Connectome

{{short description|Comprehensive map of neural connections in the brain}}

File:White Matter Connections Obtained with MRI Tractography.png within a human brain, as visualized by MRI tractography]]

File:The Human Connectome.png architecture of the human brain are visualized color-coded by traversing direction (xyz-directions mapping to RGB colors respectively). Visualization of fibers was done using TrackVis software.{{Cite journal |vauthors=Horn A, Ostwald D, Reisert M, Blankenburg F |date=November 2014 |title=The structural-functional connectome and the default mode network of the human brain |journal=NeuroImage |volume=102 Pt 1 |pages=142–51 |doi=10.1016/j.neuroimage.2013.09.069 |pmid=24099851|s2cid=6455982 }}]]

A connectome ({{IPAc-en|k|ə|ˈ|n|ɛ|k|t|oʊ|m}}) is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram".{{cite journal |last1=Mackenzie |first1=Dana |title=How animals follow their nose |journal=Knowable Magazine |publisher=Annual Reviews |date=6 March 2023 |doi=10.1146/knowable-030623-4 |doi-access=free |url=https://knowablemagazine.org/article/living-world/2023/how-animals-follow-their-nose |access-date=13 March 2023 |language=en}} These maps are available in varying levels of detail. A functional connectome shows connections between various brain regions, but not individual neurons. These are available for large animals, including mice and humans, are normally obtained by techniques such as MRI, and have a scale of millimeters. At the other extreme are neural connectomes, which show individual neurons and their interconnections. These are usually obtained by electron microscopy (EM) and have a scale of nanometers. They are only available for small creatures such as the worm C. Elegans and the fruit fly Drosophila melanogaster, and small regions of mammal brains. Finally there are chemical connectomes, showing which neurons emit, and are sensitive to, a wide variety of neuromodulators.

The significance of the connectome stems from the realization that the structure and function of any brain are intricately linked, through multiple levels and modes of brain connectivity. There are strong natural constraints on which neurons or neural populations can interact, or how strong or direct their interactions are. Indeed, the foundation of human cognition lies in the pattern of dynamic interactions shaped by the connectome.

Despite such complex and variable structure-function mappings, connectomes are an indispensable basis for the mechanistic interpretation of dynamic brain data, from single-cell recordings to functional neuroimaging.

The terms connectome and connectomics were introduced independently by Olaf Sporns at Indiana University and Patric Hagmann at Lausanne University Hospital to refer to a map of the neural connections within the brain. This term was directly inspired by the ongoing effort to sequence the human genetic code—to build a genome. It was more recently popularized by Sebastian Seung's I am my Connectome speech given at the 2010 TED conference.{{cite web|last=Seung|first=Sebastian|name-list-style=vanc|date=September 2010|orig-year=recorded July 2010|title=Sebastian Seung: I am my connectome|url=http://www.ted.com/talks/sebastian_seung.html|access-date=2011-08-07|publisher=TEDTalks}} In 2012, Seung published the book Connectome: How the Brain's Wiring Makes Us Who We Are.

Types of connectomes

Brain networks can be defined at different levels of scale, corresponding to levels of spatial resolution in brain imaging.{{cite book|last1=Kötter|first1=Rolf|title=Handbook of Brain Connectivity|year=2007|isbn=978-3-540-71462-0|series=Understanding Complex Systems|pages=149–67|chapter=Anatomical Concepts of Brain Connectivity|doi=10.1007/978-3-540-71512-2_5|name-list-style=vanc}}{{cite book|last1=Sporns|first1=Olaf|title=Networks of the Brain|date=2011|publisher=MIT Press|isbn=978-0-262-01469-4|location=Cambridge, Mass.|name-list-style=vanc}} These scales can be roughly categorized as macroscale, mesoscale and microscale. Ultimately, it may be possible to join connectomic maps obtained at different scales into a single hierarchical map of the neural organization of a given species that ranges from single neurons to populations of neurons to larger systems like cortical areas. Given the methodological uncertainties involved in inferring connectivity from the primary experimental data, and given that there are likely to be large differences in the connectomes of different individuals, any unified map will likely rely on probabilistic representations of connectivity data.{{cite journal | vauthors = Sporns O, Tononi G, Kötter R | title = The human connectome: A structural description of the human brain | journal = PLOS Computational Biology | volume = 1 | issue = 4 | pages = e42 | date = September 2005 | pmid = 16201007 | pmc = 1239902 | doi = 10.1371/journal.pcbi.0010042 | bibcode = 2005PLSCB...1...42S | doi-access = free }} {{open access}}

= Macroscale =

A connectome at the macroscale (millimeter resolution) attempts to capture large brain systems that can be parcellated into anatomically distinct modules (areas, parcels or nodes), each having a distinct pattern of connectivity. Connectomic databases at the mesoscale and macroscale may be significantly more compact than those at cellular resolution, but they require effective strategies for accurate anatomical or functional parcellation of the neural volume into network nodes.{{cite journal|vauthors=Wallace MT, Ramachandran R, Stein BE|date=February 2004|title=A revised view of sensory cortical parcellation|journal=Proceedings of the National Academy of Sciences of the United States of America|volume=101|issue=7|pages=2167–72|bibcode=2004PNAS..101.2167W|doi=10.1073/pnas.0305697101|pmc=357070|pmid=14766982|doi-access=free}}

Established methods of brain research, such as axonal tracing, provided early avenues for building connectome data sets. However, more recent advances in living subjects has been made by the use of non-invasive imaging technologies such as diffusion-weighted magnetic resonance imaging (DW-MRI) and functional magnetic resonance imaging (fMRI). The first, when combined with tractography allows reconstruction of the major fiber bundles in the brain. The second allows the researcher to capture the brain's network activity (either at rest or while performing directed tasks), enabling the identification of structurally and anatomically distinct areas of the brain that are functionally connected.

Notably, the goal of the Human Connectome Project, led by the WU-Minn consortium, was to build a structural and functional map of the healthy human brain at the macro scale, using a combination of multiple imaging technologies and resolutions.

==Initial attempts at connectivity mapping==

Image:DTI-sagittal-fibers.jpg ]]

Throughout the 2000s, several investigators have attempted to map the large-scale structural architecture of the human cerebral cortex. One attempt exploited cross-correlations in cortical thickness or volume across individuals.{{cite journal|vauthors=He Y, Chen ZJ, Evans AC|date=October 2007|title=Small-world anatomical networks in the human brain revealed by cortical thickness from MRI|journal=Cerebral Cortex|volume=17|issue=10|pages=2407–19|doi=10.1093/cercor/bhl149|pmid=17204824|doi-access=free}} Such gray-matter thickness correlations have been postulated as indicators for the presence of structural connections. A drawback of the approach is that it provides highly indirect information about cortical connection patterns and requires data from large numbers of individuals to derive a single connection data set across a subject group. Other investigators have attempted to build whole-brain connection matrices from DW-MRI imaging data.

