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Edited heading to remove excess information and included "macro" and "microscale" connectomics under a new header "methods". Ideally these could live on seperate pages of wikipedia given the large gap that exists between the methods in modern neuroscience, however for now i've just seperated the page to show they are seperate methods.
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{{short description|Study of mapping wiring diagrams}}
{{short description|Study of mapping wiring diagrams}}
'''Connectomics''' is the production and study of [[connectomes]]: comprehensive maps of [[Biological neural network|connections]] neural connectivity within an [[organism]]'s [[nervous system]]. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The [[nervous system]] is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected. Because these structures are extremely complex, methods within this field use a [[High-throughput screening|high-throughput]] application of functional and structural neural imaging, most commonly electron microscopy, and [[histology|histological]] techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected that spanning the nervous system including the various areas of cortex, cerebellum,<ref>{{Cite journal|last=Quartarone|first=Angelo|last2=Cacciola|first2=Alberto|last3=Milardi|first3=Demetrio|last4=Ghilardi|first4=Maria Felice|last5=Calamuneri|first5=Alessandro|last6=Chillemi|first6=Gaetana|last7=Anastasi|first7=Giuseppe|last8=Rothwell|first8=John|date=2020-02-01|title=New insights into cortico-basal-cerebellar connectome: clinical and physiological considerations|url=https://doi.org/10.1093/brain/awz310|journal=Brain|volume=143|issue=2|pages=396–406|doi=10.1093/brain/awz310|issn=0006-8950}}</ref><ref>{{Cite journal|last=Nguyen|first=Tri M.|last2=Thomas|first2=Logan A.|last3=Rhoades|first3=Jeff L.|last4=Ricchi|first4=Ilaria|last5=Yuan|first5=Xintong Cindy|last6=Sheridan|first6=Arlo|last7=Hildebrand|first7=David G. C.|last8=Funke|first8=Jan|last9=Regehr|first9=Wade G.|last10=Lee|first10=Wei-Chung Allen|date=2021-11-30|title=Structured connectivity in the cerebellum enables noise-resilient pattern separation|url=https://www.biorxiv.org/content/10.1101/2021.11.29.470455v1|language=en|pages=2021.11.29.470455|doi=10.1101/2021.11.29.470455v1}}</ref> the retina,<ref>{{Cite journal|last=Helmstaedter|first=Moritz|last2=Briggman|first2=Kevin L.|last3=Turaga|first3=Srinivas C.|last4=Jain|first4=Viren|last5=Seung|first5=H. Sebastian|last6=Denk|first6=Winfried|date=2013-08-07|title=Connectomic reconstruction of the inner plexiform layer in the mouse retina|url=http://dx.doi.org/10.1038/nature12346|journal=Nature|volume=500|issue=7461|pages=168–174|doi=10.1038/nature12346|issn=0028-0836}}</ref> the peripheral nervous system<ref>{{Cite journal|last=Phelps|first=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|date=2021-02-04|title=Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy|url=https://www.sciencedirect.com/science/article/pii/S0092867420316834|journal=Cell|language=en|volume=184|issue=3|pages=759–774.e18|doi=10.1016/j.cell.2020.12.013|pmc=8312698|issn=0092-8674}}</ref> and [[neuromuscular junctions]].<ref name="pmid26634293">{{cite journal|last1=Boonstra|first1=Tjeerd W.|last2=Danna-Dos-Santos|first2=Alessander|last3=Hong-Bo|first3=Xie.|last4=Roerdink|first4=Melvyn|last5=Stins|first5=John F.|authorlink6=Michael Breakspear|last6=Breakspear|first6=Michael|title=Muscle networks: Connectivity analysis of EMG activity during postural control|journal=Scientific Reports|volume=5|pages=17830|year=2015|pmid=26634293|doi=10.1038/srep17830|pmc=4669476|bibcode=2015NatSR...517830B }}</ref>
'''Connectomics''' is the production and study of [[connectomes]]: comprehensive maps of [[Biological neural network|connections]] within an [[organism]]'s [[nervous system]]. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The [[nervous system]] is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a [[High-throughput screening|high-throughput]] application of functional and structural neural imaging, most commonly [[Magnetic resonance imaging of the brain|magnetic resonance imaging]] (MRI), electron microscopy, and [[histology|histological]] techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected that spanning the nervous system including the various areas of cortex, cerebellum,<ref>{{Cite journal|last=Quartarone|first=Angelo|last2=Cacciola|first2=Alberto|last3=Milardi|first3=Demetrio|last4=Ghilardi|first4=Maria Felice|last5=Calamuneri|first5=Alessandro|last6=Chillemi|first6=Gaetana|last7=Anastasi|first7=Giuseppe|last8=Rothwell|first8=John|date=2020-02-01|title=New insights into cortico-basal-cerebellar connectome: clinical and physiological considerations|url=https://doi.org/10.1093/brain/awz310|journal=Brain|volume=143|issue=2|pages=396–406|doi=10.1093/brain/awz310|issn=0006-8950}}</ref><ref>{{Cite journal|last=Nguyen|first=Tri M.|last2=Thomas|first2=Logan A.|last3=Rhoades|first3=Jeff L.|last4=Ricchi|first4=Ilaria|last5=Yuan|first5=Xintong Cindy|last6=Sheridan|first6=Arlo|last7=Hildebrand|first7=David G. C.|last8=Funke|first8=Jan|last9=Regehr|first9=Wade G.|last10=Lee|first10=Wei-Chung Allen|date=2021-11-30|title=Structured connectivity in the cerebellum enables noise-resilient pattern separation|url=https://www.biorxiv.org/content/10.1101/2021.11.29.470455v1|language=en|pages=2021.11.29.470455|doi=10.1101/2021.11.29.470455v1}}</ref> the retina,<ref>{{Cite journal|last=Helmstaedter|first=Moritz|last2=Briggman|first2=Kevin L.|last3=Turaga|first3=Srinivas C.|last4=Jain|first4=Viren|last5=Seung|first5=H. Sebastian|last6=Denk|first6=Winfried|date=2013-08-07|title=Connectomic reconstruction of the inner plexiform layer in the mouse retina|url=http://dx.doi.org/10.1038/nature12346|journal=Nature|volume=500|issue=7461|pages=168–174|doi=10.1038/nature12346|issn=0028-0836}}</ref> the peripheral nervous system<ref>{{Cite journal|last=Phelps|first=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|date=2021-02-04|title=Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy|url=https://www.sciencedirect.com/science/article/pii/S0092867420316834|journal=Cell|language=en|volume=184|issue=3|pages=759–774.e18|doi=10.1016/j.cell.2020.12.013|pmc=8312698|issn=0092-8674}}</ref> and [[neuromuscular junctions]].<ref name="pmid26634293">{{cite journal|last1=Boonstra|first1=Tjeerd W.|last2=Danna-Dos-Santos|first2=Alessander|last3=Hong-Bo|first3=Xie.|last4=Roerdink|first4=Melvyn|last5=Stins|first5=John F.|authorlink6=Michael Breakspear|last6=Breakspear|first6=Michael|title=Muscle networks: Connectivity analysis of EMG activity during postural control|journal=Scientific Reports|volume=5|pages=17830|year=2015|pmid=26634293|doi=10.1038/srep17830|pmc=4669476|bibcode=2015NatSR...517830B }}</ref>


