Toward large-scale connectome reconstructions

Current Opinion in Neurobiology

Volumn 25, Issue , 2014, Pages 201-210

  Plaza, Stephen M ^a   Scheffer, Louis K ^a   Chklovskii, Dmitri B ^a  

^a HOWARD HUGHES MEDICAL INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; CAENORHABDITIS ELEGANS; CELL TYPE; COMPUTER ANALYSIS; CONNECTOME; ELECTRON MICROSCOPY; HUMAN; IMAGE ANALYSIS; IMAGE DISPLAY; IMAGE PROCESSING; IMAGE RECONSTRUCTION; IMAGING SOFTWARE; INFORMATION PROCESSING; MICROSCOPY; NERVE CELL; NERVOUS SYSTEM ELECTROPHYSIOLOGY; NONHUMAN; PRIORITY JOURNAL; REVIEW; SYNAPSE; ANIMAL; DEVICES; NERVE CELL NETWORK; PHYSIOLOGY; PROCEDURES; STANDARDS;

ANIMALS; CONNECTOME; NERVE NET;

EID: 84896899683     PISSN: 09594388     EISSN: 18736882     Source Type: Journal    
DOI: 10.1016/j.conb.2014.01.019     Document Type: Review

References (69)

1. Seung H.S. Reading the book of memory: sparse sampling versus dense mapping of connectomes. Neuron 2009, 62:17-29.
2. Denk W., Briggman K.L., Helmstaedter M. Structural neurobiology: missing link to a mechanistic understanding of neural computation. Nat Rev Neurosci 2012, 13:351-358.
3. Bargmann C.I., Marder E. From the connectome to brain function. Nat Methods 2013, 10:483-490.
4. Morgan J.L., Lichtman J.W. Why not connectomics?. Nat Methods 2013, 10:494-500.
5. da Costa N.M., Martin K.A.C. Sparse reconstruction of brain circuits: or, how to survive without a microscopic connectome. NeuroImage 2013, 80:27-36.
6. Takemura S., Bharioke A., Lu Z., Nern A., Vitaladevuni S., Rivlin P.K., Katz W.T., Olbris D.J., Plaza S.M., Winston P. A visual motion detection circuit suggested by Drosophila connectomics. Nature 2013, 500:175-181.
7. Jarrell T.A., Wang Y., Bloniarz A.E., Brittin C.A., Xu M., Thomson J.N., Albertson D.G., Hall D.H., Emmons S.W. The connectome of a decision-making neural network. Science 2012, 337:437-444.
8. Helmstaedter M., Briggman K.L., Turaga S., Jain V., Seung H.S., Denk W. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 2013, 500:168-174.
9. Chalfie M., Sulston J.E., White J.G., Southgate E., Thomson J.N., Brenner S. The neural circuit for touch sensitivity in C. elegans. J Neurosci 1985, 5:956-964.
10. White J.G., Southgate E., Thomson J.N., Brenner S. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos Trans R Soc Lond B: Biol Sci 1986, 314:1-340.
11. Varshney L.R., Chen B.L., Paniagua E., Hall D.H., Chklovskii D.B. Structural properties of the Caenorhabditis elegans neuronal network. PLoS Comput Biol 2011, 7(2):1-21. e1001066.
12. Hassenstein B., Reichardt W. Systemtheoretische Analyse der Zeit-, Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Rüsselkäfers Chlorophanus. Z Naturforschg B 1956, 11:513-524.
13. Borst A., Haag J., Reiff D.F. Fly motion vision. Annu Rev Neurosci 2010, 33:49-70.
14. Joesch M., Schnell B., Raghu S.V., Reiff D.F., Borst A. ON and OFF pathways in Drosophila motion vision. Nature 2010, 468:300-304.
15. Rister J., et al. Dissection of the peripheral motion channel in the visual system of Drosophila melanogaster. Neuron 2007, 56:155-170.
16. Stepanyants A., Hof P.R., Chklovskii D.B. Geometry and structural plasticity of synaptic connectivity. Neuron 2002, 34:275-288.
17. Chklovskii D.B., Mel B.W., Svoboda K. Cortical rewiring and information storage. Nature 2004, 431:782-788.
18. Mishchenko Y., Hu T., Spacek J., Mendenhall J., Harris K.M., Chklovskii D.B. Ultrastructural analysis of hippocampal neuropil from the connectomics perspective. Neuron 2010, 67:1009-1020.
19. Hu T., Towfic Z.J., Pehlevan C., Genkin A., Chklovskii D.B. A neuron as a signal processing device. Proceedings of the Asilomar Conference on Signals, Systems, and Computers 2014, IEEE.
20. Druckmann S., Chklovskii D.B. Neuronal circuits underlying persistent representations despite time varying activity. Curr Biol 2012, 22:2095-2103.
21. Hubel D.H., Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol 1962, 160:106-154.
22. Chklovskii D.B., Shiv V., Scheffer L.K. Semi-automated reconstruction of neural circuits using electron microscopy. Curr Opin Neurobiol 2010, 20.5:667-675.
