Whole-Brain Imaging Using Genetically Encoded Activity Sensors in Vertebrates



In the mid-twentieth century, the development of electrophysiology revolutionized the way that the brain could be studied, allowing scientists to advance beyond anatomy and neuroethology and address questions involving brain function. These recordings offered a temporally and spatially high-resolution readout of the activity of single cells and enabled a detailed understanding of the input–output function of individual neurons. Nevertheless, understanding the brain one neuron at a time seems like a daunting task. Over the last two decades, a considerable amount of research has focused on understanding the brain at the mesoscale of brain circuits and networks, trying to bridge the gap from single neurons to the function of the whole brain in generating behavior. This is a large, open and exciting field that encompasses theory, computational models, behavioral studies, genetic manipulations and many more approaches. Importantly, the current interest in brain circuits is fueled by the development of new techniques that allow us to acquire data relevant to addressing network function and the activity of large populations of neurons. In this chapter, we present an introduction to whole-brain, single-cell resolution imaging in a behaving vertebrate model organism, the larval zebrafish. We describe the fundamental concepts developed during the last five years that are important for understanding large-scale imaging techniques in vertebrates from experimental design to data acquisition and analysis.


Calcium imaging Light-sheet microscopy Two-photon microscopy Larval zebrafish 



We thank Vilim Stih, Patricia Cooney and Joel Ryan for helpful comments on the manuscript. LK was funded by the Humboldt Foundation, Carl von Siemens Foundation and the Max-Planck-Gesellschaft. AMK, DM, and TY were funded by IMPRS and the Max-Planck Gesellschaft. RP was funded by the Max-Planck-Gesellschaft.


  1. Ahrens MB, Li JM, Orger MB et al (2012) Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485:471–477. doi: 10.1038/nature11057 PubMedPubMedCentralGoogle Scholar
  2. Ahrens MB, Orger MB, Robson DN et al (2013) Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat Methods 10:413–420. doi: 10.1038/nmeth.2434 CrossRefPubMedGoogle Scholar
  3. Albus JS (1971) A theory of cerebellar function. Math Biosci 10:25–61. doi: 10.1016/0025-5564(71)90051-4 CrossRefGoogle Scholar
  4. Amat F, Höckendorf B, Wan Y et al (2015) Efficient processing and analysis of large-scale light-sheet microscopy data. Nat Protoc 10:1679–1696CrossRefPubMedGoogle Scholar
  5. Andermann ML, Gilfoy NB, Goldey GJ et al (2013) Chronic cellular imaging of entire cortical columns in awake mice using microprisms. Neuron 80(4). doi: 10.1016/j.neuron.2013.07.052
  6. Averbeck BB, Latham PE, Pouget A (2006) Neural correlations, population coding and computation. Nat Rev Neurosci 7:358–366. doi: 10.1038/nrn1888 CrossRefPubMedGoogle Scholar
  7. Bargmann CI, Marder E (2013) From the connectome to brain function. Nat Methods 10:483–490. doi: 10.1016/j.cub.2012.01.061 CrossRefPubMedGoogle Scholar
  8. Beck JC, Gilland E, Tank DW, Baker R (2004) Quantifying the ontogeny of optokinetic and vestibuloocular behaviors in zebrafish, medaka, and goldfish. J Neurophysiol 92:3546–3561CrossRefPubMedGoogle Scholar
  9. Bennett DV, Ahrens MB (2016) A practical guide to light sheet microscopy. Methods Mol Biol (Clifton, NJ) 1451:321Google Scholar
  10. Borgius L, Restrepo CE, Leao RN et al (2010) A transgenic mouse line for molecular genetic analysis of excitatory glutamatergic neurons. Mol Cell Neurosci 45:245–257. doi: 10.1016/j.mcn.2010.06.016 CrossRefPubMedGoogle Scholar
  11. Bouchard MB, Voleti V, Mendes CS et al (2015) Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms. Nat Photonics 9:113–119CrossRefPubMedPubMedCentralGoogle Scholar
  12. Butler AB, Hodos W (2005) Comparative vertebrate neuroanatomy: evolution and adaptation. John Wiley & SonsGoogle Scholar
  13. Chen Q et al (2012) Imaging neural activity using Thy1-GCaMP transgenic mice. Neuron 76:297–308CrossRefPubMedPubMedCentralGoogle Scholar