Generally speaking, there are two types of connectomes; macroscale and microscale. Macroscale connectomics refers to using [[Functional magnetic resonance imaging|functional]] and [[Diffusion MRI|structural]] MRI data to map out large fiber tracts and functional gray matter areas within the brain in terms of blood flow (functional) and water diffusivity (structural). Microscale connectomics is the mapping of small organisms’ complete connectome using microscopy and histology. That is, all connections that exist in their central nervous system.
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Generally speaking, there are two types of connectomes; macroscale and microscale. Macroscale connectomes are commonly collected using [[Diffusion MRI|diffusion magnetic resonance imaging]] (dMRI). dMRI datasets can span the entire brain imaging white matter between the [[Cerebral cortex|cortex]] and [[subcortex]]. One of the benefits is it offers in vivo information about connectivity between different brain areas. Macroscale connectomics has furthered our understanding of various brain pathways including the visual pathway,<ref>{{Cite journal|last=Kammen|first=Alexandra|last2=Law|first2=Meng|last3=Tjan|first3=Bosco S.|last4=Toga|first4=Arthur W.|last5=Shi|first5=Yonggang|date=January 2016|title=Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis|url=http://dx.doi.org/10.1016/j.neuroimage.2015.11.005|journal=NeuroImage|volume=125|pages=767–779|doi=10.1016/j.neuroimage.2015.11.005|issn=1053-8119}}</ref><ref>{{Cite journal|last=Yogarajah|first=M.|last2=Focke|first2=N. K.|last3=Bonelli|first3=S.|last4=Cercignani|first4=M.|last5=Acheson|first5=J.|last6=Parker|first6=G. J. M.|last7=Alexander|first7=D. C.|last8=McEvoy|first8=A. W.|last9=Symms|first9=M. R.|last10=Koepp|first10=M. J.|last11=Duncan|first11=J. S.|date=2009-05-21|title=Defining Meyer's loop-temporal lobe resections, visual field deficits and diffusion tensor tractography|url=http://dx.doi.org/10.1093/brain/awp114|journal=Brain|volume=132|issue=6|pages=1656–1668| pmc=2685925|doi=10.1093/brain/awp114|issn=0006-8950}}</ref> brainstem pathways<ref>{{Cite journal|last=Nieuwenhuys|first=Rudolf|last2=Voogd|first2=Jan|last3=van Huijzen|first3=Christiaan|date=2008|title=The Human Central Nervous System|url=http://dx.doi.org/10.1007/978-3-540-34686-9|doi=10.1007/978-3-540-34686-9}}</ref><ref>{{Citation|last=Paxinos|first=George|title=Organization of Brainstem Nuclei|date=2012|url=http://dx.doi.org/10.1016/b978-0-12-374236-0.10008-2|work=The Human Nervous System|pages=260–327|publisher=Elsevier|access-date=2021-12-07|last2=Xu-Feng|first2=Huang|last3=Sengul|first3=Gulgun|last4=Watson|first4=Charles}}</ref> and language pathways<ref>{{Cite journal|last=Glasser|first=Matthew F.|last2=Rilling|first2=James K.|date=2008-02-14|title=DTI Tractography of the Human Brain's Language Pathways|url=http://dx.doi.org/10.1093/cercor/bhn011|journal=Cerebral Cortex|volume=18|issue=11|pages=2471–2482|doi=10.1093/cercor/bhn011|issn=1460-2199}}</ref><ref>{{Cite journal|last=Catani|first=Marco|last2=Jones|first2=Derek K.|last3=ffytche|first3=Dominic H.|date=2004|title=Perisylvian language networks of the human brain|url=http://dx.doi.org/10.1002/ana.20319|journal=Annals of Neurology|volume=57|issue=1|pages=8–16|doi=10.1002/ana.20319|issn=0364-5134}}</ref> among others.