The Blue Brain Project attempted to reconstruct the entire mouse connectome using a diamond knife sharpened to an atomic edge, and electron microscopy for imaging tissue slices. They ended up in 2018 with an atlas providing information about major cell types, numbers, and positions in 737 regions of the brain.{{Cite web |title=Blue Brain Cell Atlas |url=https://bbp.epfl.ch/nexus/cell-atlas/?v=v2&std=1 |access-date=2024-05-10 |website=bbp.epfl.ch}}

==Challenge for macroscale connectomics==

The initial explorations in macroscale human connectomics were done using either equally sized regions or anatomical regions with unclear relationship to the underlying functional organization of the brain (e.g. gyral and sulcal-based regions). While much can be learned from these approaches, it is highly desirable to parcellate the brain into functionally distinct parcels: brain regions with distinct architectonics, connectivity, function, and/or topography.{{cite journal|vauthors=Felleman DJ, Van Essen DC|year=1991|title=Distributed hierarchical processing in the primate cerebral cortex|journal=Cerebral Cortex|volume=1|issue=1|pages=1–47|doi=10.1093/cercor/1.1.1-a|pmid=1822724|doi-access=free}} Accurate parcellation allows each node in the macroscale connectome to be more informative by associating it with a distinct connectivity pattern and functional profile. Parcellation of localized areas of cortex have been accomplished using diffusion tractography{{cite journal|vauthors=Beckmann M, Johansen-Berg H, Rushworth MF|date=January 2009|title=Connectivity-based parcellation of human cingulate cortex and its relation to functional specialization|journal=The Journal of Neuroscience|volume=29|issue=4|pages=1175–90|doi=10.1523/JNEUROSCI.3328-08.2009|pmc=6665147|pmid=19176826}} and functional connectivity{{cite journal|vauthors=Nelson SM, Cohen AL, Power JD, Wig GS, Miezin FM, Wheeler ME, Velanova K, Donaldson DI, Phillips JS, Schlaggar BL, Petersen SE|date=July 2010|title=A parcellation scheme for human left lateral parietal cortex|journal=Neuron|volume=67|issue=1|pages=156–70|doi=10.1016/j.neuron.2010.05.025|pmc=2913443|pmid=20624599}} to non-invasively measure connectivity patterns and define cortical areas based on distinct connectivity patterns. Such analyses may best be done on a whole brain scale and by integrating non-invasive modalities. Accurate whole brain parcellation may lead to more accurate macroscale connectomes for the normal brain, which can then be compared to disease states.

Pathways through cerebral white matter can be charted by histological dissection and staining, by degeneration methods, and by axonal tracing. Axonal tracing methods form the primary basis for the systematic charting of long-distance pathways into extensive, species-specific anatomical connection matrices between gray matter regions. Landmark studies have included the areas and connections of the visual cortex of the macaque and the thalamocortical system in the feline brain.{{cite journal|vauthors=Scannell JW, Burns GA, Hilgetag CC, O'Neil MA, Young MP|year=1999|title=The connectional organization of the cortico-thalamic system of the cat|journal=Cerebral Cortex|volume=9|issue=3|pages=277–99|doi=10.1093/cercor/9.3.277|pmid=10355908|doi-access=free}} The development of neuroinformatics databases for anatomical connectivity allow for continual updating and refinement of such anatomical connection maps. The online macaque cortex connectivity tool CoCoMac{{cite journal|vauthors=Kötter R|year=2004|title=Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database|journal=Neuroinformatics|volume=2|issue=2|pages=127–44|doi=10.1385/NI:2:2:127|pmid=15319511|s2cid=19789717}} and the temporal lobe connectome of the rat{{cite journal|vauthors=van Strien NM, Cappaert NL, Witter MP|date=April 2009|title=The anatomy of memory: an interactive overview of the parahippocampal-hippocampal network|url=https://zenodo.org/record/3426276|journal=Nature Reviews. Neuroscience|volume=10|issue=4|pages=272–82|doi=10.1038/nrn2614|pmid=19300446|s2cid=15232243}} are prominent examples of such a database.

= Chemical connectome =

Nerve cells communicate with adjacent cells through synapses and gap junctions, but they also communicate with distant cells via chemicals (typically neuropeptides) that diffuse through tissue and trigger receptors on cells far away. There are hundreds of such neuromodulators, with any given nerve cell emitting and responding to at most as few of them. The graph that describes these interactions is another form of connectome.{{cite journal

|last13= Schafer |first13= William R.

|url=https://www.cell.com/neuron/fulltext/S0896-6273(23)00756-0}}

= Microscale (neural) connectome =

Mapping the connectome at the "microscale" (micrometer resolution) means building a complete map of the neural systems, neuron-by-neuron. The challenge of doing this becomes obvious: the number of neurons comprising the brain easily ranges into the billions in more complex organisms. The human cerebral cortex alone contains on the order of 9×10¹⁰ neurons linked by 10¹⁴ synaptic connections.{{cite journal|vauthors=Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, Ferretti RE, Leite RE, Jacob Filho W, Lent R, Herculano-Houzel S|date=April 2009|title=Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain|journal=The Journal of Comparative Neurology|volume=513|issue=5|pages=532–41|doi=10.1002/cne.21974|pmid=19226510|s2cid=5200449}} By comparison, the number of base-pairs in a human genome is 3×10⁹. A few of the main challenges of building a human connectome at the microscale today include: data collection would take years given current technology, machine vision tools to annotate the data remain in their infancy, and are inadequate, and neither theory nor algorithms are readily available for the analysis of the resulting brain-graphs. To address the data collection issues, several groups are building high-throughput serial electron microscopes.{{cite journal

|title=New technique for ultra-thin serial brain section imaging using scanning electron microscopy

|journal=Microscopy and Microanalysis

|volume=13

|number=S02

|pages=26--27

|year=2007

|publisher=Cambridge University Press }}{{cite journal |title=Large-scale automated histology in the pursuit of connectomes

|last19= Sakmann |first19= Bert

|journal=Journal of Neuroscience

|volume=31

|number=45

|pages=16125--16138

|year=2011

|url=https://pubmed.ncbi.nlm.nih.gov/22072665/

|publisher=Society for Neuroscience}} To address the machine-vision and image-processing issues, the Open Connectome Project{{cite journal|vauthors=Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K|date=October 2013|title=The WU-Minn Human Connectome Project: an overview|journal=NeuroImage|volume=80|pages=62–79|doi=10.1016/j.neuroimage.2013.05.041|pmc=3724347|pmid=23684880}} is alg-sourcing (algorithm outsourcing) this hurdle. Finally, statistical graph theory is an emerging discipline which is developing sophisticated pattern recognition and inference tools to parse these brain-graphs.

Current non-invasive imaging techniques cannot capture the brain's activity on a neuron-by-neuron level, except for small animals that are optically transparent (such as Danionella and larval zebrafish). Mapping the connectome at the cellular level in larger vertebrates currently requires post-mortem (after death) microscopic analysis of limited portions of brain tissue. Non-optical techniques that rely on high-throughput DNA sequencing have been proposed by Anthony Zador (CSHL).{{cite journal|vauthors=Zador AM, Dubnau J, Oyibo HK, Zhan H, Cao G, Peikon ID|year=2012|title=Sequencing the connectome|journal=PLOS Biology|volume=10|issue=10|pages=e1001411|doi=10.1371/journal.pbio.1001411|pmc=3479097|pmid=23109909 |doi-access=free }} {{open access}}

Traditional histological circuit-mapping approaches rely on imaging and include light-microscopic techniques for cell staining, injection of labeling agents for tract tracing, or chemical brain preservation, staining and reconstruction of serially sectioned tissue blocks via electron microscopy (EM). Each of these classical approaches has specific drawbacks when it comes to deployment for connectomics. The staining of single cells, e.g. with the Golgi stain, to trace cellular processes and connectivity suffers from the limited resolution of light-microscopy as well as difficulties in capturing long-range projections. Tract tracing, often described as the "gold standard" of neuroanatomy for detecting long-range pathways across the brain, generally only allows the tracing of fairly large cell populations and single axonal pathways. EM reconstruction was successfully used for the compilation of the C. elegans connectome.{{cite journal|vauthors=White JG, Southgate E, Thomson JN, Brenner S|date=November 1986|title=The structure of the nervous system of the nematode Caenorhabditis elegans|journal=Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences|volume=314|issue=1165|pages=1–340|bibcode=1986RSPTB.314....1W|doi=10.1098/rstb.1986.0056|pmid=22462104}} However, applications to larger tissue blocks of entire nervous systems have traditionally had difficulty with projections that span longer distances.

