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High-Resolution Wide-Field Optical Imaging of Microvascular Characteristics: From the Neocortex to the Eye

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Neurovascular Coupling Methods

Part of the book series: Neuromethods ((NM,volume 88))

Abstract

Measuring microvascular characteristics in cortical tissue and individual microvessels has important applications for functional imaging, biomedical research, and clinical diagnostics. Multiphoton fluorescence microscopy approaches are most effective and allow to reliably record red blood cell (RBC) velocity in individual vessels, but require injecting fluorescent tracers. Moreover, only one or few vessels in a small area can be imaged at a time. Wide-field CCD/CMOS-based optical imaging of intrinsic absorption or reflection changes in macroscopic vascular networks allows to overcome these shortcomings, by recording RBCs’ trajectories over several mm2 of cortical surface. The RBC velocity can then be extracted from these wide-field data using specialized algorithms. Here, we describe two of those, which provide robust RBC velocity estimations that are independent and can thus be used as a control one for another. Although this approach can be used in any part of the body with optically accessible blood vessels, here we show its application in two cases: first the cerebral cortex and then the eye. In this latter application, we go into some more detail in describing the retinal function imager (RFI): a unique, noninvasive multiparameter functional imaging instrument that directly measures hemodynamic parameters such as retinal RBC velocity, oximetric state, and metabolic responses to photic activation. In addition, it allows capillary perfusion mapping without any contrast agent. These parameters of retinal function are degraded by retinal abnormalities. Here, we thus focus on the characterization of microvessels properties. Indeed, clinical studies suggest that knowing these properties should yield multiple clinical applications for early diagnosis of retinal diseases, possible critical guidance of their treatment, as well as implications for vascular diseases of cortex and eye.

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References

  1. Vanzetta I, Grinvald A (2008) Coupling between neuronal activity and microcirculation: implications for functional brain imaging. HFSP J 2:79–98

    Article  PubMed Central  PubMed  Google Scholar 

  2. Bonhoeffer T, Grinvald A (1996) Optical imaging based on intrinsic signals: the methodology. In: Toga AW, Mazziotta JC (eds) Brain mapping; the methods. Academic, San Diego, pp 55–97

    Google Scholar 

  3. Grinvald A, Shoham D, Shmuel A, Glaser D, Vanzetta I, Shtoyerman E, Shlovin H, Wijnbergen C, Hildesheim R, Arieli A (1999) In-vivo optical imaging of cortical architecture and dynamics. In: Windhorst U, Johansson H (eds) Modern techniques in neuroscience research. Springer, Heidelberg, pp 893–970, Chapter 34

    Chapter  Google Scholar 

  4. Grinvald A, Malonek D, Shmuel A, Glaser D, Vanzetta I, Shtoyerman E, Shoham D, Arieli A (1999) Intrinsic signal imaging in the neocortex. In: Imaging of neuronal activity. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, pp 1–17, Chapter 45

    Google Scholar 

  5. Grinvald A, Sharon D, Omer DB, Vanzetta I (2011) Imaging the neocortex functional architecture based on multiple intrinsic signals; implications for hemodynamic based functional imaging. In: Helmchen F, Konnerth A, Yuste R (eds) Imaging in neuroscience. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, pp 907–926, Chapter 83

    Google Scholar 

  6. Grinvald A, Lieke E, Frostig RD, Gilbert CD, Wiesel TN (1986) Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature 324:361–364

    Article  CAS  PubMed  Google Scholar 

  7. Burgansky-Eliash Z, Nelson DA, Bar-Tal OP, Lowenstein A, Grinvald A, Barak A (2010) Reduced retinal blood flow velocity in diabetic retinopathy. Retina 30:765–773

    Article  PubMed  Google Scholar 

  8. Izhaky D, Nelson DA, Burgansky-Eliash Z, Grinvald A (2009) Functional imaging using the retinal function imager: direct imaging of blood velocity, achieving fluorescein angiography-like images without any contrast agent, qualitative oximetry, and functional metabolic signals. Jpn J Ophthalmol 53:345–351

