Network anatomy and in vivo physiology of visual cortical neurons

Journal name:
Nature
Volume:
471,
Pages:
177–182
Date published:
DOI:
doi:10.1038/nature09802
Received
Accepted
Published online

Abstract

In the cerebral cortex, local circuits consist of tens of thousands of neurons, each of which makes thousands of synaptic connections. Perhaps the biggest impediment to understanding these networks is that we have no wiring diagrams of their interconnections. Even if we had a partial or complete wiring diagram, however, understanding the network would also require information about each neuron's function. Here we show that the relationship between structure and function can be studied in the cortex with a combination of in vivo physiology and network anatomy. We used two-photon calcium imaging to characterize a functional property—the preferred stimulus orientation—of a group of neurons in the mouse primary visual cortex. Large-scale electron microscopy of serial thin sections was then used to trace a portion of these neurons’ local network. Consistent with a prediction from recent physiological experiments, inhibitory interneurons received convergent anatomical input from nearby excitatory neurons with a broad range of preferred orientations, although weak biases could not be rejected.

At a glance

Figures

  1. Functional characterization of neurons before anatomical reconstruction.
    Figure 1: Functional characterization of neurons before anatomical reconstruction.

    a, Schematic representation of diverse input to inhibitory interneurons. Excitatory pyramidal cells (triangles) with varied preferred orientations (different colours) provide input (coloured arrows) to an inhibitory interneuron (white circle). b, Cell-based visual orientation preference map in the mouse visual cortex from in vivo two-photon calcium imaging. Visually responsive cells are coloured according to their estimated preferred orientation (colour coding shown at top), with broadly tuned cells (orientation selectivity index ≤0.2) shown white. Dark diagonal band, region targeted for acquisition of EM sections, cut orthogonal to image plane. c, In vivo two-photon fluorescence image of the three-dimensional volume (red, blood vessels or astrocytes; green, OGB-loaded somata or yellow fluorescent protein (YFP)-labelled apical dendrites) separated to expose the functionally imaged plane. Scale bars, 100μm.

  2. Large-scale EM.
    Figure 2: Large-scale EM.

    a, Electron micrograph of an entire 120,000×80,000 pixel thin section, showing the pial surface (top) and cortical layers 1 through to upper 4 (bottom). b, e, Three-dimensional renderings of the EM volume through the entire series (b, Supplementary Movie 3) and through 50 sections of the cube in d (e, red outline in b, Supplementary Movie 4). d, A cube of the EM volume with c as one face. c, f, Zoomed-in view of two functionally characterized cells (c; red outline in a), and the neuropile between them (f; blue outlines in a, c and d), illustrating the density of axons and dendrites coursing between cell bodies. Pink represents electron transparent regions (for example, blood vessels), yellow represents regions that are electron dense (such as nucleoli, oligodendrocyte nuclei, and myelin), and aqua denotes regions with pixel values in between (for example, nuclei, cell bodies and dendrites). Scale bars: a, b, 100μm; ce, 10μm; f, 1μm.

  3. Correspondence between in vivo fluorescence anatomy and EM.
    Figure 3: Correspondence between in vivo fluorescence anatomy and EM.

    a, Maximum-intensity projection of the in vivo fluorescence anatomy corresponding to the EM volume (red, blood vessels or astrocytes; green, OGB or YFP as in Fig. 1c). Colours between arrowheads correspond to orientation preference of neurons in the imaged plane, as in d. b, Projection through 37 EM sections evenly spaced in the series. c, Merge of EM and fluorescence projections. d, Zoomed-in view of the region outlined in c, showing the overlay of in vivo and EM data in a single thin section. Horizontal grey lines delineate the functionally imaged plane. Within the functionally imaged plane, the cell bodies of six neurons of known orientation preference (from left to right: cells 13, 12, 11, 10, 9 and 8, coloured as in Fig. 1b) are well registered with their EM ultrastructure. Outside the functionally imaged plane, blood vessels and astrocytes in the EM are well registered with the red SR101 staining. Scale bars, 50μm.