Recent advances in mapping neural connectivity at the cellular level offer significant new hope for overcoming the limitations of classical techniques and for compiling cellular connectome data sets.{{cite journal|vauthors=Livet J, Weissman TA, Kang H, Draft RW, Lu J, Bennis RA, Sanes JR, Lichtman JW|date=November 2007|title=Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system|journal=Nature|volume=450|issue=7166|pages=56–62|bibcode=2007Natur.450...56L|doi=10.1038/nature06293|pmid=17972876|s2cid=4402093}}{{cite journal|vauthors=Lichtman JW, Sanes JR|date=June 2008|title=Ome sweet ome: what can the genome tell us about the connectome?|journal=Current Opinion in Neurobiology|volume=18|issue=3|pages=346–53|doi=10.1016/j.conb.2008.08.010|pmc=2735215|pmid=18801435}}{{cite journal|vauthors=Lichtman JW, Livet J, Sanes JR|date=June 2008|title=A technicolour approach to the connectome|journal=Nature Reviews. Neuroscience|volume=9|issue=6|pages=417–22|doi=10.1038/nrn2391|pmc=2577038|pmid=18446160}} Using Brainbow, a combinatorial color labeling method based on the stochastic expression of several fluorescent proteins, Jeff W. Lichtman and colleagues were able to mark individual neurons with one of over 100 distinct colors. The labeling of individual neurons with a distinguishable hue then allows the tracing and reconstruction of their cellular structure including long processes within a block of tissue.

In March 2011, the journal Nature published a pair of articles on micro-connectomes: Bock et al.{{cite journal|vauthors=Bock DD, Lee WC, Kerlin AM, Andermann ML, Hood G, Wetzel AW, Yurgenson S, Soucy ER, Kim HS, Reid RC|date=March 2011|title=Network anatomy and in vivo physiology of visual cortical neurons|journal=Nature|volume=471|issue=7337|pages=177–82|bibcode=2011Natur.471..177B|doi=10.1038/nature09802|pmc=3095821|pmid=21390124}} and Briggman et al.{{cite journal|vauthors=Briggman KL, Helmstaedter M, Denk W|date=March 2011|title=Wiring specificity in the direction-selectivity circuit of the retina|journal=Nature|volume=471|issue=7337|pages=183–8|bibcode=2011Natur.471..183B|doi=10.1038/nature09818|pmid=21390125|s2cid=4425160}} In both articles, the authors first characterized the functional properties of a small subset of cells, and then manually traced a subset of the processes emanating from those cells to obtain a partial subgraph. In alignment with the principles of open science, the authors of Bock et al. (2011) have released their data for public access. The full resolution 12 terabyte dataset from Bock et al. is available at NeuroData. Independently, important topologies of functional interactions among several hundred cells are also gradually going to be declared.{{cite journal|vauthors=Shimono M, Beggs JM|date=October 2015|title=Functional Clusters, Hubs, and Communities in the Cortical Microconnectome|journal=Cerebral Cortex|volume=25|issue=10|pages=3743–57|doi=10.1093/cercor/bhu252|pmc=4585513|pmid=25336598}}

In addition to EM, an alternative approach to mapping connectivity was proposed in 2012 by Zador and colleagues. Zador's technique, called BOINC (barcoding of individual neuronal connections) uses high-throughput DNA sequencing to map neural circuits. Briefly, the approach consists of labelling each neuron with a unique DNA barcode, transferring barcodes between synaptically coupled neurons (for example using Suid herpesvirus 1, SuHV1), and fusion of barcodes to represent a synaptic pair. This approach has the potential to be cheap, fast, and extremely high-throughput.

In 2016, the Intelligence Advanced Research Projects Activity of the United States government launched MICrONS, a five-year, multi-institute project to map one cubic millimeter of rodent visual cortex, as part of the BRAIN Initiative.{{Cite web|last=Cepelewicz|first=Jordana|date=March 8, 2016|title=The U.S. Government Launches a $100-Million "Apollo Project of the Brain"|url=http://www.scientificamerican.com/article/the-u-s-government-launches-a-100-million-apollo-project-of-the-brain/|access-date=November 27, 2018|work=Scientific American|publisher=Springer Nature America}}{{Cite web|last=Emily|first=Singer|date=April 6, 2016|title=Mapping the Brain to Build Better Machines|url=https://www.quantamagazine.org/mapping-the-brain-to-build-better-machines-20160406/|access-date=November 27, 2018|website=Quanta Magazine|publisher=Simons Foundation}} Though only a small volume of biological tissue, this project will yield one of the largest micro-scale connectomics datasets currently in existence.

In 2024, a new technique called LICONN combined hydrogel expansion with light microscopy (as opposed to electron microscopy) to generate neuron level connectomes.{{cite bioRxiv

|last13= Danzl |first13= Johann

|date=1 Mar 2024 |title=Light-microscopy based dense connectomic reconstruction of mammalian brain tissue

|biorxiv=10.1101/2024.03.01.582884}} The chief advantages are cheaper equipment (optical vs EM microscopes), faster data acquisition, and multi-color labelling.

Plasticity of the connectome

At the beginning of the connectome project, it was thought that the connections between neurons were unchangeable once established and that only individual synapses could be altered. However, recent evidence suggests that connectivity is also subject to change, termed neuroplasticity. There are two ways that the brain can rewire: formation and removal of synapses in an established connection or formation or removal of entire connections between neurons.{{cite journal |last1=Greenough |first1=William T. |last2=Bailey |first2=Craig H. | name-list-style = vanc |title=The anatomy of a memory: convergence of results across a diversity of tests |journal=Trends in Neurosciences |date=January 1988 |volume=11 |issue=4 |pages=142–147 |doi=10.1016/0166-2236(88)90139-7|s2cid=54348379 }}

