connectomics

File:Connectomics.png image series are used to map white matter tracts, and fMRI series are used to assess how blood flow correlates between connected gray matter areas.]]

Macroscale connectomes are commonly collected using diffusion-weighted magnetic resonance imaging (DW-MRI) and functional magnetic resonance imaging (fMRI). DW-MRI datasets can span the entire brain, imaging white matter between the cortex and subcortex, providing information about the diffusion of water molecules in brain tissue, and allowing researchers to infer the orientation and integrity of white matter pathways.{{Cite journal |last1=Sotiropoulous |first1=Statmotios |last2=Zalesky |first2=Andrew |date=June 27, 2017 |title=Building connectomes using diffusion MRI: why, how and but |journal= NMR in Biomedicine|volume=32 |issue=4 |pages=e3752 |doi=10.1002/nbm.3752 |pmid=28654718 |pmc=6491971 }} DW-MRI can be used in conjunction with tractography where it enables the reconstruction of white matter tracts in the brain. It does so by measuring the diffusion of water molecules in multiple directions, as DW-MRI can estimate the local fiber orientations and generate a model of the brain's fiber pathways. Meanwhile, tractography algorithms trace the likely trajectories of these pathways, providing a representation of the brain's anatomical connectivity. Metrics such as fractional anisotropy (FA), mean diffusivity (MD), or connectivity strength can be computed from DW-MRI data to assess the microstructural properties of white matter and quantify the strength of (long-range) connections between brain regions.{{Cite journal |journal=eLife |title= Strong long-range connections can delocalize a subset of eigenvectors. |doi=10.7554/elife.01239.010 |doi-access=free }} Figure 7.

In contrast to DW-MRI, fMRI measures the blood oxygenation level-dependent (BOLD) signal, which reflects changes in cerebral blood flow and oxygenation associated with neural activity, as regulated by the neurovascular unit.{{Cite journal |last1=Deligianni |first1=Fani |last2=Centeno |first2=Maria |last3=Carmichael |first3=David W. |last4=Clayden |first4=Jonathan D. |date=2014 |title=Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands |journal=Frontiers in Neuroscience |volume=8 |page=258 |doi=10.3389/fnins.2014.00258 |pmid=25221467 |pmc=4148011 |issn=1662-453X |doi-access=free}} When used together, a resting-state fMRI and a DW-MRI dataset provide a comprehensive view of how regions of the brain are structurally connected, and how closely they are communicating.{{cite journal | vauthors = Damoiseaux JS, Greicius MD | title = Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity | journal = Brain Structure & Function | volume = 213 | issue = 6 | pages = 525–533 | date = October 2009 | pmid = 19565262 | doi = 10.1007/s00429-009-0208-6 | s2cid = 16792748}}{{cite journal | vauthors = Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P | title = Predicting human resting-state functional connectivity from structural connectivity | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 6 | pages = 2035–2040 | date = February 2009 | pmid = 19188601 | pmc = 2634800 | doi = 10.1073/pnas.0811168106 | doi-access = free | bibcode = 2009PNAS..106.2035H}} Resting-state functional connectivity (RSFC) analysis is a common method to measure connectomes using fMRI that involves acquiring fMRI data while the subject is at rest and not performing any specific tasks or stimuli.{{Cite journal |last1=Zhu |first1=Feiyin |last2=Tang |first2=Liying |date=July 5, 2019 |title=Resting-state functional magnetic resonance imaging (fMRI) and functional connectivity density mapping in patients with corneal ulcer |journal= Neuropsychiatric Disease and Treatment|volume=15 |pages=1833–1844 |doi=10.2147/NDT.S210658 |pmid=31308676 |pmc=6617566 |doi-access=free }} RSFC examines the temporal correlation of the BOLD signals between different brain regions (after accounting for the confounding effect of other regions), providing insights into functional connectivity.

Techniques that actively manipulate the brain, often called neuromodulation, can provide insights into the connectome.{{Cite journal |last1=Horn |first1=Andreas |last2=Fox |first2=Michael D. |date=2020-11-01 |title=Opportunities of connectomic neuromodulation |journal=NeuroImage |language=en |volume=221 |pages=117180 |doi=10.1016/j.neuroimage.2020.117180 |issn=1053-8119 |pmc=7847552 |pmid=32702488}} For example, transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that applies strong magnetic pulses between scalp electrodes which target specific brain regions with electrical currents.{{Cite journal |last1=Xia |first1=Mingrui |last2=He |first2=Yong |date=2022-10-01 |title=Connectome-guided transcranial magnetic stimulation treatment in depression |journal=European Child & Adolescent Psychiatry |language=en |volume=31 |issue=10 |pages=1481–1483 |doi=10.1007/s00787-022-02089-1 |issn=1435-165X |pmid=36151354 |s2cid=252497452 |doi-access=free}} This can temporarily disrupt or enhance the activity of specific brain areas and observe changes in connectivity. Transcranial direct current stimulation (tDCS) is another non-invasive neuromodulation technique that applies a constant but relatively weak electrical current for a few minutes, modulating neuronal excitability.{{Cite journal |last1=Kunze |first1=Tim |last2=Hunold |first2=Alexander |last3=Haueisen |first3=Jens |last4=Jirsa |first4=Viktor |last5=Spiegler |first5=Andreas |date=2016-10-15 |title=Transcranial direct current stimulation changes resting state functional connectivity: A large-scale brain network modeling study |url= |journal=NeuroImage |series=Transcranial electric stimulation (tES) and Neuroimaging |language=en |volume=140 |pages=174–187 |doi=10.1016/j.neuroimage.2016.02.015 |issn=1053-8119 |pmid=26883068 |s2cid=17716820 |hdl-access=free |hdl=11858/00-001M-0000-002C-EBE2-7}} It allows researchers to investigate the causal relationship between targeted brain regions and changes in connectivity. tDCS increases the functional connectivity within the brain, with a bias towards specific networks (e.g., cortical processing), and may even cause structural changes to take place in the white matter via myelination and in the gray matter via synaptic plasticity. Another neuromodulation technique is deep brain stimulation (DBS), an invasive technique that involves surgically implanting electrodes into specific brain regions in order to apply localized, high-frequency electrical impulses.{{Cite journal |last1=Wang |first1=Qiang |last2=Akram |first2=Harith |last3=Muthuraman |first3=Muthuraman |last4=Gonzalez-Escamilla |first4=Gabriel |last5=Sheth |first5=Sameer A. |last6=Oxenford |first6=Simón |last7=Yeh |first7=Fang-Cheng |last8=Groppa |first8=Sergiu |last9=Vanegas-Arroyave |first9=Nora |last10=Zrinzo |first10=Ludvic |last11=Li |first11=Ningfei |last12=Kühn |first12=Andrea |last13=Horn |first13=Andreas |date=2021-01-01 |title=Normative vs. patient-specific brain connectivity in deep brain stimulation |journal=NeuroImage |language=en |volume=224 |pages=117307 |doi=10.1016/j.neuroimage.2020.117307 |issn=1053-8119 |pmid=32861787 |s2cid=216357803 |doi-access=free}} This technique modulates brain networks and is often used to alleviate motor symptoms from disorders like Parkinson's, essential tremor, and dystonia.{{Cite journal |last=Benabid |first=Alim Louis |date=2003-12-01 |title=Deep brain stimulation for Parkinson's disease |url= |journal=Current Opinion in Neurobiology |language=en |volume=13 |issue=6 |pages=696–706 |doi=10.1016/j.conb.2003.11.001 |issn=0959-4388 |pmid=14662371 |s2cid=27174469}} The functional and structural connectivity between electrodes can be used to predict patient outcomes and estimate optimal connectivity profiles.

Electrophysiological methods measure the difference in signals from different parts of the brain to estimate the connectivity between them, a process that requires a low signal-to-noise ratio to maintain the accuracy of the measurements and sufficient spatial resolution to support the connectivity between specific regions of the brain.{{Cite journal |last1=Sadaghiani |first1=Sepideh |last2=Brookes |first2=Matthew J |last3=Baillet |first3=Sylvain |date=2022-02-15 |title=Connectomics of human electrophysiology |journal=NeuroImage |language=en |volume=247 |pages=118788 |doi=10.1016/j.neuroimage.2021.118788 |pmid=34906715 |pmc=8943906 |issn=1053-8119}} These methods offer insights into real-time neural dynamics and functional connectivity between brain regions. Electroencephalography (EEG) measures the differences in the electrical potential generated by oscillating currents at the surface of the scalp, due to the non-invasive, external placement of the electrodes.{{Cite journal |date=2022-02-15 |title=Connectomics of human electrophysiology |journal=NeuroImage |language=en |volume=247 |pages=118788 |doi=10.1016/j.neuroimage.2021.118788 |issn=1053-8119 |last1=Sadaghiani |first1=Sepideh |last2=Brookes |first2=Matthew J. |last3=Baillet |first3=Sylvain |pmid=34906715 |pmc=8943906}} Meanwhile, magnetoencephalography (MEG) relies on the magnetic fields generated by the electrical activity of the brain to collect information.

