Abstract

We present volumetric imaging and computational techniques to quantify neuronal and myelin architecture with intrinsic scattering contrast. Using spectral / Fourier domain Optical Coherence Microscopy (OCM) and software focus-tracking we validate imaging of neuronal cytoarchitecture and demonstrate quantification in the rodent cortex in vivo. Additionally, by ex vivo imaging in conjunction with optical clearing techniques, we demonstrate that intrinsic scattering contrast is preserved in the brain, even after sacrifice and fixation. We volumetrically image cytoarchitecture and myeloarchitecture ex vivo across the entire depth of the rodent cortex. Cellular-level imaging up to the working distance of our objective (~3 mm) is demonstrated ex vivo. Architectonic features show the expected laminar characteristics; moreover, changes in contrast after the application of acetic acid suggest that entire neuronal cell bodies are responsible for the “negative contrast” present in the images. Clearing and imaging techniques that preserve tissue architectural integrity have the potential to enable non-invasive studies of the brain during development, disease, and remodeling, even in samples where exogenous labeling is impractical.

© 2013 Optical Society of America

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  1. K. A. Kasischke, H. D. Vishwasrao, P. J. Fisher, W. R. Zipfel, and W. W. Webb, “Neural activity triggers neuronal oxidative metabolism followed by astrocytic glycolysis,” Science 305(5680), 99–103 (2004).
    [Crossref] [PubMed]
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  9. M. Snuderl, D. Wirth, S. A. Sheth, S. K. Bourne, C. S. Kwon, M. Ancukiewicz, W. T. Curry, M. P. Frosch, and A. N. Yaroslavsky, “Dye-enhanced multimodal confocal imaging as a novel approach to intraoperative diagnosis of brain tumors,” Brain Pathol. 23(1), 73–81 (2013).
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  17. T. Collier, P. Shen, B. de Pradier, K. B. Sung, R. Richards-Kortum, M. Follen, and A. Malpica, “Near real time confocal microscopy of amelanotic tissue: dynamics of aceto-whitening enable nuclear segmentation,” Opt. Express 6(2), 40–48 (2000).
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  19. T. S. Skoglund, R. Pascher, and C. H. Berthold, “Heterogeneity in the columnar number of neurons in different neocortical areas in the rat,” Neurosci. Lett. 208(2), 97–100 (1996).
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  20. D. J. Faber, F. J. van der Meer, M. C. G. Aalders, and T. van Leeuwen, “Quantitative measurement of attenuation coefficients of weakly scattering media using optical coherence tomography,” Opt. Express 12(19), 4353–4365 (2004).
    [Crossref] [PubMed]
  21. K. Chung, J. Wallace, S. Y. Kim, S. Kalyanasundaram, A. S. Andalman, T. J. Davidson, J. J. Mirzabekov, K. A. Zalocusky, J. Mattis, A. K. Denisin, S. Pak, H. Bernstein, C. Ramakrishnan, L. Grosenick, V. Gradinaru, and K. Deisseroth, “Structural and molecular interrogation of intact biological systems,” Nature 497(7449), 332–337 (2013).
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  22. K. Chung and K. Deisseroth, “CLARITY for mapping the nervous system,” Nat. Methods 10(6), 508–513 (2013).
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  25. A. L. Pistorio, S. H. Hendry, and X. Wang, “A modified technique for high-resolution staining of myelin,” J. Neurosci. Methods 153(1), 135–146 (2006).
    [Crossref] [PubMed]
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    [Crossref] [PubMed]
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    [Crossref] [PubMed]
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    [Crossref] [PubMed]
  31. M. Hawrylycz, R. A. Baldock, A. Burger, T. Hashikawa, G. A. Johnson, M. Martone, L. Ng, C. Lau, S. D. Larson, J. Nissanov, L. Puelles, S. Ruffins, F. Verbeek, I. Zaslavsky, and J. Boline, “Digital atlasing and standardization in the mouse brain,” PLOS Comput. Biol. 7(2), e1001065 (2011).
    [Crossref] [PubMed]
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  33. T. Ragan, L. R. Kadiri, K. U. Venkataraju, K. Bahlmann, J. Sutin, J. Taranda, I. Arganda-Carreras, Y. Kim, H. S. Seung, and P. Osten, “Serial two-photon tomography for automated ex vivo mouse brain imaging,” Nat. Methods 9(3), 255–258 (2012).
    [Crossref] [PubMed]

2013 (6)

O. Assayag, K. Grieve, B. Devaux, F. Harms, J. Pallud, F. Chretien, C. Boccara, and P. Varlet, “Imaging of non-tumorous and tumorous human brain tissues with full-field optical coherence tomography,” NeuroImage. Clinical 2, 549–557 (2013).