  14. Chen T-W, Wardill TJ, Sun Y et al (2013) Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499:295–300. doi: 10.1038/nature12354 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Denk W, Strickler JH, Webb WW (1990) Two-photon laser scanning fluorescence microscopy. Science 248:73–76CrossRefPubMedGoogle Scholar
  16. Deneux T, Kaszas A, Szalay G et al (2016) Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo. Nat Commun 7:12190. doi: 10.1038/ncomms12190 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Dombeck DA, Khabbaz AN, Collman F et al (2007) Imaging large-scale neural activity with cellular resolution in awake, mobile mice. Neuron 56:43–57CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dombeck DA, Harvey CD, Tian L et al (2010) Functional imaging of hippocampal place cells at cellular resolution during virtual navigation. Nat Neurosci 13:1433–1440. doi: 10.1038/nn.2648 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Dunn TW, Mu Y, Narayan S et al (2016) Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion. Elife 5:1–29. doi: 10.7554/eLife.12741 CrossRefGoogle Scholar
  20. Esposti F, Johnston J, Rosa J et al (2013) Olfactory stimulation selectively modulates the OFF pathway in the Retina of Zebrafish. Neuron 79:97–110. doi: 10.1016/j.neuron.2013.05.001 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Feng G, Mellor RH, Bernstein M et al (2000) Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron 28:41–51. doi: 10.1016/S0896-6273(00)00084-2 CrossRefPubMedGoogle Scholar
  22. Filosa A, Barker AJ, Dal Maschio M, Baier H (2016) Feeding state modulates behavioral choice and processing of Prey Stimuli in the Zebrafish Tectum. Neuron 90:596–608CrossRefPubMedGoogle Scholar
  23. Fosque BF, Sun Y, Dana H et al (2015) Neural circuits. Labeling of active neural circuits in vivo with designed calcium integrators. Science 347:755–760. doi: 10.1126/science.1260922 CrossRefPubMedGoogle Scholar
  24. Freeman J, Vladimirov N, Kawashima T et al (2014) Mapping brain activity at scale with cluster computing. Nat Methods 11:941–950. doi: 10.1038/nmeth.3041 CrossRefPubMedGoogle Scholar
  25. Friedrich J, Soudry D, Mu Y, et al (2015) Fast constrained non-negative matrix factorization for whole-brain calcium imaging data. Conf Neural Inf Process Syst 1–5Google Scholar
  26. Gao P, Ganguli S (2015) On simplicity and complexity in the brave new world of large-scale neuroscience. Curr Opin Neurobiol 32:148–155. doi: 10.1016/j.conb.2015.04.003 CrossRefPubMedGoogle Scholar
  27. Ghosh KK, Burns LD, Cocker ED et al (2011) Miniaturized integration of a fluorescence microscope. Nat Methods 8:871–878CrossRefPubMedPubMedCentralGoogle Scholar
  28. Groneberg AH, Herget U, Ryu S, De Marco RJ (2015) Positive taxis and sustained responsiveness to water motions in larval zebrafish. Front Neural Circuits 9:9. doi: 10.3389/fncir.2015.00009 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Hamel EJO, Grewe BF, Parker JG, Schnitzer MJ (2015) Cellular level brain imaging in behaving mammals: an engineering approach. Neuron 86:140–159CrossRefPubMedGoogle Scholar
  30. Harris KD, Quiroga RQ, Freeman J, Smith SL (2016) Improving data quality in neuronal population recordings. Nat Neurosci 19:1165–1174. doi: 10.1038/nn.4365 CrossRefPubMedPubMedCentralGoogle Scholar
  31. Helmchen F, Borst JG, Sakmann B (1997) Calcium dynamics associated with a single action potential in a CNS presynaptic terminal. Biophys J 72:1458CrossRefPubMedPubMedCentralGoogle Scholar
  32. Helmchen F, Denk W, Kerr JND (2013) Miniaturization of two-photon microscopy for imaging in freely moving animals. Cold Spring Harb Protoc 2013:pdb–top078147Google Scholar
  33. Horton NG, Wang K, Kobat D et al (2013) In vivo three-photon microscopy of subcortical structures within an intact mouse brain. Nat Phot 7:205–209CrossRefGoogle Scholar
  34. Huisken J, Swoger J, Bene F Del, et al (2004) Live Embryos by Selective Plane Illumination Microscopy. 13–16. doi: 10.1126/science.1100035
  35. Huisken J, Stainier DYR (2009) Selective plane illumination microscopy techniques in developmental biology. Development 136:1963–1975CrossRefPubMedPubMedCentralGoogle Scholar
  36. Izhikevich EM (2007) Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, Cambridge, MAGoogle Scholar