  4. Convergent synaptic input onto inhibitory interneurons.
    Figure 4: Convergent synaptic input onto inhibitory interneurons.

    a, Three-dimensional rendering of axonal contacts onto a postsynaptic neuron. Large balls at the top represent cell bodies of neurons within the functionally imaged plane. Axons of a horizontally tuned neuron (cell 4; green) and a vertically tuned neuron (cell 10; red) descend and make synapses (small yellow balls) onto dendrites of an inhibitory interneuron (cyan). The axonal and dendritic segments leading to the convergence were independently traced by a second person, blind to the original segmentation (thick tracing). Cell bodies and axons coloured by orientation preference, as in Fig. 1b. Scale bar, 50μm. b, c, Electron micrographs showing the synapses onto the inhibitory neuron from cell 4 (b) and cell 10 (c) with corresponding colours overlaid. Scale bar, 1μm. d, e, Orientation tuning curves derived from in vivo calcium imaging of the cell bodies of cell 4 (d) and cell 10 (e). Coloured bars and arrows, stimulus orientation and direction. ΔF/F, change in fluorescence. Error bars, ±s.e.m.

  5. From anatomy to connectivity graphs.
    Figure 5: From anatomy to connectivity graphs.

    a, Three-dimensional rendering of the dendrites, axons and cell bodies of 14 neurons in the functionally imaged plane (coloured according to their orientation preference, key right, as in Fig. 1b), and the dendrites and cell bodies of all their postsynaptic targets traced in the EM volume (magenta, excitatory targets; cyan, inhibitory targets; spines on postsynaptic targets not shown; Supplementary Movie 5). Scale bar, 100μm. b, Directed network diagram of the functionally characterized cells and their targets, derived from a. Postsynaptic excitatory (magenta) and inhibitory (cyan) targets with cell bodies contained within the EM volume are drawn as circles. Other postsynaptic targets (dendritic fragments) are drawn as squares. (From top to bottom and left to right: functionally characterized cells 5, 2, 7; 13, 6, 14; 1; 10; 11, 3; 9; 12, 4; and 8.) c, Three-dimensional rendering of the arbors and cell bodies of functionally characterized neurons, along with postsynaptic targets that either receive convergent input from multiple functionally characterized neurons, or were themselves functionally characterized (Supplementary Movie 5). d, A subset of the network graph showing only the connections in c, all independently verified (from top to bottom and left to right: functionally characterized cells 5, 2, 7; 13, 6; 10; 11, 3; 12, 9, 8 and 4).

  6. Convergent synaptic input onto inhibitory interneurons is predicted by proximity, not function.
    Figure 6: Convergent synaptic input onto inhibitory interneurons is predicted by proximity, not function.

    a, A portion of the aligned and registered EM image series re-sliced parallel to the functionally imaged plane, through 1,153 EM sections. Overlaid is a network graph of the convergences onto inhibitory interneurons. Visually responsive cell bodies are pseudo-coloured according to their preferred orientation (as in Fig. 1b), and numbered as in Figs 4 and 5. Convergences onto inhibitory neuronal targets are represented by lines, corresponding to one or more synapses, leading either to filled cyan circles (targets traced to cell bodies in the EM volume) or squares (dendritic fragments). Cell 14 was partially contained in the EM volume and is not shown. Scale bars, 10μm. b, Diagram of cumulative synaptic proximity (CSP). Line segments represent axons, with three-dimensional Gaussians centred at each synapse. A CSP was calculated for each pair of orientation-tuned neurons by summing all pair-wise overlaps of Gaussians from the two axons (σ12μm; Methods). c, Pairs of axons whose synaptic boutons were in close proximity were more likely to converge onto a common target. The CSP of axon pairs participating in convergences (red) was significantly greater than for non-converging pairs (blue; P<1.3×10−5, two-sample Kolmogorov-Smirnov test, nconvergent pairs = 21, nnon-convergent pairs = 24). d, Convergences were not predicted by the difference in orientation preference between presynaptic cell pairs. The distribution of differences in orientation preference was not significantly different from a uniform distribution (P>0.30, two-sample Kolmogorov-Smirnov test, nconvergences = 29) or a model distribution (Methods) based on CSP (P>0.68, two-sample Kolmogorov-Smirnov test, nconvergences = 29).

