174:
Connectomes of higher organism's brains requires considerable data. For the fruit fly, for example, roughly 10 terabytes of image data are processed, by humans and computers, to generate several gigabyte of connectome data. Easy interaction with this data requires an interactive query interface,
150:, released in 2021, is a 1.4 petabyte volume of a small sample of human brain tissue imaged at nanoscale-resolution by serial section electron microscopy, reconstructed and annotated by automated computational techniques, and analyzed for preliminary insights into the structure of human cortex.
115:. The second techniques uses computer vision software to identify voxels belonging to the same neuron. The second technique uses Machine Learning software to identify voxels belonging to the same neuron. Popular approaches are U-Net architectures to predict voxel-wise affinities paired with a
119:
segmentation and flood-filling networks. These approaches produce an over-segmentation which can be manually or automatically agglomerated to correctly represent a neuron. Even for automatically agglomerated segmentations, large manual proofreading efforts are employed for highest accuracy.
135:
was the seminal work in this field. This circuit was obtained with great effort using manually cut sections and purely manual annotation on photographic film. For many years this was the only circuit reconstruction
691:
Loomba, Sahil; Straehle, Jakob; Gangadharan, Vijayan; Heike, Natalie; Khalifa, Abdelrahman; Motta, Alessandro; Ju, Niansheng; Sievers, Meike; Gempt, Jens; Meyer, Hanno S.; Helmstaedter, Moritz (2022-07-08).
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where researchers can look at the portion of data they are interested in without downloading the whole data set, and without specific training. A specific example of this technology is the
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Januszewski, Michał; Kornfeld, Jörgen; Li, Peter H.; Pope, Art; Blakely, Tim; Lindsey, Larry; Maitin-Shepard, Jeremy; Tyka, Mike; Denk, Winfried; Jain, Viren (August 2018).
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Shapson-Coe, Alexander; Januszewski, Michał; Berger, Daniel R.; Pope, Art; Wu, Yuelong; Blakely, Tim; Schalek, Richard L.; Li, Peter H.; Wang, Shuohong (2021-11-25),
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Bock, Davi D.; Lee, Wei-Chung Allen; Kerlin, Aaron M.; Andermann, Mark L.; Hood, Greg; Wetzel, Arthur W.; Yurgenson, Sergey; Soucy, Edward R.; et al. (2011).
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Chklovskii, Dmitri B; Vitaladevuni, Shiv; Scheffer, Louis K (2010). "Semi-automated reconstruction of neural circuits using electron microscopy".
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In their 2022 study “Connectomic comparison of mouse and human cortex”, the researchers reconstructed 9 connectomes across species: Datasets of
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can diffuse across large distances and still strongly affect function. Currently these features must be obtained through other techniques.
100:, or trimmed using an in-microscope microtome. Then the sample is re-imaged, and the process repeated until the desired volume is processed.
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interface to the connectomes generate at HHMI. This mimics the infrastructure of genetics, where interactive query tools such as
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are normally used to look at genes of interest, which for most research comprise only a small portion of the genome.
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The volume is then annotated using one of two main methods. The first manually identifies the skeletons of each
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523:"Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction"
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was released in 2020. This data release introduced the first on-line tools to query the connectome.
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Principles and techniques of scanning electron microscopy. Biological applications, fourth edition
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Understanding the detailed operation of the reconstructed networks also requires knowledge of
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The first step is to align the individual images into a coherent three dimensional volume.
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Saalfeld, Stephan, Albert
Cardona, Volker Hartenstein, and Pavel Tomančák (2009).
548:"High-precision automated reconstruction of neurons with flood-filling networks"
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52:
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461:"CATMAID: collaborative annotation toolkit for massive amounts of image data"
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587:"Semi-automated reconstruction of neural circuits using electron microscopy"
340:"The structure of the nervous system of the nematode Caenorhabditis elegans"
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23:(or a portion of the nervous system) of an animal. It is sometimes called
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Chklovskii, Dmitri B., Shiv
Vitaladevuni, and Louis K. Scheffer. (2010).