M. Snuderl, D. Wirth, S. A. Sheth, S. K. Bourne, C. S. Kwon, M. Ancukiewicz, W. T. Curry, M. P. Frosch, and A. N. Yaroslavsky, “Dye-enhanced multimodal confocal imaging as a novel approach to intraoperative diagnosis of brain tumors,” Brain Pathol. 23(1), 73–81 (2013).
[Crossref] [PubMed]

K. Chung, J. Wallace, S. Y. Kim, S. Kalyanasundaram, A. S. Andalman, T. J. Davidson, J. J. Mirzabekov, K. A. Zalocusky, J. Mattis, A. K. Denisin, S. Pak, H. Bernstein, C. Ramakrishnan, L. Grosenick, V. Gradinaru, and K. Deisseroth, “Structural and molecular interrogation of intact biological systems,” Nature 497(7449), 332–337 (2013).
[Crossref] [PubMed]

K. Chung and K. Deisseroth, “CLARITY for mapping the nervous system,” Nat. Methods 10(6), 508–513 (2013).
[Crossref] [PubMed]

M. T. Ke, S. Fujimoto, and T. Imai, “SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction,” Nat. Neurosci. 16(8), 1154–1161 (2013).
[Crossref] [PubMed]

O. O. Ahsen, Y. K. Tao, B. M. Potsaid, Y. Sheikine, J. Jiang, I. Grulkowski, T.-H. Tsai, V. Jayaraman, M. F. Kraus, J. L. Connolly, J. Hornegger, A. Cable, and J. G. Fujimoto, “Swept source optical coherence microscopy using a 1310 nm VCSEL light source,” Opt. Express 21(15), 18021–18033 (2013).
[Crossref] [PubMed]

2012 (3)

V. J. Srinivasan, H. Radhakrishnan, J. Y. Jiang, S. Barry, and A. E. Cable, “Optical coherence microscopy for deep tissue imaging of the cerebral cortex with intrinsic contrast,” Opt. Express 20(3), 2220–2239 (2012).
[Crossref] [PubMed]

W. Denk, K. L. Briggman, and M. Helmstaedter, “Structural neurobiology: missing link to a mechanistic understanding of neural computation,” Nat. Rev. Neurosci. 13(5), 351–358 (2012).
[PubMed]

T. Ragan, L. R. Kadiri, K. U. Venkataraju, K. Bahlmann, J. Sutin, J. Taranda, I. Arganda-Carreras, Y. Kim, H. S. Seung, and P. Osten, “Serial two-photon tomography for automated ex vivo mouse brain imaging,” Nat. Methods 9(3), 255–258 (2012).
[Crossref] [PubMed]

2011 (8)

M. Hawrylycz, R. A. Baldock, A. Burger, T. Hashikawa, G. A. Johnson, M. Martone, L. Ng, C. Lau, S. D. Larson, J. Nissanov, L. Puelles, S. Ruffins, F. Verbeek, I. Zaslavsky, and J. Boline, “Digital atlasing and standardization in the mouse brain,” PLOS Comput. Biol. 7(2), e1001065 (2011).
[Crossref] [PubMed]

A. F. McCaslin, B. R. Chen, A. J. Radosevich, B. Cauli, and E. M. Hillman, “In vivo 3D morphology of astrocyte-vasculature interactions in the somatosensory cortex: implications for neurovascular coupling,” J. Cereb. Blood Flow Metab. 31(3), 795–806 (2011).
[Crossref] [PubMed]

M. Rieckher, U. J. Birk, H. Meyer, J. Ripoll, and N. Tavernarakis, “Microscopic optical projection tomography in vivo,” PLoS ONE 6(4), e18963 (2011).
[Crossref] [PubMed]

S. Witte, A. Negrean, J. C. Lodder, C. P. de Kock, G. Testa Silva, H. D. Mansvelder, and M. Louise Groot, “Label-free live brain imaging and targeted patching with third-harmonic generation microscopy,” Proc. Natl. Acad. Sci. U.S.A. 108(15), 5970–5975 (2011).
[Crossref] [PubMed]