Microscale connectomes focuses on resolving individual cell-to-cell connectivity within much smaller volumes of nervous system tissue. The most common method for neural circuit reconstruction is chemical brain preservation followed by 3D electron microscopy,{{cite journal | vauthors = Anderson JR, Jones BW, Watt CB, Shaw MV, Yang JH, Demill D, Lauritzen JS, Lin Y, Rapp KD, Mastronarde D, Koshevoy P, Grimm B, Tasdizen T, Whitaker R, Marc RE | title = Exploring the retinal connectome | journal = Molecular Vision | volume = 17 | pages = 355–379 | date = February 2011 | pmid = 21311605 | pmc = 3036568 }} which offers single synapse resolution. The first microscale connectome encompassing an entire nervous system was produced for the nematode C. elegans in 1986.{{Cite journal |date=1986-11-12 |title=The structure of the nervous system of the nematode Caenorhabditis elegans |url=https://royalsocietypublishing.org/doi/10.1098/rstb.1986.0056 |journal=Philosophical Transactions of the Royal Society of London. B, Biological Sciences |language=en |volume=314 |issue=1165 |pages=1–340 |doi=10.1098/rstb.1986.0056 |bibcode=1986RSPTB.314....1W |issn=0080-4622 |last1=White |first1=J. G. |last2=Southgate |first2=E. |last3=Thomson |first3=J. N. |last4=Brenner |first4=S. |pmid=22462104 |url-access=subscription }} This was done by manually annotating printouts of the EM scans. Advances in EM acquisition, image alignment and segmentation, and manipulation of large datasets have since allowed for larger volumes to be imaged and segmented more easily. EM has been used to produce connectomes from a variety of nervous system samples, including publicly available datasets that encompass the entire brain{{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}} and ventral nerve cord{{Cite journal |last1=Phelps |first1=Jasper S. |last2=Hildebrand |first2=David Grant Colburn |last3=Graham |first3=Brett J. |last4=Kuan |first4=Aaron T. |last5=Thomas |first5=Logan A. |last6=Nguyen |first6=Tri M. |last7=Buhmann |first7=Julia |last8=Azevedo |first8=Anthony W. |last9=Sustar |first9=Anne |last10=Agrawal |first10=Sweta |last11=Liu |first11=Mingguan |last12=Shanny |first12=Brendan L. |last13=Funke |first13=Jan |last14=Tuthill |first14=John C. |last15=Lee |first15=Wei-Chung Allen |date=February 2021 |title=Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy |url=|journal=Cell |volume=184 |issue=3 |pages=759–774.e18 |doi=10.1016/j.cell.2020.12.013 |issn=0092-8674 |pmc=8312698 |pmid=33400916}}{{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}} of adult Drosophila melanogaster, the full central nervous system (connected brain and ventral nerve cord) of larval Drosophila melanogaster,{{Cite journal |last1=Winding |first1=Michael |last2=Pedigo |first2=Benjamin D. |last3=Barnes |first3=Christopher L. |last4=Patsolic |first4=Heather G. |last5=Park |first5=Youngser |last6=Kazimiers |first6=Tom |last7=Fushiki |first7=Akira |last8=Andrade |first8=Ingrid V. |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=Richard D. |date=2023-03-10 |title=The connectome of an insect brain |journal=Science |language=en |volume=379 |issue=6636 |pages=eadd9330 |doi=10.1126/science.add9330 |issn=0036-8075 |pmc=7614541 |pmid=36893230}} and volumes from mouse{{Cite report |url=http://biorxiv.org/lookup/doi/10.1101/2021.07.28.454025 |title=Functional connectomics spanning multiple areas of mouse visual cortex |last1=The MICrONS Consortium |last2=Bae |first2=J. Alexander |date=2021-07-29 |publisher=Neuroscience |doi=10.1101/2021.07.28.454025 |language=en |last3=Baptiste |first3=Mahaly |last4=Bishop |first4=Caitlyn A. |last5=Bodor |first5=Agnes L. |last6=Brittain |first6=Derrick |last7=Buchanan |first7=JoAnn |last8=Bumbarger |first8=Daniel J. |last9=Castro |first9=Manuel A.}} and human cortex.{{Cite report |url=http://biorxiv.org/lookup/doi/10.1101/2021.05.29.446289 |title=A connectomic study of a petascale fragment of human cerebral cortex |last1=Shapson-Coe |first1=Alexander |last2=Januszewski |first2=Michał |date=2021-05-30 |publisher=Neuroscience |doi=10.1101/2021.05.29.446289 |language=en |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}}{{Cite journal |last1=Loomba |first1=Sahil |last2=Straehle |first2=Jakob |last3=Gangadharan |first3=Vijayan |last4=Heike |first4=Natalie |last5=Khalifa |first5=Abdelrahman |last6=Motta |first6=Alessandro |last7=Ju |first7=Niansheng |last8=Sievers |first8=Meike |last9=Gempt |first9=Jens |last10=Meyer |first10=Hanno S. |last11=Helmstaedter |first11=Moritz |date=2022-07-08 |title=Connectomic comparison of mouse and human cortex |journal=Science |language=en |volume=377 |issue=6602 |pages=eabo0924 |doi=10.1126/science.abo0924 |issn=0036-8075|doi-access=free |pmid=35737810 }} The National Institutes of Health (NIH) has now invested in creating an EM connectome of an entire mouse brain.{{Cite web |title=BRAIN CONNECTS: A Center for High-throughput Integrative Mouse Connectomics |url=https://reporter.nih.gov/search/OZaPZ82vPUynldKK6St1Pg/project-details/10665380 |access-date=October 17, 2023 |website=NIH RePORTER}} EM can be combined with fluorescence in correlative microscopy, which can generate more interpretable data as is it able to automatically detect specific neuron types and can trace them in their entirety using fluorescent markers.{{Cite web|url=http://request.delmic.com/neuroscience|title=Neuroscience: Synaptic connectivity in the songbird brain - Application Note {{!}} DELMIC|last=BV|first=DELMIC|website=request.delmic.com|language=en|access-date=2017-02-16}}

Other imaging modalities are approaching the nanometer-scale resolution necessary for microscale connectomics. X-ray nanotomography using a synchrotron source can now reach <100 nm resolution, and can theoretically continue to improve.{{Cite journal |last1=Kuan |first1=Aaron T. |last2=Phelps |first2=Jasper S. |last3=Thomas |first3=Logan A. |last4=Nguyen |first4=Tri M. |last5=Han |first5=Julie |last6=Chen |first6=Chiao-Lin |last7=Azevedo |first7=Anthony W. |last8=Tuthill |first8=John C. |last9=Funke |first9=Jan |last10=Cloetens |first10=Peter |last11=Pacureanu |first11=Alexandra |last12=Lee |first12=Wei-Chung Allen |date=December 2020 |title=Dense neuronal reconstruction through X-ray holographic nano-tomography |journal=Nature Neuroscience |language=en |volume=23 |issue=12 |pages=1637–1643 |doi=10.1038/s41593-020-0704-9 |issn=1097-6256 |pmc=8354006 |pmid=32929244}} Unlike EM, this technique does not require the tissue being imaged to be stained with heavy metals or to be physically sectioned. Conventional light microscopy is constrained by light diffraction. Researchers have recently used stimulated emission depletion (STED) microscopy, a super-resolution light microscopy technique, to image the extracellular space of living human brain organoids and mouse hippocampal slice cultures, allowing for reconstruction of all neurites within this volume by implementing a two-stage machine learning approach.{{Cite journal |last1=Velicky |first1=Philipp |last2=Miguel |first2=Eder |last3=Michalska |first3=Julia M. |last4=Lyudchik |first4=Julia |last5=Wei |first5=Donglai |last6=Lin |first6=Zudi |last7=Watson |first7=Jake F. |last8=Troidl |first8=Jakob |last9=Beyer |first9=Johanna |last10=Ben-Simon |first10=Yoav |last11=Sommer |first11=Christoph |last12=Jahr |first12=Wiebke |last13=Cenameri |first13=Alban |last14=Broichhagen |first14=Johannes |last15=Grant |first15=Seth G. N. |date=August 2023 |title=Dense 4D nanoscale reconstruction of living brain tissue |journal=Nature Methods |language=en |volume=20 |issue=8 |pages=1256–1265 |doi=10.1038/s41592-023-01936-6 |issn=1548-7091 |pmc=10406607 |pmid=37429995}} They combined this with fluorescently-tagged synaptic markers to find synaptic connectivity in the sample as well as with calcium imaging to monitor neuronal activity . However, this live-imaging approach was limited to ~130 nm resolution, and was therefore not able to reliably reconstruct thin axons over long distances. 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

|last1= Tavakoli |first1= Mojtaba R. |last2= Lyudchik |first2= Julia |last3= Januszewski |first3= Michał

|last4= Vistunou |first4= Vitali |last5= Agudelo |first5= Nathalie |last6= Vorlaufer |first6= Jakob

|last7= Sommer |first7= Christoph |last8= Kreuzinger |first8= Caroline |last9= Oliveira |first9= Barbara

|last10= Cenameri |first10= Alban |last11= Novarino |first11= Gaia |last12= Jain |first12= Viren

|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}} Potential advantages include cheaper equipment (optical vs EM microscopes), faster data acquisition, and multi-color labelling.

