Connectomics

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system, typically its brain or eye. Because these structures are extremely complex, methods within this field use a high-throughput application of neural imaging and histological techniques in order to increase the speed, efficiency, and resolution of maps of the multitude of neural connections in a nervous system. While the principal focus of such a project is the brain, any neural connections could theoretically be mapped by connectomics, including, for example, neuromuscular junctions.[1] This study is sometimes referred to by its previous name of hodology.

Tools

One of the main tools used for connectomics research at the macroscale level is diffusion MRI.[2] The main tool for connectomics research at the microscale level is chemical brain preservation followed by 3D electron microscopy,[3] 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.[4]

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,[5] the fruit fly,[6][7] the nematode C. elegans,[8][9] and the barn owl.[10]

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.[11] Current neural networks mostly rely on probabilistic representations of connectivity patterns.[12] Connectograms (circular diagrams of connectomics) have been used in traumatic brain injury cases to document the extent of damage to neural networks.[13][14]

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.[15][16] 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.

Human connectomes have an individual variability, which can be measured with the cumulative distribution function, as it was shown in [17]. 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.[18] 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.[19]

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.

gollark: https://dragcave.net/lineage/MpmwIWeird thing on hub.
gollark: Each CB can breed lots of 2Gs which can breed lots of 3Gs which can breed lots of 4Gs.
gollark: Not really; it's exponential growth, sort of thing.
gollark: Or at least value; the value of 2G prizes does not reflect their rarity well.
gollark: I got offers of a gold+silver on my ND and those are around 2G prizes in rarity.

See also

References
  1. 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.
  2. Wedeen, V.J.; Wang, R.P.; Schmahmann, J.D.; Benner, T.; Tseng, W.Y.I.; Dai, G.; Pandya, D.N.; Hagmann, P.; et al. (2008). "Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers". NeuroImage. 41 (4): 1267–77. doi:10.1016/j.neuroimage.2008.03.036. PMID 18495497.
  3. Anderson, JR; Jones, BW; Watt, CB; Shaw, MV; Yang, JH; Demill, D; Lauritzen, JS; Lin, Y; et al. (2011). "Exploring the retinal connectome". Molecular Vision. 17: 355–79. PMC 3036568. PMID 21311605.
  4. BV, DELMIC. "Neuroscience: Synaptic connectivity in the songbird brain - Application Note | DELMIC". request.delmic.com. Retrieved 2017-02-16.
  5. Bock, Davi D.; Lee, Wei-Chung Allen; Kerlin, Aaron M.; Andermann, Mark L.; Hood, Greg; Wetzel, Arthur W.; Yurgenson, Sergey; Soucy, Edward R.; et al. (2011). "Network anatomy and in vivo physiology of visual cortical neurons". Nature. 471 (7337): 177–82. Bibcode:2011Natur.471..177B. doi:10.1038/nature09802. PMC 3095821. PMID 21390124.
  6. Chklovskii, Dmitri B; Vitaladevuni, Shiv; Scheffer, Louis K (2010). "Semi-automated reconstruction of neural circuits using electron microscopy". Current Opinion in Neurobiology. 20 (5): 667–75. doi:10.1016/j.conb.2010.08.002. PMID 20833533.
  7. Zheng, Z (2018). "A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster". Cell. 174 (3): 730–743. doi:10.1016/j.cell.2018.06.019. PMC 6063995. PMID 30033368.
  8. Chen, B. L.; Hall, D. H.; Chklovskii, D. B. (2006). "Wiring optimization can relate neuronal structure and function". Proceedings of the National Academy of Sciences. 103 (12): 4723–8. Bibcode:2006PNAS..103.4723C. doi:10.1073/pnas.0506806103. PMC 1550972. PMID 16537428.
  9. Perez-Escudero, A.; Rivera-Alba, M.; De Polavieja, G. G. (2009). "Structure of deviations from optimality in biological systems". Proceedings of the National Academy of Sciences. 106 (48): 20544–9. Bibcode:2009PNAS..10620544P. doi:10.1073/pnas.0905336106. PMC 2777958. PMID 19918070.
  10. Pena, JL; Debello, WM (2010). "Auditory processing, plasticity, and learning in the barn owl". ILAR Journal. 51 (4): 338–52. doi:10.1093/ilar.51.4.338. PMC 3102523. PMID 21131711.
  11. http://www.scholarpedia.org/article/Connectome%5B%5D%5B%5D%5B%5D
  12. Nordlie, Eilen; Gewaltig, Marc-Oliver; Plesser, Hans Ekkehard (2009). Friston, Karl J. (ed.). "Towards Reproducible Descriptions of Neuronal Network Models". PLoS Computational Biology. 5 (8): e1000456. Bibcode:2009PLSCB...5E0456N. doi:10.1371/journal.pcbi.1000456. PMC 2713426. PMID 19662159.
  13. Van Horn, John D.; Irimia, A.; Torgerson, C.M.; Chambers, M.C.; Kikinis, R.; Toga, A.W. (2012). "Mapping connectivity damage in the case of Phineas Gage". PLoS ONE. 7 (5): e37454. Bibcode:2012PLoSO...737454V. doi:10.1371/journal.pone.0037454. PMC 3353935. PMID 22616011.
  14. Irimia, Andrei; Chambers, M.C.; Torgerson, C.M.; Filippou, M.; Hovda, D.A.; Alger, J.R.; Gerig, G.; Toga, A.W.; Vespa, P.M.; Kikinis, R.; Van Horn, J.D. (6 February 2012). "Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury". Frontiers in Neurology. 3: 10. doi:10.3389/fneur.2012.00010. PMC 3275792. PMID 22363313.
  15. Szalkai, Balazs; Varga, Balint; Grolmusz, Vince (2015). "Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's". PLoS ONE. 10 (7): e0130045. arXiv:1501.00727. Bibcode:2015PLoSO..1030045S. doi:10.1371/journal.pone.0130045. PMC 4488527. PMID 26132764.
  16. Szalkai, Balázs; Varga, Bálint; Grolmusz, Vince (2017). "Brain size bias compensated graph-theoretical parameters are also better in women's structural connectomes". Brain Imaging and Behavior. 12 (3): 663–673. doi:10.1007/s11682-017-9720-0. ISSN 1931-7565. PMID 28447246.
  17. Kerepesi, Csaba; Szalkai, Balázs; Varga, Bálint; Grolmusz, Vince (2018). "Comparative connectomics: Mapping the inter-individual variability of connections within the regions of the human brain". Neuroscience Letters. 662 (1): 17–21. arXiv:1507.00327. doi:10.1016/j.neulet.2017.10.003. PMID 28988973.
  18. Lichtman, J; Sanes, J (2008). "Ome sweet ome: what can the genome tell us about the connectome?". Current Opinion in Neurobiology. 18 (3): 346–53. doi:10.1016/j.conb.2008.08.010. PMC 2735215. PMID 18801435.
  19. Vance, Ashlee (27 December 2010). "Seeking the Connectome, a Mental Map, Slice by Slice". The New York Times.

Further reading
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