J. Ben Arous, J. Binding, J. F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” J. Biomed. Opt. 16(11), 116012 (2011).
[Crossref] [PubMed]

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: multi-contrast optical coherence tomography based tractography,” Neuroimage 58(4), 984–992 (2011).
[Crossref] [PubMed]

H. Hama, H. Kurokawa, H. Kawano, R. Ando, T. Shimogori, H. Noda, K. Fukami, A. Sakaue-Sawano, and A. Miyawaki, “Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain,” Nat. Neurosci. 14(11), 1481–1488 (2011).
[Crossref] [PubMed]

M. J. Farrar, F. W. Wise, J. R. Fetcho, and C. B. Schaffer, “In vivo imaging of myelin in the vertebrate central nervous system using third harmonic generation microscopy,” Biophys. J. 100(5), 1362–1371 (2011).
[Crossref] [PubMed]

2009 (2)

P. S. Tsai, J. P. Kaufhold, P. Blinder, B. Friedman, P. J. Drew, H. J. Karten, P. D. Lyden, and D. Kleinfeld, “Correlations of Neuronal and Microvascular Densities in Murine Cortex Revealed by Direct Counting and Colocalization of Nuclei and Vessels,” J. Neurosci. 29(46), 14553–14570 (2009).
[Crossref] [PubMed]

J. W. Bohland, C. Wu, H. Barbas, H. Bokil, M. Bota, H. C. Breiter, H. T. Cline, J. C. Doyle, P. J. Freed, R. J. Greenspan, S. N. Haber, M. Hawrylycz, D. G. Herrera, C. C. Hilgetag, Z. J. Huang, A. Jones, E. G. Jones, H. J. Karten, D. Kleinfeld, R. Kötter, H. A. Lester, J. M. Lin, B. D. Mensh, S. Mikula, J. Panksepp, J. L. Price, J. Safdieh, C. B. Saper, N. D. Schiff, J. D. Schmahmann, B. W. Stillman, K. Svoboda, L. W. Swanson, A. W. Toga, D. C. Van Essen, J. D. Watson, and P. P. Mitra, “A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale,” PLOS Comput. Biol. 5(3), e1000334 (2009).
[Crossref] [PubMed]

2008 (1)

2006 (1)

A. L. Pistorio, S. H. Hendry, and X. Wang, “A modified technique for high-resolution staining of myelin,” J. Neurosci. Methods 153(1), 135–146 (2006).
[Crossref] [PubMed]

2005 (2)

F. Helmchen and W. Denk, “Deep tissue two-photon microscopy,” Nat. Methods 2(12), 932–940 (2005).
[Crossref] [PubMed]

C. Schmitz and P. R. Hof, “Design-based stereology in neuroscience,” Neuroscience 130(4), 813–831 (2005).
[Crossref] [PubMed]

2004 (4)

D. J. Faber, F. J. van der Meer, M. C. G. Aalders, and T. van Leeuwen, “Quantitative measurement of attenuation coefficients of weakly scattering media using optical coherence tomography,” Opt. Express 12(19), 4353–4365 (2004).
[Crossref] [PubMed]

K. A. Kasischke, H. D. Vishwasrao, P. J. Fisher, W. R. Zipfel, and W. W. Webb, “Neural activity triggers neuronal oxidative metabolism followed by astrocytic glycolysis,” Science 305(5680), 99–103 (2004).
[Crossref] [PubMed]

Y. He and R. K. Wang, “Dynamic optical clearing effect of tissue impregnated with hyperosmotic agents and studied with optical coherence tomography,” J. Biomed. Opt. 9(1), 200–206 (2004).
[Crossref] [PubMed]

W. Denk and H. Horstmann, “Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure,” PLoS Biol. 2(11), e329 (2004).
[Crossref] [PubMed]

2003 (1)

D. A. Dombeck, K. A. Kasischke, H. D. Vishwasrao, M. Ingelsson, B. T. Hyman, and W. W. Webb, “Uniform polarity microtubule assemblies imaged in native brain tissue by second-harmonic generation microscopy,” Proc. Natl. Acad. Sci. U.S.A. 100(12), 7081–7086 (2003).
[Crossref] [PubMed]

2000 (2)

R. A. Drezek, T. Collier, C. K. Brookner, A. Malpica, R. Lotan, R. R. Richards-Kortum, and M. Follen, “Laser scanning confocal microscopy of cervical tissue before and after application of acetic acid,” Am. J. Obstet. Gynecol. 182(5), 1135–1139 (2000).
[Crossref] [PubMed]

