Neuronal tracing

Digital reconstruction or tracing of neuron morphology is a fundamental task in computational neuroscience.{{cite journal|last1=Peng|first1=Hanchuan|last2=Roysam|first2=Badri|last3=Ascoli|first3=Giorgio|title=Automated image computing reshapes computational neuroscience|journal=BMC Bioinformatics|date=2013|volume=14|page=293|doi=10.1186/1471-2105-14-293|pmid=24090217|pmc=3853071 |doi-access=free }}{{cite journal|last1=Meijering|first1=Erik|title=Neuron Tracing in Perspective|journal=Cytometry Part A|date=2010|volume=77|issue=7|pages=693–704|doi=10.1002/cyto.a.20895|pmid=20583273|citeseerx=10.1.1.623.3000|s2cid=14047936}}{{cite book|last1=Schwartz E|title=Computational neuroscience|date=1990|publisher=MIT Press|location=Cambridge, Mass|isbn=978-0-262-19291-0}} It is also critical for mapping neuronal circuits based on advanced microscope images, usually based on light microscopy (e.g. laser scanning microscopy, bright field imaging) or electron microscopy or other methods. Due to the high complexity of neuron morphology and often seen heavy noise in such images, as well as the typically encountered massive amount of image data, it has been widely viewed as one of the most challenging computational tasks for computational neuroscience. Many image analysis based methods have been proposed to trace neuron morphology, usually in 3D, manually, semi-automatically or completely automatically. There are normally three processing steps: generation and proof editing, and annotating of a reconstruction.{{cite journal|last1=Peng|first1=H., Long, F., Zhao, T., and Myers, E.W.|title=Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions|journal=Neuroinformatics|volume=9|issue=2–3|pages=103–105|url=https://link.springer.com/article/10.1007/s12021-010-9090-x|doi=10.1007/s12021-010-9090-x|pmid=21170608|year=2011|s2cid=4995280|url-access=subscription}}{{cite journal|last1=Peng|first1=H., Tang, J., Xiao, H., Bria, A.|display-authors=etal|title=Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis|journal=Nature Communications|date=2014|doi=10.1038/ncomms5342|volume=5|pages=4342|pmid=25014658|pmc=4104457|bibcode=2014NatCo...5.4342P }}{{Cite journal |last1=Dorkenwald |first1=Sven |last2=Matsliah |first2=Arie |last3=Sterling |first3=Amy R. |last4=Schlegel |first4=Philipp |last5=Yu |first5=Szi-chieh |last6=McKellar |first6=Claire E. |last7=Lin |first7=Albert |last8=Costa |first8=Marta |last9=Eichler |first9=Katharina |last10=Yin |first10=Yijie |last11=Silversmith |first11=Will |last12=Schneider-Mizell |first12=Casey |last13=Jordan |first13=Chris S. |last14=Brittain |first14=Derrick |last15=Halageri |first15=Akhilesh |date=3 October 2024 |title=Neuronal wiring diagram of an adult brain |journal=Nature |language=en |volume=634 |issue=8032 |pages=124–138 |doi=10.1038/s41586-024-07558-y |issn=0028-0836 |pmc=11446842 |pmid=39358518}}

The need to describe or reconstruct a neuron's morphology probably began in early days of neuroscience when neurons were labeled or visualized using Golgi's methods. Many of the known neuron types, such as pyramidal neurons and Chandelier cells, were described based on their morphological characterization.

The first computer-assisted neuron reconstruction system, now known as [http://www.mbfbioscience.com/neurolucida Neurolucida], was developed by Dr. Edmund Glaser and Dr. Hendrik Van der Loos in the 1960s.{{Cite journal|last1=Glaser|first1=E. M.|last2=Vanderloos|first2=H.|date=1965-01-01|journal=IEEE Transactions on Biomedical Engineering|volume=12|pages=22–31|issn=0018-9294|pmid=14291539|title=A Semi-Automatic Computer-Microscope for the Analysis of Neuronal Morphology|doi=10.1109/TBME.1965.4502337}}