23. Bock D.D., Lee W.-C.A., Kerlin A.M., Andermann M.L., Hood G., Wetzel A.W., Yurgenson S., Soucy E.R., Kim H.S., Reid R.C. Network anatomy and in vivo physiology of visual cortical neurons. Nature 2011, 471:177-182.
24. Hayworth K.J., Kasthuri N., Schalek R., Lichtman J.W. Automating the collection of ultrathin serial sections for large volume TEM reconstructions. Microsci Microanal 2006, 12(Suppl. 2):86-87.
25. Denk W., Heinz H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol 2004, 2(11):1900-1909. e0020329.
26. Knott G., Marchman H., Wall D., Lich B. Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. J Neurosci 2008, 28:2959-2964.
27. Veeraraghavan A., Genkin A.V., Vitaladevuni S., Scheffer L., Xu S., Hess H., Fetter R., Cantoni M., Knott G., Chklovskii D. Increasing depth resolution of electron microscopy of neural circuits using sparse tomographic reconstruction. CVPR 2010, 1767-1774.
28. Hu T., Nunez-Iglesias J., Vitaladevuni S., Scheffer L., Xu S., Bolorizadeh M., Hess H., Fetter R., Chklovskii D.B. Electron microscopy reconstruction of brain structure using sparse representations over learned dictionaries. IEEE Trans Med Imag 2013, 32:2179-2188.
29. Hayworth K.J., Tapia J.C., Kasthuri N., Schalek R., Lichtman J.W. Divide and conquer lossless thick sectioning of plastic-embedded brain tissue to parallelize large volume serial reconstructions. Neuroscience Meeting Planner, PPP4 2010, Society for Neuroscience, 516.91.
30. Fiala J.C. Reconstruct: a free editor for serial section microscopy. J Microsc 2005, 218:52-61.
31. Carlbom I., Terzopoulos D., Harris K.M. Computer-assisted registration, segmentation, and 3D reconstruction from images of neuronal tissue sections. IEEE Trans Med Imaging 1994, 13:351-362.
32. Bunke H. Recognition of cursive Roman handwriting: past, present and future. Document Analysis and Recognition 2003, 448-459.
33. Saalfeld S., Cardona A., Hartenstein V., Tomančák P. CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 2009, 25:1984-1986.
34. Helmstaedter M., Briggman K.L., Denk W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci 2011, 14:1081-1088.
35. Xu M., Jarrell T.A., Wang Y., Cook S.J., Hall D.H., Emmons S.W. Computer assisted assembly of connectomes from electron micrographs: application to Caenorhabditis elegans. PLoS ONE 2013, 8(1):1-6. e0054050.
36. Jeong W.-K., Beyer J., Hadwiger M., Blue R., Law C., Vazquez-Reina A., Reid R.C., Lichtman J., Pfister H. Ssecrett and NeuroTrace: interactive visualization and analysis tools for large-scale neuroscience data sets. IEEE Comput Graph Appl 2010, 30:58-70.
37. Sommer C., Straehle C., Koethe U., Hamprecht F.A. ilastik: interactive learning and segmentation toolkit. Proc IEEE International Symposium on Biomedical Imaging, vol. 8 2011, 230-233.
38. Straehle C.N., Kothe U., Knott G., Hamprecht F.A. Carving: scalable interactive segmentation of neural volume electron microscopy images. Med Image Comput Comput Assist Interv 2011, 14:653-660.
39. Rivera-Alba M., Vitaladevuni S.N., Mishchenko Y., Lu Z., Takemura S.Y., Scheffer L., Meinertzhagen I.A., Chklovskii D.B., de Polavieia G.G. Wiring economy and volume exclusion determine neuronal placement in the Drosophila brain. Curr Biol 2011, 21:2000-2005.
40. Holcomb P.S., Hoffpauir B.K., Hoyson M.C., Jackson D.R., Deerinck T.J., Marrs G.S., Dehoff M., Wu J., Ellisman M.H., Spirou G.A. Synaptic inputs compete during rapid formation of the calyx of held: a new model system for neural development. J Neurosci 2013, 33:12954-12969.
41. Kreshuk A., Straehle C.N., Sommer C., Koethe U., Cantoni M., Knott G., Hamprecht F.A. Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PLoS ONE 2011, e0024899.
42. Becker C., Ali K., Knott G., Fua P. Learning context cues for synapse segmentation in EM volumes. Med Image Comput Comput Assist Interv 2012, 15:585-592.