  37. Jercog P, Rogerson T, Schnitzer MJ (2016) Large-scale fluorescence calcium-imaging methods for studies of long-term memory in behaving mammals. Cold Spring Harb Perspect Biol 8:a021824CrossRefPubMedGoogle Scholar
  38. Ji N, Freeman J, Smith SL (2016) Technologies for imaging neural activity in large volumes. Nat Neurosci 19:1154–1164CrossRefPubMedPubMedCentralGoogle Scholar
  39. Kato S, Kaplan HS, Schrödel T et al (2015) Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell 163:656–669CrossRefPubMedGoogle Scholar
  40. Keller PJ, Schmidt AD, Wittbrodt J, Stelzer EHK (2008) Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322:1065–1069. doi: 10.1126/science.1162493 CrossRefPubMedGoogle Scholar
  41. Kim C-H, Ueshima E, Muraoka O et al (1996) Zebrafish elav/HuC homologue as a very early neuronal marker. Neurosci Lett 216:109–112CrossRefPubMedGoogle Scholar
  42. Kim CK, Miri A, Leung LC et al (2014) Prolonged, brain-wide expression of nuclear-localized GCaMP3 for functional circuit mapping. Front Neural Circuits 8:1–12. doi: 10.3389/fncir.2014.00138 CrossRefGoogle Scholar
  43. Koester HJ, Sakmann B (2000) Calcium dynamics associated with action potentials in single nerve terminals of pyramidal cells in layer 2/3 of the young rat neocortex. J Physiol 529:625–646CrossRefPubMedPubMedCentralGoogle Scholar
  44. Krishnan S, Mathuru AS, Kibat C et al (2014) The right dorsal habenula limits attraction to an odor in zebrafish. Curr Biol 24:1167–1175. doi: 10.1016/j.cub.2014.03.073 CrossRefPubMedGoogle Scholar
  45. Lacoste AMB, Schoppik D, Robson DN, et al (2015) A convergent and essential interneuron pathway for mauthner-cell-mediated escapes. Curr Biol 1–9. doi: 10.1016/j.cub.2015.04.025
  46. Lin MZ, Schnitzer MJ (2016) Genetically encoded indicators of neuronal activity. Nat Neurosci 19:1142–1153. doi: 10.1038/nn.4359 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Marr D, Thach WT (1969) A theory of cerebellar cortex. In: From the Retina to the Neocortex. Springer, pp 11–50Google Scholar
  48. Maruyama R, Maeda K, Moroda H et al (2014) Detecting cells using non-negative matrix factorization on calcium imaging data. Neural Networks 55:11–19. doi: 10.1016/j.neunet.2014.03.007 CrossRefPubMedGoogle Scholar
  49. Masino M, Fetcho JR (2005) Fictive swimming motor patterns in wild type and mutant larval zebrafish. J Neurophysiol 93:3177–3188. doi: 10.1152/jn.01248.2004 CrossRefPubMedGoogle Scholar
  50. Miri A, Daie K, Burdine RD et al (2011a) Regression-based identification of behavior-encoding neurons during large-scale optical imaging of neural activity at cellular resolution. J Neurophysiol 105:964–980. doi: 10.1152/jn.00702.2010 CrossRefPubMedGoogle Scholar
  51. Miri A, Daie K, Arrenberg AB et al (2011b) Spatial gradients and multidimensional dynamics in a neural integrator circuit. Nat Neurosci 14:1150–1159. doi: 10.1038/nn.2888 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Mukamel EA, Nimmerjahn A, Schnitzer MJ (2009) Automated analysis of cellular signals from large-scale calcium imaging data. Neuron 63:747–760. doi: 10.1016/j.neuron.2009.08.009 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Orger MB, Baier H (2005) Channeling of red and green cone inputs to the zebrafish optomotor response. Vis Neurosci 22:275–281. doi: 10.1007/3-540-35375-5 CrossRefPubMedGoogle Scholar
  54. Panier T, Romano SA, Olive R et al (2013) Fast functional imaging of multiple brain regions in intact zebrafish larvae using selective plane illumination microscopy. Front Neural Circuits 7:1–11. doi: 10.3389/fncir.2013.00065 CrossRefGoogle Scholar
  55. Peters AJ, Chen SX, Komiyama T (2014) Emergence of reproducible spatiotemporal activity during motor learning. Nature 510:263–267CrossRefPubMedGoogle Scholar
  56. Pillow JW, Shlens J, Paninski L et al (2008) Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454:995–999CrossRefPubMedPubMedCentralGoogle Scholar
  57. Pnevmatikakis EA, Soudry D, Gao Y et al (2016) Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89:299. doi: 10.1016/j.neuron.2015.11.037 CrossRefGoogle Scholar
  58. Portugues R, Engert F (2011) Adaptive locomotor behavior in larval zebrafish. Front Syst Neurosci 5:72. doi: 10.3389/fnsys.2011.00072 CrossRefPubMedPubMedCentralGoogle Scholar