Modern approaches to trace a neuron started when digitized pictures of neurons were acquired using microscopes. Initially this was done in 2D. Quickly after the advanced 3D imaging, especially the fluorescence imaging and electron microscopic imaging, there were a huge demand of tracing neuron morphology from these imaging data. To reconstruct 3D volumes of neurons, a popular method is serial section EM, in which the sample is sliced into thin layers and each slice is imaged, resulting in a voxel dataset.{{Cite journal |last1=Svara |first1=Fabian |last2=Förster |first2=Dominique |last3=Kubo |first3=Fumi |last4=Januszewski |first4=Michał |last5=dal Maschio |first5=Marco |last6=Schubert |first6=Philipp J. |last7=Kornfeld |first7=Jörgen |last8=Wanner |first8=Adrian A. |last9=Laurell |first9=Eva |last10=Denk |first10=Winfried |last11=Baier |first11=Herwig |date=24 October 2022 |title=Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain |journal=Nature Methods |language=en |volume=19 |issue=11 |pages=1357–1366 |doi=10.1038/s41592-022-01621-0 |issn=1548-7091 |pmc=9636024 |pmid=36280717}}{{Cite journal |last1=Zheng |first1=Zhihao |last2=Lauritzen |first2=J. Scott |last3=Perlman |first3=Eric |last4=Robinson |first4=Camenzind G. |last5=Nichols |first5=Matthew |last6=Milkie |first6=Daniel |last7=Torrens |first7=Omar |last8=Price |first8=John |last9=Fisher |first9=Corey B. |last10=Sharifi |first10=Nadiya |last11=Calle-Schuler |first11=Steven A. |last12=Kmecova |first12=Lucia |last13=Ali |first13=Iqbal J. |last14=Karsh |first14=Bill |last15=Trautman |first15=Eric T. |date=26 July 2018 |title=A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster |journal=Cell |language=English |volume=174 |issue=3 |pages=730–743.e22 |doi=10.1016/j.cell.2018.06.019 |issn=0092-8674 |pmc=6063995 |pmid=30033368}}{{Cite journal |last1=Randel |first1=Nadine |last2=Shahidi |first2=Réza |last3=Verasztó |first3=Csaba |last4=Bezares-Calderón |first4=Luis A |last5=Schmidt |first5=Steffen |last6=Jékely |first6=Gáspár |date=10 June 2015 |editor-last=Marder |editor-first=Eve |title=Inter-individual stereotypy of the Platynereis larval visual connectome |journal=eLife |volume=4 |pages=e08069 |doi=10.7554/eLife.08069 |doi-access=free |issn=2050-084X |pmc=4477197 |pmid=26061864}}{{Citation |last1=Macrina |first1=Thomas |title=Petascale neural circuit reconstruction: automated methods |date=5 August 2021 |url=http://biorxiv.org/lookup/doi/10.1101/2021.08.04.455162 |access-date=30 May 2025 |language=en |doi=10.1101/2021.08.04.455162 |last2=Lee |first2=Kisuk |last3=Lu |first3=Ran |last4=Turner |first4=Nicholas L. |last5=Wu |first5=Jingpeng |last6=Popovych |first6=Sergiy |last7=Silversmith |first7=William |last8=Kemnitz |first8=Nico |last9=Bae |first9=J. Alexander}}

Modern neuronal tracing effort have been completed on voxel volumes with a resolution of up to 4 × 4 × 34 nm³ and database sizes of over 1.4 petabytes.{{Cite journal |last1=Shapson-Coe |first1=Alexander |last2=Januszewski |first2=Michał |last3=Berger |first3=Daniel R. |last4=Pope |first4=Art |last5=Wu |first5=Yuelong |last6=Blakely |first6=Tim |last7=Schalek |first7=Richard L. |last8=Li |first8=Peter H. |last9=Wang |first9=Shuohong |last10=Maitin-Shepard |first10=Jeremy |last11=Karlupia |first11=Neha |last12=Dorkenwald |first12=Sven |last13=Sjostedt |first13=Evelina |last14=Leavitt |first14=Laramie |last15=Lee |first15=Dongil |date=10 May 2024 |title=A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution |journal=Science |language=en |volume=384 |issue=6696 |pages=eadk4858 |doi=10.1126/science.adk4858 |issn=0036-8075 |pmc=11718559 |pmid=38723085|bibcode=2024Sci...384k4858S }}

File:Neuron reconstruction and tracing illustration.png

Neurons can be often traced manually either in 2D or 3D. To do so, one may either directly paint the trajectory of neuronal processes in individual 2D sections of a 3D image volume and manage to connect them, or use the [http://www.nature.com/ncomms/2014/140711/ncomms5342/full/ncomms5342.html 3D Virtual Finger] painting which directly converts any 2D painted trajectory in a projection image to real 3D neuron processes. The major limitation of manual tracing of neurons is the huge amount of labor in the work.