== Methods ==
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On the other hand, microscale connectomes focus on a much smaller area of the nervous system with much higher resolution. These datasets are commonly collected using [[electron microscopy]] imaging and offer single synapse resolution of entire local circuits. Some of the milestones in EM connectomics include the entire nervous system of [[C. elegans]],<ref>{{Cite journal|date=1986-11-12|title=The structure of the nervous system of the nematodeCaenorhabditis elegans|url=http://dx.doi.org/10.1098/rstb.1986.0056|journal=Philosophical Transactions of the Royal Society of London. B, Biological Sciences|volume=314|issue=1165|pages=1–340|doi=10.1098/rstb.1986.0056|issn=0080-4622}}</ref> an entire fly brain,<ref>{{Cite journal|last=Scheffer|first=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|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|url=https://doi.org/10.7554/eLife.57443|journal=eLife|volume=9|pages=e57443|pmid=32880371 | doi=10.7554/eLife.57443|issn=2050-084X}}</ref> and most recently a millimeter cube from both mouse<ref>{{Cite journal|last=MICrONS Consortium|last2=Bae|first2=J. Alexander|last3=Baptiste|first3=Mahaly|last4=Bodor|first4=Agnes L.|last5=Brittain|first5=Derrick|last6=Buchanan|first6=JoAnn|last7=Bumbarger|first7=Daniel J.|last8=Castro|first8=Manuel A.|last9=Celii|first9=Brendan|last10=Cobos|first10=Erick|last11=Collman|first11=Forrest|date=2021-07-29|title=Functional connectomics spanning multiple areas of mouse visual cortex|url=http://biorxiv.org/lookup/doi/10.1101/2021.07.28.454025|language=en|doi=10.1101/2021.07.28.454025}}</ref> and human cortex.<ref>{{Cite journal|last=Shapson-Coe|first=Alexander|last2=Januszewski|first2=Michał|last3=Berger|first3=Daniel R.|last4=Pope|first4=Art|last5=Wu|first5=Yuelong|last6=Blakely|first6=Tim|last7=Schalek|first7=Richard L.|last8=Li|first8=Peter|last9=Wang|first9=Shuohong|last10=Maitin-Shepard|first10=Jeremy|last11=Karlupia|first11=Neha|date=2021-05-30|title=A connectomic study of a petascale fragment of human cerebral cortex|url=https://www.biorxiv.org/content/10.1101/2021.05.29.446289v1|language=en|pages=2021.05.29.446289|doi=10.1101/2021.05.29.446289v1}}</ref>

=== Macroscale Connectomics ===
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Macroscale connectomes are commonly collected using [[Diffusion MRI|diffusion magnetic resonance imaging]] (dMRI) and functional magnetic resonance imaging (fMRI). dMRI datasets can span the entire brain imaging, white matter between the [[Cerebral cortex|cortex]] and [[subcortex]]. In contrast, fMRI datasets measure cerebral blood flow in the brain, as a marker of neuronal activation. One of the benefits of MRI is it offers in vivo information about connectivity between different brain areas. Macroscale connectomics has furthered our understanding of various [[Large-scale brain network|brain networks]] including visual,<ref>{{Cite journal|last=Kammen|first=Alexandra|last2=Law|first2=Meng|last3=Tjan|first3=Bosco S.|last4=Toga|first4=Arthur W.|last5=Shi|first5=Yonggang|date=January 2016|title=Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis|url=http://dx.doi.org/10.1016/j.neuroimage.2015.11.005|journal=NeuroImage|volume=125|pages=767–779|doi=10.1016/j.neuroimage.2015.11.005|issn=1053-8119}}</ref><ref>{{Cite journal|last=Yogarajah|first=M.|last2=Focke|first2=N. K.|last3=Bonelli|first3=S.|last4=Cercignani|first4=M.|last5=Acheson|first5=J.|last6=Parker|first6=G. J. M.|last7=Alexander|first7=D. C.|last8=McEvoy|first8=A. W.|last9=Symms|first9=M. R.|last10=Koepp|first10=M. J.|last11=Duncan|first11=J. S.|date=2009-05-21|title=Defining Meyer's loop-temporal lobe resections, visual field deficits and diffusion tensor tractography|url=http://dx.doi.org/10.1093/brain/awp114|journal=Brain|volume=132|issue=6|pages=1656–1668|doi=10.1093/brain/awp114|issn=0006-8950|pmc=2685925}}</ref> brainstem, <ref>{{Cite journal|last=Nieuwenhuys|first=Rudolf|last2=Voogd|first2=Jan|last3=van Huijzen|first3=Christiaan|date=2008|title=The Human Central Nervous System|url=http://dx.doi.org/10.1007/978-3-540-34686-9|doi=10.1007/978-3-540-34686-9}}</ref><ref>{{Citation|last=Paxinos|first=George|title=Organization of Brainstem Nuclei|date=2012|url=http://dx.doi.org/10.1016/b978-0-12-374236-0.10008-2|work=The Human Nervous System|pages=260–327|publisher=Elsevier|access-date=2021-12-07|last2=Xu-Feng|first2=Huang|last3=Sengul|first3=Gulgun|last4=Watson|first4=Charles}}</ref> and language networks,<ref>{{Cite journal|last=Glasser|first=Matthew F.|last2=Rilling|first2=James K.|date=2008-02-14|title=DTI Tractography of the Human Brain's Language Pathways|url=http://dx.doi.org/10.1093/cercor/bhn011|journal=Cerebral Cortex|volume=18|issue=11|pages=2471–2482|doi=10.1093/cercor/bhn011|issn=1460-2199}}</ref><ref>{{Cite journal|last=Catani|first=Marco|last2=Jones|first2=Derek K.|last3=ffytche|first3=Dominic H.|date=2004|title=Perisylvian language networks of the human brain|url=http://dx.doi.org/10.1002/ana.20319|journal=Annals of Neurology|volume=57|issue=1|pages=8–16|doi=10.1002/ana.20319|issn=0364-5134}}</ref> among others.

