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Automated synaptic connectivity inference for volume electron microscopy

Nature Methods volume 14, pages 435442 (2017) | Download Citation

Abstract

Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest classifiers to infer a richly annotated synaptic connectivity matrix from manual neurite skeleton reconstructions by automatically identifying mitochondria, synapses and their types, axons, dendrites, spines, myelin, somata and cell types. We tested our approach on serial block-face electron microscopy data sets from zebrafish, mouse and zebra finch, and computed the synaptic wiring of songbird basal ganglia. We found that, for example, basal-ganglia cell types with high firing rates in vivo had higher densities of mitochondria and vesicles and that synapse sizes and quantities scaled systematically, depending on the innervated postsynaptic cell types.

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References

  1. 1.

    & Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012).

  2. 2.

    , & Structural neurobiology: missing link to a mechanistic understanding of neural computation. Nat. Rev. Neurosci. 13, 351–358 (2012).

  3. 3.

    , & Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011).

  4. 4.

    et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174 (2013).

  5. 5.

    et al. A visual motion detection circuit suggested by Drosophila connectomics. Nature 500, 175–181 (2013).

  6. 6.

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

  7. 7.

    , , , & Dense EM-based reconstruction of the interglomerular projectome in the zebrafish olfactory bulb. Nat. Neurosci. 19, 816–825 (2016).

  8. 8.

    et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511–538 (2010).

  9. 9.

    et al. Boundary learning by optimization with topological constraints. in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conf. 2488–2495 (2010).

  10. 10.

    , & SegEM: efficient image analysis for high-resolution connectomics. Neuron 87, 1193–1206 (2015).

  11. 11.

    , , , & Extracellular space preservation aids the connectomic analysis of neural circuits. Elife 4, e08206 (2015).

  12. 12.

    et al. Annotating synapses in large em datasets. Preprint available at (2014).

  13. 13.

    , & Fully-automatic synapse prediction and validation on a large data set. Preprint available at (2016).

  14. 14.

    et al. Theano: a CPU and GPU math expression compiler. in Proceedings of the Python for Scientific Computing Conference (SciPy) 4, 3 (2010).

  15. 15.

    , , , & Fast image scanning with deep max-pooling convolutional neural networks. Preprint available at (2013).

  16. 16.

    et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).

  17. 17.

    et al. Synaptic circuits and their variations within different columns in the visual system of Drosophila. Proc. Natl. Acad. Sci. USA 112, 13711–13716 (2015).

  18. 18.

    et al. Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PLoS One 6, e24899 (2011).

  19. 19.

    , , , & Automated detection of synapses in serial section transmission electron microscopy image stacks. PLoS One 9, e87351 (2014).

  20. 20.

    , , & Learning context cues for synapse segmentation. IEEE Trans. Med. Imaging 32, 1864–1877 (2013).

  21. 21.

    & Identifying synapses using deep and wide multiscale recursive networks. Preprint available at (2014).

  22. 22.

    et al. A Fast method for the segmentation of synaptic junctions and mitochondria in serial electron microscopic images of the brain. Neuroinformatics 14, 235–250 (2016).

  23. 23.

    et al. VESICLE: volumetric evaluation of synaptic interfaces using computer vision at large scale. Preprint available at (2014).

  24. 24.

    et al. A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front. Neuroanat. 8, 126 (2014).

  25. 25.

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

  26. 26.

    Synaptic patterns on different cell types in the different laminae of the cat visual cortex. An electron microscope study. Brain Res. 9, 268–287 (1968).

  27. 27.

    Axo-somatic and axo-dendritic synapses of the cerebral cortex: an electron microscope study. J. Anat. 93, 420–433 (1959).

  28. 28.

    , & The Fine Structure of the Nervous System: Neurons and Their Supporting Cells (Oxford University Press, NY, 1991).

  29. 29.

    & The fine structure of nerve endings in the nucleus of the trapezoid body and the ventral cochlear nucleus. Am. J. Anat. 118, 375–389 (1966).

  30. 30.

    & Is the songbird Area X striatal, pallidal, or both? An anatomical study. J. Comp. Neurol. 473, 415–437 (2004).

  31. 31.

    , , , & An immunohistochemical and pathway tracing study of the striatopallidal organization of area X in the male zebra finch. J. Comp. Neurol. 469, 239–261 (2004).

  32. 32.

    & Singing-related neural activity distinguishes four classes of putative striatal neurons in the songbird basal ganglia. J. Neurophysiol. 103, 2002–2014 (2010).

  33. 33.