Automated reconstructions of neurons can be done using model (e.g. spheres or tubes) fitting and marching,{{cite journal|last1=Al-Kofahi, K.A.|title=Rapid automated three-dimensional tracing of neurons from confocal image stacks|journal=IEEE Trans. Inf. Technol. Biomed.|date=2002|volume=6|issue=2|pages=171–187|doi=10.1109/titb.2002.1006304|pmid=12075671|display-authors=etal|citeseerx=10.1.1.57.9339|s2cid=12413677}} pruning of over-reconstruction,{{cite journal|last1=Peng|first1=H.|title=Automatic 3D neuron tracing using all-path pruning|journal=Bioinformatics|volume=27|issue=13|pages=i239–i247|doi=10.1093/bioinformatics/btr237|pmid=21685076|display-authors=etal|pmc=3117353|year=2011}} minimal cost connection of key points, ray-bursting and many others.{{cite journal|last1=Rodriguez, A.|title=Three-dimensional neuron tracing by voxel scooping|journal=J. Neurosci. Methods|date=2009|volume=184|issue=1|pages=169–175|doi=10.1016/j.jneumeth.2009.07.021|pmid=19632273|pmc=2753723|display-authors=etal}} Skeletonization is a critical step in automated neuron reconstruction, but in the case of all-path-pruning and its variants{{cite journal|last1=Xiao|first1=H|title=APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of gray-weighted image distance-trees|journal=Bioinformatics|date=2013|volume=29|issue=11|pages=1448–1454|doi=10.1093/bioinformatics/btt170|pmid=23603332|display-authors=etal|pmc=3661058}} it is combined with estimation of model parameters (e.g. tube diameters). The major limitation of automated tracing is the lack of precision especially when the neuron morphology is complicated or the image has substantial amount of noise. Newer methods also include the use of convolutional neural networks to segment, reconstruct, and annotate large datasets.{{Cite web |date=2023-11-06 |title=Using AI to optimize for rapid neural imaging |url=https://news.mit.edu/2023/using-ai-optimize-rapid-neural-imaging-1106 |access-date=2025-05-30 |website=MIT News {{!}} Massachusetts Institute of Technology |language=en}} These AI models are trained off of 'ground truth' provided by manual or semi-automatic reconstructions and have since achieved superhuman accuracy.{{Cite web |date=2017-09-11 |title=Using Artificial Intelligence to Map the Brain's Wiring |url=https://www.simonsfoundation.org/2017/09/11/using-artificial-intelligence-to-map-the-brains-wiring/ |access-date=2025-05-30 |website=Simons Foundation |language=en-US}}

Semi-automated neuron tracing often depends on two strategies. One is to run the completely automated neuron tracing followed by manual curation of such reconstructions.{{Cite web |last=Michalowski |first=Jennifer |date=2020-09-28 |title=AI learns to trace neuronal pathways |url=https://www.cshl.edu/ai-learns-to-trace-neuronal-pathways/ |access-date=2025-05-30 |website=Cold Spring Harbor Laboratory |language=en-US}} The alternative way is to produce some prior knowledge, such as the termini locations of a neuron, with which a neuron can be more easily traced automatically. Semi-automated tracing is often thought to be a balanced solution that has acceptable time cost and reasonably good reconstruction accuracy. The open source software Vaa3D-Neuron, [http://www.mbfbioscience.com/neurolucida360 Neurolucida 360], Imaris Filament Tracer and [https://www.drvtechnologies.com/aivia5 Aivia] all provide both categories of methods.