    Article  PubMed  Google Scholar 

  9. Nelson DA, Krupsky S, Pollack A, Aloni E, Belkin M, Vanzetta I, Rosner M, Grinvald A (2005) Special report: noninvasive multi-parameter functional optical imaging of the eye. Ophthalmic Surg Lasers Imaging 36:57–66

    PubMed  Google Scholar 

  10. Schaffer CB, Friedman B, Nishimura N, Schroeder LF, Tsai PS, Ebner FF, Lyden PD, Kleinfeld D (2006) Two-photon imaging of cortical surface microvessels reveals a robust redistribution in blood flow after vascular occlusion. PLoS Biol 4:e22

    Article  PubMed Central  PubMed  Google Scholar 

  11. Kamoun WS, Chae SS, Lacorre DA, Tyrrell JA, Mitre M, Gillissen MA, Fukumura D, Jain RK, Munn LL (2010) Simultaneous measurement of RBC velocity, flux, hematocrit and shear rate in vascular networks. Nat Methods 7:655–660

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Buxton RB, Uludag K, Dubowitz DJ, Liu TT (2004) Modelling the hemodynamic response to brain activation. Neuroimage 23:220–233

    Article  Google Scholar 

  13. Vazquez AL, Cohen ER, Gulani V, Hernandez-Garcia L, Zheng Y, Lee GR, Kim SG, Grotberg JB, Noll DC (2006) Vascular dynamics and BOLD fMRI: CBF level effects and analysis considerations. Neuroimage 32:1642–1655

    Article  PubMed  Google Scholar 

  14. Wei HL, Zheng Y, Pan Y, Coca D, Li LM, Mayhew JE, Billings SA (2009) Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach. IEEE Trans Biomed Eng 56:1606–1616

    Article  PubMed  Google Scholar 

  15. Mullinger KJ, Mayhew SD, Bagshaw AP, Bowtell R, Francis ST (2013) Poststimulus undershoots in cerebral blood flow and BOLD fMRI responses are modulated by poststimulus neuronal activity. Proc Natl Acad Sci U S A 110:13636–13641

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Okell TW, Chappell MA, Kelly ME, Jezzard P, Piilgaard H, Lauritzen M (2013) Contribution of somatosensory cortex to evoked cerebellar blood flow responses. Cerebral blood flow quantification using vessel-encoded arterial spin labeling. J Cereb Blood Flow Metab 33:1716–1724

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Guibert R, Fonta C, Estève F, Plouraboué F (2013) On the normalization of cerebral blood flow. J Cereb Blood Flow Metab 33:669–672

    Article  PubMed Central  PubMed  Google Scholar 

  18. Briers JD (2001) Laser Doppler, speckle and related techniques for blood perfusion mapping and imaging. Physiol Meas 22:35–66

    Article  Google Scholar 

  19. Briers JD, Richards G, He XW (1999) Capillary blood flow monitoring using laser speckle contrast analysis. J Biomed Opt 4:164–175

    Article  CAS  PubMed  Google Scholar 

  20. Dunn AK, Bolay H, Moskovitz MA, Boas DA (2001) Dynamic imaging of cerebral blood flow using laser speckle. J Cereb Blood Flow Metab 21:195–201

    Article  CAS  PubMed  Google Scholar 

  21. Royl G, Leithner C, Sellien H, Müller JP, Megow D, Offenhauser N, Steinbrink J, Kohl-Bareis M, Dirnagl U, Lindauer U (2006) Functional imaging with laser speckle contrast analysis: vascular compartment analysis and correlation with laser Doppler flowmetry and somatosensory evoked potentials. Brain Res 1121:95–103

    Article  CAS  PubMed  Google Scholar 

  22. Winship IR (2013) Improved cerebral blood flow measurement with multiexposure speckle imaging. J Cereb Blood Flow Metab 33:797

