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Whole-brain functional imaging at cellular resolution using light-sheet microscopy

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Abstract

Brain function relies on communication between large populations of neurons across multiple brain areas, a full understanding of which would require knowledge of the time-varying activity of all neurons in the central nervous system. Here we use light-sheet microscopy to record activity, reported through the genetically encoded calcium indicator GCaMP5G, from the entire volume of the brain of the larval zebrafish in vivo at 0.8 Hz, capturing more than 80% of all neurons at single-cell resolution. Demonstrating how this technique can be used to reveal functionally defined circuits across the brain, we identify two populations of neurons with correlated activity patterns. One circuit consists of hindbrain neurons functionally coupled to spinal cord neuropil. The other consists of an anatomically symmetric population in the anterior hindbrain, with activity in the left and right halves oscillating in antiphase, on a timescale of 20 s, and coupled to equally slow oscillations in the inferior olive.

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Figure 1: Whole-brain, neuron-level light-sheet imaging in larval zebrafish in vivo.
Figure 2: Whole-brain imaging of neuronal activity with cellular resolution.
Figure 3: Correlations and activity patterns across brain regions.
Figure 4: Single-neuron activity in the left habenula and correlation to hindbrain activity.
Figure 5: Semiautomated analysis procedure for volumetric imaging of non–stimulus locked, long-timescale activity.
Figure 6: Whole-brain, neuron-level identification of two functionally defined circuits.

Change history

  • 05 April 2013

    The authors note that Michael B. Orger, Drew N. Robson and Jennifer M. Li should be added to the author list of the version of this article initially published online. This change affects the author list, affiliations, Acknowledgments and Author Contributions. The error has been corrected for the print, PDF and HTML versions of this article.

References

  1. 1

    Averbeck, B.B., Latham, P.E. & Pouget, A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358–366 (2006).

    CAS  Article  Google Scholar 

  2. 2

    Friedrich, R.W., Habermann, C.J. & Laurent, G. Multiplexing using synchrony in the zebrafish olfactory bulb. Nat. Neurosci. 7, 862–871 (2004).

    CAS  Article  Google Scholar 

  3. 3

    Schneidman, E., Berry, M.J. II, Segev, R. & Bialek, W. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440, 1007–1012 (2006).

    CAS  Article  Google Scholar 

  4. 4

    Stopfer, M., Jayaraman, V. & Laurent, G. Intensity versus identity coding in an olfactory system. Neuron 39, 991–1004 (2003).

    CAS  Article  Google Scholar 

  5. 5

    Druckmann, S. & Chklovskii, D.B. Neuronal circuits underlying persistent representations despite time varying activity. Curr. Biol. 22, 2095–2103 (2012).

    CAS  Article  Google Scholar 

  6. 6

    Gregoriou, G.G., Gotts, S.J. & Desimone, R. Cell-type-specific synchronization of neural activity in FEF with V4 during attention. Neuron 73, 581–594 (2012).

    CAS  Article  Google Scholar 

  7. 7

    Churchland, M.M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).

    CAS  Article  Google Scholar 

  8. 8

    Deco, G., Jirsa, V.K. & McIntosh, A.R. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat. Rev. Neurosci. 12, 43–56 (2011).

    CAS  Article  Google Scholar 

  9. 9

    Ahrens, M.B. et al. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471–477 (2012).

    CAS  Article  Google Scholar 

  10. 10

    Flusberg, B.A. et al. High-speed, miniaturized fluorescence microscopy in freely moving mice. Nat. Methods 5, 935–938 (2008).

    CAS  Article  Google Scholar 

  11. 11

    Katona, G. et al. Fast two-photon in vivo imaging with three-dimensional random-access scanning in large tissue volumes. Nat. Methods 9, 201–208 (2012).

    CAS  Article  Google Scholar 

  12. 12

    Holekamp, T.F., Turaga, D. & Holy, T.E. Fast three-dimensional fluorescence imaging of activity in neural populations by objective-coupled planar illumination microscopy. Neuron 57, 661–672 (2008).

    CAS  Article  Google Scholar 

  13. 13

    Grewe, B.F., Langer, D., Kasper, H., Kampa, B.M. & Helmchen, F. High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision. Nat. Methods 7, 399–405 (2010).

    CAS  Article  Google Scholar 

  14. 14

    Cheng, A., Goncalves, J.T., Golshani, P., Arisaka, K. & Portera-Cailliau, C. Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing. Nat. Methods 8, 139–142 (2011).

    CAS  Article  Google Scholar 

  15. 15

    Marre, O. et al. Mapping a complete neural population in the retina. J. Neurosci. 32, 14859–14873 (2012).

    CAS  Article  Google Scholar 

  16. 16

    Akerboom, J. et al. Optimization of a GCaMP calcium indicator for neural activity imaging. J. Neurosci. 32, 13819–13840 (2012).

    CAS  Article  Google Scholar 

  17. 17

    Park, H.C. et al. Analysis of upstream elements in the HuC promoter leads to the establishment of transgenic zebrafish with fluorescent neurons. Dev. Biol. 227, 279–293 (2000).