Tracing of electron microscopy image is thought to be more challenging than tracing light microscopy images, while the latter is still quite difficult, according to the [http://diademchallenge.org/ DIADEM competition].{{cite journal|last1=Liu|first1=Y|title=The DIADEM and beyond|journal=Neuroinformatics|date=2011|volume=9|issue=2–3|pages=99–102|doi=10.1007/s12021-011-9102-5|pmid=21431331|doi-access=free}} For tracing electron microscopy data, manual tracing is used more often than the alternative automated or semi-automated methods.{{cite journal|vauthors=Helmstaedter M, Briggman KL, Denk W |title=High-accuracy neurite reconstruction for high-throughput neuroanatomy|journal=Nat Neurosci|date=2011|volume=14|issue=8|pages=1081–1088|doi=10.1038/nn.2868|pmid=21743472|s2cid=17795934|url=https://hal.archives-ouvertes.fr/hal-00658165/document}} For tracing light microscopy data, more times the automated or semi-automated methods are used.

Since tracing electron microscopy images takes substantial amount time, collaborative manual tracing software is useful. Crowdsourcing is an alternative way to effectively collect collaborative manual reconstruction results for such image data sets.{{cite journal|last1=Kim|title=Space–time wiring specificity supports direction selectivity in the retina|journal=Nature|date=2014|volume=509|issue=7500|pages=331–336|doi=10.1038/nature13240|display-authors=etal|pmid=24805243|pmc=4074887|bibcode=2014Natur.509..331. }}

A number of neuron tracing tools especially software packages are available. One comprehensive Open Source software package that contains implementation of a number of neuron tracing methods developed in different research groups as well as many neuron utilities functions such as quantitative measurement, parsing, comparison, is [http://home.penglab.com/proj/vaa3d/home/index.html Vaa3D and its Vaa3D-Neuron modules]. Some other free tools such as NeuronStudio{{cite web |url=http://research.mssm.edu/cnic/tools-ns.html |title=Tools: NeuronStudio (Beta) Version 0.9.92, last updated on November 19, 2009 |last=Rodriguez |first=Alfredo |date=2010-02-18 |publisher=CNIC, Mount Sinai School of Medicine |archive-url=https://web.archive.org/web/20180916171008/http://research.mssm.edu/cnic/tools-ns.html |archive-date=2018-09-16}} also provide tracing function based on specific methods. Neuroscientists also use commercial tools such as [http://www.mbfbioscience.com/neurolucida Neurolucida], [http://www.mbfbioscience.com/neurolucida360 Neurolucida 360], [https://www.drvtechnologies.com/aivia5 Aivia], Amira, etc. to trace and analyse neurons. A 2012 study show that Neurolucida is cited over 7 times more than all other available neuron tracing programs combined,{{Cite journal|last1=Halavi|first1=Maryam|last2=Hamilton|first2=Kelly A.|last3=Parekh|first3=Ruchi|last4=Ascoli|first4=Giorgio A.|date=2012-01-01|title=Digital reconstructions of neuronal morphology: three decades of research trends|journal=Frontiers in Neuroscience|volume=6|pages=49|doi=10.3389/fnins.2012.00049|issn=1662-453X|pmc=3332236|pmid=22536169|doi-access=free}} and is also the most widely used and versatile system to produce neuronal reconstruction.{{Cite journal|last1=Aguiar|first1=Paulo|last2=Sousa|first2=Mafalda|last3=Szucs|first3=Peter|date=2013-06-14|title=Versatile Morphometric Analysis and Visualization of the Three-Dimensional Structure of Neurons|journal=Neuroinformatics|language=en|volume=11|issue=4|pages=393–403|doi=10.1007/s12021-013-9188-z|pmid=23765606|s2cid=16591493|issn=1539-2791}} The [https://alleninstitute.org/bigneuron/about/ BigNeuron project (https://alleninstitute.org/bigneuron/about/)] {{Cite journal |last1=Peng |first1=Hanchuan |last2= Hawrylycz |first2=Michael |last3= Roskams |first3=Jane |title= BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images|url= |journal=Neuron|language=en|volume=87|issue=2|pages=252–256|doi=10.1016/j.neuron.2015.06.036 |pmid=26182412 |pmc=4725298|date=2015-07-15 }} is a recent substantial international collaboration effort to integrate the majority of known neuron tracing tools onto a common platform to facilitate Open Source, easy accessing of various tools at one single place. Powerful new tools such as UltraTracer,{{cite bioRxiv|last1=Peng|first1=Hanchuan|last2=Zhou|first2=Zhi|last3=Meijering|first3=Erik|date=2016|title=Automatic Tracing of Ultra-Volume of Neuronal Images|biorxiv=10.1101/087726}} that can trace arbitrarily large image volume, have been produced through this effort. The online tool [https://webknossos.org/ WEBKNOSSOS] has a Flight Mode for high-speed tracing of axons or dendrites, in which trained annotator crowds achieve tracing speeds of 1.5 ± 0.6 mm/h for axons and 2.1 ± 0.9 mm/h for dendrites in 3D electron microscopy data.{{Cite journal |last1=Boergens |first1=Kevin M. |last2=Berning |first2=Manuel |last3=Bocklisch |first3=Tom |last4=Bräunlein |first4=Dominic |last5=Drawitsch |first5=Florian |last6=Frohnhofen |first6=Johannes |last7=Herold |first7=Tom |last8=Otto |first8=Philipp |last9=Rzepka |first9=Norman |last10=Werkmeister |first10=Thomas |last11=Werner |first11=Daniel |last12=Wiese |first12=Georg |last13=Wissler |first13=Heiko |last14=Helmstaedter |first14=Moritz |date=July 2017 |title=webKnossos: efficient online 3D data annotation for connectomics |url=https://www.nature.com/articles/nmeth.4331 |journal=Nature Methods |language=en |volume=14 |issue=7 |pages=691–694 |doi=10.1038/nmeth.4331 |pmid=28604722 |s2cid=30609228 |issn=1548-7105|url-access=subscription }}

