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FlyWire: online community for whole-brain connectomics

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.

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Fig. 1: Assessing segmentation quality using known neurons.
Fig. 2: Proofreading the supervoxel graph.
Fig. 3: The ChunkedGraph approach for proofreading supervoxel graphs.
Fig. 4: Attaching automatically detected synapses to neurons.
Fig. 5: Proofreading in FlyWire.
Fig. 6: Connectivity between mechanosensory neurons extracted with FlyWire.

Data availability

FlyWire’s EM data and unproofread segmentation are publicly available. FlyWire’s proofread segmentation is available to the community first as outlined in FlyWire’s principle. Published proofread neurons are publicly available. FlyWire’s website (flywire.ai) describes how to access these different data sources.

All neuron reconstructions used in this manuscript are available and linked in Supplementary Table 2. Additionally, all data necessary to reproduce the analyses in this manuscript are available through the data analysis GitHub repository at https://github.com/seung-lab/FlyWirePaper. This includes the connectivity map between all neurons included in the mechanosensory analyses.

For the comparison with FlyCircuit neurons we used the dotprops of a public dataset60 (https://zenodo.org/record/5205616). Source data are provided with this paper.

Code availability

All repositories presented in this manuscript are open-sourced and available through the Seung laboratory GitHub project. Specifically, our implementation of the ChunkedGraph is available at https://github.com/seung-lab/PyChunkedGraph. Further, the code to reproduce all figures in this manuscript is also available on GitHub at https://github.com/seung-lab/FlyWirePaper.

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Acknowledgements

We acknowledge support from National Institutes of Health (NIH) BRAIN Initiative RF1 MH117815 to H.S.S. and M.M. M.M. further received funding through an HHMI Faculty Scholar award and an NIH R35 Research Program Award. H.S.S. also received NIH funding through RF1MH123400, U01MH117072, U01MH114824. H.S.S. also acknowledges support from the Mathers Foundation, as well as assistance from Google and Amazon. These companies had no influence on the research. We are grateful for support with FAFB imagery from S. Saalfeld, E. Trautman and D. Bock. We are grateful to D. Bock and Z. Zheng for discussions about FAFB. We thank G. Jefferis, D. Bock, A. Cardona, A. Seeds, S. Hampel and R. Wilson for advice regarding the community. We thank G. Jefferis and P. Schlegel (both with Medical Research Council Laboratory of Molecular Biology and University of Cambridge) for help with the brain renderings, transformations to FlyCircuit and NBLAST comparison with FlyCircuit neurons. We thank G. McGrath for computer system administration and M. Husseini for project administration. We are grateful to J. Maitin-Shepard for Neuroglancer. We are grateful to J. Buhmann and J. Funke for discussions about their synapse resource. We thank N. da Costa, A. Bodor, C. David and the Eyewire team for feedback on the proofreading system. We thank the Allen Institute for Brain Science founder, P. G. Allen, for his vision, encouragement and support. This work was also supported by the Intelligence Advanced Research Projects Activity via Department of Interior/Interior Business Center contract no. D16PC0005 to H.S.S. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Intelligence Advanced Research Projects Activity, Department of Interior/Interior Business Center or the US Government.

Author information

Authors and Affiliations

Authors

Contributions

T.M. and N.K. realigned the dataset with methods developed by E.M., B.N. and T.M. and infrastructure developed by S.P., Z.J. J.A.B., S.M. wrote code for masking defects and misalignments. K.L. trained the convolutional net for boundary detection, using ground-truth data realigned by D.I. J.W. used the convolutional net to generate an affinity map that was segmented by R.L. S.D. and N.K. created the proofreading system with input from J.Z. and Z.A. N.K., M.A.C., O.O., A.H., C.S.J., K.K. and A.R.S. adapted and improved Neuroglancer for proofreading and annotations. S.D., F.C., C.S.M., C.S.J. and D. Brittain built the server infrastructure to host FlyWire and manage users. W.M.S. added the images into cloud storage. C.E.M. managed the community and trained proofreaders. C.E.M., C.J. and A.R.S. designed the training tutorials. C.E.M., C.B., J.G., D.D., L.E.R., S.K., A.B., J.H., M.M., S.M., B.S., K.W., R.W. and D. Bland tested the site and proofread neurons. C.E.M. and J.G. devised neuron annotation procedures. S.C.Y. managed proofreaders and evaluated twigs and synapses. S.D. evaluated the proofreading system. S.D. and C.E.M. analyzed the data. S.D., C.E.M., H.S.S. and M.M. wrote the manuscript. H.S.S. and M.M. led the effort.

Corresponding authors

Correspondence to Mala Murthy or H. Sebastian Seung.

Ethics declarations

Competing interests

T.M. and H.S.S. are owners of Zetta AI LLC, which provides neural circuit reconstruction services for research laboratories. R.L. and N.K. are employees of Zetta AI LLC.

Additional information

Peer review information Nature Methods thanks Ann-Shyn Chiang, Scott Emmons and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Full brain rendering and comparison with the hemibrain.