References

  1. Ramón y Cajal, S. Textura del Sistema Nervioso del Hombre y de los Vertebrados (Moya, 1904)
  2. Binzegger, T., Douglas, R. J. & Martin, K. A. A quantitative map of the circuit of cat primary visual cortex. J. Neurosci. 24, 84418453 (2004)
  3. Stepanyants, A. et al. Local potential connectivity in cat primary visual cortex. Cereb. Cortex 18, 1328 (2008)
  4. Mason, A., Nicoll, A. & Stratford, K. Synaptic transmission between individual pyramidal neurons of the rat visual cortex in vitro. J. Neurosci. 11, 7284 (1991)
  5. Markram, H., Lubke, J., Frotscher, M., Roth, A. & Sakmann, B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J. Physiol. (Lond.) 500, 409440 (1997)
  6. Thomson, A. M. & Bannister, A. P. Interlaminar connections in the neocortex. Cereb. Cortex 13, 514 (2003)
  7. Braitenberg, V. & Schüz, A. Cortex : Statistics and Geometry of Neuronal Connectivity 2nd edn (Springer, Berlin, 1998)
  8. Song, S., Sjostrom, P. J., Reigl, M., Nelson, S. & Chklovskii, D. B. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 3, e68 (2005)
  9. Yoshimura, Y. & Callaway, E. M. Fine-scale specificity of cortical networks depends on inhibitory cell type and connectivity. Nature Neurosci. 8, 15521559 (2005)
  10. Yoshimura, Y., Dantzker, J. L. & Callaway, E. M. Excitatory cortical neurons form fine-scale functional networks. Nature 433, 868873 (2005)
  11. Heuser, J. E. et al. Synaptic vesicle exocytosis captured by quick freezing and correlated with quantal transmitter release. J. Cell Biol. 81, 275300 (1979)
  12. White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. Lond. B 314, 1340 (1986)
  13. Sorra, K. E. & Harris, K. M. Stability in synapse number and size at 2 hr after long-term potentiation in hippocampal area CA1. J. Neurosci. 18, 658671 (1998)
  14. Dacheux, R. F. & Raviola, E. The rod pathway in the rabbit retina: a depolarizing bipolar and amacrine cell. J. Neurosci. 6, 331345 (1986)
  15. Sterling, P. Microcircuitry of the cat retina. Annu. Rev. Neurosci. 6, 149185 (1983)
  16. Hamos, J. E., Van Horn, S. C., Raczkowski, D. & Sherman, S. M. Synaptic circuits involving an individual retinogeniculate axon in the cat. J. Comp. Neurol. 259, 165192 (1987)
  17. Kisvárday, Z. F. et al. Synaptic targets of HRP-filled layer III pyramidal cells in the cat striate cortex. Exp. Brain Res. 64, 541552 (1986)
  18. Anderson, J. C., Douglas, R. J., Martin, K. A. & Nelson, J. C. Map of the synapses formed with the dendrites of spiny stellate neurons of cat visual cortex. J. Comp. Neurol. 341, 2538 (1994)
  19. Ahmed, B., Anderson, J. C., Martin, K. A. & Nelson, J. C. Map of the synapses onto layer 4 basket cells of the primary visual cortex of the cat. J. Comp. Neurol. 380, 230242 (1997)
  20. Tamas, G., Somogyi, P. & Buhl, E. H. Differentially interconnected networks of GABAergic interneurons in the visual cortex of the cat. J. Neurosci. 18, 42554270 (1998)
  21. Shepherd, G. M. & Harris, K. M. Three-dimensional structure and composition of CA3right arrowCA1 axons in rat hippocampal slices: implications for presynaptic connectivity and compartmentalization. J. Neurosci. 18, 83008310 (1998)
  22. Mishchenko, Y. et al. Ultrastructural analysis of hippocampal neuropil from the connectomics perspective. Neuron 67, 10091020 (2010)
  23. Anderson, J. R. et al. A computational framework for ultrastructural mapping of neural circuitry. PLoS Biol. 7, e1000074 (2009)
  24. Sohya, K., Kameyama, K., Yanagawa, Y., Obata, K. & Tsumoto, T. GABAergic neurons are less selective to stimulus orientation than excitatory neurons in layer II/III of visual cortex, as revealed by in vivo functional Ca2+ imaging in transgenic mice. J. Neurosci. 27, 21452149 (2007)
  25. Niell, C. M. & Stryker, M. P. Highly selective receptive fields in mouse visual cortex. J. Neurosci. 28, 75207536 (2008)
  26. Liu, B. H. et al. Visual receptive field structure of cortical inhibitory neurons revealed by two-photon imaging guided recording. J. Neurosci. 29, 1052010532 (2009)
  27. Kerlin, A. M., Andermann, M. L., Berezovskii, V. K. & Reid, R. C. Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex. Neuron 67, 858871 (2010)
  28. Runyan, C. A. et al. Response features of parvalbumin-expressing interneurons suggest precise roles for subtypes of inhibition in visual cortex. Neuron 67, 847857 (2010)