In addition to advanced microscopy techniques, connectomics heavily relies on software analysis tools and machine learning pipelines for reconstructing and analyzing neural networks. These tools are designed to process and interpret the vast amounts of data generated by volume electron microscopy and other imaging methods. Key steps in connectomic reconstruction include image segmentation, where individual neurons and their components are identified and annotated, and network mapping, where the connections between these neurons are established.{{Cite journal |last1=Motta |first1=Alessandro |last2=Berning |first2=Manuel |last3=Boergens |first3=Kevin M. |last4=Staffler |first4=Benedikt |last5=Beining |first5=Marcel |last6=Loomba |first6=Sahil |last7=Hennig |first7=Philipp |last8=Wissler |first8=Heiko |last9=Helmstaedter |first9=Moritz |date=2019-11-29 |title=Dense connectomic reconstruction in layer 4 of the somatosensory cortex |url=https://www.science.org/doi/10.1126/science.aay3134 |journal=Science |language=en |volume=366 |issue=6469 |doi=10.1126/science.aay3134 |pmid=31649140 |issn=0036-8075}}

Several software platforms facilitate these tasks. [https://www.virtualflybrain.org/blog/releases/catmaid/ CATMAID] (Collaborative Annotation Toolkit for Massive Amounts of Image Data) is a decentralized web interface allowing seamless navigation of large image stacks. It is designed to facilitate the collaborative exploration, annotation, and efficient sharing of regions of interests by bookmarking.{{Cite journal |last1=Saalfeld |first1=Stephan |last2=Cardona |first2=Albert |last3=Hartenstein |first3=Volker |last4=Tomančák |first4=Pavel |date=2009-04-17 |title=CATMAID: collaborative annotation toolkit for massive amounts of image data |url=|journal=Bioinformatics |volume=25 |issue=15 |pages=1984–1986 |doi=10.1093/bioinformatics/btp266 |issn=1367-4811 |pmc=2712332 |pmid=19376822}} Another example is WEBKNOSSOS, an online platform used for viewing, annotating, and sharing large 3D images, aiding in the detailed analysis of neural structures by allowing efficient navigation and annotation of 3D datasets.{{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 }} [https://neuroglancer-demo.appspot.com/ Neuroglancer], a web-based tool designed for visualizing and navigating large-scale neuroscience data, offers features like 3D rendering and interactive exploration of brain datasets.

To see one of the first micro-connectomes at full-resolution, visit the [https://neurodata.io/project/ocp/ Open Connectome Project], which is hosting several connectome datasets, including the 12TB dataset from Bock et al. (2011).

Comparative connectomics is a subfield in neuroscience that focuses on comparing the connectomes, or neural network maps, across different species, developmental stages, or pathological states.{{Cite journal |last1=van den Heuvel |first1=Martijn P. |last2=Bullmore |first2=Edward T. |last3=Sporns |first3=Olaf |date=May 2016 |title=Comparative Connectomics |url=https://www.repository.cam.ac.uk/handle/1810/254415|journal=Trends in Cognitive Sciences |volume=20 |issue=5 |pages=345–361 |doi=10.1016/j.tics.2016.03.001 |pmid=27026480 |s2cid=3629442 |issn=1364-6613}} This comparative approach aims to uncover fundamental principles of brain organization and function by identifying conserved and divergent patterns in neural circuitry. By analyzing similarities and differences in the wiring diagrams of various organisms, researchers can gain insights into the evolutionary processes shaping the nervous system, as well as into the neural basis of behavior and cognition. For example, a 2022 study comparing synaptic connectivity in the mouse and human/macaque cortex revealed that, even though the human cortex contains three times more interneurons than the mouse cortex, the excitation-to-inhibition ratio is similar between the species.

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}} Both mechanisms of rewiring are useful for learning completely novel tasks that may require entirely new connections between regions of the brain.{{cite journal |vauthors=Bennett SH, Kirby AJ, Finnerty GT |title=Rewiring the connectome: Evidence and effects |journal=Neuroscience and Biobehavioral Reviews |volume=88 |pages=51–62 |date=May 2018 |pmid=29540321 |pmc=5903872 |doi=10.1016/j.neubiorev.2018.03.001}} However, the ability of the brain to gain or lose entire connections poses an issue for mapping a universal species connectome. Although rewiring happens on different scales, from microscale to macroscale, each scale does not occur in isolation. For example, in the C. elegans connectome, the total number of synapses increases 5-fold from birth to adulthood, changing both local and global network properties.{{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}} Other developmental connectomes, such as the muscle connectome, retain some global network properties even though the number of synapses decreases by 10-fold in early postnatal life.{{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}}

Evidence for macroscale rewiring mostly comes from research on grey and white matter density, which could indicate new connections or changes in axon density. Direct evidence for this level of rewiring comes from primate studies, using viral tracing to map the formation of connections. Primates that were taught to use novel tools developed new connections between the interparietal cortex and higher visual areas of the brain.{{cite journal|vauthors=Hihara S, Notoya T, Tanaka M, Ichinose S, Ojima H, Obayashi S, Fujii N, Iriki A|date=2006|title=Extension of corticocortical afferents into the anterior bank of the intraparietal sulcus by tool-use training in adult monkeys|journal=Neuropsychologia|volume=44|issue=13|pages=2636–46|doi=10.1016/j.neuropsychologia.2005.11.020|pmid=16427666|s2cid=12738783}} Further viral tracing studies have provided evidence that macroscale rewiring occurs in adult animals during associative learning.{{cite journal|vauthors=Boele HJ, Koekkoek SK, De Zeeuw CI, Ruigrok TJ|date=November 2013|title=Axonal sprouting and formation of terminals in the adult cerebellum during associative motor learning|journal=The Journal of Neuroscience|volume=33|issue=45|pages=17897–907|doi=10.1523/JNEUROSCI.0511-13.2013|pmc=6618426|pmid=24198378}} However, it is not likely that long-distance neural connections undergo extensive rewiring in adults. Small changes in an already established nerve tract are likely what is observed in macroscale rewiring.

Rewiring at the mesoscale involves studying the presence or absence of entire connections between neurons. Evidence for this level of rewiring comes from observations that local circuits form new connections as a result of experience-dependent plasticity in the visual cortex. Additionally, the number of local connections between pyramidal neurons in the primary somatosensory cortex increases following altered whisker sensory experience in rodents.{{cite journal | vauthors = Ko H, Cossell L, Baragli C, Antolik J, Clopath C, Hofer SB, Mrsic-Flogel TD | title = The emergence of functional microcircuits in visual cortex | journal = Nature | volume = 496 | issue = 7443 | pages = 96–100 | date = April 2013 | pmid = 23552948 | pmc = 4843961 | doi = 10.1038/nature12015 | bibcode = 2013Natur.496...96K}}

Microscale rewiring is the formation or removal of synaptic connections between two neurons and can be studied with longitudinal two-photon imaging. Dendritic spines on pyramidal neurons can be shown forming within days following sensory experience and learning.{{cite journal|author5-link=Karel Svoboda (scientist)|vauthors=Holtmaat A, Wilbrecht L, Knott GW, Welker E, Svoboda K|date=June 2006|title=Experience-dependent and cell-type-specific spine growth in the neocortex|journal=Nature|language=En|volume=441|issue=7096|pages=979–83|bibcode=2006Natur.441..979H|doi=10.1038/nature04783|pmid=16791195|s2cid=4428322}}{{cite journal|vauthors=Knott GW, Holtmaat A, Wilbrecht L, Welker E, Svoboda K|date=September 2006|title=Spine growth precedes synapse formation in the adult neocortex in vivo|journal=Nature Neuroscience|language=En|volume=9|issue=9|pages=1117–24|doi=10.1038/nn1747|pmid=16892056|s2cid=18143285}}{{cite journal |vauthors=Xu T, Yu X, Perlik AJ, Tobin WF, Zweig JA, Tennant K, Jones T, Zuo Y |date=December 2009 |title=Rapid formation and selective stabilization of synapses for enduring motor memories |journal=Nature |language=En |volume=462 |issue=7275 |pages=915–9 |bibcode=2009Natur.462..915X |doi=10.1038/nature08389 |pmc=2844762 |pmid=19946267}} Changes can even be seen within five hours on apical tufts of layer five pyramidal neurons in the primary motor cortex after a seed reaching task in primates.