=== Microscale Connectomics ===
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On the other hand, microscale connectomes focus on a much smaller area of the nervous system with much higher resolution. These datasets are commonly collected using [[electron microscopy]] imaging and offer single synapse resolution of entire local circuits. Some of the milestones in EM connectomics include the entire nervous system of [[C. elegans]],<ref>{{Cite journal|date=1986-11-12|title=The structure of the nervous system of the nematodeCaenorhabditis elegans|url=http://dx.doi.org/10.1098/rstb.1986.0056|journal=Philosophical Transactions of the Royal Society of London. B, Biological Sciences|volume=314|issue=1165|pages=1–340|doi=10.1098/rstb.1986.0056|issn=0080-4622}}</ref> an entire fly brain,<ref>{{Cite journal|last=Scheffer|first=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|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|url=https://doi.org/10.7554/eLife.57443|journal=eLife|volume=9|pages=e57443|doi=10.7554/eLife.57443|issn=2050-084X|pmid=32880371}}</ref> and most recently a millimeter cube from both mouse<ref>{{Cite journal|last=MICrONS Consortium|last2=Bae|first2=J. Alexander|last3=Baptiste|first3=Mahaly|last4=Bodor|first4=Agnes L.|last5=Brittain|first5=Derrick|last6=Buchanan|first6=JoAnn|last7=Bumbarger|first7=Daniel J.|last8=Castro|first8=Manuel A.|last9=Celii|first9=Brendan|last10=Cobos|first10=Erick|last11=Collman|first11=Forrest|date=2021-07-29|title=Functional connectomics spanning multiple areas of mouse visual cortex|url=http://biorxiv.org/lookup/doi/10.1101/2021.07.28.454025|language=en|doi=10.1101/2021.07.28.454025}}</ref> and human cortex.<ref>{{Cite journal|last=Shapson-Coe|first=Alexander|last2=Januszewski|first2=Michał|last3=Berger|first3=Daniel R.|last4=Pope|first4=Art|last5=Wu|first5=Yuelong|last6=Blakely|first6=Tim|last7=Schalek|first7=Richard L.|last8=Li|first8=Peter|last9=Wang|first9=Shuohong|last10=Maitin-Shepard|first10=Jeremy|last11=Karlupia|first11=Neha|date=2021-05-30|title=A connectomic study of a petascale fragment of human cerebral cortex|url=https://www.biorxiv.org/content/10.1101/2021.05.29.446289v1|language=en|pages=2021.05.29.446289|doi=10.1101/2021.05.29.446289v1}}</ref>