    Article  PubMed  Google Scholar 

  23. Kleinfeld D, Mitra PP, Helmchen F, Denk W (1998) Fluctuations and stimulus-induced changes in blood flow observed in individual capillaries in layers 2 through 4 of rat neocortex. Proc Natl Acad Sci U S A 95:15741–15746

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Chaigneau E, Oheim M, Audinat E, Charpak S (2003) Two-photon imaging of capillary blood flow in olfactory bulb glomeruli. Proc Natl Acad Sci U S A 100:13081–13086

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Vanzetta I, Deneux T, Masson GS, Faugeras O (2006) Cerebral blood flow recorded at high sensitivity in two dimensions using high resolution optical imaging. In: Proceedings of IEEE international symposium on biomedical imaging, pp 1264–1267

    Google Scholar 

  26. Hillman EM, Devor A, Bouchard MB, Dunn AK, Krauss GW, Skoch J, Bacskai BJ, Dale AM, Boas DA (2007) Depth-resolved optical imaging and microscopy of vascular compartment dynamics during somatosensory stimulation. Neuroimage 35:89–104

    Article  PubMed Central  PubMed  Google Scholar 

  27. Drew PJ, Blinder P, Cauwenberghs G, Shih AY, Kleinfeld D (2010) Rapid determination of particle velocity from space-time images using the Radon transform. J Comput Neurosci 29:5–11

    Article  PubMed  Google Scholar 

  28. Deneux T, Faugeras O, Takerkart S, Masson GS, Vanzetta I (2011) A new variational method for erythrocyte velocity estimation in wide-field imaging in-vivo. IEEE Trans Med Imaging 30:1527–1545

    Article  PubMed  Google Scholar 

  29. Deneux T, Takerkart S, Grinvald A, Masson GS, Vanzetta I (2012) A processing work-flow for measuring erythrocytes velocity in extended vascular networks from wide field high-resolution optical imaging data. Neuroimage 59:2569–2588

    Article  PubMed  Google Scholar 

  30. Vanzetta I, Hildesheim R, Grinvald A (2005) Compartment-resolved imaging of activity-dependent dynamics of cortical blood volume and oximetry. J Neurosci 25:2233–2244

    Article  CAS  PubMed  Google Scholar 

  31. Frostig RD, Lieke EE, Ts’o DY, Grinvald A (1990) Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. Proc Natl Acad Sci U S A 87:6082–6086

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Ts’o DY, Frostig RD, Lieke EE, Grinvald A (1990) Functional organization of primate visual cortex revealed by high resolution optical imaging. Science 249:417–420

    Article  PubMed  Google Scholar 

  33. Grinvald A, Bonhoeffer T, Vanzetta I, Pollack A, Aloni E, Ofri R, Nelson D (2004) High-resolution functional optical imaging: From the neocortex to the eye. Ophthalmol Clin North Am 17:53–67

    Article  PubMed  Google Scholar 

  34. Horn BKP (1986) Robot vision. MIT, Cambridge, MA

    Google Scholar 

  35. Coleman TF, Li Y (1994) On the convergence of reflective Newton methods for large-scale nonlinear minimization subject to bounds. Math Program 67:189–224

    Article  Google Scholar 

  36. Nishimura N, Schaffer CB, Friedman B, Tsai PS, Lyden PD, Kleinfeld D (2006) Targeted insult to subsurface cortical blood vessels using ultrashort laser pulses: three models of stroke. Nat Methods 3:99–108

    Article  CAS  PubMed  Google Scholar 

  37. Ayata C, Shin HK, Salomone S, Ozdemir-Gursoy Y, Boas DA, Dunn AK, Moskowitz MA (2004) Pronounced hypoperfusion during spreading depression in mouse cortex. J Cereb Blood Flow Metab 24:1172–1182

    Article  PubMed  Google Scholar 

  38. Piilgaard H, Lauritzen M (2004) Contribution of somatosensory cortex to evoked cerebellar blood flow responses. Neuroreport 15:695–698