Pathology

Connectomics have been used to assess brain states in both health and disease.{{Cite journal |last1=Fornito |first1=Alex |last2=Zalesky |first2=Andrew |last3=Pantelis |first3=Christos |last4=Bullmore |first4=Edward T. |date=October 2012 |title=Schizophrenia, neuroimaging and connectomics |url=https://linkinghub.elsevier.com/retrieve/pii/S1053811912002133 |journal=NeuroImage |language=en |volume=62 |issue=4 |pages=2296–2314 |doi=10.1016/j.neuroimage.2011.12.090|url-access=subscription }}{{Cite journal |last1=De Micco |first1=Rosa |last2=Agosta |first2=Federica |last3=Basaia |first3=Silvia |last4=Siciliano |first4=Mattia |last5=Cividini |first5=Camilla |last6=Tedeschi |first6=Gioacchino |last7=Filippi |first7=Massimo |last8=Tessitore |first8=Alessandro |date=July 2021 |title=Functional Connectomics and Disease Progression in Drug-Naïve Parkinson's Disease Patients |url=https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.28541 |journal=Movement Disorders |language=en |volume=36 |issue=7 |pages=1603–1616 |doi=10.1002/mds.28541 |pmid=33639029 |issn=0885-3185|url-access=subscription }} Moreover, connectome-based methods have had an impact on planning or understanding therapeutic options, such as invasive and noninvasive brain stimulation procedures.{{Cite journal |last1=Horn |first1=Andreas |last2=Reich |first2=Martin |last3=Vorwerk |first3=Johannes |last4=Li |first4=Ningfei |last5=Wenzel |first5=Gregor |last6=Fang |first6=Qianqian |last7=Schmitz-Hübsch |first7=Tanja |last8=Nickl |first8=Robert |last9=Kupsch |first9=Andreas |last10=Volkmann |first10=Jens |last11=Kühn |first11=Andrea A. |last12=Fox |first12=Michael D. |date=July 2017 |title=Connectivity Predicts deep brain stimulation outcome in P arkinson disease |journal=Annals of Neurology |language=en |volume=82 |issue=1 |pages=67–78 |doi=10.1002/ana.24974 |issn=0364-5134 |pmc=5880678 |pmid=28586141}}{{Cite journal |last1=Weigand |first1=Anne |last2=Horn |first2=Andreas |last3=Caballero |first3=Ruth |last4=Cooke |first4=Danielle |last5=Stern |first5=Adam P. |last6=Taylor |first6=Stephan F. |last7=Press |first7=Daniel |last8=Pascual-Leone |first8=Alvaro |last9=Fox |first9=Michael D. |date=July 2018 |title=Prospective Validation That Subgenual Connectivity Predicts Antidepressant Efficacy of Transcranial Magnetic Stimulation Sites |journal=Biological Psychiatry |language=en |volume=84 |issue=1 |pages=28–37 |doi=10.1016/j.biopsych.2017.10.028 |pmc=6091227 |pmid=29274805}}{{Cite journal |last1=Fox |first1=Michael D. |last2=Buckner |first2=Randy L. |last3=Liu |first3=Hesheng |last4=Chakravarty |first4=M. Mallar |last5=Lozano |first5=Andres M. |last6=Pascual-Leone |first6=Alvaro |date=2014-10-14 |title=Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases |journal=Proceedings of the National Academy of Sciences |language=en |volume=111 |issue=41 |pages=E4367-75 |doi=10.1073/pnas.1405003111 |doi-access=free |issn=0027-8424 |pmc=4205651 |pmid=25267639}} In this context, the term 'connectomic surgery' was introduced in 2012,{{Cite journal |last=Henderson |first=Jaimie M. |date=2012 |title="Connectomic surgery": diffusion tensor imaging (DTI) tractography as a targeting modality for surgical modulation of neural networks |journal=Frontiers in Integrative Neuroscience |volume=6 |page=15 |doi=10.3389/fnint.2012.00015 |doi-access=free |issn=1662-5145 |pmc=3334531 |pmid=22536176}} as a framework to define or refine surgical targets by identifying pathological brain circuits using neuroimaging techniques such as diffusion-imaging based tractography that are also leveraged for macroscale connectomics. Dysfunctional brain circuits are thought to mediate neurological or psychiatric symptoms in various disorders, and have also been referred to as 'oscillopathies', with the idea that aberrant oscillations unfold along brain circuits, carrying meaningless noise, instead of meaningful information flow throughout the brain.{{Cite journal |last1=Vedam-Mai |first1=Vinata |last2=Deisseroth |first2=Karl |last3=Giordano |first3=James |last4=Lazaro-Munoz |first4=Gabriel |last5=Chiong |first5=Winston |last6=Suthana |first6=Nanthia |last7=Langevin |first7=Jean-Philippe |last8=Gill |first8=Jay |last9=Goodman |first9=Wayne |last10=Provenza |first10=Nicole R. |last11=Halpern |first11=Casey H. |last12=Shivacharan |first12=Rajat S. |last13=Cunningham |first13=Tricia N. |last14=Sheth |first14=Sameer A. |last15=Pouratian |first15=Nader |date=2021-04-19 |title=Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies |journal=Frontiers in Human Neuroscience |volume=15 |doi=10.3389/fnhum.2021.644593 |doi-access=free |issn=1662-5161 |pmc=8092047 |pmid=33953663}} Once identified, dysfunctional circuits may be lesioned by means of ablative neurosurgery or disrupted by means of deep brain stimulation. The (hypothetical) complete library that maps dysfunctional circuits onto specific neurological or psychiatric symptoms has been termed the 'dysfunctome' of the human brain, which could be iteratively mapped and used to inform interventional brain circuit therapeutics.{{Cite journal |last1=Hollunder |first1=Barbara |last2=Ostrem |first2=Jill L. |last3=Sahin |first3=Ilkem Aysu |last4=Rajamani |first4=Nanditha |last5=Oxenford |first5=Simón |last6=Butenko |first6=Konstantin |last7=Neudorfer |first7=Clemens |last8=Reinhardt |first8=Pablo |last9=Zvarova |first9=Patricia |last10=Polosan |first10=Mircea |last11=Akram |first11=Harith |last12=Vissani |first12=Matteo |last13=Zhang |first13=Chencheng |last14=Sun |first14=Bomin |last15=Navratil |first15=Pavel |date=March 2024 |title=Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation |journal=Nature Neuroscience |language=en |volume=27 |issue=3 |pages=573–586 |doi=10.1038/s41593-024-01570-1 |issn=1097-6256 |pmc=10917675 |pmid=38388734}}{{Cite journal |date=March 2024 |title=Mapping the dysfunctome provides an avenue for targeted brain circuit therapy |url=https://www.nature.com/articles/s41593-024-01572-z |journal=Nature Neuroscience |language=en |volume=27 |issue=3 |pages=401–402 |doi=10.1038/s41593-024-01572-z |issn=1097-6256|url-access=subscription }}{{Cite journal |last1=Rajamani |first1=Nanditha |last2=Friedrich |first2=Helen |last3=Butenko |first3=Konstantin |last4=Dembek |first4=Till |last5=Lange |first5=Florian |last6=Navrátil |first6=Pavel |last7=Zvarova |first7=Patricia |last8=Hollunder |first8=Barbara |last9=de Bie |first9=Rob M. A. |last10=Odekerken |first10=Vincent J. J. |last11=Volkmann |first11=Jens |last12=Xu |first12=Xin |last13=Ling |first13=Zhipei |last14=Yao |first14=Chen |last15=Ritter |first15=Petra |date=2024-05-31 |title=Deep brain stimulation of symptom-specific networks in Parkinson's disease |journal=Nature Communications |language=en |volume=15 |issue=1 |page=4662 |doi=10.1038/s41467-024-48731-1 |issn=2041-1723 |pmc=11143329 |pmid=38821913}}