== Tools ==
== Tools ==
One of the main tools used for connectomics research at the macroscale level is [[diffusion MRI]].<ref name=pmid18495497>{{cite journal|last1=Wedeen|first1=V.J.|last2=Wang|first2=R.P.|last3=Schmahmann|first3=J.D.|last4=Benner|first4=T.|last5=Tseng|first5=W.Y.I.|last6=Dai|first6=G.|last7=Pandya|first7=D.N.|last8=Hagmann|first8=P.|last9=D'arceuil|first9=H.|last10=De Crespigny|first10=A.J.|title=Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers|journal=NeuroImage|volume=41|issue=4|pages=1267–77|year=2008|pmid=18495497|doi=10.1016/j.neuroimage.2008.03.036|s2cid=2660208|display-authors=8 }}</ref> The main tool for connectomics research at the microscale level is chemical brain preservation followed by 3D [[electron microscopy]],<ref name=pmid21311605>{{cite journal|last1=Anderson|first1=JR|last2=Jones|first2=BW|last3=Watt|first3=CB|last4=Shaw|first4=MV|last5=Yang|first5=JH|last6=Demill|first6=D|last7=Lauritzen|first7=JS|last8=Lin|first8=Y|last9=Rapp|first9=KD|last10=Mastronarde|first10=D|last11=Koshevoy|first11=P|last12=Grimm|first12=B|last13=Tasdizen|first13=T|last14=Whitaker|first14=R|last15=Marc|first15=R. E.|title=Exploring the retinal connectome|journal=Molecular Vision|volume=17|pages=355–79|year=2011|pmid=21311605|pmc=3036568|display-authors=8 }}</ref> used for [[neural circuit reconstruction]]. [[Correlative light-electron microscopy|Correlative microscopy]], which combines fluorescence with 3D electron microscopy, results in more interpretable data as is it able to automatically detect specific neuron types and can trace them in their entirety using fluorescent markers.<ref>{{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}}</ref>
One of the main tools used for connectomics research at the macroscale level is [[Magnetic resonance imaging|MRI]].<ref name=pmid18495497>{{cite journal|last1=Wedeen|first1=V.J.|last2=Wang|first2=R.P.|last3=Schmahmann|first3=J.D.|last4=Benner|first4=T.|last5=Tseng|first5=W.Y.I.|last6=Dai|first6=G.|last7=Pandya|first7=D.N.|last8=Hagmann|first8=P.|last9=D'arceuil|first9=H.|last10=De Crespigny|first10=A.J.|title=Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers|journal=NeuroImage|volume=41|issue=4|pages=1267–77|year=2008|pmid=18495497|doi=10.1016/j.neuroimage.2008.03.036|s2cid=2660208|display-authors=8 }}</ref> When used together, a resting-state fMRI and a dMRI dataset provide a comprehensive view of how regions of the brain are structurally connected, and how closely they are communicating. <ref>{{Cite journal|last=Damoiseaux|first=Jessica S.|last2=Greicius|first2=Michael D.|date=2009-10-01|title=Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity|url=https://doi.org/10.1007/s00429-009-0208-6|journal=Brain Structure and Function|language=en|volume=213|issue=6|pages=525–533|doi=10.1007/s00429-009-0208-6|issn=1863-2661}}</ref><ref>{{Cite journal|last=Honey|first=C. J.|last2=Sporns|first2=O.|last3=Cammoun|first3=L.|last4=Gigandet|first4=X.|last5=Thiran|first5=J. P.|last6=Meuli|first6=R.|last7=Hagmann|first7=P.|date=2009-02-10|title=Predicting human resting-state functional connectivity from structural connectivity|url=https://www.pnas.org/content/106/6/2035|journal=Proceedings of the National Academy of Sciences|language=en|volume=106|issue=6|pages=2035–2040|doi=10.1073/pnas.0811168106|issn=0027-8424|pmid=19188601}}</ref> The main tool for connectomics research at the microscale level is chemical brain preservation followed by 3D [[electron microscopy]],<ref name=pmid21311605>{{cite journal|last1=Anderson|first1=JR|last2=Jones|first2=BW|last3=Watt|first3=CB|last4=Shaw|first4=MV|last5=Yang|first5=JH|last6=Demill|first6=D|last7=Lauritzen|first7=JS|last8=Lin|first8=Y|last9=Rapp|first9=KD|last10=Mastronarde|first10=D|last11=Koshevoy|first11=P|last12=Grimm|first12=B|last13=Tasdizen|first13=T|last14=Whitaker|first14=R|last15=Marc|first15=R. E.|title=Exploring the retinal connectome|journal=Molecular Vision|volume=17|pages=355–79|year=2011|pmid=21311605|pmc=3036568|display-authors=8 }}</ref> used for [[neural circuit reconstruction]]. [[Correlative light-electron microscopy|Correlative microscopy]], which combines fluorescence with 3D electron microscopy, results in more interpretable data as is it able to automatically detect specific neuron types and can trace them in their entirety using fluorescent markers.<ref>{{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}}</ref>


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).
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).
Line 15: Line 21:


== Applications ==
== Applications ==
By comparing diseased connectome and healthy connectomes, we should 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.<ref name="sporns">http://www.scholarpedia.org/article/Connectome{{Unreliable medical source|date=March 2011}}{{Dead link|date=July 2019 |bot=InternetArchiveBot |fix-attempted=yes }}</ref>{{Self-published inline|date=March 2011}} Current neural networks mostly rely on probabilistic representations of connectivity patterns.<ref name=pmid19662159>{{cite journal|last1=Nordlie|first1=Eilen|last2=Gewaltig|first2=Marc-Oliver|last3=Plesser|first3=Hans Ekkehard|editor1-last=Friston|editor1-first=Karl J.|title=Towards Reproducible Descriptions of Neuronal Network Models|journal=PLOS Computational Biology|volume=5|issue=8|pages=e1000456|year=2009|pmid=19662159|pmc=2713426|doi=10.1371/journal.pcbi.1000456|bibcode=2009PLSCB...5E0456N }}</ref> [[Connectogram]]s (circular diagrams of connectomics) have been used in [[traumatic brain injury]] cases to document the extent of damage to neural networks.<ref name="Van Horn">{{cite journal|last=Van Horn|first=John D.|author2=Irimia, A.|author3=Torgerson, C.M.|author4=Chambers, M.C.|author5=Kikinis, R.|author6=Toga, A.W.|title=Mapping connectivity damage in the case of Phineas Gage|journal=PLOS ONE|year=2012|volume=7|issue=5|doi=10.1371/journal.pone.0037454|pmid=22616011|pmc=3353935|pages=e37454|bibcode=2012PLoSO...737454V|doi-access=free}}</ref><ref name=Irimia>{{cite journal|last=Irimia|first=Andrei|author2=Chambers, M.C.|author3=Torgerson, C.M.|author4=Filippou, M.|author5=Hovda, D.A.|author6=Alger, J.R.|author7=Gerig, G.|author8=Toga, A.W.|author9=Vespa, P.M.|author10=Kikinis, R.|author11=Van Horn, J.D.|title=Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury|journal=Frontiers in Neurology|date=6 February 2012|doi=10.3389/fneur.2012.00010|pmid=22363313|pmc=3275792|volume=3|pages=10|doi-access=free}}</ref>
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.<ref name="sporns">http://www.scholarpedia.org/article/Connectome{{Unreliable medical source|date=March 2011}}{{Dead link|date=July 2019 |bot=InternetArchiveBot |fix-attempted=yes }}</ref>{{Self-published inline|date=March 2011}} Current neural networks mostly rely on probabilistic representations of connectivity patterns.<ref name=pmid19662159>{{cite journal|last1=Nordlie|first1=Eilen|last2=Gewaltig|first2=Marc-Oliver|last3=Plesser|first3=Hans Ekkehard|editor1-last=Friston|editor1-first=Karl J.|title=Towards Reproducible Descriptions of Neuronal Network Models|journal=PLOS Computational Biology|volume=5|issue=8|pages=e1000456|year=2009|pmid=19662159|pmc=2713426|doi=10.1371/journal.pcbi.1000456|bibcode=2009PLSCB...5E0456N }}</ref> Connectivity matrices (checkerboard diagrams of connectomics) have been used in stroke recovery to evaluate the response to treatment via [[Transcranial magnetic stimulation|Transcranial Magnetic Stimulation]].<ref>{{Cite journal|last=Yeung|first=Jacky T.|last2=Young|first2=Isabella M.|last3=Doyen|first3=Stephane|last4=Teo|first4=Charles|last5=Sughrue|first5=Michael E.|date=2021-10|title=Changes in the Brain Connectome Following Repetitive Transcranial Magnetic Stimulation for Stroke Rehabilitation|url=https://pubmed.ncbi.nlm.nih.gov/34858752/|journal=Cureus|volume=13|issue=10|pages=e19105|doi=10.7759/cureus.19105|issn=2168-8184|pmc=8614179|pmid=34858752}}</ref> Similarly, [[connectogram]]s (circular diagrams of connectomics) have been used in [[traumatic brain injury]] cases to document the extent of damage to neural networks.<ref name="Van Horn">{{cite journal|last=Van Horn|first=John D.|author2=Irimia, A.|author3=Torgerson, C.M.|author4=Chambers, M.C.|author5=Kikinis, R.|author6=Toga, A.W.|title=Mapping connectivity damage in the case of Phineas Gage|journal=PLOS ONE|year=2012|volume=7|issue=5|doi=10.1371/journal.pone.0037454|pmid=22616011|pmc=3353935|pages=e37454|bibcode=2012PLoSO...737454V|doi-access=free}}</ref><ref name=Irimia>{{cite journal|last=Irimia|first=Andrei|author2=Chambers, M.C.|author3=Torgerson, C.M.|author4=Filippou, M.|author5=Hovda, D.A.|author6=Alger, J.R.|author7=Gerig, G.|author8=Toga, A.W.|author9=Vespa, P.M.|author10=Kikinis, R.|author11=Van Horn, J.D.|title=Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury|journal=Frontiers in Neurology|date=6 February 2012|doi=10.3389/fneur.2012.00010|pmid=22363313|pmc=3275792|volume=3|pages=10|doi-access=free}}</ref>


The human connectome can be viewed as a [[Graph (discrete mathematics)|graph]], and the rich tools, definitions and algorithms of the [[Graph theory]] can be applied to these graphs. Comparing the connectomes (or braingraphs) of healthy women and men, Szalkai et al.<ref name=":47">{{Cite journal|title = Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's|last1 = Szalkai|first1 = Balazs|last2 = Varga|first2 = Balint|last3 = Grolmusz|first3 = Vince|date = 2015|journal = PLOS ONE|doi = 10.1371/journal.pone.0130045|volume=10|number=7|pages=e0130045|pmid=26132764|pmc=4488527|arxiv=1501.00727|bibcode=2015PLoSO..1030045S|doi-access = free}}</ref><ref name="Szalkai2017">{{cite journal|last1=Szalkai|first1=Balázs|last2=Varga|first2=Bálint|last3=Grolmusz|first3=Vince|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|year=2017|issn=1931-7565|doi=10.1007/s11682-017-9720-0|pmid=28447246|s2cid=4028467}}</ref> 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 (graph theory)|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.
The human connectome can be viewed as a [[Graph (discrete mathematics)|graph]], and the rich tools, definitions and algorithms of the [[Graph theory]] can be applied to these graphs. Comparing the connectomes (or braingraphs) of healthy women and men, Szalkai et al.<ref name=":47">{{Cite journal|title = Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's|last1 = Szalkai|first1 = Balazs|last2 = Varga|first2 = Balint|last3 = Grolmusz|first3 = Vince|date = 2015|journal = PLOS ONE|doi = 10.1371/journal.pone.0130045|volume=10|number=7|pages=e0130045|pmid=26132764|pmc=4488527|arxiv=1501.00727|bibcode=2015PLoSO..1030045S|doi-access = free}}</ref><ref name="Szalkai2017">{{cite journal|last1=Szalkai|first1=Balázs|last2=Varga|first2=Bálint|last3=Grolmusz|first3=Vince|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|year=2017|issn=1931-7565|doi=10.1007/s11682-017-9720-0|pmid=28447246|s2cid=4028467}}</ref> 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 (graph theory)|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.
Line 50: Line 56:


==See also==
==See also==
* [[Dynamic functional connectivity|Dynamic Functional Connectivity]]
* [[List of functional connectivity software|List of Functional Connectivity Software]]
* [[Human Connectome Project]]
* [[Human Connectome Project]]
* [[Budapest Reference Connectome]]
* [[Budapest Reference Connectome]]

Revision as of 06:55, 27 January 2022

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected that spanning the nervous system including the various areas of cortex, cerebellum,[1][2] the retina,[3] the peripheral nervous system[4] and neuromuscular junctions.[5]

Generally speaking, there are two types of connectomes; macroscale and microscale. Macroscale connectomics refers to using functional and structural MRI data to map out large fiber tracts and functional gray matter areas within the brain in terms of blood flow (functional) and water diffusivity (structural). Microscale connectomics is the mapping of small organisms’ complete connectome using microscopy and histology. That is, all connections that exist in their central nervous system.

Methods

Macroscale Connectomics

Macroscale connectomes are commonly collected using diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI). dMRI datasets can span the entire brain imaging, white matter between the cortex and subcortex. In contrast, fMRI datasets measure cerebral blood flow in the brain, as a marker of neuronal activation. One of the benefits of MRI is it offers in vivo information about connectivity between different brain areas. Macroscale connectomics has furthered our understanding of various brain networks including visual,[6][7] brainstem, [8][9] and language networks,[10][11] among others.

Microscale Connectomics

On the other hand, microscale connectomes focus on a much smaller area of the nervous system with much higher resolution. These datasets are commonly collected using electron microscopy imaging and offer single synapse resolution of entire local circuits. Some of the milestones in EM connectomics include the entire nervous system of C. elegans,[12] an entire fly brain,[13] and most recently a millimeter cube from both mouse[14] and human cortex.[15]

Tools

One of the main tools used for connectomics research at the macroscale level is MRI.[16] When used together, a resting-state fMRI and a dMRI dataset provide a comprehensive view of how regions of the brain are structurally connected, and how closely they are communicating. [17][18] The main tool for connectomics research at the microscale level is chemical brain preservation followed by 3D electron microscopy,[19] used for neural circuit reconstruction. Correlative microscopy, which combines fluorescence with 3D electron microscopy, results in more interpretable data as is it able to automatically detect specific neuron types and can trace them in their entirety using fluorescent markers.[20]

To see one of the first micro-connectomes at full-resolution, visit the Open Connectome Project, which is hosting several connectome datasets, including the 12TB dataset from Bock et al. (2011).

Model systems

Aside from the human brain, some of the model systems used for connectomics research are the mouse,[21] the fruit fly,[22][23] the nematode C. elegans,[24][25] and the barn owl.[26]

Applications

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.[27][self-published source?] Current neural networks mostly rely on probabilistic representations of connectivity patterns.[28] Connectivity matrices (checkerboard diagrams of connectomics) have been used in stroke recovery to evaluate the response to treatment via Transcranial Magnetic Stimulation.[29] Similarly, connectograms (circular diagrams of connectomics) have been used in traumatic brain injury cases to document the extent of damage to neural networks.[30][31]

The human connectome can be viewed as a graph, and the rich tools, definitions and algorithms of the Graph theory can be applied to these graphs. Comparing the connectomes (or braingraphs) of healthy women and men, Szalkai et al.[32][33] 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.

Local measures of difference between populations of those graph have been also introduced (e.g. to compare case versus control groups).[34] Those can be found by using either an adjusted t-test,[35] or a sparsity model,[34] with the aim of finding statistically significant connections which are different among those groups.

Human connectomes have an individual variability, which can be measured with the cumulative distribution function, as it was shown in.[36] 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.

Comparison to genomics

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.[37] Others have criticized attempts towards a microscale connectome, arguing that we don't have enough knowledge about where to look for insights, or that it cannot be completed within a realistic time frame.[38]

Eyewire game

Main articles: Eyewire and Sebastian Seung

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.