T. Collier, P. Shen, B. de Pradier, K. B. Sung, R. Richards-Kortum, M. Follen, and A. Malpica, “Near real time confocal microscopy of amelanotic tissue: dynamics of aceto-whitening enable nuclear segmentation,” Opt. Express 6(2), 40–48 (2000).
[Crossref] [PubMed]

1996 (1)

T. S. Skoglund, R. Pascher, and C. H. Berthold, “Heterogeneity in the columnar number of neurons in different neocortical areas in the rat,” Neurosci. Lett. 208(2), 97–100 (1996).
[Crossref] [PubMed]

1994 (1)

1989 (1)

A. Burkhalter and K. L. Bernardo, “Organization of corticocortical connections in human visual cortex,” Proc. Natl. Acad. Sci. U.S.A. 86(3), 1071–1075 (1989).
[Crossref] [PubMed]

Aalders, M. C. G.

Abosch, A.

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: multi-contrast optical coherence tomography based tractography,” Neuroimage 58(4), 984–992 (2011).
[Crossref] [PubMed]

Ahsen, O. O.

Akkin, T.

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: multi-contrast optical coherence tomography based tractography,” Neuroimage 58(4), 984–992 (2011).
[Crossref] [PubMed]

Al-Qaisi, M. K.

H. Wang, A. J. Black, J. Zhu, T. W. Stigen, M. K. Al-Qaisi, T. I. Netoff, A. Abosch, and T. Akkin, “Reconstructing micrometer-scale fiber pathways in the brain: multi-contrast optical coherence tomography based tractography,” Neuroimage 58(4), 984–992 (2011).
[Crossref] [PubMed]

Ancukiewicz, M.

M. Snuderl, D. Wirth, S. A. Sheth, S. K. Bourne, C. S. Kwon, M. Ancukiewicz, W. T. Curry, M. P. Frosch, and A. N. Yaroslavsky, “Dye-enhanced multimodal confocal imaging as a novel approach to intraoperative diagnosis of brain tumors,” Brain Pathol. 23(1), 73–81 (2013).
[Crossref] [PubMed]

Andalman, A. S.

K. Chung, J. Wallace, S. Y. Kim, S. Kalyanasundaram, A. S. Andalman, T. J. Davidson, J. J. Mirzabekov, K. A. Zalocusky, J. Mattis, A. K. Denisin, S. Pak, H. Bernstein, C. Ramakrishnan, L. Grosenick, V. Gradinaru, and K. Deisseroth, “Structural and molecular interrogation of intact biological systems,” Nature 497(7449), 332–337 (2013).
[Crossref] [PubMed]

Ando, R.

H. Hama, H. Kurokawa, H. Kawano, R. Ando, T. Shimogori, H. Noda, K. Fukami, A. Sakaue-Sawano, and A. Miyawaki, “Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain,” Nat. Neurosci. 14(11), 1481–1488 (2011).
[Crossref] [PubMed]

Arganda-Carreras, I.

T. Ragan, L. R. Kadiri, K. U. Venkataraju, K. Bahlmann, J. Sutin, J. Taranda, I. Arganda-Carreras, Y. Kim, H. S. Seung, and P. Osten, “Serial two-photon tomography for automated ex vivo mouse brain imaging,” Nat. Methods 9(3), 255–258 (2012).
[Crossref] [PubMed]

Assayag, O.

O. Assayag, K. Grieve, B. Devaux, F. Harms, J. Pallud, F. Chretien, C. Boccara, and P. Varlet, “Imaging of non-tumorous and tumorous human brain tissues with full-field optical coherence tomography,” NeuroImage. Clinical 2, 549–557 (2013).

Bahlmann, K.

T. Ragan, L. R. Kadiri, K. U. Venkataraju, K. Bahlmann, J. Sutin, J. Taranda, I. Arganda-Carreras, Y. Kim, H. S. Seung, and P. Osten, “Serial two-photon tomography for automated ex vivo mouse brain imaging,” Nat. Methods 9(3), 255–258 (2012).
[Crossref] [PubMed]

Baldock, R. A.