    , , & Singing-related neural activity distinguishes two putative pallidal cell types in the songbird basal ganglia: comparison to the primate internal and external pallidal segments. J. Neurosci. 30, 7088–7098 (2010).

  34. 34.

    & A telencephalic nucleus essential for song learning contains neurons with physiological characteristics of both striatum and globus pallidus. J. Neurosci. 22, 3776–3787 (2002).

  35. 35.

    , & Evidence for “direct” and “indirect” pathways through the song system basal ganglia. J. Comp. Neurol. 484, 93–104 (2005).

  36. 36.

    & Automatic neuron type identification by neurite localization in the Drosophila medulla. Preprint available at (2014).

  37. 37.

    & Automatic discovery of cell types and microcircuitry from neural connectomics. eLife 4, e04250 (2015).

  38. 38.

    , , & Focal expression of mutant huntingtin in the songbird basal ganglia disrupts cortico-basal ganglia networks and vocal sequences. Proc. Natl. Acad. Sci. USA 113, E1720–E1727 (2016).

  39. 39.

    , , & Millisecond timescale disinhibition mediates fast information transmission through an avian basal ganglia loop. J. Neurosci. 29, 15420–15433 (2009).

  40. 40.

    & Inhibitory control of neostriatal projection neurons by GABAergic interneurons. Nat. Neurosci. 2, 467–472 (1999).

  41. 41.

    & Synaptic input and output of parvalbumin-immunoreactive neurons in the neostriatum of the rat. Neuroscience 62, 707–719 (1994).

  42. 42.

    , & Surround inhibition among projection neurons is weak or nonexistent in the rat neostriatum. J. Neurophysiol. 72, 2555–2558 (1994).

  43. 43.

    et al. Synaptic connectivity between rat striatal spiny projection neurons in vivo: Unexpected multiple somatic innervation in the context of overall sparse proximal connectivity. Basal Ganglia 3, 93–108 (2013).

  44. 44.

    & Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci. 13, 266–271 (1990).

  45. 45.

    , , , & Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10, 413–420 (2013).

  46. 46.

    & A universal scaling law between gray matter and white matter of cerebral cortex. Proc. Natl. Acad. Sci. USA 97, 5621–5626 (2000).

  47. 47.

    , , , & Monitoring neural activity with bioluminescence during natural behavior. Nat. Neurosci. 13, 513–520 (2010).

  48. 48.

    , & Cellular scaling rules for rodent brains. Proc. Natl. Acad. Sci. USA 103, 12138–12143 (2006).

  49. 49.

    & High-resolution whole-brain staining for electron microscopic circuit reconstruction. Nat. Methods 12, 541–546 (2015).

  50. 50.

    , & High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat. Neurosci. 14, 1081–1088 (2011).

  51. 51.

    et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

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Acknowledgements

We thank W. Denk for generous support and many helpful discussions. We thank M.S. Fee, K. Doroschak, S. Mikula, L. Heinrich and W. Denk for comments on the manuscript. We thank R. Illichmann, D. Dimitrov, O. Fedorashko, K. Kiesl and G. Patzer for ground truth annotations and many others for the skeleton annotations, and the KNOSSOS development team for continuous software support. We thank J. Buhmann for neurite segmentation training data, L. Heinrich for optimizing the watershed parameters, L. Schott for myelin training data, L. Burchartz for spine ground truth, C. Roome and M. Hilpert for IT support, and J. Kuhl for support with the figures. J.K. and F.S. were supported by the Boehringer Ingelheim Fonds.

Author information

Affiliations

  1. Max Planck Institute of Neurobiology, Planegg-Martinsried, Germany.

    • Sven Dorkenwald
    • , Philipp J Schubert
    • , Marius F Killinger
    • , Shawn Mikula
    • , Fabian Svara
    •  & Joergen Kornfeld
  2. Max Planck Institute for Medical Research, Heidelberg, Germany.

    • Sven Dorkenwald
    • , Philipp J Schubert
    • , Marius F Killinger
    •  & Gregor Urban

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Contributions

S.D., P.J.S. and J.K. developed and evaluated SyConn. M.F.K. and G.U. developed ElektroNN. S.M., F.S. and J.K. contributed data sets. S.D., P.J.S. and J.K. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Joergen Kornfeld.

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    Supplementary Text and Figures

    Supplementary Figures 1–7 and Supplementary Tables 1, 2 and 5

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    Supplementary Table 3

    Parameters of the CNNs used in this study.

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    Supplementary Table 4

    Boundaries used for the volume annotations in this study.

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    Source code and documentation.

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DOI

https://doi.org/10.1038/nmeth.4206

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