  29. Ma, W. P. et al. Visual representations by cortical somatostatin inhibitory neurons — selective but with weak and delayed responses. J. Neurosci. 30, 1437114379 (2010)
  30. Ohki, K., Chung, S., Ch'ng, Y. H., Kara, P. & Reid, R. C. Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature 433, 597603 (2005)
  31. Stosiek, C., Garaschuk, O., Holthoff, K. & Konnerth, A. In vivo two-photon calcium imaging of neuronal networks. Proc. Natl Acad. Sci. USA 100, 73197324 (2003)
  32. Harris, K. M. et al. Uniform serial sectioning for transmission electron microscopy. J. Neurosci. 26, 1210112103 (2006)
  33. Peters, A., Palay, S. L. & Webster, H. D. The Fine Structure of the Nervous System : Neurons and Their Supporting Cells 3rd edn (Oxford Univ. Press, 1991)
  34. Alitto, H. J. & Dan, Y. Function of inhibition in visual cortical processing. Curr. Opin. Neurobiol. 20, 340346 (2010)
  35. Liu, B. H. et al. Intervening inhibition underlies simple-cell receptive field structure in visual cortex. Nature Neurosci. 13, 8996 (2010)
  36. Reynolds, J. H. & Heeger, D. J. The normalization model of attention. Neuron 61, 168185 (2009)
  37. Helmstaedter, M., Briggman, K. L. & Denk, W. 3D structural imaging of the brain with photons and electrons. Curr. Opin. Neurobiol. 18, 633641 (2008)
  38. Turaga, S. C. et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511538 (2010)
  39. Peters, A. & Kara, D. A. The neuronal composition of area 17 of rat visual cortex. II. The nonpyramidal cells. J. Comp. Neurol. 234, 242263 (1985)
  40. White, E. L. & Keller, A. Cortical Circuits: Synaptic Organization of the Cerebral Cortex — Structure, Function and Theory (Birkhauser, 1989)
  41. Markram, H. et al. Interneurons of the neocortical inhibitory system. Nature Rev. Neurosci. 5, 793807 (2004)
  42. McGuire, B. A., Gilbert, C. D., Rivlin, P. K. & Wiesel, T. N. Targets of horizontal connections in macaque primary visual cortex. J. Comp. Neurol. 305, 370392 (1991)
  43. Holmgren, C., Harkany, T., Svennenfors, B. & Zilberter, Y. Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J. Physiol. (Lond.) 551, 139153 (2003)
  44. Thomson, A. M., West, D. C., Wang, Y. & Bannister, A. P. Synaptic connections and small circuits involving excitatory and inhibitory neurons in layers 2-5 of adult rat and cat neocortex: triple intracellular recordings and biocytin labelling in vitro. Cereb. Cortex 12, 936953 (2002)
  45. Denk, W. & Horstmann, H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2, e329 (2004)
  46. Hayworth, K., Kasthuri, N., Schalek, R. & Lichtman, J. Automating the collection of ultrathin serial sections for large volume TEM reconstructions. Microsc. Microanal. 12, 8687 (2006)
  47. Jia, H., Rochefort, N. L., Chen, X. & Konnerth, A. Dendritic organization of sensory input to cortical neurons in vivo. Nature 464, 13071312 (2010)
  48. Ohki, K. & Reid, R. C. Specificity and randomness in the visual cortex. Curr. Opin. Neurobiol. 17, 401407 (2007)
  49. Grewe, B. F. & Helmchen, F. Optical probing of neuronal ensemble activity. Curr. Opin. Neurobiol. 19, 520529 (2009)
  50. Tian, L. et al. Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nature Methods 6, 875881 (2009)
  51. Feng, G. et al. Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron 28, 4151 (2000)
  52. Peltier, S. et al. Design of a new 8k x 8k lens coupled detector for wide-field, high-resolution transmission electron microscopy. Microsc. Microanal. 11, 610611 (2005)
  53. Martone, M. E. et al. A cell-centered database for electron tomographic data. J. Struct. Biol. 138, 145155 (2002)
  54. Cardona, A. et al. An integrated micro- and macroarchitectural analysis of the Drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol. 8, e1000502 (2010)
  55. Fiala, J. C. & Harris, K. M. Cylindrical diameters method for calibrating section thickness in serial electron microscopy. J. Microsc. 202, 468472 (2001)
  56. Ringach, D. L., Shapley, R. M. & Hawken, M. J. Orientation selectivity in macaque V1: diversity and laminar dependence. J. Neurosci. 22, 56395651 (2002)
  57. Worgotter, F. & Eysel, U. T. Quantitative determination of orientational and directional components in the response of visual cortical cells to moving stimuli. Biol. Cybern. 57, 349355 (1987)