Reconstructions of single neurons can be stored in various formats. This largely depends on the software that have been used to trace such neurons. The SWC format, which consists of a number of topologically connected structural compartments (e.g. a single tube or sphere), is often used to store digital traced neurons, especially when the morphology lacks or does not need detailed 3D shape models for individual compartments. Other more sophisticated neuron formats have separate geometrical modeling of the neuron cell body and neuron processes using Neurolucida {{Cite journal|last1=Bianchi|first1=Serena|last2=Stimpson|first2=Cheryl D.|last3=Bauernfeind|first3=Amy L.|last4=Schapiro|first4=Steven J.|last5=Baze|first5=Wallace B.|last6=McArthur|first6=Mark J.|last7=Bronson|first7=Ellen|last8=Hopkins|first8=William D.|last9=Semendeferi|first9=Katerina|date=2013-10-01|title=Dendritic Morphology of Pyramidal Neurons in the Chimpanzee Neocortex: Regional Specializations and Comparison to Humans|journal=Cerebral Cortex|volume=23|issue=10|pages=2429–2436|doi=10.1093/cercor/bhs239|issn=1047-3211|pmc=3767963|pmid=22875862}}{{Cite journal|last1=Silberberg|first1=Gilad|last2=Markram|first2=Henry|date=2007-03-01|title=Disynaptic Inhibition between Neocortical Pyramidal Cells Mediated by Martinotti Cells|journal=Neuron|language=English|volume=53|issue=5|pages=735–746|doi=10.1016/j.neuron.2007.02.012|issn=0896-6273|pmid= 17329212|s2cid=15624023|doi-access=free}}{{Cite journal|last1=Bianchi|first1=Serena|last2=Bauernfeind|first2=Amy L.|last3=Gupta|first3=Kanika|last4=Stimpson|first4=Cheryl D.|last5=Spocter|first5=Muhammad A.|last6=Bonar|first6=Christopher J.|last7=Manger|first7=Paul R.|last8=Hof|first8=Patrick R.|last9=Jacobs|first9=Bob|date=2011-04-01|title=Neocortical neuron morphology in Afrotheria: comparing the rock hyrax with the African elephant|journal=Annals of the New York Academy of Sciences|volume=1225|issue=1 |pages=37–46|doi=10.1111/j.1749-6632.2011.05991.x|issn=1749-6632|pmid=21534991|bibcode=2011NYASA1225...37B |s2cid=18281955}} among others.

There are a few common single neuron reconstruction databases. A widely used database is [http://NeuroMorpho.org http://NeuroMorpho.Org] {{cite journal|vauthors=Ascoli GA, Donohue DE, Halavi M |title=NeuroMorpho.Org - A central resource for neuronal morphologies|journal=J Neurosci|date=2007|volume=27|issue=35|pages=9247–9251|doi=10.1523/jneurosci.2055-07.2007|pmid=17728438|pmc=6673130}} which contains over 86,000 neuron morphology of >40 species contributed worldwide by a number of research labs. Allen Institute for Brain Science, HHMI's Janelia Research Campus, and other institutes are also generating large-scale single neuron databases.