Model organisms and datasets

For macroscacle connectomes, the most common research subject is the human. For microscale connectomes, the most common subjects are the mouse,{{cite journal | vauthors = Bock DD, Lee WC, Kerlin AM, Andermann ML, Hood G, Wetzel AW, Yurgenson S, Soucy ER, Kim HS, Reid RC | title = Network anatomy and in vivo physiology of visual cortical neurons | journal = Nature | volume = 471 | issue = 7337 | pages = 177–182 | date = March 2011 | pmid = 21390124 | pmc = 3095821 | doi = 10.1038/nature09802 | bibcode = 2011Natur.471..177B }} the fruit fly,{{cite journal | vauthors = Chklovskii DB, Vitaladevuni S, Scheffer LK | title = Semi-automated reconstruction of neural circuits using electron microscopy | journal = Current Opinion in Neurobiology | volume = 20 | issue = 5 | pages = 667–675 | date = October 2010 | pmid = 20833533 | doi = 10.1016/j.conb.2010.08.002 | s2cid = 206950616 }}{{cite journal | vauthors = Zheng Z, Lauritzen JS, Perlman E, Robinson CG, Nichols M, Milkie D, Torrens O, Price J, Fisher CB, Sharifi N, Calle-Schuler SA, Kmecova L, Ali IJ, Karsh B, Trautman ET, Bogovic JA, Hanslovsky P, Jefferis GS, Kazhdan M, Khairy K, Saalfeld S, Fetter RD, Bock DD | title = A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster | journal = Cell | volume = 174 | issue = 3 | pages = 730–743.e22 | date = July 2018 | pmid = 30033368 | pmc = 6063995 | doi = 10.1016/j.cell.2018.06.019 }} the nematode C. elegans,{{cite journal | vauthors = Chen BL, Hall DH, Chklovskii DB | title = Wiring optimization can relate neuronal structure and function | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 103 | issue = 12 | pages = 4723–4728 | date = March 2006 | pmid = 16537428 | pmc = 1550972 | doi = 10.1073/pnas.0506806103 | doi-access = free | bibcode = 2006PNAS..103.4723C }}{{cite journal | vauthors = Pérez-Escudero A, Rivera-Alba M, de Polavieja GG | title = Structure of deviations from optimality in biological systems | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 48 | pages = 20544–20549 | date = December 2009 | pmid = 19918070 | pmc = 2777958 | doi = 10.1073/pnas.0905336106 | doi-access = free | bibcode = 2009PNAS..10620544P }} and the barn owl.{{cite journal | vauthors = Pena JL, DeBello WM | title = Auditory processing, plasticity, and learning in the barn owl | journal = ILAR Journal | volume = 51 | issue = 4 | pages = 338–352 | year = 2010 | pmid = 21131711 | pmc = 3102523 | doi = 10.1093/ilar.51.4.338 }}

= Human =

The Human Connectome Project (HCP) was an initiative launched in 2009 by the National Institutes of Health (NIH) to map the neural pathways that underlie human brain function.{{Cite web |title=Connectome Programs {{!}} Blueprint |url=https://neuroscienceblueprint.nih.gov/human-connectome/connectome-programs |access-date=2023-06-19 |website=neuroscienceblueprint.nih.gov}} The goal was to obtain and distribute information regarding the structural and functional connections within the human brain, improving imaging and analysis methods to enhance resolution and practicality in the realm of connectomics. By understanding the wiring patterns within and across individuals, researchers hope to unravel the electrical signals that give rise to our thoughts, emotions, and behaviors. Additional programs within the Connectome Initiative, such as the Lifespan Connectome and Disease Connectome, focus on mapping brain connections across different age groups and studying connectome variations in individuals with specific clinical diagnoses. The Connectome Coordination Facility serves as a centralized repository for HCP data and provides support to researchers. The success of this project has opened the door to understanding how connectomics might be influential in other areas of neuroscience. The potential of a "Connectome II" project has been referenced recently, which would focus on developing a scanner designed for high-throughput studies involving multiple subjects.{{cite journal | vauthors = Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JL, Burgess GC, Curtiss SW, Oostenveld R, Larson-Prior LJ, Schoffelen JM, Hodge MR, Cler EA, Marcus DM, Barch DM, Yacoub E, Smith SM, Ugurbil K, Van Essen DC | title = The Human Connectome Project: A retrospective | journal = NeuroImage | volume = 244 | pages = 118543 | date = December 2021 | pmid = 34508893 | doi = 10.1016/j.neuroimage.2021.118543 | pmc = 9387634 }} The project would aim to utilize recent advancements in visualization technologies towards a higher spatial resolution in imaging structural connectivity. Advancements in this area might also involve incorporating wearable mobile technology to acquire various types of behavioral data, complementing the neuroimaging information gathered by the scanner.

= Small animals=

Caenorhabditis elegans., commonly referred to as C. Elegans, is a small (<1 mm) nematode (or roundworm). It has a very small nervous system with 302 neurons and about 5000 synaptic connections (as compared, say, to the human brain which has 100 billion neurons and more than 100 trillion chemical synapses).{{Cite web |title=Overview |url=https://medicine.yale.edu/lab/colon_ramos/overview/ |access-date=2023-06-19 |website=medicine.yale.edu |language=en}} It was the first animal with a fully reconstructed connectome.''{{cite journal |last1=Brouillette |first1=Monique |title=Mapping the brain to understand the mind |journal=Knowable Magazine |date=21 April 2022 |doi=10.1146/knowable-042122-1 |url=https://knowablemagazine.org/article/mind/2022/mapping-brain-understand-mind|doi-access=free |language=en}}

The C. elegans connectome reconstruction began with electron micrographs published by White, Brenner et al., 1986. Based on this seminal work, the first ever connectome (then called "neural circuitry database" by the authors) for C. elegans was published in book form with accompanying floppy disks by Achacoso and Yamamoto in 1992.{{Cite web|title=Ay's Neuroanatomy of C. elegans for Computation|url=https://www.crcpress.com/Ays-Neuroanatomy-of-C-Elegans-for-Computation/Achacoso-Yamamoto/p/book/9780849342349|access-date=2019-10-15|website=CRC Press|language=en|archive-date=2019-10-15|archive-url=https://web.archive.org/web/20191015035549/https://www.crcpress.com/Ays-Neuroanatomy-of-C-Elegans-for-Computation/Achacoso-Yamamoto/p/book/9780849342349|url-status=dead}}{{Cite journal|last1=Yamamoto|first1=William S.|last2=Achacoso|first2=Theodore B.|date=1992-06-01|title=Scaling up the nervous system of Caenorhabditis elegans: Is one ape equal to 33 million worms?|journal=Computers and Biomedical Research|volume=25|issue=3|pages=279–291|doi=10.1016/0010-4809(92)90043-A|issn=0010-4809|pmid=1611892}} The very first paper on the computer representation of its connectome was presented and published three years earlier in 1989 by Achacoso at the Symposium on Computer Application in Medical Care (SCAMC).{{Cite journal|last1=Achacoso|first1=Theodore B.|last2=Fernandez|first2=Victor|last3=Nguyen|first3=Duc C.|last4=Yamamoto|first4=William S.|date=1989-11-08|title=Computer Representation of the Synaptic Connectivity of Caenorhabditis Elegans|journal=Proceedings of the Annual Symposium on Computer Application in Medical Care|pages=330–334|issn=0195-4210|pmc=2245716}} The C. elegans connectome was later revised.{{cite journal|vauthors=Varshney LR, Chen BL, Paniagua E, Hall DH, Chklovskii DB|date=February 2011|title=Structural properties of the Caenorhabditis elegans neuronal network|journal=PLOS Computational Biology|volume=7|issue=2|pages=e1001066|bibcode=2011PLSCB...7E1066V|doi=10.1371/journal.pcbi.1001066|pmc=3033362|pmid=21304930|veditors=Sporns O |doi-access=free }} {{open access}}{{cite journal|last1=Cook|first1=Steven J.|last2=Jarrell|first2=Travis A.|last3=Brittin|first3=Christopher A.|last4=Wang|first4=Yi|last5=Bloniarz|first5=Adam E.|last6=Yakovlev|first6=Maksim A.|last7=Nguyen|first7=Ken C. Q.|last8=Tang|first8=Leo T.-H.|last9=Bayer|first9=Emily A.|last10=Duerr|first10=Janet S.|last11=Bülow|first11=Hannes E.|date=3 July 2019|title=Whole-animal connectomes of both Caenorhabditis elegans sexes|journal=Nature|volume=571|issue=7763|pages=63–71|bibcode=2019Natur.571...63C|doi=10.1038/s41586-019-1352-7|pmc=6889226|pmid=31270481|first13=David H.|last14=Emmons|first14=Scott W.|last13=Hall|first12=Oliver|last12=Hobert}}

