Nature MethodsNeurosciencePeer-reviewed journal article
FlyWire: Online community for whole-brain connectomics
Sven DorkenwaldiD, Claire E. McKellariD, Thomas MacrinaiD, Nico KemnitziD, Kisuk LeeiD, Ran LuiD, Jingpeng WuiD, Sergiy PopovychiD, Eric MitchelliD, Barak NehoraniD, Zhen JiaiD, J. Alexander BaeiD, Shang MuiD, Dodam IhiD, Manuel CastroiD, Oluwaseun Ogedengbe, Akhilesh HalageriiD, Kai Kuehner, Amy Robinson SterlingiD, Zoe C. AshwoodiD, Jonathan ZungiD, Derrick BrittainiD, Forrest CollmaniD, Casey M Schneider-MizelliD, Chris A. JordaniD, , H. Sebastian SeungiD
- Citations
- 289
- FWCI
- 20.00
- Field percentile
- top 1%
Abstract
Due to advances in automated image acquisition and analysis, new whole-brain connectomes beyond C. elegans are finally 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 fly brain, and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based 3D 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 analysing the connectome of mechanosensory neurons.
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