Recently, databases of entire reconstructed neural volumes have also been made avaliable. The [https://mapzebrain.org/ Max Planck Zebrafish Brain Atlas] and its associated dataset are the reconstructed connectome of the 208 neurons of a zebrafish larva.{{Cite journal |last1=Svara |first1=Fabian |last2=Förster |first2=Dominique |last3=Kubo |first3=Fumi |last4=Januszewski |first4=Michał |last5=dal Maschio |first5=Marco |last6=Schubert |first6=Philipp J. |last7=Kornfeld |first7=Jörgen |last8=Wanner |first8=Adrian A. |last9=Laurell |first9=Eva |last10=Denk |first10=Winfried |last11=Baier |first11=Herwig |date=24 October 2022 |title=Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain |journal=Nature Methods |language=en |volume=19 |issue=11 |pages=1357–1366 |doi=10.1038/s41592-022-01621-0 |issn=1548-7091 |pmc=9636024 |pmid=36280717}} The [https://catmaid.jekelylab.ex.ac.uk/ CATMAID] database contains the traced connectome of a Platynereis larval connectome with 1,500 neurons and 6,500 non-neural cells.{{Citation |last1=Verasztó |first1=Csaba |title=Whole-animal connectome and cell-type complement of the three-segmented Platynereis dumerilii larva |date=22 August 2022 |url=http://biorxiv.org/lookup/doi/10.1101/2020.08.21.260984 |access-date=30 May 2025 |language=en |doi=10.1101/2020.08.21.260984 |last2=Jasek |first2=Sanja |last3=Gühmann |first3=Martin |last4=Shahidi |first4=Réza |last5=Ueda |first5=Nobuo |last6=Beard |first6=James David |last7=Mendes |first7=Sara |last8=Heinz |first8=Konrad |last9=Bezares-Calderón |first9=Luis Alberto}} The [https://flywire.ai/ Flywire] connectome is a reconstruction and annotation of the approximately 140,000 annotated neurons making up the brain of an adult Drosophila.{{Cite journal |last1=Schlegel |first1=Philipp |last2=Yin |first2=Yijie |last3=Bates |first3=Alexander S. |last4=Dorkenwald |first4=Sven |last5=Eichler |first5=Katharina |last6=Brooks |first6=Paul |last7=Han |first7=Daniel S. |last8=Gkantia |first8=Marina |last9=dos Santos |first9=Marcia |last10=Munnelly |first10=Eva J. |last11=Badalamente |first11=Griffin |last12=Serratosa Capdevila |first12=Laia |last13=Sane |first13=Varun A. |last14=Fragniere |first14=Alexandra M. C. |last15=Kiassat |first15=Ladann |date=3 October 2024 |title=Whole-brain annotation and multi-connectome cell typing of Drosophila |journal=Nature |language=en |volume=634 |issue=8032 |pages=139–152 |doi=10.1038/s41586-024-07686-5 |issn=0028-0836 |pmc=11446831 |pmid=39358521}}{{Cite journal |last1=Dorkenwald |first1=Sven |last2=Matsliah |first2=Arie |last3=Sterling |first3=Amy R. |last4=Schlegel |first4=Philipp |last5=Yu |first5=Szi-chieh |last6=McKellar |first6=Claire E. |last7=Lin |first7=Albert |last8=Costa |first8=Marta |last9=Eichler |first9=Katharina |last10=Yin |first10=Yijie |last11=Silversmith |first11=Will |last12=Schneider-Mizell |first12=Casey |last13=Jordan |first13=Chris S. |last14=Brittain |first14=Derrick |last15=Halageri |first15=Akhilesh |date=3 October 2024 |title=Neuronal wiring diagram of an adult brain |journal=Nature |language=en |volume=634 |issue=8032 |pages=124–138 |doi=10.1038/s41586-024-07558-y |issn=0028-0836 |pmc=11446842 |pmid=39358518}} The [https://h01-release.storage.googleapis.com/landing.html H01] dataset is a reconstructed cubic millimeter of human brain tissue.

Many of related neuron data databases at different scales also exist.

{{Reflist}}

Category:Neuroscience

Category:Cellular neuroscience