For macroscale connectomes, the most common subject is the human. For microscale connectomes, some of the model systems 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}}

{{Main|Human Connectome Project}}

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}} 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 C. elegans roundworm has a simple nervous system of 302 neurons and 5000 synaptic connections, (as compared 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 of the very few animals in which a full connectome has been mapped using various imaging techniques, mainly serial-electron microscopy.{{Cite web |title=WormWiring |url=https://www.wormwiring.org/ |access-date=2023-06-19 |website=www.wormwiring.org}} This has made it a natural target for connectomics.

One project studied the aging process of the C. elegans brain by comparing varying worms from birth to adulthood.{{cite journal | vauthors = Witvliet D, Mulcahy B, Mitchell JK, Meirovitch Y, Berger DR, Wu Y, Liu Y, Koh WX, Parvathala R, Holmyard D, Schalek RL, Shavit N, Chisholm AD, Lichtman JW, Samuel AD, Zhen M | title = Connectomes across development reveal principles of brain maturation | journal = Nature | volume = 596 | issue = 7871 | pages = 257–261 | date = August 2021 | pmid = 34349261 | doi = 10.1038/s41586-021-03778-8 | pmc = 8756380 | bibcode = 2021Natur.596..257W}} Researchers found the biggest change with age is the wiring circuits, and that connectivity between and within brain regions increases with age. Additional findings are likely through comparative connectomics, comparing and contrasting different species' brain networks to pinpoint relations in behavior.

Another study analyzed connections about sensory neurons, interneurons, neck motor neurons, behavior, environmental influences, and more in detail.{{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}}

{{Main|Drosophila connectome}}

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. The full fly connectome contains on the order of 100 thousand neurons and 100 million synapses.

The largest current dataset is the FlyWire segmentation and annotation of the female adult fly brain (FAFB) volume, which encompasses the entire brain of an adult. Another adult brain dataset available is the Hemibrain, generated as a collaboration between the Janelia FlyEM team and Google.{{Cite journal |last1=Scheffer |first1=Louis K |last2=Xu |first2=C Shan |last3=Januszewski |first3=Michal |last4=Lu |first4=Zhiyuan |last5=Takemura |first5=Shin-ya |last6=Hayworth |first6=Kenneth J |last7=Huang |first7=Gary B |last8=Shinomiya |first8=Kazunori |last9=Maitlin-Shepard |first9=Jeremy |last10=Berg |first10=Stuart |last11=Clements |first11=Jody |last12=Hubbard |first12=Philip M |last13=Katz |first13=William T |last14=Umayam |first14=Lowell |last15=Zhao |first15=Ting |date=2020-09-03 |editor-last=Marder |editor-first=Eve |editor2-last=Eisen |editor2-first=Michael B |editor3-last=Pipkin |editor3-first=Jason |editor4-last=Doe |editor4-first=Chris Q |title=A connectome and analysis of the adult Drosophila central brain |journal=eLife |volume=9 |pages=e57443 |doi=10.7554/eLife.57443 |doi-access=free |pmid=32880371 |pmc=7546738 |issn=2050-084X}}{{Cite web |title=Hemibrain |url=https://www.janelia.org/project-team/flyem/hemibrain |access-date=2024-02-22 |website=Janelia Research Campus |language=en}} This dataset is an incomplete but large section of the fly central brain.

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 SEM by Dr. Wei-Chung Allen Lee’s lab at Harvard Medical School. The male adult nerve cord (MANC) was collected at Janelia using FIB-SEM. The connectome of a complete central nervous system (connected brain and VNC) of a 1st instar D. melanogaster larva has been collected as a single volume. This dataset of 3016 neurons was segmented and annotated manually using CATMAID by a team of people mainly led by researchers at Janelia, Cambridge, and the MRC LMB.

An online database known as [http://www.mouselight.janelia.org/ 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 the imaging technology is advanced enough to do so.

Mice models in the lab have provided insight into genetic brain disorders, one study manipulated mice with a deletion of 22q11.2 (chromosome 22, a likely known genetic risk factor that leads to schizophrenia).{{cite journal | vauthors = Sigurdsson T, Stark KL, Karayiorgou M, Gogos JA, Gordon JA | title = Impaired hippocampal-prefrontal synchrony in a genetic mouse model of schizophrenia | journal = Nature | volume = 464 | issue = 7289 | pages = 763–767 | date = April 2010 | pmid = 20360742 | doi = 10.1038/nature08855 | pmc = 2864584 | bibcode = 2010Natur.464..763S }} The findings of this study showed that this impaired neural activity in mice's working memory is similar to what it does in humans.

Macroscale and microscale connectomics have very different applications. Macroscale connectomics has furthered our understanding of various brain networks including visual,{{cite journal | vauthors = Kammen A, Law M, Tjan BS, Toga AW, Shi Y | title = Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis | journal = NeuroImage | volume = 125 | pages = 767–779 | date = January 2016 | pmid = 26551261 | pmc = 4691391 | doi = 10.1016/j.neuroimage.2015.11.005 }}{{cite journal | vauthors = Yogarajah M, Focke NK, Bonelli S, Cercignani M, Acheson J, Parker GJ, Alexander DC, McEvoy AW, Symms MR, Koepp MJ, Duncan JS | title = Defining Meyer's loop-temporal lobe resections, visual field deficits and diffusion tensor tractography | journal = Brain | volume = 132 | issue = Pt 6 | pages = 1656–1668 | date = June 2009 | pmid = 19460796 | pmc = 2685925 | doi = 10.1093/brain/awp114}} brainstem,{{Cite book | vauthors = Nieuwenhuys R, Voogd J, Van Huijzen C |date=2008|title=The Human Central Nervous System|doi=10.1007/978-3-540-34686-9|isbn=978-3-540-34684-5}}{{cite book | vauthors = Paxinos G, Huang XF, Sengul G, Watson C |chapter=Organization of Brainstem Nuclei|date=2012|title=The Human Nervous System|pages=260–327|publisher=Elsevier |doi=10.1016/b978-0-12-374236-0.10008-2|isbn=978-0-12-374236-0|url=https://ro.uow.edu.au/cgi/viewcontent.cgi?article=4108&context=hbspapers}} and language networks,{{cite journal | vauthors = Glasser MF, Rilling JK | title = DTI tractography of the human brain's language pathways | journal = Cerebral Cortex | volume = 18 | issue = 11 | pages = 2471–2482 | date = November 2008 | pmid = 18281301 | doi = 10.1093/cercor/bhn011 }}{{cite journal | vauthors = Catani M, Jones DK, ffytche DH | title = Perisylvian language networks of the human brain | journal = Annals of Neurology | volume = 57 | issue = 1 | pages = 8–16 | date = January 2005 | pmid = 15597383 | doi = 10.1002/ana.20319 | s2cid = 17743067 | doi-access = free}} among others. Microscale connectomics, on the other hand, concentrates on mechanistic explanations of how the neural circuits of the brain perform specific functions. Examples include motion vision,{{cite journal |title=How flies see motion

|last1=Borst |first1=Alexander |last2=Groschner |first2=Lukas N

|journal=Annual Review of Neuroscience

|volume=46

|number=1

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|doi=10.1146/annurev-neuro-080422-111929 |pmid=37428604 |url=https://www.annualreviews.org/content/journals/10.1146/annurev-neuro-080422-111929

|doi-access=free

}} olfactory learning,{{cite journal |title=The connectome of the adult Drosophila mushroom body provides insights into function

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|doi= 10.7554/eLife.62576 |doi-access= free |pmid= 33315010 |pmc= 7909955 |url=https://elifesciences.org/articles/62576.pdf}}

navigation,{{cite journal |title=A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

|last1= Hulse |first1= Brad K |last2= Haberkern |first2= Hannah |last3= Franconville |first3= Romain

|last4= Evans |first4= Daniel Turner- |last5= Takemura |first5= Shin- ya |last6= Wolff |first6= Tanya

|last7= Noorman |first7= Marcella |last8= Dreher |first8= Marisa |last9= Dan |first9= Chuntao

|last10= Parekh |first10= Ruchi |last11= Hermundstad |first11= Ann M |last12= Rubin |first12= Gerald M

|last13= Jayaraman |first13= Vivek

|journal=eLife

|volume=10

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|publisher=eLife Sciences Publications, Ltd

|doi= 10.7554/eLife.66039 |doi-access= free |pmid= 34696823 |pmc= 9477501 }} and escape responses,{{cite journal |title=Neural basis for looming size and velocity encoding in the Drosophila giant fiber escape pathway

|last1= Ache |first1= Jan M. |last2= Polsky |first2= Jason |last3= Alghailani |first3= Shada

|last4= Parekh |first4= Ruchi |last5= Breads |first5= Patrick |last6= Peek |first6= Martin Y.

|last7= Bock |first7= Davi D. |last8= Reyn |first8= Catherine R. von |last9= Card |first9= Gwyneth M.