60:
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A connectomic study of a petascale fragment of human cerebral cortex
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276:"Network anatomy and in vivo physiology of visual cortical neurons"
747:"Beyond the connectome: how neuromodulators shape neural circuits"
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51:
Some of the model systems used for circuit reconstruction are the
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414:"Volume electron microscopy for neuronal circuit reconstruction"
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The sample must be fixed, stained, and embedded in plastic.
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158:
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195:(hard to see with existing techniques), the identity of
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is the reconstruction of the detailed circuitry of the
35:of human-made devices, and is part of the field of
694:"Connectomic comparison of mouse and human cortex"
92:. Alternatively, the sample may be imaged with a
338:; Nichol Thomson, J.; Brenner, Sydney (1986).
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84:The sample may be cut into thin slices with a
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631:: CS1 maint: multiple names: authors list (
507:: CS1 maint: multiple names: authors list (
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412:Briggman, Kevin L.; Davi D. Bock (2012).
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31:(EM). This field is a close relative of
648:"Connectomes: Mapping the mind of a fly"
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199:and the locations and identities of
211:may provide an alternative method.
139:The central brain of the fruit fly
96:, then the surface abraded using a
734:. Howard Hughes Medical Institute.
344:Philos Trans R Soc Lond B Biol Sci
39:, which in turn is a sub-field of
27:since the main method used is the
14:
732:"Analysis tools for connectomics"
90:transmission electron microscopy
591:Current Opinion in Neurobiology
418:Current Opinion in Neurobiology
233:Current Opinion in Neurobiology
745:Bargmann, Cornelia I. (2012).
391:. Cambridge University Press.
1:
477:10.1093/bioinformatics/btp266
17:Neural circuit reconstruction
646:Jason Pipkin (Oct 8, 2020).
94:scanning electron microscope
187:Limitations and future work
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603:10.1016/j.conb.2010.08.002
430:10.1016/j.conb.2011.10.022
245:10.1016/j.conb.2010.08.002
673:10.1101/2021.05.29.446289
564:10.1038/s41592-018-0049-4
710:10.1126/science.abo0924
387:Hayat, M. Arif (2000).
170:Querying the connectome
141:Drosophila Melanogaster
764:10.1002/bies.201100185
365:10.1098/rstb.1986.0056
209:Expansion microscopy
88:, then imaged using
356:1986RSPTB.314....1W
300:10.1038/nature09802
292:2011Natur.471..177B
33:reverse engineering
29:electron microscope
527:scholar.google.com
129:The connectome of
72:Sample preparation
650:. Elife Sciences.
471:(15): 1984–1986.
336:Southgate, Eileen
197:neurotransmitters
146:The Human Cortex
25:EM reconstruction
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552:Nature Methods
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465:Bioinformatics
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398:978-0521632874
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803:Neuroimaging
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41:neuroanatomy
37:connectomics
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787:Categories
678:2024-02-14
532:2024-02-14
215:References
136:available.
132:C. elegans
65:C. elegans
59:, and the
751:BioEssays
718:0036-8075
619:206950616
572:1548-7105
261:206950616
201:receptors
117:watershed
86:microtome
53:fruit fly
773:22396302
704:(6602).
611:20833533
495:19376822
446:22657332
438:22119321
374:22462104
318:21390124
253:20833533
177:NeuPrint
61:nematode
698:Science
486:2712332
352:Bibcode
309:3095821
288:Bibcode
159:Macaque
113:neurite
80:Imaging
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280:Nature
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55:, the
793:Brain
615:S2CID
442:S2CID
257:S2CID
181:BLAST
163:Human
155:Mouse
57:mouse
769:PMID
714:ISSN
633:link
607:PMID
568:ISSN
509:link
491:PMID
434:PMID
393:ISBN
370:PMID
314:PMID
249:PMID
161:and
759:doi
706:doi
702:377
669:doi
599:doi
560:doi
481:PMC
473:doi
426:doi
360:doi
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304:PMC
296:doi
284:471
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