M. Hawrylycz, R. A. Baldock, A. Burger, T. Hashikawa, G. A. Johnson, M. Martone, L. Ng, C. Lau, S. D. Larson, J. Nissanov, L. Puelles, S. Ruffins, F. Verbeek, I. Zaslavsky, and J. Boline, “Digital atlasing and standardization in the mouse brain,” PLOS Comput. Biol. 7(2), e1001065 (2011).
[Crossref] [PubMed]

Barbas, H.

J. W. Bohland, C. Wu, H. Barbas, H. Bokil, M. Bota, H. C. Breiter, H. T. Cline, J. C. Doyle, P. J. Freed, R. J. Greenspan, S. N. Haber, M. Hawrylycz, D. G. Herrera, C. C. Hilgetag, Z. J. Huang, A. Jones, E. G. Jones, H. J. Karten, D. Kleinfeld, R. Kötter, H. A. Lester, J. M. Lin, B. D. Mensh, S. Mikula, J. Panksepp, J. L. Price, J. Safdieh, C. B. Saper, N. D. Schiff, J. D. Schmahmann, B. W. Stillman, K. Svoboda, L. W. Swanson, A. W. Toga, D. C. Van Essen, J. D. Watson, and P. P. Mitra, “A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale,” PLOS Comput. Biol. 5(3), e1000334 (2009).
[Crossref] [PubMed]

Barry, S.

Ben Arous, J.

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M. Rieckher, U. J. Birk, H. Meyer, J. Ripoll, and N. Tavernarakis, “Microscopic optical projection tomography in vivo,” PLoS ONE 6(4), e18963 (2011).
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T. S. Skoglund, R. Pascher, and C. H. Berthold, “Heterogeneity in the columnar number of neurons in different neocortical areas in the rat,” Neurosci. Lett. 208(2), 97–100 (1996).
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Stigen, T. W.

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J. W. Bohland, C. Wu, H. Barbas, H. Bokil, M. Bota, H. C. Breiter, H. T. Cline, J. C. Doyle, P. J. Freed, R. J. Greenspan, S. N. Haber, M. Hawrylycz, D. G. Herrera, C. C. Hilgetag, Z. J. Huang, A. Jones, E. G. Jones, H. J. Karten, D. Kleinfeld, R. Kötter, H. A. Lester, J. M. Lin, B. D. Mensh, S. Mikula, J. Panksepp, J. L. Price, J. Safdieh, C. B. Saper, N. D. Schiff, J. D. Schmahmann, B. W. Stillman, K. Svoboda, L. W. Swanson, A. W. Toga, D. C. Van Essen, J. D. Watson, and P. P. Mitra, “A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale,” PLOS Comput. Biol. 5(3), e1000334 (2009).
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Sutin, J.

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J. W. Bohland, C. Wu, H. Barbas, H. Bokil, M. Bota, H. C. Breiter, H. T. Cline, J. C. Doyle, P. J. Freed, R. J. Greenspan, S. N. Haber, M. Hawrylycz, D. G. Herrera, C. C. Hilgetag, Z. J. Huang, A. Jones, E. G. Jones, H. J. Karten, D. Kleinfeld, R. Kötter, H. A. Lester, J. M. Lin, B. D. Mensh, S. Mikula, J. Panksepp, J. L. Price, J. Safdieh, C. B. Saper, N. D. Schiff, J. D. Schmahmann, B. W. Stillman, K. Svoboda, L. W. Swanson, A. W. Toga, D. C. Van Essen, J. D. Watson, and P. P. Mitra, “A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale,” PLOS Comput. Biol. 5(3), e1000334 (2009).
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Swanson, L. W.

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Taranda, J.

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M. Rieckher, U. J. Birk, H. Meyer, J. Ripoll, and N. Tavernarakis, “Microscopic optical projection tomography in vivo,” PLoS ONE 6(4), e18963 (2011).
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van Leeuwen, T.

Varlet, P.

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Supplementary Material (2)

» Media 1: MOV (2349 KB)     
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Figures (7)

Fig. 1
Fig. 1 Geometries for ex vivo brain imaging. Imaging was performed in an optically cleared brain, via the intact cortical surface (a), or via a cut coronal plane (b). The mouse brain for this figure was rendered using Brain Explorer 2 software (http://mouse.brain-map.org/static/brainexplorer). (OBJ – objective lens)

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Fig. 2
Fig. 2 OCM performs quantitative neuromorphometry in vivo. (a) Two-photon microscopy, after co-labeling with OGB-1 (green) and SR-101 (red), depicts neuronal cell bodies as green spherical regions labeled with OGB-1 but not SR-101. Astrocytes are labeled with both OGB-1 and SR-101 and hence appear orange. (b) Co-registered OCM of the same brain shows approximate correspondence between low scattering regions in OCM images and neuronal cell bodies. (c) Automated software segmentation and rendering of cell bodies from OCM data reveals three-dimensional neuronal architecture. (d) OCM cell density compared with TPM neuron and astrocyte density profiles, showing better agreement with neuron density profile. (e) Cell density and spacing profiles computed from OCM data. Unlike conventional histology, these profiles are obtained directly from three-dimensional data, and do not require stereological assumptions. (See Media 1).