    CAS  Article  Google Scholar 

  18. 18

    Naumann, E.A., Kampff, A.R., Prober, D.A., Schier, A.F. & Engert, F. Monitoring neural activity with bioluminescence during natural behavior. Nat. Neurosci. 13, 513–520 (2010).

    CAS  Article  Google Scholar 

  19. 19

    Tomer, R., Khairy, K., Amat, F. & Keller, P.J. Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy. Nat. Methods 9, 755–763 (2012).

    CAS  Article  Google Scholar 

  20. 20

    Keller, P.J., Schmidt, A.D., Wittbrodt, J. & Stelzer, E.H.K. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008).

    CAS  Article  Google Scholar 

  21. 21

    Sumbre, G., Muto, A., Baier, H. & Poo, M.M. Entrained rhythmic activities of neuronal ensembles as perceptual memory of time interval. Nature 456, 102–106 (2008).

    CAS  Article  Google Scholar 

  22. 22

    Kinkhabwala, A. et al. A structural and functional ground plan for neurons in the hindbrain of zebrafish. Proc. Natl. Acad. Sci. USA 108, 1164–1169 (2011).

    CAS  Article  Google Scholar 

  23. 23

    Koyama, M., Kinkhabwala, A., Satou, C., Higashijima, S. & Fetcho, J. Mapping a sensory-motor network onto a structural and functional ground plan in the hindbrain. Proc. Natl. Acad. Sci. USA 108, 1170–1175 (2011).

    CAS  Article  Google Scholar 

  24. 24

    Leopold, D.A., Murayama, Y. & Logothetis, N.K. Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. Cereb. Cortex 13, 422–433 (2003).

    Article  Google Scholar 

  25. 25

    Junek, S., Chen, T.W., Alevra, M. & Schild, D. Activity correlation imaging: visualizing function and structure of neuronal populations. Biophys. J. 96, 3801–3809 (2009).

    CAS  Article  Google Scholar 

  26. 26

    Hatta, K. & Korn, H. Tonic inhibition alternates in paired neurons that set direction of fish escape reaction. Proc. Natl. Acad. Sci. USA 96, 12090–12095 (1999).

    CAS  Article  Google Scholar 

  27. 27

    Orger, M.B., Kampff, A.R., Severi, K.E., Bollmann, J.H. & Engert, F. Control of visually guided behavior by distinct populations of spinal projection neurons. Nat. Neurosci. 11, 327–333 (2008).

    CAS  Article  Google Scholar 

  28. 28

    Miri, A. et al. Spatial gradients and multidimensional dynamics in a neural integrator circuit. Nat. Neurosci. 14, 1150–1159 (2011).

    CAS  Article  Google Scholar 

  29. 29

    Chorev, E., Yarom, Y. & Lampl, I. Rhythmic episodes of subthreshold membrane potential oscillations in the rat inferior olive nuclei in vivo. J. Neurosci. 27, 5043–5052 (2007).

    CAS  Article  Google Scholar 

  30. 30

    Nikolaou, N. et al. Parametric functional maps of visual inputs to the tectum. Neuron 76, 317–324 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Del Bene, F. et al. Filtering of visual information in the tectum by an identified neural circuit. Science 330, 669–673 (2010).

    CAS  Article  Google Scholar 

  32. 32

    Blumhagen, F. et al. Neuronal filtering of multiplexed odour representations. Nature 479, 493–498 (2011).

    CAS  Article  Google Scholar 

  33. 33

    Aizenberg, M. & Schuman, E.M. Cerebellar-dependent learning in larval zebrafish. J. Neurosci. 31, 8708–8712 (2011).

    CAS  Article  Google Scholar 

  34. 34

    Douglass, A.D., Kraves, S., Deisseroth, K., Schier, A.F. & Engert, F. Escape behavior elicited by single, channelrhodopsin-2-evoked spikes in zebrafish somatosensory neurons. Curr. Biol. 18, 1133–1137 (2008).

    CAS  Article  Google Scholar 

  35. 35

    Paninski, L. et al. A new look at state-space models for neural data. J. Comput. Neurosci. 29, 107–126 (2010).

    Article  Google Scholar 

  36. 36

    Yu, B.M. et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol. 102, 614–635 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

We thank M. Coleman (Coleman Technologies) for custom microscope operating software; Janelia Farm Research Campus Instrument Design and Fabrication for custom mechanical parts; Janelia Farm Research Campus Vivarium staff for animal care; C. Riegler (Harvard) for crossing the elavl3:GCaMP5G fish line into the albino background; R. Tomer, L. Lagnado and L. Looger for their contributions to early GCaMP test experiments using light-sheet microscopy; K. Branson, V. Jayaraman, L. Looger, K. Svoboda, F. Amat, W. Lemon and M. Yartsev for helpful discussions and critical reading of the manuscript; A. Schier (under US National Institutes of Health (NIH) grants R01HL109525 and R01GM085357) and F. Engert (under NIH grant DP1NS082121) for supporting the work of D.N.R. and J.M.L.; and A. Kampff (Harvard) for providing M.B.O. with access to a custom two-photon microscope. This work was supported by the Howard Hughes Medical Institute.