The small size of the nervous system of C. elegans has allowed studies that would be difficult or impractical in larger organisms. These include changes during the animal's development.{{cite journal|last1=Witvliet|first1=Daniel|last2=Mulcahy|first2=Ben|last3=Mitchell|first3=James K.|last4=Meirovitch|first4=Yaron|last5=Berger|first5=Daniel R.|last6=Wu|first6=Yuelong|last7=Liu|first7=Yufang|last8=Koh|first8=Wan Xian|last9=Parvathala|first9=Rajeev|last10=Holmyard|first10=Douglas|last11=Schalek|first11=Richard L.|date=August 2021|title=Connectomes across development reveal principles of brain maturation|journal=Nature|language=en|volume=596|issue=7871|pages=257–261|biorxiv=10.1101/2020.04.30.066209|doi=10.1038/s41586-021-03778-8|issn=1476-4687|last15=Samuel|first16=Mei|first12=Nir|first15=Aravinthan D. T.|first14=Jeff W.|last16=Zhen|last14=Lichtman|first13=Andrew D.|last13=Chisholm|last12=Shavit|pmid=34349261 |pmc=8756380 |bibcode=2021Natur.596..257W }}{{cite journal|vauthors=Vogelstein JV, Perlman E, Falk B, Baden A, Gray-Roncal W, Chandrashekhar V, Collman C, Seshamani S, Patsolic JL, Lillaney K, Kazhdan M, Hider R, Pryor D, Matelsky J, Gion T, Manavalan P, Wester B, Chevillet M, Trautman ET, Khairy K, Bridgeford E, Kleissas DM, Tward DJ, Crow AK, Hsueh B, Wright MA, Miller MI, Smith SJ, Vogelstein JR, Deisseroth K, Burns R|date=October 2018|title=A community-developed open-source computational ecosystem for big neuro data|journal=Nature Methods|volume=15|issue=11|pages=846–847|arxiv=1804.02835|bibcode=2018arXiv180402835B|doi=10.1038/s41592-018-0181-1|pmc=6481161|pmid=30377345}}, variability between individuals, both at the level of synapse and connection, despite an invariant cell lineage{{Cite journal |last1=Cook |first1=Steven J. |last2=Kalinski |first2=Cristine A. |last3=Hobert |first3=Oliver |date=2023-06-05 |title=Neuronal contact predicts connectivity in the C. elegans brain |journal=Current Biology |volume=33 |issue=11 |pages=2315–2320.e2 |doi=10.1016/j.cub.2023.04.071 |pmid=37236179 |issn=0960-9822 |quote=C. elegans neurons show inter-individual variability in adjacency and connectivity|doi-access=free |bibcode=2023CBio...33E2315C }} and changes during development and aging.{{Citation |last1=Witvliet |first1=Daniel |title=Connectomes across development reveal principles of brain maturation in C. elegans |date=2020-04-30 |url=https://www.biorxiv.org/content/10.1101/2020.04.30.066209v1 |access-date=2024-01-23 |language=en |doi=10.1101/2020.04.30.066209 |quote=About 43% of all connections and 16% of all synapses were not conserved between animals. This degree of variability contrasts with the widely held view that the C. elegans connectome is hardwired. |last2=Mulcahy |first2=Ben |last3=Mitchell |first3=James K. |last4=Meirovitch |first4=Yaron |last5=Berger |first5=Daniel R. |last6=Wu |first6=Yuelong |last7=Liu |first7=Yufang |last8=Koh |first8=Wan Xian |last9=Parvathala |first9=Rajeev|s2cid=263532508 |hdl=1721.1/143880 |hdl-access=free }} Researchers found the biggest change with age is the wiring circuits, and that connectivity between and within brain regions increases with age. Other studies have combined the connectome with behavior, environmental influences, and other available information to study the connection between neuroanatomy and behavior,{{cite journal | vauthors = Izquierdo EJ, Beer RD | title = Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis | journal = PLOS Computational Biology | volume = 9 | issue = 2 | pages = e1002890 | date = 2013-02-07 | pmid = 23408877 | doi = 10.1371/journal.pcbi.1002890 | pmc = 3567170 | bibcode = 2013PLSCB...9E2890I | doi-access = free }} and suggested comparing the connectome to that of other animals, once available.

Two other small animals with complete connectomes are the larvi of the ascidian Ciona intestinalis{{cite journal

|title=The CNS connectome of a tadpole larva of Ciona intestinalis (L.) highlights sidedness in the brain of a chordate sibling

|journal=Elife

|volume=5

|pages=e16962

|year=2016

|publisher={eLife Sciences Publications, Ltd

|url=https://elifesciences.org/articles/16962.pdf}}

(177 CNS neurons, 6618 synapses including 1772 neuromuscular junctions and 1206 gap junctions) and Platynereis dumerilii (2728 neurons, 25,509 synapses).{{cite bioRxiv

|title=Whole-animal connectome and cell-type complement of the three-segmented Platynereis dumerilii larva

|last10= Williams |first10= Elizabeth |last11= Jékely |first11= Gáspár

|biorxiv=10.1101/2020.08.21.260984

|pages=2020--08

|year=2020

}}

=Fruit fly =

The fruit fly, Drosophila melanogaster, serves as an appealing model for exploring the structure and operation of nervous systems. Its central nervous system (CNS) is notably compact, housing approximately 3,000 neurons in the larva and 200,000 neurons in adults, and the fly exhibits reasonably stereotyped neural connections across individual flies.{{cite journal |last1=Schlegel |first1=Philipp |title=Information flow, cell types and stereotypy in a full olfactory connectome |journal=eLife |date=2021-05-25 |volume=10 |issue=10 |doi=10.7554/eLife.66018 |pmid=34032214 |pmc=8298098 |doi-access=free }} Despite its small size, this CNS supports a broad spectrum of complex and well-studied behaviors, plus there are many genetic tools that enable experiments on the CNS. Obtaining an anatomical dataset of the fly's CNS could be a pivotal step, potentially offering insights into the nervous systems of other organisms. Within the last decade, largely owing to technological advancements in EM data collection and image processing, multiple synapse-scale connectome datasets have been generated for the fruit fly Drosophila melanogaster in its adult and larval forms.

Drosophila connectomics started in 1991 with a description of the circuits of the lamina.