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Fig. 3
Fig. 3 OCM, when combined with ex vivo optical clearing techniques, depicts laminar cortical cytoarchitecture and myeloarchitecture in three-dimensions, over the entire cortical depth and beyond. The imaging geometry is shown in Fig. 1(a). (a) Cytoarchitecture is visualized with a minimum intensity projection. (b) Myeloarchitecture is visualized with a maximum intensity projection and displayed on an inverted colorscale. (c-h) Individual en face images in the transverse plane show the expected trends in cytoarchitecture and myeloarchitecture, with myelination increasing with cortical depth until the white matter (wm) is reached. (i) Quantification of cortical en face myelin content vs. depth confirms the trend of increasing myelination with depth as well as the cortical myelination in layer I (c). (Media 2)

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Fig. 4
Fig. 4 Contrast mechanisms were investigated by application of acetic acid. (a) Image of a cleared mouse cortex cut along a coronal plane, imaged via the coronal surface (as shown in Fig. 1(b)). (b) Comparable image after application of acetic acid show highly scattering centers corresponding to the cell nuclei. Registration between (a) and (b) was not preserved due to tissue volume swelling after acetic acid application. (c) Zoom of boxed region in (b) reveals punctate scattering centers, resembling nuclei, surrounded by hyporeflective regions, resembling soma. These results are consistent with the assertion that OCM, without the application of acetic acid, selectively visualizes the neuronal soma.

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Fig. 5
Fig. 5 (a) OCM contrast, derived from optical backscattering, is sensitive to imaging geometry. Imaging via a cut coronal plane (as shown in Fig. 1(b)) reveals vertically oriented cortical myelin fibers, as compared with Fig. 3, obtained by imaging via the cortical surface (as shown in Fig. 1(a)), which visualizes horizontally oriented myelin fibers. (b) Example image of a Gallyas myelin stain, taken from the same brain region. Retrieved from the Mouse Brain Architecture Project (http://brainarchitecture.org/mouse/), available under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

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Fig. 6
Fig. 6 A comparison of in vivo OCM without clearing and ex vivo OCM after clearing suggests that the clearing procedure reduces signal attenuation and blur. (a) In vivo axial signal profile around the focus demonstrates attenuation as the focus moves deeper in tissue. (b) With increasing focus depth, OCM after fixation and clearing reveals less attenuation of signal from the focus than in vivo OCM. (c) In vivo normalized axial signal profile demonstrates broadening as the focus moves deeper in tissue. (d) With increasing focus depth, OCM after fixation and clearing reveals less broadening of the axial signal profile than in vivo OCM. A broad axial signal profile is a possible indicator of detected multiple forward scattered light, which degrades image contrast. (e-f) In agreement with these observations, in vivo OCM contrast degraded at depths of ~1 mm, whereas ex vivo OCM with clearing revealed contrast at depths of ~3 mm (the maximum depth allowed by the working distance of our objective). Maximum intensity projection (MIP) images are shown to highlight myelinated axons. The imaging geometry is shown in Fig. 1(a).

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Fig. 7
Fig. 7 (a) The lipid-rich myelin sheath has a higher refractive index than the cylindrical axon, leading to directional scattering properties. (b-c) If the axon is oriented in the plane perpendicular to the optic axis, backscattered light is detected along the optic axis (thick red arrow). (d) However, if the axon is oriented at an oblique angle, the amount of light that is backscattered and detected is reduced.

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Equations (3)

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A ^ (x,y, d brain )= ∑ i w[ z( d brain , Z i )− z focus ( x,y, Z i ) ] A[ x,y,z( d brain , Z i ), Z i ]
M pqr = ∭ x p y q z r f( x,y,z ) dxdydz
{ x ¯ , y ¯ , z ¯ }={ M 100 M 000 , M 010 M 000 , M 001 M 000 }

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