Author information

Affiliations

Authors

Contributions

M.B.A. and P.J.K. conceived of the research, performed the experiments, analyzed the data and wrote the paper. D.N.R., J.M.L. and M.B.O. generated the elavl3:GCaMP5G fish line. M.B.O. acquired the two-photon cell-counting image stack and participated in preliminary experiments.

Corresponding authors

Correspondence to Misha B Ahrens or Philipp J Keller.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Table 1 and Supplementary Notes 1 and 2 (PDF 5060 kb)

Supplementary Software

Image processing and analysis of whole-brain functional recordings (ZIP 350 kb)

Fast volumetric imaging of the zebrafish brain with light-sheet microscopy

Slicing series of the 800 × 600 × 200 μm3 volume of an elavl3:GCaMP5G-labeled larval zebrafish brain, recorded in 5-μm steps with a 4.25 ± 0.80–μm–thick light sheet (full width at half maximum mean ± s.d. across the brain volume, n = 81). The video playback rate of 30 frames per second is equivalent to the acquisition rate in the microscope. The entire brain volume was recorded at a rate of 0.8 Hz. To reduce the size of this video, we downsampled microscopy images by a factor of 4. Detection objective: Nikon CFI75 LWD 16×/0.80 W. (AVI 4248 kb)

Selected slices from a zebrafish whole-brain light-sheet microscopy recording

Set of 10 out of 41 slices from a volumetric light-sheet recording of an elavl3:GCaMP5G-labeled larval zebrafish brain, showing functional activity recorded at different depths of the 600 × 800 × 200 μm3 volume. The video shows raw microscopy data prior to image registration. To reduce the size of this video, we downsampled microscopy images by a factor of 20. Detection objective: Nikon CFI75 LWD 16×/0.80 W. (AVI 15524 kb)

Whole-brain imaging of neuronal activity (visualization A, slices)

Whole-brain, neuron-level functional activity in a complete set of slices from a volumetric light-sheet recording of an elavl3:GCaMP5G-labeled larval zebrafish brain, superimposed on the reference anatomy (gray). Supplementary Videos 26 show different visualizations of the same whole-brain recording. To reduce the size, we downsampled microscopy images by a factor of 64. Detection objective: Nikon CFI75 LWD 16×/0.80 W. (AVI 20114 kb)

Whole-brain imaging of neuronal activity (visualization B, projections)

Dorsal, lateral and frontal maximum-intensity projections of whole-brain, neuron-level functional activity, reported by the genetically encoded calcium indicator GCaMP5G in an elavl3:GCaMP5G fish, superimposed on maximum-intensity projections of the reference anatomy (gray). Supplementary Videos 26 show different visualizations of the same whole-brain recording. To reduce the size, we downsampled microscopy images by a factor of 4. Detection objective: Nikon CFI75 LWD 16×/0.80 W. (AVI 20138 kb)

Whole-brain imaging of neuronal activity (visualization C, raw ΔF/F)

Left, dorsal maximum-intensity projections of whole-brain, neuron-level functional activity, reported by the genetically encoded calcium indicator GCaMP5G in an elavl3:GCaMP5G fish, superimposed on maximum-intensity projections of the reference anatomy (gray). Right, functional activity only. Supplementary Videos 26 show different visualizations of the same whole-brain recording. To reduce the size, we downsampled microscopy images by a factor of 4. Detection objective: Nikon CFI75 LWD 16×/0.80 W. (AVI 19565 kb)

Neuronal activity in the forebrain

Dorsal maximum-intensity projections of neuron-level functional activity in the forebrain, reported by the genetically encoded calcium indicator GCaMP5G in an elavl3:GCaMP5G fish, superimposed on maximum-intensity projections of the reference anatomy (gray). This video shows full-resolution images for the forebrain region of the whole-brain recording visualized in Supplementary Video 5. Supplementary Videos 26 show different visualizations of the same whole-brain recording. Detection objective: Nikon CFI75 LWD 16×/0.80 W. (AVI 22405 kb)

Three-dimensional visualization of the hindbrain oscillator (fish from Fig. 6)

Rotating maximum-intensity projection of the three-dimensional data set underlying Figure 6a. (AVI 5743 kb)

Three-dimensional visualization of hindbrain-spinal circuit (fish from Fig. 6)

Rotating maximum-intensity projection of the three-dimensional data set underlying Figure 6f. (AVI 6142 kb)

Three-dimensional visualization of the hindbrain oscillator (fish from Supplementary Fig. 3e)

Rotating maximum-intensity projection of the three-dimensional data set underlying Supplementary Fig. 3e. (AVI 5796 kb)

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Ahrens, M., Orger, M., Robson, D. et al. Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat Methods 10, 413–420 (2013). https://doi.org/10.1038/nmeth.2434

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