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|doi-access= free }} all in Drosophila.

File:Connectivity matrix.jpg

By comparing diseased and healthy connectomes, we can gain insight into certain psychopathologies, such as neuropathic pain, and potential therapies for them. Generally, the field of neuroscience would benefit from standardization and raw data. For example, connectome maps can be used to inform computational models of whole-brain dynamics.http://www.scholarpedia.org/article/Connectome{{Unreliable medical source|date=March 2011}} {{dead link|date=July 2019 |bot=InternetArchiveBot |fix-attempted=yes}}{{Self-published inline|date=March 2011}} Current neural networks mostly rely on probabilistic representations of connectivity patterns.{{cite journal | vauthors = Nordlie E, Gewaltig MO, Plesser HE | title = Towards reproducible descriptions of neuronal network models | journal = PLOS Computational Biology | volume = 5 | issue = 8 | pages = e1000456 | date = August 2009 | pmid = 19662159 | pmc = 2713426 | doi = 10.1371/journal.pcbi.1000456 | editor1-last = Friston | bibcode = 2009PLSCB...5E0456N | editor1-first = Karl J. | doi-access = free}} Connectivity matrices (checkerboard diagrams of connectomics) have been used in stroke recovery to evaluate the response to treatment via Transcranial Magnetic Stimulation.{{cite journal | vauthors = Yeung JT, Young IM, Doyen S, Teo C, Sughrue ME | title = Changes in the Brain Connectome Following Repetitive Transcranial Magnetic Stimulation for Stroke Rehabilitation | journal = Cureus | volume = 13 | issue = 10 | pages = e19105 | date = October 2021 | pmid = 34858752 | pmc = 8614179 | doi = 10.7759/cureus.19105 | doi-access = free}} Similarly, connectograms (circular diagrams of connectomics) have been used in traumatic brain injury cases to document the extent of damage to neural networks.{{cite journal | vauthors = Van Horn JD, Irimia A, Torgerson CM, Chambers MC, Kikinis R, Toga AW | title = Mapping connectivity damage in the case of Phineas Gage | journal = PLOS ONE | volume = 7 | issue = 5 | pages = e37454 | year = 2012 | pmid = 22616011 | pmc = 3353935 | doi = 10.1371/journal.pone.0037454 | doi-access = free | bibcode = 2012PLoSO...737454V}}{{cite journal | vauthors = Irimia A, Chambers MC, Torgerson CM, Filippou M, Hovda DA, Alger JR, Gerig G, Toga AW, Vespa PM, Kikinis R, Van Horn JD | title = Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury | journal = Frontiers in Neurology | volume = 3 | pages = 10 | date = 6 February 2012 | pmid = 22363313 | pmc = 3275792 | doi = 10.3389/fneur.2012.00010 | doi-access = free}}

Looking into these methods of research, they can reveal information about different mental illnesses and brain disorders. The tracking of brain networks in alignment with diseases and illnesses would be enhanced by these advanced technologies that can produce complex images of neural networks.{{Cite journal |last1=Fornito |first1=Alex |last2=Zalesky |first2=Andrew |last3=Breakspear |first3=Michael |date=March 2015 |title=The connectomics of brain disorders |url=https://www.nature.com/articles/nrn3901 |journal=Nature Reviews Neuroscience |language=en |volume=16 |issue=3 |pages=159–172 |doi=10.1038/nrn3901 |pmid=25697159 |s2cid=1792111 |issn=1471-0048|url-access=subscription }} With this in mind, diseases can not only be tracked, but predicted based on behavior of previous cases, a process that would take an extensive period of time to collect and record. Specifically, studies on different brain disorders such as schizophrenia and bipolar disorder with a focus on the connectomics involved reveal information. Both of these disorders have a similar genetic origin,{{Cite journal |last1=Stahl |first1=Eli A. |last2=Breen |first2=Gerome |last3=Forstner |first3=Andreas J. |last4=McQuillin |first4=Andrew |last5=Ripke |first5=Stephan |last6=Trubetskoy |first6=Vassily |last7=Mattheisen |first7=Manuel |last8=Wang |first8=Yunpeng |last9=Coleman |first9=Jonathan R. I. |last10=Gaspar |first10=Héléna A. |last11=de Leeuw |first11=Christiaan A. |last12=Steinberg |first12=Stacy |last13=Pavlides |first13=Jennifer M. Whitehead |last14=Trzaskowski |first14=Maciej |last15=Byrne |first15=Enda M. |date=May 2019 |title=Genome-wide association study identifies 30 loci associated with bipolar disorder |journal=Nature Genetics |language=en |volume=51 |issue=5 |pages=793–803 |doi=10.1038/s41588-019-0397-8 |pmid=31043756 |pmc=6956732 |hdl=10481/58017 |issn=1546-1718}} and research found that those with higher polygenic scores for schizophrenia and bipolar disorder have lower amounts of connectivity shown through neuroimaging.{{Cite journal |date=2022-11-09 |title=Associated Genetics and Connectomic Circuitry in Schizophrenia and Bipolar Disorder |journal=Biological Psychiatry |language=en |doi=10.1016/j.biopsych.2022.11.006 |issn=0006-3223 |last1=Wei |first1=Yongbin |last2=De Lange |first2=Siemon C. |last3=Savage |first3=Jeanne E. |last4=Tissink |first4=Elleke |last5=Qi |first5=Ting |last6=Repple |first6=Jonathan |last7=Gruber |first7=Marius |last8=Kircher |first8=Tilo |last9=Dannlowski |first9=Udo |last10=Posthuma |first10=Danielle |last11=Van Den Heuvel |first11=Martijn P. |volume=94 |issue=2 |pages=174–183 |pmid=36803976 |s2cid=253426641 |doi-access=free }} This method of research tackles real-world applications of connectomics, combining methods of imaging with genetics to dig deeper into the origins and outcomes of genetically related disorders. Another study supports the finding that there is relation between connectivity and likelihood of disease, as researchers found those diagnosed with schizophrenia have less structurally complete brain networks.{{Cite journal |last1=Heuvel |first1=Martijn P. van den |last2=Mandl |first2=René C. W. |last3=Stam |first3=Cornelis J. |last4=Kahn |first4=René S. |last5=Pol |first5=Hilleke E. Hulshoff |date=2010-11-24 |title=Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis |url=https://www.jneurosci.org/content/30/47/15915 |journal=Journal of Neuroscience |language=en |volume=30 |issue=47 |pages=15915–15926 |doi=10.1523/JNEUROSCI.2874-10.2010 |issn=0270-6474 |pmid=21106830|pmc=6633761}} The main drawback in this area of connectomics is not being able to achieve images of whole-brain networks, therefore it is hard to make complete and accurate assumptions about cause and effect of diseases' neural pathways. Connectomics has been used to study patients with strokes using MRI imaging, however because such little research is done in this specific area, conclusions cannot be drawn regarding the relation between strokes and connectivity.{{Cite journal |last1=Bian |first1=Rong |last2=Huo |first2=Ming |last3=Liu |first3=Wan |last4=Mansouri |first4=Negar |last5=Tanglay |first5=Onur |last6=Young |first6=Isabella |last7=Osipowicz |first7=Karol |last8=Hu |first8=Xiaorong |last9=Zhang |first9=Xia |last10=Doyen |first10=Stephane |last11=Sughrue |first11=Michael E. |last12=Liu |first12=Li |date=2023 |title=Connectomics underlying motor functional outcomes in the acute period following stroke |journal=Frontiers in Aging Neuroscience |volume=15 |doi=10.3389/fnagi.2023.1131415 |pmid=36875697 |pmc=9975347 |issn=1663-4365 |doi-access=free }} The research did find results that highlight an association between poor connectivity in the language system and poor motor coordination, however the results were not substantial enough to make a bold claim. For behavioral disorders, it can be difficult to diagnose and treat because most situations revolve on a symptoms-based approach. However, this can be difficult because many disorders have overlapping symptoms. Connectomics has been used to find neuromarkers associated with social anxiety disorder (SAD) at a high precision rate in improving related symptoms.{{Cite journal |last1=Whitfield-Gabrieli |first1=S. |last2=Ghosh |first2=S. S. |last3=Nieto-Castanon |first3=A. |last4=Saygin |first4=Z. |last5=Doehrmann |first5=O. |last6=Chai |first6=X. J. |last7=Reynolds |first7=G. O. |last8=Hofmann |first8=S. G. |last9=Pollack |first9=M. H. |last10=Gabrieli |first10=J. D. E. |date=May 2016 |title=Brain connectomics predict response to treatment in social anxiety disorder |journal=Molecular Psychiatry |language=en |volume=21 |issue=5 |pages=680–685 |doi=10.1038/mp.2015.109 |pmid=26260493 |s2cid=1654492 |issn=1476-5578|doi-access=free}} This is an expanding field and there is room for greater application to mental health disorders and brain malfunction, in which current research is building on neural networks and the psychopathology involved.{{Cite journal |last=Menon |first=Vinod |date=2011-10-01 |title=Large-scale brain networks and psychopathology: a unifying triple network model |url= |journal=Trends in Cognitive Sciences |language=en |volume=15 |issue=10 |pages=483–506 |doi=10.1016/j.tics.2011.08.003 |pmid=21908230 |s2cid=26653572 |issn=1364-6613}}

