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      A connectome and analysis of the adult Drosophila central brain

      research-article
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      eLife
      eLife Sciences Publications, Ltd
      connectome, brain regions, cell types, graph properties, connectome reconstuction methods, synapse detecton, D. melanogaster

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain.

          eLife digest

          Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about 100,000 neurons – compared to some 86 billion in humans – the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning.

          Thanks to decades of research, scientists now have a good understanding of which parts of the fruit fly brain support particular behaviors. But exactly how they do this is often unclear. This is because previous studies showing the connections between cells only covered small areas of the brain. This is like trying to understand a novel when all you can see is a few isolated paragraphs.

          To solve this problem, Scheffer, Xu, Januszewski, Lu, Takemura, Hayworth, Huang, Shinomiya et al. prepared the first complete map of the entire central region of the fruit fly brain. The central brain consists of approximately 25,000 neurons and around 20 million connections. To prepare the map – or connectome – the brain was cut into very thin 8nm slices and photographed with an electron microscope. A three-dimensional map of the neurons and connections in the brain was then reconstructed from these images using machine learning algorithms. Finally, Scheffer et al. used the new connectome to obtain further insights into the circuits that support specific fruit fly behaviors.

          The central brain connectome is freely available online for anyone to access. When used in combination with existing methods, the map will make it easier to understand how the fly brain works, and how and why it can fail to work correctly. Many of these findings will likely apply to larger brains, including our own. In the long run, studying the fly connectome may therefore lead to a better understanding of the human brain and its disorders. Performing a similar analysis on the brain of a small mammal, by scaling up the methods here, will be a likely next step along this path.

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          • Record: found
          • Abstract: found
          • Article: not found

          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
            Bookmark
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            Deep Residual Learning for Image Recognition

              Bookmark
              • Record: found
              • Abstract: not found
              • Book Chapter: not found

              U-Net: Convolutional Networks for Biomedical Image Segmentation

                Bookmark

                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                07 September 2020
                2020
                : 9
                : e57443
                Affiliations
                [1 ]Janelia Research Campus, Howard Hughes Medical Institute AshburnUnited States
                [2 ]Google Research Mountain ViewUnited States
                [3 ]Life Sciences Centre, Dalhousie University HalifaxCanada
                [4 ]Google Research, Google LLC ZurichSwitzerland
                [5 ]Institute for Quantitative Biosciences, University of Tokyo TokyoJapan
                [6 ]MRC Laboratory of Molecular Biology CambridgeUnited States
                [7 ]Institute of Zoology, Biocenter Cologne, University of Cologne CologneGermany
                [8 ]Department of Zoology, University of Cambridge CambridgeUnited Kingdom
                Brandeis University United States
                University of California, Berkeley United States
                Brandeis University United States
                Brandeis University United States
                Howard Hughes Medical Institute, University of Oregon United States
                Author notes
                [‡]

                Max Delbrueck Centre for Developmental Medicine, Berlin, Germany.

                [§]

                Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, United States.

                [#]

                Two Six Labs, Arlington, United States.

                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-3289-6564
                http://orcid.org/0000-0002-8564-7836
                https://orcid.org/0000-0002-3480-2744
                http://orcid.org/0000-0002-4128-9774
                http://orcid.org/0000-0003-2400-6426
                http://orcid.org/0000-0002-9606-3510
                http://orcid.org/0000-0003-0262-6421
                https://orcid.org/0000-0001-8453-7961
                http://orcid.org/0000-0002-6746-5035
                http://orcid.org/0000-0002-9417-6212
                http://orcid.org/0000-0003-0172-6594
                https://orcid.org/0000-0003-0995-5471
                http://orcid.org/0000-0002-4829-9457
                http://orcid.org/0000-0002-9830-2415
                https://orcid.org/0000-0002-9274-5928
                https://orcid.org/0000-0001-6193-4454
                https://orcid.org/0000-0001-8588-0569
                http://orcid.org/0000-0002-1195-0445
                http://orcid.org/0000-0002-5623-8339
                http://orcid.org/0000-0002-8681-1749
                http://orcid.org/0000-0001-8374-6008
                http://orcid.org/0000-0002-5633-1314
                https://orcid.org/0000-0002-5663-5967
                http://orcid.org/0000-0002-5973-7512
                http://orcid.org/0000-0002-4675-8373
                http://orcid.org/0000-0002-4402-9230
                http://orcid.org/0000-0002-8454-865X
                http://orcid.org/0000-0002-7892-6845
                http://orcid.org/0000-0002-0041-9229
                http://orcid.org/0000-0002-9288-2009
                http://orcid.org/0000-0002-6601-4830
                http://orcid.org/0000-0002-8086-206X
                http://orcid.org/0000-0003-2139-7599
                http://orcid.org/0000-0003-1974-7174
                http://orcid.org/0000-0001-5794-6321
                http://orcid.org/0000-0001-7864-7734
                https://orcid.org/0000-0001-8223-9522
                http://orcid.org/0000-0002-1242-1836
                http://orcid.org/0000-0002-9120-1136
                http://orcid.org/0000-0001-8830-1892
                https://orcid.org/0000-0002-4918-2058
                https://orcid.org/0000-0001-7615-301X
                https://orcid.org/0000-0002-1764-0245
                http://orcid.org/0000-0002-4271-6774
                https://orcid.org/0000-0002-8879-6108
                http://orcid.org/0000-0003-4516-5928
                http://orcid.org/0000-0002-6358-9567
                https://orcid.org/0000-0002-8271-9873
                http://orcid.org/0000-0003-1321-8183
                https://orcid.org/0000-0002-2863-0050
                https://orcid.org/0000-0002-0173-8053
                http://orcid.org/0000-0002-7063-6165
                https://orcid.org/0000-0002-7176-4708
                http://orcid.org/0000-0003-1131-0410
                http://orcid.org/0000-0001-9673-2692
                https://orcid.org/0000-0002-8060-2807
                http://orcid.org/0000-0003-3680-7378
                http://orcid.org/0000-0001-5948-3092
                http://orcid.org/0000-0002-0587-9355
                https://orcid.org/0000-0002-7274-5533
                https://orcid.org/0000-0002-4106-1761
                http://orcid.org/0000-0001-8762-8703
                http://orcid.org/0000-0003-3000-1533
                https://orcid.org/0000-0001-7425-8555
                Article
                57443
                10.7554/eLife.57443
                7546738
                32880371
                cd6b7a43-ff82-48af-94e7-5a9266f05732
                © 2020, Scheffer et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 31 March 2020
                : 01 September 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award ID: Internal funding
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006785, Google;
                Award ID: Internal funding
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome;
                Award ID: 203261/Z/16/Z
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Computational and Systems Biology
                Neuroscience
                Custom metadata
                New reconstruction methods are used to create a publicly available dense reconstruction of the neurons and chemical synapses of central brain of Drosophila, with analysis of its graph properties.

                Life sciences
                connectome,brain regions,cell types,graph properties,connectome reconstuction methods,synapse detecton,d. melanogaster

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