Public Datasets

Websites to explore publicly available connectomics datasets:

Macroscale Connectomics (Healthy Young Adult Datasets)

For a more comprehensive list of open macroscale datasets, check out this article

Microscale Connectomics

See also

References

  1. ^ Quartarone, Angelo; Cacciola, Alberto; Milardi, Demetrio; Ghilardi, Maria Felice; Calamuneri, Alessandro; Chillemi, Gaetana; Anastasi, Giuseppe; Rothwell, John (2020-02-01). "New insights into cortico-basal-cerebellar connectome: clinical and physiological considerations". Brain. 143 (2): 396–406. doi:10.1093/brain/awz310. ISSN 0006-8950.
  2. ^ Nguyen, Tri M.; Thomas, Logan A.; Rhoades, Jeff L.; Ricchi, Ilaria; Yuan, Xintong Cindy; Sheridan, Arlo; Hildebrand, David G. C.; Funke, Jan; Regehr, Wade G.; Lee, Wei-Chung Allen (2021-11-30). "Structured connectivity in the cerebellum enables noise-resilient pattern separation": 2021.11.29.470455. doi:10.1101/2021.11.29.470455v1. {{cite journal}}: Cite journal requires |journal= (help)
  3. ^ Helmstaedter, Moritz; Briggman, Kevin L.; Turaga, Srinivas C.; Jain, Viren; Seung, H. Sebastian; Denk, Winfried (2013-08-07). "Connectomic reconstruction of the inner plexiform layer in the mouse retina". Nature. 500 (7461): 168–174. doi:10.1038/nature12346. ISSN 0028-0836.
  4. ^ Phelps, Jasper S.; Hildebrand, David Grant Colburn; Graham, Brett J.; Kuan, Aaron T.; Thomas, Logan A.; Nguyen, Tri M.; Buhmann, Julia; Azevedo, Anthony W.; Sustar, Anne; Agrawal, Sweta; Liu, Mingguan (2021-02-04). "Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy". Cell. 184 (3): 759–774.e18. doi:10.1016/j.cell.2020.12.013. ISSN 0092-8674. PMC 8312698.
  5. ^ Boonstra, Tjeerd W.; Danna-Dos-Santos, Alessander; Hong-Bo, Xie.; Roerdink, Melvyn; Stins, John F.; Breakspear, Michael (2015). "Muscle networks: Connectivity analysis of EMG activity during postural control". Scientific Reports. 5: 17830. Bibcode:2015NatSR...517830B. doi:10.1038/srep17830. PMC 4669476. PMID 26634293.
  6. ^ Kammen, Alexandra; Law, Meng; Tjan, Bosco S.; Toga, Arthur W.; Shi, Yonggang (January 2016). "Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis". NeuroImage. 125: 767–779. doi:10.1016/j.neuroimage.2015.11.005. ISSN 1053-8119.
  7. ^ Yogarajah, M.; Focke, N. K.; Bonelli, S.; Cercignani, M.; Acheson, J.; Parker, G. J. M.; Alexander, D. C.; McEvoy, A. W.; Symms, M. R.; Koepp, M. J.; Duncan, J. S. (2009-05-21). "Defining Meyer's loop-temporal lobe resections, visual field deficits and diffusion tensor tractography". Brain. 132 (6): 1656–1668. doi:10.1093/brain/awp114. ISSN 0006-8950. PMC 2685925.
  8. ^ Nieuwenhuys, Rudolf; Voogd, Jan; van Huijzen, Christiaan (2008). "The Human Central Nervous System". doi:10.1007/978-3-540-34686-9. {{cite journal}}: Cite journal requires |journal= (help)
  9. ^ Paxinos, George; Xu-Feng, Huang; Sengul, Gulgun; Watson, Charles (2012), "Organization of Brainstem Nuclei", The Human Nervous System, Elsevier, pp. 260–327, retrieved 2021-12-07
  10. ^ Glasser, Matthew F.; Rilling, James K. (2008-02-14). "DTI Tractography of the Human Brain's Language Pathways". Cerebral Cortex. 18 (11): 2471–2482. doi:10.1093/cercor/bhn011. ISSN 1460-2199.
  11. ^ Catani, Marco; Jones, Derek K.; ffytche, Dominic H. (2004). "Perisylvian language networks of the human brain". Annals of Neurology. 57 (1): 8–16. doi:10.1002/ana.20319. ISSN 0364-5134.
  12. ^ "The structure of the nervous system of the nematodeCaenorhabditis elegans". Philosophical Transactions of the Royal Society of London. B, Biological Sciences. 314 (1165): 1–340. 1986-11-12. doi:10.1098/rstb.1986.0056. ISSN 0080-4622.
  13. ^ Scheffer, Louis K; Xu, C Shan; Januszewski, Michal; Lu, Zhiyuan; Takemura, Shin-ya; Hayworth, Kenneth J; Huang, Gary B; Shinomiya, Kazunori; Maitlin-Shepard, Jeremy; Berg, Stuart; Clements, Jody (2020-09-03). Marder, Eve; Eisen, Michael B; Pipkin, Jason; Doe, Chris Q (eds.). "A connectome and analysis of the adult Drosophila central brain". eLife. 9: e57443. doi:10.7554/eLife.57443. ISSN 2050-084X. PMID 32880371.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  14. ^ MICrONS Consortium; Bae, J. Alexander; Baptiste, Mahaly; Bodor, Agnes L.; Brittain, Derrick; Buchanan, JoAnn; Bumbarger, Daniel J.; Castro, Manuel A.; Celii, Brendan; Cobos, Erick; Collman, Forrest (2021-07-29). "Functional connectomics spanning multiple areas of mouse visual cortex". doi:10.1101/2021.07.28.454025. {{cite journal}}: Cite journal requires |journal= (help)
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Further reading

External links

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