Human connectomes have an individual variability, which can be measured with the cumulative distribution function, as it was shown in.{{cite journal | vauthors = Kerepesi C, Szalkai B, Varga B, Grolmusz V | title = Comparative connectomics: Mapping the inter-individual variability of connections within the regions of the human brain | journal = Neuroscience Letters | volume = 662 | issue = 1 | pages = 17–21 | date = January 2018 | pmid = 28988973 | doi = 10.1016/j.neulet.2017.10.003 | arxiv = 1507.00327 | s2cid = 378080}} By analyzing the individual variability of the human connectomes in distinct cerebral areas, it was found that the frontal and the limbic lobes are more conservative, and the edges in the temporal and occipital lobes are more diverse. A "hybrid" conservative/diverse distribution was detected in the paracentral lobule and the fusiform gyrus. Smaller cortical areas were also evaluated: precentral gyri were found to be more conservative, and the postcentral and the superior temporal gyri to be very diverse.

The recent advancements in the field of connectomics have sparked conversation around its relation to the field of genomics. Recently, scientists in the field have highlighted the parallels between this project and large-scale genomics initiatives.{{cite journal | vauthors = Chen PB, Flint J | title = What connectomics can learn from genomics | journal = PLOS Genetics | volume = 17 | issue = 7 | pages = e1009692 | date = July 2021 | pmid = 34270560 | pmc = 8318269 | doi = 10.1371/journal.pgen.1009692 | doi-access = free}} Additionally, they have referenced the need for integration with other scientific disciplines, particularly genetics. While genomics focuses on the genetic blueprint of an organism, connectomics provides insights into the structural and functional connectivity of the brain. By integrating these two fields, researchers can explore how genetic variations and gene expression patterns influence the wiring and organization of neural circuits.{{cite journal | vauthors = Arnatkeviciute A, Fulcher BD, Bellgrove MA, Fornito A | title = Where the genome meets the connectome: Understanding how genes shape human brain connectivity | journal = NeuroImage | volume = 244 | pages = 118570 | date = December 2021 | pmid = 34508898 | doi = 10.1016/j.neuroimage.2021.118570 | s2cid = 237448401 | doi-access = free}} This interdisciplinary approach helps uncover the relationship between genes, neural connectivity, and brain function. Additionally, connectomics can benefit from genomics by leveraging genetic tools and techniques to manipulate specific genes or neuronal populations to study their impact on neural circuitry and behavior. Understanding the genetic basis of neural connectivity can enhance our understanding of brain development, neural plasticity, and the mechanisms underlying various neurological disorders.

The human genome project initially faced many of the above criticisms, but was nevertheless completed ahead of schedule and has led to many advances in genetics. Some have argued that analogies can be made between genomics and connectomics, and therefore we should be at least slightly more optimistic about the prospects in connectomics.{{cite journal | vauthors = Lichtman JW, Sanes JR | title = Ome sweet ome: what can the genome tell us about the connectome? | journal = Current Opinion in Neurobiology | volume = 18 | issue = 3 | pages = 346–353 | date = June 2008 | pmid = 18801435 | pmc = 2735215 | doi = 10.1016/j.conb.2008.08.010}} Others have criticized attempts towards a microscale connectome, arguing that we do not have enough knowledge about where to look for insights, or that it cannot be completed within a realistic time frame.{{cite news|url=https://www.nytimes.com/2010/12/28/science/28brain.html|work=The New York Times|first=Ashlee|last=Vance|author-link=Ashlee Vance|title=Seeking the Connectome, a Mental Map, Slice by Slice|date=27 December 2010}}

Using fMRI in the resting state and during tasks, functions of the connectome circuits are being studied.{{cite journal|vauthors=Van Dijk KR, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL|date=January 2010|title=Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization|journal=Journal of Neurophysiology|volume=103|issue=1|pages=297–321|doi=10.1152/jn.00783.2009|pmc=2807224|pmid=19889849}} Just as detailed road maps of the Earth's surface do not tell us much about the kind of vehicles that travel those roads or what cargo they are hauling, to understand how neural structures result in specific functional behavior such as consciousness, it is necessary to build theories that relate functions to anatomical connectivity.{{cite journal|vauthors=Allen M, Williams G|year=2011|title=Consciousness, plasticity, and connectomics: the role of intersubjectivity in human cognition|journal=Frontiers in Psychology|volume=2|pages=20|doi=10.3389/fpsyg.2011.00020|pmc=3110420|pmid=21687435|doi-access=free}} However, the bond between structural and functional connectivity is not straightforward. Computational models of whole-brain network dynamics are valuable tools to investigate the role of the anatomical network in shaping functional connectivity.{{cite journal|vauthors=Cabral J, Kringelbach ML, Deco G|date=March 2014|title=Exploring the network dynamics underlying brain activity during rest|journal=Progress in Neurobiology|volume=114|pages=102–31|doi=10.1016/j.pneurobio.2013.12.005|pmid=24389385|doi-access=free|s2cid=9423875|hdl=10230/23083|hdl-access=free}}{{cite journal|vauthors=Honey CJ, Kötter R, Breakspear M, Sporns O|date=June 2007|title=Network structure of cerebral cortex shapes functional connectivity on multiple time scales|journal=Proceedings of the National Academy of Sciences of the United States of America|volume=104|issue=24|pages=10240–5|bibcode=2007PNAS..10410240H|doi=10.1073/pnas.0701519104|pmc=1891224|pmid=17548818|doi-access=free}} In particular, computational models can be used to predict the dynamic effect of lesions in the connectome.{{cite journal|vauthors=Cabral J, Hugues E, Kringelbach ML, Deco G|date=September 2012|title=Modeling the outcome of structural disconnection on resting-state functional connectivity|journal=NeuroImage|volume=62|issue=3|pages=1342–53|doi=10.1016/j.neuroimage.2012.06.007|pmid=22705375|s2cid=10548492}}{{cite journal|vauthors=Honey CJ, Sporns O|date=July 2008|title=Dynamical consequences of lesions in cortical networks|journal=Human Brain Mapping|volume=29|issue=7|pages=802–9|doi=10.1002/hbm.20579|pmc=6870962|pmid=18438885}}

A connectome can be viewed as a graph, and the rich tools, definitions and algorithms of graph theory and network science can be applied to these graphs. In case of a micro-scale connectome, the nodes of this network (or graph) are the neurons, and the edges correspond to the synapses between those neurons. For the macro-scale connectome, the nodes correspond to the ROIs (regions of interest), while the edges of the graph are derived from the axons interconnecting those areas. Thus connectomes are sometimes referred to as brain graphs, as they are indeed graphs in a mathematical sense which describe the connections in the brain (or, in a broader sense, the whole nervous system).