(a, b) A neuropil rendering of the fly brain (white) is overlaid with a rendering of the hemibrain and proofread reconstructions of neurons from the antennal mechanosensory and motor center (AMMC). The proofread reconstructions of (a) the AMMC-A2 neuron from the right hemisphere and (b) an WV-WV neuron are added. Scale bar: 50 μm.

Extended Data Fig. 2 Quality of EM image alignment.

(a, b) Chunked pearson correlation (CPC) between two neighboring sections in the original alignment (v14) and our realigned data (v14.1). (a) Relative change of CPC between the original and our realigned data per section. (b) Histogram of the CPC improvements from (a) (dashed red line is at 0). (c, d, e) Example images used for the CPC calculation in (a) where (c) the CPC improved through a better alignment around an artifact, (d) the CPC is almost identical and (e) the CPC overall improved due to a stretch of poorly aligned sections in the original data that were resolved in v14.1.

Source data

Extended Data Fig. 3 Chunking the dataset.

(a) Automated segmentation overlayed on the EM data. Each different color represents an individual putative neuron. (b) The underlying supervoxel data is chunked (white dotted lines) such that each supervoxel is fully contained in one chunk. (c) A close up view of the box in (b). (d) Application of the same chunking scheme to the meshes, requiring only minimal mesh recomputations after edits. (e) Diversity of the number of supervoxels in each chunk (median: 25661). (f) The median supervoxel contains 792 voxels. Most very small supervoxels (< 200 voxels) are the result of chunking.

Source data

Extended Data Fig. 4 Proofreading with the ChunkedGraph.

(a,)In the ChunkedGraph connected component information is stored in an octree structure where each abstract node (black nodes in levels >1) represents the connected component in the spatially underlying graph (dashed lines represent chunk boundaries). Nodes on the highest layer represent entire neuronal components. (b) Edits in the ChunkedGraph (here, a merge; indicated by the red arrow and added red edge) affect the supervoxel graph to recompute the neuronal connected components. (c) The same neuron shown in Fig. 2 after proofreading with each merged component shown in a different color. Scale bar (c): 10 μm.

Extended Data Fig. 5 The FlyWire proofreading platform.

(a) The most common view in FlyWire displays four panels: a bar with links and a leaderboard of top proofreaders (left), the EM image in grayscale overlaid with segmentation in color (second panel from left), a 3D view of selected cell segments (third panel), and menus with multiple tools (right). (b) Annotation tools include points, which can be used for a variety of purposes such as marking particular cells or synapses.

Extended Data Fig. 6 Fast proofreading in FlyWire.

Analysis of 60 neurons included in the triple proofreading analysis and fast proofreading analysis. (a) Comparison of the F1-Scores (0-1, higher is better; with respect to proofreading results after three rounds) between different proofreading rounds according to volumetric completeness (medians: Auto: 0.777, 1: 0.992, 2: 0.999, Fast: 0.988 means: Auto: 0.729, 1: 0.975, 2: 0.992, Fast: 0.968) and (b) assigned synapses (medians: Auto: 0.799, 1: 0.992, 2: 0.999, Fast: 0.988, means: Auto: 0.746, 1: 0.958, 2: 0.986, Fast: 0.945). ‘Auto’ refers to reconstructions without proofreading. Boxes are interquartile ranges (IQR), whiskers are set at 1.5 x IQR.

Source data

Extended Data Fig. 7 NBLAST-based analysis of segmentation accuracy.

Comparison of NBLAST matches and scores of 183 neurons before and after proofreading to assess the quality of the automated segmentation. (a) NBLAST scores of all 183 triple-proofread neurons (Fig. 5) against 16129 neurons in FlyCircuit. For each neuron in FlyWire we found the best hit in FlyCircuit according to the mean of the two NBLAST scores. (b) scores for the best matches labeled by manual labels of match vs. no match (N(match)=174 out of 183). (c) mean scores of the FlyWire neurons with matches before and after proofreading (N = 174 neurons). (d) Histogram of the change in NBLAST score before and after proofreading. (e) Rankings of each FlyCircuit neuron matched to a triple-proofread neuron in FlyWire among the 16129 neurons before proofreading and after one round of proofreading. (f) NBLAST scores of the unproofread segments grouped by whether they matched or did not match the broad cell type after proofreading.

Source data

Extended Data Fig. 8 Renderings of AMMC-B1 subtypes.

Neurons grouped by subtype and hemisphere. AMMC, WED brain regions are shown for reference. The neuropil mesh is shown to the same scale. Scale bar: 50 μm.

Extended Data Fig. 9 Connectivity diagrams.

(a) Diagram from Fig. 6b reordered by putative subtype (b) Same diagram as in Fig. 6b with different colormap threshold.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, Supplementary Fig. 1 and Supplementary Note 1

Reporting Summary

Supplementary Video 1

Introductory Video to FlyWire.

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Dorkenwald, S., McKellar, C.E., Macrina, T. et al. FlyWire: online community for whole-brain connectomics. Nat Methods 19, 119–128 (2022). https://doi.org/10.1038/s41592-021-01330-0

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