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The big data challenges of connectomics

Abstract

The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes. Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s with a the study of the small nervous system of a worm and has recently garnered general interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains. However, modern connectomics produces 'big data', unprecedented quantities of digital information at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions. Here we describe some of the key difficulties that may arise and provide suggestions for managing them.

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Figure 1: Segmenting brain images.
Figure 2: Transformation of segmented data into a connectivity graph.

References

  1. Helmstaedter, M. Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nat. Methods 10, 501–507 (2013).

    CAS  Article  Google Scholar 

  2. Lichtman, J.W. & Denk, W. The big and the small: challenges of imaging the brain's circuits. Science 334, 618–623 (2011).

    CAS  Article  Google Scholar 

  3. Hell, S.W. Far-field optical nanoscopy. Science 316, 1153–1158 (2007).

    CAS  Article  Google Scholar 

  4. Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).

    CAS  Article  Google Scholar 

  5. Cai, D., Cohen, K.B., Luo, T., Lichtman, J.W. & Sanes, J.R. Improved tools for the Brainbow toolbox. Nat. Methods 10, 540–547 (2013) Epub 2013 May 5.

    CAS  Article  Google Scholar 

  6. Lakadamyali, M., Babcock, H., Bates, M., Zhuang, X. & Lichtman, J. 3D multicolor super-resolution imaging offers improved accuracy in neuron tracing. PLoS ONE 7, e30826 (2012).

    CAS  Article  Google Scholar 

  7. O'Rourke, N.A., Weiler, N.C., Micheva, K.D. & Smith, S.J. Deep molecular diversity of mammalian synapses: why it matters and how to measure it. Nat. Rev. Neurosci. 13, 365–379 (2012).

    CAS  Article  Google Scholar 

  8. Peddie, C.J. & Collinson, L.M. Exploring the third dimension: volume electron microscopy comes of age. Micron 61, 9–19 (2014).

    Article  Google Scholar 

  9. Denk, W. & Horstmann, H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2, 329 (2004).

    Article  Google Scholar 

  10. Knott, G., Marchman, H., Wall, D. & Lich, B. Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. J. Neurosci. 28, 2959–2964 (2008).

    CAS  Article  Google Scholar 

  11. Bock, D.D. et al. Network anatomy and in vivo physiology of visual cortical neurons. Nature 471, 177–182 (2011).

    CAS  Article  Google Scholar 

  12. Briggman, K.L. & Bock, D.D. Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012).

    CAS  Article  Google Scholar 

  13. Schüz, A. & Palm, G. Density of neurons and synapses in the cerebral cortex of the mouse. J. Comp. Neurol. 286, 442–455 (1989).

    Article  Google Scholar 

  14. Korbo, L. et al. An efficient method for estimating the total number of neurons in rat brain cortex. J. Neurosci. Methods 31, 93–100 (1990).

    CAS  Article  Google Scholar 

  15. Kaynig, V. et al. Large-scale automatic reconstruction of neuronal processes from electron microscopy images. Preprint at http://arxiv.org/abs/1303.7186 (2013).

  16. Kim, J.S. et al. Space-time wiring specificity supports direction selectivity in the retina. Nature 509, 331–336 (2014).

    CAS  Article  Google Scholar 

  17. Plaza, S.M., Scheffer, L.K. & Chklovskii, D.B. Toward large-scale connectome reconstructions. Curr. Opin. Neurobiol. 25, 201–210 (2014).

    CAS  Article  Google Scholar 

  18. Bunke, H. & Varga, T. Off-line roman cursive handwriting recognition: digital document processing. Adv. Pattern Recognit. 2007, 165–183 (2007).

    Google Scholar 

  19. Becker, C., Ali, K., Knott, G. & Fua, P. Learning context cues for synapse segmentation. IEEE Trans. Med. Imaging 32, 1864–1877 (2013).

    Article  Google Scholar 

  20. 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. 7, e1001066 (2011).

    CAS  Article  Google Scholar 

  21. Eppstein, D., Goodrich, M.T. & Sun, J.Z. The skip quadtree: a simple dynamic data structure for multidimensional data. Proc. 21st Ann. Symp. Comput. Geom. 296–305 (ACM, New York, 2005).

  22. Herlihy, M. & Shavit, N. The Art of Multiprocessor Programming (Revised Edition) (Morgan Kaufmann Publishers, San Francisco, California, 2012).

  23. Amdahl, G.M. Validity of the single processor approach to achieving large-scale computing capabilities. AFIPS Conf. Proc. 30, 483–485 (1967).

    Google Scholar 

  24. Burns, R. et al. The Open Connectome Project Data Cluster: scalable analysis and vision for high-throughput neuroscience. Proc. 25th Int. Conf. Sci. Stat. Database Manag. 27, 1–11 (2012).

    Google Scholar 

  25. Lichtman, J.W. & Sanes, J.R. Ome sweet ome: what can the genome tell us about the connectome? Curr. Opin. Neurobiol. 18, 346–353 (2008).

    CAS  Article  Google Scholar 

  26. Morgan, J.L. & Lichtman, J.W. Digital tissue. in Cellular Connectomics. (eds. Helmsteder, M. & Brigmann, K.) (Academic Press, in the press).

Download references

Acknowledgements

Support is gratefully acknowledged from the US National Institute of Mental Health Silvio Conte Center (1P50MH094271 to J.W.L.), the US National Institutes of Health (NS076467 to J.W.L. and 2R44MH088088-03 to H.P.), the National Science Foundation (OIA-1125087 to H.P., CCF-1217921 to N.S., CCF-1301926 to N.S., IIS-1447786 to N.S., and IIS-1447344 to H.P. and J.W.L.), a Department of Energy Advanced Scientific Computing Research grant (ER26116/DE-SC0008923 to N.S.), Nvidia (H.P.), Oracle (N.S.) and Intel (N.S.).

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Correspondence to Jeff W Lichtman.

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Lichtman, J., Pfister, H. & Shavit, N. The big data challenges of connectomics. Nat Neurosci 17, 1448–1454 (2014). https://doi.org/10.1038/nn.3837

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