One group of researchers (Iturria-Medina et al., 2008){{cite journal|vauthors=Iturria-Medina Y, Sotero RC, Canales-Rodríguez EJ, Alemán-Gómez Y, Melie-García L|date=April 2008|title=Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory|journal=NeuroImage|volume=40|issue=3|pages=1064–76|doi=10.1016/j.neuroimage.2007.10.060|pmid=18272400|s2cid=3593098}} has constructed connectome data sets using diffusion tensor imaging (DTI){{cite journal|vauthors=Basser PJ, Mattiello J, LeBihan D|date=January 1994|title=MR diffusion tensor spectroscopy and imaging|journal=Biophysical Journal|volume=66|issue=1|pages=259–67|bibcode=1994BpJ....66..259B|doi=10.1016/S0006-3495(94)80775-1|pmc=1275686|pmid=8130344}}{{cite journal|vauthors=Basser PJ, Mattiello J, LeBihan D|date=March 1994|title=Estimation of the effective self-diffusion tensor from the NMR spin echo|url=https://zenodo.org/record/1229906|journal=Journal of Magnetic Resonance, Series B|volume=103|issue=3|pages=247–54|bibcode=1994JMRB..103..247B|doi=10.1006/jmrb.1994.1037|pmid=8019776}} followed by the derivation of average connection probabilities between 70 and 90 cortical and basal brain gray matter areas. All networks were found to have small-world attributes and "broad-scale" degree distributions. An analysis of betweenness centrality in these networks demonstrated high centrality for the precuneus, the insula, the superior parietal and the superior frontal cortex. Another group (Gong et al. 2008){{cite journal|vauthors=Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, Beaulieu C|date=March 2009|title=Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography|journal=Cerebral Cortex|volume=19|issue=3|pages=524–36|doi=10.1093/cercor/bhn102|pmc=2722790|pmid=18567609}} has applied DTI to map a network of anatomical connections between 78 cortical regions. This study also identified several hub regions in the human brain, including the precuneus and the superior frontal gyrus.

Hagmann et al. (2007){{cite journal|vauthors=Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, Thiran JP|date=July 2007|title=Mapping human whole-brain structural networks with diffusion MRI|journal=PLOS ONE|volume=2|issue=7|pages=e597|bibcode=2007PLoSO...2..597H|doi=10.1371/journal.pone.0000597|pmc=1895920|pmid=17611629|doi-access=free|veditors=Sporns O}} {{open access}} constructed a connection matrix from fiber densities measured between homogeneously distributed and equal-sized ROIs numbering between 500 and 4000. A quantitative analysis of connection matrices obtained for approximately 1,000 ROIs and approximately 50,000 fiber pathways from two subjects demonstrated an exponential (one-scale) degree distribution as well as robust small-world attributes for the network. The data sets were derived from diffusion spectrum imaging (DSI) (Wedeen, 2005),{{cite journal|vauthors=Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM|date=December 2005|title=Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging|journal=Magnetic Resonance in Medicine|volume=54|issue=6|pages=1377–86|doi=10.1002/mrm.20642|pmid=16247738|doi-access=free}} a variant of diffusion-weighted imaging{{cite journal|vauthors=Le Bihan D, Breton E|date=1985|title=Imagerie de diffusion in vivo par résonance magnétique nucléaire|trans-title=Imagery of diffusion in vivo by nuclear magnetic resonance|journal=Comptes Rendus de l'Académie des Sciences|language=fr|volume=93|issue=5|pages=27–34}}{{cite journal|vauthors=Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M|date=November 1986|title=MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders|journal=Radiology|volume=161|issue=2|pages=401–7|doi=10.1148/radiology.161.2.3763909|pmid=3763909}} that is sensitive to intra-voxel heterogeneities in diffusion directions caused by crossing fiber tracts and thus allows more accurate mapping of axonal trajectories than other diffusion imaging approaches (Wedeen, 2008).{{cite journal|vauthors=Wedeen VJ, Wang RP, Schmahmann JD, Benner T, Tseng WY, Dai G, Pandya DN, Hagmann P, D'Arceuil H, de Crespigny AJ|date=July 2008|title=Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers|journal=NeuroImage|volume=41|issue=4|pages=1267–77|doi=10.1016/j.neuroimage.2008.03.036|pmid=18495497|s2cid=2660208}}

The combination of whole-head DSI datasets acquired and processed according to the approach developed by Hagmann et al. (2007) with the graph analysis tools conceived initially for animal tracing studies (Sporns, 2006; Sporns, 2007){{cite journal|vauthors=Sporns O|date=July 2006|title=Small-world connectivity, motif composition, and complexity of fractal neuronal connections|journal=Bio Systems|volume=85|issue=1|pages=55–64|doi=10.1016/j.biosystems.2006.02.008|pmid=16757100|bibcode=2006BiSys..85...55S }}{{cite journal|vauthors=Sporns O, Honey CJ, Kötter R|date=October 2007|title=Identification and classification of hubs in brain networks|journal=PLOS ONE|volume=2|issue=10|pages=e1049|bibcode=2007PLoSO...2.1049S|doi=10.1371/journal.pone.0001049|pmc=2013941|pmid=17940613|doi-access=free|veditors=Kaiser M}} {{open access}} allow a detailed study of the network structure of human cortical connectivity (Hagmann et al., 2008).{{cite journal|vauthors=Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O|date=July 2008|title=Mapping the structural core of human cerebral cortex|journal=PLOS Biology|volume=6|issue=7|pages=e159|doi=10.1371/journal.pbio.0060159|pmc=2443193|pmid=18597554|veditors=Friston KJ |doi-access=free }} {{open access}} The human brain network was characterized using a broad array of network analysis methods including core decomposition, modularity analysis, hub classification and centrality. Hagmann et al. presented evidence for the existence of a structural core of highly and mutually interconnected brain regions, located primarily in posterior medial and parietal cortex. The core comprises portions of the posterior cingulate cortex, the precuneus, the cuneus, the paracentral lobule, the isthmus of the cingulate, the banks of the superior temporal sulcus, and the inferior and superior parietal cortex, all located in both cerebral hemispheres.

A subfield of connectomics deals with the comparison of the brain graphs of multiple subjects. It is possible to build a consensus graph such the Budapest Reference Connectome by allowing only edges that are present in at least $k$ connectomes, for a selectable $k$ parameter. The Budapest Reference Connectome has led the researchers to the discovery of the Consensus Connectome Dynamics of the human brain graphs. The edges appeared in all of the brain graphs form a connected subgraph around the brainstem. By allowing gradually less frequent edges, this core subgraph grows continuously, as a shrub. The growth dynamics may reflect the individual brain development and provide an opportunity to direct some edges of the human consensus brain graph.{{cite journal|vauthors=Kerepesi C, Szalkai B, Varga B, Grolmusz V|year=2016|title=How to Direct the Edges of the Connectomes: Dynamics of the Consensus Connectomes and the Development of the Connections in the Human Brain|journal=PLOS ONE|volume=11|issue=6|pages=e0158680|arxiv=1509.05703|bibcode=2016PLoSO..1158680K|doi=10.1371/journal.pone.0158680|pmc=4928947|pmid=27362431|doi-access=free}}

Alternatively, local difference which are statistically significantly different among groups have attracted more attention as they highlight specific connections and therefore shed more light on specific brain traits or pathology. Hence, algorithms to find local difference between graph populations have also been introduced (e.g. to compare case versus control groups).{{cite journal|last1=Crimi|first1=Alessandro|last2=Giancardo|first2=Luca|last3=Sambataro|first3=Fabio|last4=Diego|first4=Sona|year=2019|title=MultiLink analysis: brain network comparison via sparse connectivity analysis|journal=Scientific Reports|volume=9|issue=1|pages=1–13|bibcode=2019NatSR...9...65C|doi=10.1038/s41598-018-37300-4|pmc=6329758|pmid=30635604}} Those can be found by using either an adjusted t-test,{{cite journal|last1=Zalesky|first1=Andrew|last2=Fornito|first2=Alex|last3=Bullmore|first3=Edward|year=2010|title=Network-based statistic: identifying differences in brain networks.|journal=NeuroImage|volume=53|issue=4|pages=1197–1207|doi=10.1016/j.neuroimage.2010.06.041|pmid=20600983|s2cid=17760084}} or a sparsity model, with the aim of finding statistically significant connections which are different among those groups.