43. Butcher N.J., Friedrich A.B., Lu Z., Tanimoto H., Meinertzhagen I.A. Different classes of input and output neurons reveal new features in microglomeruli of the adult Drosophila mushroom body calyx. J Comp Neurol 2012, 520:2185-2201.
44. Holderith N., Lorincz A., Katona G., Rozsa B., Kulik A., Watanabe M., Nusser Z. Release probability of hippocampal glutamatergic terminals scales with the size of the active zone. Nat Neurosci 2012, 15:988-997.
45. Schikorski T., Stevens C.F. Quantitative ultrastructural analysis of hippocampal excitatory synapses. J Neurosci 1997, 7:5858-5867.
46. Meinertzhagen I.A., Sorra K.E. Synaptic organization in the fly's optic lamina: few cells, many synapses and divergent microcircuits. Prog Brain Res 2001, 131:53-69.
47. Plaza S.M., Scheffer L.K., Saunders M. Minimizing manual image segmentation turn-around time for neuronal reconstruction by embracing uncertainty. PLoS ONE 2012, e0044448.
48. Straehle C., Köthe U., Briggman K., Denk W., Hamprecht F.A. Seeded watershed cut uncertainty estimators for guided interactive segmentation. CVPR 2012, 765-772.
49. Bell G., Hey T., Szalay A. Beyond the data deluge. Science 2009, 323:1297-1298.
50. Helmstaedter M. Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nat Methods 2013, 10:501-507.
51. http://eyewire.org/.
52. Lichtman J.W., Sanes J.R. Ome sweet ome: what can the genome tell us about the connectome?. Curr Opin Neurobiol 2008, 18.3:346-353.
53. Rosenbloom K.R., Dreszer T.R., Pheasant M., Barber G.P., Meyer L.R., Pohl A., Raney B.J., Wang T., Hinrichs A.S., Zweig A.S., et al. ENCODE whole-genome data in the UCSC Genome Browser. Nucl Acids Res 2010, 38:D620-D625.
54. Chiang A.-S., Lin C.-Y., Chuang C.-C., Chang H.-M., Hsieh C.-H., Yeh C.-W., Shih C.-T., Wu J.-J., Wang G.-T., Chen Y.-C., et al. Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Curr Biol 2011, 21:1-11.
55. Wang Z., Gerstein M., Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10:57-63.
56. Salvaterra P.M., Kitamoto T. Drosophila cholinergic neurons and processes visualized with Gal4/UAS-GFP. Gene Expr Patterns 2001, 1:73-82.
57. Song S., Sjöström P.J., Reigl M., Nelson S., Chklovskii D.B. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol 2005, 3:e68.
58. Reigl M., Alon U., Chklovskii D.B. Search for computational modules in the C. elegans brain. BMC Biol 2004, 2:25.
59. Strausfeld N.J. Arthropod Brains: Evolution, Functional Elegance, and Historical Significance 2012, Belknap Press of Harvard University Press, Cambridge, MA.
60. Hansen M.C., Hakan Y., Hayes J.P. Unveiling the ISCAS-85 benchmarks: a case study in reverse engineering. Des Test Comput 1999, 16:72-80.
61. Finn R.D., Tate J., Mistry J., Coggill P.C., Sammut S.J., Hotz H.-R., Ceric G., Forslund K., Eddy S., Sonnhammer E.L., et al. The Pfam protein families database. Nucl Acids Res 2004, 36:D138-D141.
62. Grillner S., Wallén P. Central pattern generators for locomotion, with special reference to vertebrates. Annu Rev Neurosci 1985, 8:233-261.
63. Sonnhammer E.L.L., Eddy S.R., Durbin R. Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins Struct Funct Genet 1997, 28:405-420.
64. Rein K., Zöckler M., Mader M.T., Grübel C., Heisenberg M. The Drosophila standard brain. Curr Biol 2002, 12:227-231.
65. Lefort S., Tomm C., Floyd Sarria J.C., Petersen C.C. The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 2009, 61:301-316.
66. Hooks B.M., Hires S.A., Zhang Y.X., Huber D., Petreanu L., Svoboda K., Shepherd G.M. Laminar analysis of excitatory local circuits in vibrissal motor and sensory cortical areas. PLoS Biol 2011, 9:e1000572.
67. Williams R.W. Mapping genes that modulate mouse brain development: a quantitative genetic approach. Mouse Brain Development 2000, 21-49. Springer, Berlin, Heidelberg.
68. Allen J.S., Damasio H., Grabowski T.J. Normal neuroanatomical variation in the human brain: an MRI-volumetric study. Am J Phys Anthropol 2002, 118:341-358.
69. Azevedo F.A., Carvalho L.R., Grinberg L.T., Farfel J.M., Ferretti R.E., Leite R.E., Jacob Filho W., Lent R., Herculano-Houzel S. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol 2009, 513:532-541.