{{cite journal | vauthors = Meinertzhagen IA, O'Neil SD | title = Synaptic organization of columnar elements in the lamina of the wild type in Drosophila melanogaster | journal = The Journal of Comparative Neurology | volume = 305 | issue = 2 | pages = 232–263 | date = March 1991 | pmid = 1902848 | doi = 10.1002/cne.903050206 | s2cid = 35301798 }} However the methods used were largely manual and further progress awaited more automated techniques. In 2011, a high-level connectome, at the level of brain compartments and interconnecting tracts of neurons, for the full fly brain was published,

{{cite journal | vauthors = Chiang AS, Lin CY, Chuang CC, Chang HM, Hsieh CH, Yeh CW, Shih CT, Wu JJ, Wang GT, Chen YC, Wu CC, Chen GY, Ching YT, Lee PC, Lin CY, Lin HH, Wu CC, Hsu HW, Huang YA, Chen JY, Chiang HJ, Lu CF, Ni RF, Yeh CY, Hwang JK | display-authors = 6 | title = Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution | journal = Current Biology | volume = 21 | issue = 1 | pages = 1–11 | date = January 2011 | pmid = 21129968 | doi = 10.1016/j.cub.2010.11.056 | s2cid = 17155338 | doi-access = free }} and is available online.{{cite web |title=FlyCircuit - A Database of Drosophila Brain Neurons |url=http://flycircuit.tw/ | work = National Center for High-Performance Computing (NCHC) | access-date=30 Aug 2013 }} New techniques such as digital image processing began to be applied to detailed neural reconstruction.{{cite journal | vauthors = Rivera-Alba M, Vitaladevuni SN, Mishchenko Y, Lu Z, Takemura SY, Scheffer L, Meinertzhagen IA, Chklovskii DB, de Polavieja GG | display-authors = 6 | title = Wiring economy and volume exclusion determine neuronal placement in the Drosophila brain | journal = Current Biology | volume = 21 | issue = 23 | pages = 2000–2005 | date = December 2011 | pmid = 22119527 | pmc = 3244492 | doi = 10.1016/j.cub.2011.10.022 }}

Reconstructions of larger regions soon followed, including a column of the medulla,

{{cite journal | vauthors = Takemura SY, Bharioke A, Lu Z, Nern A, Vitaladevuni S, Rivlin PK, Katz WT, Olbris DJ, Plaza SM, Winston P, Zhao T, Horne JA, Fetter RD, Takemura S, Blazek K, Chang LA, Ogundeyi O, Saunders MA, Shapiro V, Sigmund C, Rubin GM, Scheffer LK, Meinertzhagen IA, Chklovskii DB | display-authors = 6 | title = A visual motion detection circuit suggested by Drosophila connectomics | journal = Nature | volume = 500 | issue = 7461 | pages = 175–181 | date = August 2013 | pmid = 23925240 | pmc = 3799980 | doi = 10.1038/nature12450 | bibcode = 2013Natur.500..175T }} also in the visual system of the fruit fly, and the alpha lobe of the mushroom body.

{{cite journal | vauthors = Takemura SY, Aso Y, Hige T, Wong A, Lu Z, Xu CS, Rivlin PK, Hess H, Zhao T, Parag T, Berg S, Huang G, Katz W, Olbris DJ, Plaza S, Umayam L, Aniceto R, Chang LA, Lauchie S, Ogundeyi O, Ordish C, Shinomiya A, Sigmund C, Takemura S, Tran J, Turner GC, Rubin GM, Scheffer LK | display-authors = 6 | title = A connectome of a learning and memory center in the adult Drosophila brain | journal = eLife | volume = 6 | pages = e26975 | date = July 2017 | pmid = 28718765 | pmc = 5550281 | doi = 10.7554/eLife.26975 | doi-access = free }} In 2020, a dense connectome of half the central brain of Drosophila was released,{{cite bioRxiv | vauthors = Xu CS, Januszewski M, Lu Z, Takemura SY, Hayworth KJ, Huang G, Shinomiya K, Maitin-Shepard J, Ackerman D, Berg S, Blakely T |display-authors=6 |year=2020 |title=A connectome of the adult Drosophila central brain |biorxiv=10.1101/2020.01.21.911859 }} along with a web site that allows queries and exploration of this data.{{cite web |title=Analysis tools for connectomics |url=https://neuprint.janelia.org |publisher=Howard Hughes Medical Institute (HHMI)

}} The methods used in reconstruction and initial analysis of the 'hemibrain' connectome followed.{{cite journal | vauthors = Scheffer LK, Xu CS, Januszewski M, Lu Z, Takemura SY, Hayworth KJ, Huang GB, Shinomiya K, Maitlin-Shepard J, Berg S, Clements J, Hubbard PM, Katz WT, Umayam L, Zhao T, Ackerman D, Blakely T, Bogovic J, Dolafi T, Kainmueller D, Kawase T, Khairy KA, Leavitt L, Li PH, Lindsey L, Neubarth N, Olbris DJ, Otsuna H, Trautman ET, Ito M, Bates AS, Goldammer J, Wolff T, Svirskas R, Schlegel P, Neace E, Knecht CJ, Alvarado CX, Bailey DA, Ballinger S, Borycz JA, Canino BS, Cheatham N, Cook M, Dreher M, Duclos O, Eubanks B, Fairbanks K, Finley S, Forknall N, Francis A, Hopkins GP, Joyce EM, Kim S, Kirk NA, Kovalyak J, Lauchie SA, Lohff A, Maldonado C, Manley EA, McLin S, Mooney C, Ndama M, Ogundeyi O, Okeoma N, Ordish C, Padilla N, Patrick CM, Paterson T, Phillips EE, Phillips EM, Rampally N, Ribeiro C, Robertson MK, Rymer JT, Ryan SM, Sammons M, Scott AK, Scott AL, Shinomiya A, Smith C, Smith K, Smith NL, Sobeski MA, Suleiman A, Swift J, Takemura S, Talebi I, Tarnogorska D, Tenshaw E, Tokhi T, Walsh JJ, Yang T, Horne JA, Li F, Parekh R, Rivlin PK, Jayaraman V, Costa M, Jefferis GS, Ito K, Saalfeld S, George R, Meinertzhagen IA, Rubin GM, Hess HF, Jain V, Plaza SM | display-authors = 6 | title = A connectome and analysis of the adult Drosophila central brain | journal = eLife | volume = 9 | issue = | date = September 2020 | pmid = 32880371 | pmc = 7546738 | doi = 10.7554/eLife.57443 | doi-access = free }} In 2023, the connectome of the female adult fly brain (FAFB) volume was released,{{Cite report |title=Neuronal wiring diagram of an adult brain |last1=Dorkenwald |first1=Sven |last2=Matsliah |first2=Arie |date=2023-06-30 |publisher=Neuroscience |doi=10.1101/2023.06.27.546656 |language=en |pmc=10327113 |pmid=37425937 |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}} which encompasses the entire brain of an adult.