Comparisons between the connectomes (or braingraphs) of healthy women and men{{cite journal | vauthors = Szalkai B, Varga B, Grolmusz V | title = Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's | journal = PLOS ONE | volume = 10 | issue = 7 | pages = e0130045 | date = 2015 | pmid = 26132764 | pmc = 4488527 | doi = 10.1371/journal.pone.0130045 | arxiv = 1501.00727 | doi-access = free | bibcode = 2015PLoSO..1030045S }}{{cite journal | vauthors = Szalkai B, Varga B, Grolmusz V | title = Brain size bias compensated graph-theoretical parameters are also better in women's structural connectomes | journal = Brain Imaging and Behavior | volume = 12 | issue = 3 | pages = 663–673 | date = June 2018 | pmid = 28447246 | doi = 10.1007/s11682-017-9720-0 | s2cid = 4028467}}{{cite journal|vauthors=Ingalhalikar M, Smith A, Parker D, Satterthwaite TD, Elliott MA, Ruparel K, Hakonarson H, Gur RE, Gur RC, Verma R|date=January 2014|title=Sex differences in the structural connectome of the human brain|journal=Proceedings of the National Academy of Sciences of the United States of America|volume=111|issue=2|pages=823–8|bibcode=2014PNAS..111..823I|doi=10.1073/pnas.1316909110|pmc=3896179|pmid=24297904|doi-access=free}} have shown that in several deep graph-theoretical parameters, the structural connectome of women is significantly better connected than that of men. For example, women's connectome has more edges, higher minimum bipartition width, larger eigengap, greater minimum vertex cover than that of men. The minimum bipartition width (or, in other words, the minimum balanced cut) is a well-known measure of quality of computer multistage interconnection networks, it describes the possible bottlenecks in network communication: The higher this value is, the better is the network. The larger eigengap shows that the female connectome is better expander graph than the connectome of males. The better expanding property, the higher minimum bipartition width and the greater minimum vertex cover show deep advantages in network connectivity in the case of female braingraph.

Connectomes generally exhibit a small-world character, with overall cortical connectivity decreasing with age.{{cite journal|vauthors=Gong G, Rosa-Neto P, Carbonell F, Chen ZJ, He Y, Evans AC|date=December 2009|title=Age- and gender-related differences in the cortical anatomical network|journal=The Journal of Neuroscience|volume=29|issue=50|pages=15684–93|doi=10.1523/JNEUROSCI.2308-09.2009|pmc=2831804|pmid=20016083}} The aim of the as of 2015 ongoing [http://lifespan.humanconnectome.org/ HCP Lifespan Pilot Project] is to identify connectome differences between 6 age groups (4–6, 8–9, 14–15, 25–35, 45–55, 65–75).

More recently, connectograms have been used to visualize full-brain data by placing cortical areas around a circle, organized by lobe.{{cite journal|vauthors=Irimia A, Chambers MC, Torgerson CM, Van Horn JD|date=April 2012|title=Circular representation of human cortical networks for subject and population-level connectomic visualization|journal=NeuroImage|volume=60|issue=2|pages=1340–51|doi=10.1016/j.neuroimage.2012.01.107|pmc=3594415|pmid=22305988}}{{cite journal|vauthors=Irimia A, Chambers MC, Torgerson CM, Filippou M, Hovda DA, Alger JR, Gerig G, Toga AW, Vespa PM, Kikinis R, Van Horn JD|year=2012|title=Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury|journal=Frontiers in Neurology|volume=3|pages=10|doi=10.3389/fneur.2012.00010|pmc=3275792|pmid=22363313|doi-access=free}} Inner circles then depict cortical metrics on a color scale. White matter fiber connections in DTI data are then drawn between these cortical regions and weighted by fractional anisotropy and strength of the connection. Such graphs have even been used to analyze the damage done to the famous traumatic brain injury patient Phineas Gage.{{cite journal|vauthors=Van Horn JD, Irimia A, Torgerson CM, Chambers MC, Kikinis R, Toga AW|year=2012|title=Mapping connectivity damage in the case of Phineas Gage|journal=PLOS ONE|volume=7|issue=5|pages=e37454|bibcode=2012PLoSO...737454V|doi=10.1371/journal.pone.0037454|pmc=3353935|pmid=22616011|doi-access=free}} {{open access}}

Statistical graph theory is an emerging discipline which is developing sophisticated pattern recognition and inference tools to parse these brain graphs (Goldenberg et al., 2009).

In 2005, Dr. Olaf Sporns at Indiana University and Dr. Patric Hagmann at Lausanne University Hospital independently and simultaneously suggested the term "connectome" 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.

"Connectomics" has been defined as the science concerned with assembling and analyzing connectome data sets.{{cite thesis|url=http://infoscience.epfl.ch/record/33696 |title=From diffusion MRI to brain connectomics | last = Hagmann | first= Patric | access-date=2014-01-16 | doi = 10.5075/epfl-thesis-3230 | location = Lausanne | publisher = EPFL | date = 2005}}

In their 2005 paper, "The Human Connectome, a structural description of the human brain", Sporns et al. wrote:

To understand the functioning of a network, one must know its elements and their interconnections. The purpose of this article is to discuss research strategies aimed at a comprehensive structural description of the network of elements and connections forming the human brain. We propose to call this dataset the human "connectome," and we argue that it is fundamentally important in cognitive neuroscience and neuropsychology. The connectome will significantly increase our understanding of how functional brain states emerge from their underlying structural substrate, and will provide new mechanistic insights into how brain function is affected if this structural substrate is disrupted.{{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}}

In his 2005 Ph.D. thesis, From diffusion MRI to brain connectomics, Hagmann wrote:

It is clear that, like the genome, which is much more than just a juxtaposition of genes, the set of all neuronal connections in the brain is much more than the sum of their individual components. The genome is an entity it-self, as it is from the subtle gene interaction that [life] emerges. In a similar manner, one could consider the brain connectome, set of all neuronal connections, as one single entity, thus emphasizing the fact that the huge brain neuronal communication capacity and computational power critically relies on this subtle and incredibly complex connectivity architecture.

The term "connectome" was more recently popularized by Sebastian Seung's I am my Connectome speech given at the 2010 TED conference, which discusses the high-level goals of mapping the human connectome, as well as ongoing efforts to build a three-dimensional neural map of brain tissue at the microscale.{{cite web|last=Seung|first=Sebastian|name-list-style=vanc|date=September 2010|orig-year=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.

Websites to explore publicly available connectomics datasets:

Macroscale Connectomics (Healthy Young Adult Datasets)

• Human Connectome Project [https://www.humanconnectome.org/study/hcp-young-adult Young Adult]
• Amsterdam [https://nilab-uva.github.io/AOMIC.github.io/ Open MRI Collection]
• Harvard Brain Genomic [https://dataverse.harvard.edu/dataverse/GSP Superstruct Project]

For a more comprehensive list of open macroscale datasets, check out [https://www.sciencedirect.com/science/article/pii/S1053811921008521 this article]

Microscale Connectomics

• Whole C. elegans [https://wormwiring.org/ connectome]
• NeuPRINT [https://neuprint.janelia.org/ Fly Hemibrain]
• [https://flywire.ai/ Flywire] (whole fly brain)
• [https://www.microns-explorer.org/ MICrONS Explorer] (mouse cortical data)
• [https://h01-release.storage.googleapis.com/landing.html H01 Browser] Release (human cortical data)
• Connectomic comparison of mouse and human cortex ([https://wklink.org/9045 mouse], [https://wklink.org/1186 macaque], and [https://wklink.org/7861 human] cortical data)

• Dynamic Functional Connectivity
• List of Functional Connectivity Software
• Human Connectome Project
• Budapest Reference Connectome
• Drosophila connectome
• https://eyewire.org/explore

{{reflist|30em}}

{{refbegin}}

• {{cite journal | vauthors = Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O | title = Mapping the structural core of human cerebral cortex | journal = PLOS Biology | volume = 6 | issue = 7 | pages = e159 | date = July 2008 | pmid = 18597554 | pmc = 2443193 | doi = 10.1371/journal.pbio.0060159 | editor1-last = Friston | editor1-first = Karl J. | doi-access = free }}
• {{cite web |date=2008-07-02 |title=New map IDs the core of the human brain |website=Brain Mysteries |url=http://www.brainmysteries.com/Research/New_map_IDs_the_core_of_the_human_brain.asp |archive-url=https://web.archive.org/web/20080703192335/http://www.brainmysteries.com/Research/New_map_IDs_the_core_of_the_human_brain.asp |archive-date=2008-07-03}}

{{refend}}

• [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://braingraph.org braingraph.org: a database of hundreds of braingraphs, computed from the data of the Human Connectome Project]

{{Genomics}}

{{Neuroscience}}

Category:Omics

Category:Neuroscience