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Author information

Affiliations

  1. Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Davi D. Bock,
    • Wei-Chung Allen Lee,
    • Aaron M. Kerlin,
    • Mark L. Andermann,
    • Sergey Yurgenson,
    • Hyon Suk Kim &
    • R. Clay Reid
  2. The Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA

    • Davi D. Bock,
    • Wei-Chung Allen Lee,
    • Edward R. Soucy,
    • Hyon Suk Kim &
    • R. Clay Reid
  3. National Resource for Biomedical Supercomputing, Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA

    • Greg Hood &
    • Arthur W. Wetzel

Contributions

D.D.B., E.R.S., S.Y. and R.C.R. designed and built the TEMCA system, and D.D.B. programmed it. A.M.K. and M.L.A. performed the in vivo imaging and A.M.K. analysed it. D.D.B. processed the tissue, and D.D.B. and W.-C.A.L. aligned the block with the in vivo imaging. W.-C.A.L. sectioned the series, and D.D.B., W.-C.A.L. and H.S.K. imaged it on the TEMCA. G.H. and A.W.W. stitched and aligned the images into a volume. D.D.B., W.-C.A.L. and H.S.K. did most of the segmentation. D.D.B., W.-C.A.L. and S.Y. performed quantitative analysis on the tracing. D.D.B., W.-C.A.L. and R.C.R. designed the experiment and wrote the paper, with considerable help from the other authors.

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The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Figures (5.1M)

    The file contains Supplementary Figures 1-8 with legends.

Movies

  1. Supplementary Movie 1 (30.4M)

    The movie shows section-by-section fly-through of the aligned EM series. Please see Methods for directions to the publicly accessible high-resolution aligned dataset.

  2. Supplementary Movie 2 (30M)

    The movie shows section-by-section fly-through of a cropped (8.2 x 8.2 µm) region traversing a sixth of the aligned EM sections at the level of the functionally imaged plane. With global 3-D alignment of the EM data set, many of the finest processes in the neuropil (e.g. dendritic spines and fine axons) can be unambiguously followed for tens to hundreds of micrometres.

  3. Supplementary Movie 3 (27.7M)

    The movie shows fifty serial sections in the aligned EM series rotating as a false-colour volumetric rendering.

  4. Supplementary Movie 4 (10.3M)

    The movie shows zoomed-in view of a region through fifty serial sections rotating as a false-colour volumetric rendering.

  5. Supplementary Movie 5 (28.7M)

    The movie shows rotating 3-D renderings of the skeletonized arbors and cell bodies of the functionally characterized cells, their postsynaptic targets, and their convergence targets.

Additional data