There are also currently two publicly available datasets of the adult fly ventral nerve cord (VNC). The female adult nerve cord (FANC) was collected using high-throughput ssTEM by Wei-Chung Allen Lee’s lab at Harvard Medical School.{{cite journal | vauthors = Phelps JS, Hildebrand DG, Graham BJ, Kuan AT, Thomas LA, Nguyen TM, Buhmann J, Azevedo AW, Sustar A, Agrawal S, Liu M, Shanny BL, Funke J, Tuthill JC, Lee WA | title = Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy | journal = Cell | volume = 184 | issue = 3 | pages = 759–774.e18 | date = February 2021 | pmid = 33400916 | pmc = 8312698 | doi = 10.1016/j.cell.2020.12.013 }} and was reconstructed in 2024.{{cite journal

|title=Connectomic reconstruction of a female Drosophila ventral nerve cord},

|first34= Wei-Chung Allen |last35= Tuthill |first35= John C.

|journal=Nature

|volume=631

|number=8020

|pages=360--368

|year=2024

|publisher={Nature Publishing Group UK London

}} The male adult nerve cord (MANC) reconstructed soon after.{{Cite report |url=http://biorxiv.org/lookup/doi/10.1101/2023.06.05.543757 |title=A Connectome of the Male Drosophila Ventral Nerve Cord |last1=Takemura |first1=Shin-ya |last2=Hayworth |first2=Kenneth J |date=2023-06-06 |publisher=Neuroscience |doi=10.1101/2023.06.05.543757 |language=en |last3=Huang |first3=Gary B |last4=Januszewski |first4=Michal |last5=Lu |first5=Zhiyuan |last6=Marin |first6=Elizabeth C |last7=Preibisch |first7=Stephan |last8=Xu |first8=C Shan |last9=Bogovic |first9=John}} In addition, the connectome of a complete central nervous system (connected brain and VNC) of a 1st instar D. melanogaster larva has been reconstructed as a single dataset of 3016 neurons.{{Cite journal |last1=Winding |first1=Michael |last2=Pedigo |first2=Benjamin |last3=Barnes |first3=Christopher |last4=Patsolic |first4=Heather |last5=Park |first5=Youngser |last6=Kazimiers |first6=Tom |last7=Fushiki |first7=Akira |last8=Andrade |first8=Ingrid |last9=Khandelwal |first9=Avinash |last10=Valdes-Aleman |first10=Javier |last11=Li |first11=Feng |last12=Randel |first12=Nadine |last13=Barsotti |first13=Elizabeth |last14=Correia |first14=Ana |last15=Fetter |first15=Fetter |last16=Hartenstein |first16=Volker |last17=Priebe |first17=Carey |last18=Vogelstein |first18=Joshua |last19=Cardona |first19=Albert |last20=Zlatic |first20=Marta |date=10 Mar 2023 |title=The connectome of an insect brain |journal=Science |volume=379 |issue=6636 |pages=eadd9330 |biorxiv=10.1101/2022.11.28.516756v1 |doi=10.1126/science.add9330 |pmid=36893230|pmc=7614541 |s2cid=254070919 }}{{Cite web |first=Jill |last=Rosen |date=2023-03-09 |title=Scientists complete first map of an insect brain |url=https://hub.jhu.edu/2023/03/09/scientists-complete-first-map-of-an-insect-brain/ |access-date=2023-03-11 |website=The Hub |language=en}}{{Cite web |date=2023-03-10 |title=First wiring map of insect brain complete |url=https://www.cam.ac.uk/research/news/first-wiring-map-of-insect-brain-complete |access-date=2023-03-11 |website=University of Cambridge |language=en}}

Progress is still on-going - at least two teams are working on complete adult CNS connectomes that includes both the brain and the VNC, in both male and female flies.{{cite web |title=BANC Guide for Citizen Scientists |date=20 December 2024 |url=https://blog.flywire.ai/2024/12/20/banc-guide-for-citizen-scientists/}}{{Cite web|url=https://www.janelia.org/news/seeing-is-believing-janelia-reveals-connectome-of-the-fruit-fly-visual-system|title=Seeing is believing: Janelia reveals connectome of the fruit fly visual system|website=Janelia Research Campus}}

= Mouse =

Partial connectomes of a mouse retina and mouse primary visual cortex are available. The first full connectome of a mammalian circuit (not the whole brain) was constructed in 2021. This construction included the development of all connections between the central nervous system and a single muscle from birth to adulthood.{{cite journal|last1=Meirovitch|first1=Yaron|last2=Kang|first2=Kai|last3=Draft|first3=Ryan W.|last4=Pavarino|first4=Elisa C.|last5=Henao E.|first5=Maria F.|last6=Yang|first6=Fuming|last7=Turney|first7=Stephen G.|last8=Berger|first8=Daniel R.|last9=Peleg|first9=Adi|last10=Schalek|first10=Richard L.|last11=Lu|first11=Ju L.|date=September 2021|title=Neuromuscular connectomes across development reveal synaptic ordering rules|journal=bioRxiv|language=en|doi=10.1101/2021.09.20.460480|last12=Tapia|first12=Juan-Carlos|last13=Lichtman|first13=Jeff W.|s2cid=237598181}}

An online database known as MouseLight displays over 1000 neurons mapped in the mouse brain based on a collective database of sub-micron resolution images of these brains. This platform illustrates the thalamus, hippocampus, cerebral cortex, and hypothalamus based on single-cell projections.{{Cite web |title=MouseLight |url=http://www.mouselight.janelia.org/ |access-date=2023-06-19 |website=www.mouselight.janelia.org}} Imaging technology to produce this mouse brain does not allow an in-depth look at synapses but can show axonal arborizations which contain many synapses.{{cite journal | vauthors = Winnubst J, Bas E, Ferreira TA, Wu Z, Economo MN, Edson P, Arthur BJ, Bruns C, Rokicki K, Schauder D, Olbris DJ, Murphy SD, Ackerman DG, Arshadi C, Baldwin P, Blake R, Elsayed A, Hasan M, Ramirez D, Dos Santos B, Weldon M, Zafar A, Dudman JT, Gerfen CR, Hantman AW, Korff W, Sternson SM, Spruston N, Svoboda K, Chandrashekar J | title = Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain | journal = Cell | volume = 179 | issue = 1 | pages = 268–281.e13 | date = September 2019 | pmid = 31495573 | doi = 10.1016/j.cell.2019.07.042 | pmc = 6754285 }} A limiting factor to studying mouse connectomes, much like with humans, is the complexity of labeling all the cell types of the mouse brain; this is a process that would require the reconstruction of 100,000+ neurons and imaging technology is not yet advanced enough to do so.

Some portions of the technology needed to scale ultrastructural circuit mapping to the whole mouse brain are under investigation.{{cite journal|vauthors=Mikula S, Binding J, Denk W|date=December 2012|title=Staining and embedding the whole mouse brain for electron microscopy|journal=Nature Methods|volume=9|issue=12|pages=1198–201|doi=10.1038/nmeth.2213|pmid=23085613|s2cid=205421025}} However, the mouse brain is about 10,000 times bigger than the brain of Drosophila brain, the largest reconstructed to date. A mouse connectome will therefore require non-trivial advances in connectivity mapping.{{cite journal |title=The mind of a mouse

|url=https://www.cell.com/cell/pdf/S0092-8674(20)31001-1.pdf

|last25= and |first25= |last26= Essen |first26= David C. Van

|journal=Cell

|volume=182

|number=6

|pages=1372--1376

|year=2020

|publisher=Elsevier

}}

Eyewire game

Eyewire is an online game developed by American scientist Sebastian Seung of Princeton University. It uses social computing to help map the connectome of the brain. It has attracted over 130,000 players from over 100 countries.

References

External links

[http://www.braingraph.org/ Database of hundreds of braingraphs with different resolutions and weight functions at braingraph.org]
[http://neuroscienceblueprint.nih.gov/ The NIH Blueprint for Neuroscience Research]
TED talk by Sebastian Seung: [https://www.youtube.com/watch?v=HA7GwKXfJB0 I am my connectome]
[http://www.mitk.org/DiffusionImaging MITK Diffusion: Free software for the processing of diffusion-weighted MR data including connectomics]

Category:Cognitive neuroscience

Category:Computational neuroscience

Category:Diagrams