  59. Portugues R, Feierstein CE, Engert F, Orger MB (2014) Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior. Neuron 81:1328–1343. doi: 10.1016/j.neuron.2014.01.019 CrossRefPubMedPubMedCentralGoogle Scholar
  60. Preibisch S, Amat F, Stamataki E et al (2014) Efficient Bayesian-based multiview deconvolution. Nat Methods 11:645–648. doi: 10.1038/nmeth.2929 CrossRefPubMedPubMedCentralGoogle Scholar
  61. Prevedel R, Yoon Y-G, Hoffmann M et al (2014) Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nat Methods 11:727–730. doi: 10.1038/nmeth.2964 CrossRefPubMedPubMedCentralGoogle Scholar
  62. Randlett O, Wee CL, Naumann EA et al (2015) Whole-brain activity mapping onto a zebrafish brain atlas. Nat Methods 12:1–12. doi: 10.1038/nmeth.3581 CrossRefGoogle Scholar
  63. Renninger SL, Orger MB (2013) Two-photon imaging of neural population activity in zebrafish. Methods 62:255–267CrossRefPubMedGoogle Scholar
  64. Rohlfing T, Maurer CR (2003) Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Trans Inf Technol Biomed 7:16–25CrossRefPubMedGoogle Scholar
  65. Romano SA, Pietri T, Pérez-Schuster V, et al (2015) Spontaneous Neuronal Network Dynamics Reveal Circuit’s Functional Adaptations for Behavior. Neuron 1–16. doi: 10.1016/j.neuron.2015.01.027
  66. Ronneberger O, Liu K, Rath M et al (2012) ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains. Nat Methods 9:735–742. doi: 10.1038/nmeth.2076 CrossRefPubMedGoogle Scholar
  67. Rose T, Goltstein PM, Portugues R, Griesbeck O (2014) Putting a finishing touch on GECIs. Front Mol Neurosci 7:88CrossRefPubMedPubMedCentralGoogle Scholar
  68. Rose T, Jaepel J, Hübener M, Bonhoeffer T (2016) Cell-specific restoration of stimulus preference after monocular deprivation in the visual cortex. Science 352:1319–1322CrossRefPubMedGoogle Scholar
  69. Sadakane O, Masamizu Y, Watakabe A et al (2015) Long-term two-photon calcium imaging of neuronal populations with subcellular resolution in adult non-human primates. Cell Rep 13:1989–1999CrossRefPubMedGoogle Scholar
  70. Santisakultarm TP, Kersbergen CJ, Bandy DK et al (2016) Two-photon imaging of cerebral hemodynamics and neural activity in awake and anesthetized marmosets. J Neurosci Methods 271:55–64CrossRefPubMedPubMedCentralGoogle Scholar
  71. Sato T, Takahoko M, Okamoto H (2006) HuC:Kaede, a useful tool to label neural morphologies in networks in vivo. Genesis 44:136–142. doi: 10.1002/gene.20196 CrossRefPubMedGoogle Scholar
  72. Schrodel T, Prevedel R, Aumayr K, Zimmer M, Vaziri A (2013) Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light. Nat Meth 10:1013–1020CrossRefGoogle Scholar
  73. Scott EK et al (2007) Targeting neural circuitry in zebrafish using GAL4 enhancer trapping. Nat Methods 4:323–326PubMedGoogle Scholar
  74. Seidemann E, Chen Y, Bai Y et al (2016) Calcium imaging with genetically encoded indicators in behaving primates. Elife 5:e16178CrossRefPubMedPubMedCentralGoogle Scholar
  75. Theis L, Berens P, Froudarakis E et al (2016) Benchmarking spike rate inference in population calcium imaging. Neuron 90:471–482CrossRefPubMedPubMedCentralGoogle Scholar
  76. Thiel G, Greengard P, Südhof TC (1991) Characterization of tissue-specific transcription by the human synapsin I gene promoter. Proc Natl Acad Sci 88:3431–3435CrossRefPubMedPubMedCentralGoogle Scholar
  77. Tian L, Hires SA, Mao T et al (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881. doi: 10.1038/nmeth.1398 CrossRefPubMedPubMedCentralGoogle Scholar
  78. Tischbirek C, Birkner A, Jia H et al (2015) Deep two-photon brain imaging with a red-shifted fluorometric Ca2+ indicator. Proc Natl Acad Sci 112:11377–11382CrossRefPubMedPubMedCentralGoogle Scholar
  79. Tomer R, Lovett-Barron M, Kauvar I et al (2015) SPED light sheet microscopy: fast mapping of biological system structure and function. Cell 163:1796–1806. doi: 10.1016/j.cell.2015.11.061 CrossRefPubMedPubMedCentralGoogle Scholar
  80. Vladimirov N, Mu Y, Kawashima T et al (2014) Light-sheet functional imaging in fictively behaving zebrafish. Nat Methods 11:1–2. doi: 10.1038/nmeth.3040 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Research Group of Sensorimotor ControlMax Planck Institute of NeurobiologyMartinsriedGermany

Personalised recommendations