-chieh Yu
Mala Murthy
H. Sebastian Seung
Source :
Nature Methods. 19:119-128
Publication Year :
2021
Publisher :
Springer Science and Business Media LLC, 2021.

Abstract

Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.

Details

ISSN :
15487105 and 15487091
Volume :
19
Database :
OpenAIRE
Journal :
Nature Methods
Accession number :
edsair.doi.dedup.....c43698ac1ef46f615475049b71530803
Full Text :
https://doi.org/10.1038/s41592-021-01330-0