    Article  PubMed  Google Scholar 

  39. Kohl M, Lindauer U, Dirnagl U, Villringer A (1998) Separation of changes in light scattering and chromophore concentrations during cortical spreading depression in rats. Opt Lett 23:555–557

    Article  CAS  PubMed  Google Scholar 

  40. Nguyen J, Nishimura N, Fetcho RN, Iadecola C, Schaffer CB (2011) Occlusion of cortical ascending venules causes blood flow decreases, reversals in flow direction, and vessel dilation in upstream capillaries. J Cereb Blood Flow Metab 31:2243–2254

    Article  PubMed Central  PubMed  Google Scholar 

  41. Tomita M, Tomita M, Unekawa M, Toriumi N, Suzuki N (2011) Oscillating neuro-capillary coupling during cortical spreading depression as observed by tracking FITC-labeled RBCs in single capillaries. Neuroimage 56:1001–1010

    Article  PubMed  Google Scholar 

  42. Seylaz J, Charbonné R, Nanri K, Von Euw D, Borredon J, Kacem K, Méric P, Pinard E (1999) Dynamic in vivo measurement of erythrocyte velocity and flow in capillaries and of microvessel diameter in the rat brain by confocal laser microscopy. J Cereb Blood Flow Metab 19:863–870

    Article  CAS  PubMed  Google Scholar 

  43. Berwick J, Johnston D, Jones M, Martindale J, Redgrave P, McLoughlin N, Schiessl I, Mayhew JE (2005) Neurovascular coupling investigated with two-dimensional optical imaging spectroscopy in rat whisker barrel cortex. Eur J Neurosci 22:1655–1666

    Article  CAS  PubMed  Google Scholar 

  44. Fujimoto JG (2003) Optical coherence tomography for ultrahigh resolution in vivo imaging. Nat Biotechnol 21:1361–1367

    Article  CAS  PubMed  Google Scholar 

  45. Trick GL, Calotti FY, Skarf B (2006) Advances in imaging of the optic disc and retinal nerve fiber layer. J Neuroophthalmol 26:284–295

    Article  PubMed  Google Scholar 

  46. Singh R, Kaiser PK (2007) Advances in AMD imaging. Int Ophthalmol Clin 47:65–74

    Article  PubMed  Google Scholar 

  47. Drexler W, Fujimoto JG (2008) State-of-the-art retinal optical coherence tomography. Prog Retin Eye Res 27:45–88

    Article  PubMed  Google Scholar 

  48. Schmitz-Valckenberg S, Holz FG, Bird AC, Spaide RF (2008) Fundus autofluorescence imaging: review and perspectives. Retina 28:385–409

    Article  PubMed  Google Scholar 

  49. Podoleanu AG, Rosen RB (2008) Combinations of techniques in imaging the retina with high resolution. Prog Retin Eye Res 27:464–499

    Article  PubMed  Google Scholar 

  50. Denninghoff KR, Smith MH, Hillman L (2000) Retinal imaging techniques in diabetes. Diabetes Technol Ther 2:111–113

    Article  CAS  PubMed  Google Scholar 

  51. Harris A, Dinn RB, Kagemann L, Rechtman E (2003) A review of methods for human retinal oximetry. Ophthalmic Surg Lasers Imaging 34:152–164

    PubMed  Google Scholar 

  52. Blum M, Bachmann K, Wintzer D, Riemer T, Vilser W, Strobel J (1999) Noninvasive measurement of the Bayliss effect in retinal autoregulation. Graefes Arch Clin Exp Ophthalmol 237:296–300

    Article  CAS  PubMed  Google Scholar 

  53. Hubbard LD, Brothers RJ, King WN et al (1999) Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. Ophthalmology 106:2269–2280

    Article  CAS  PubMed  Google Scholar 

  54. Ege BM, Hejlesen OK, Larsen OV, Møller K, Jennings B, Kerr D, Cavan DA (2000) Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput Methods Programs Biomed 62:165–175

    Article  CAS  PubMed  Google Scholar 

  55. Van Hecke MV, Dekker JM, Nijpels G, Stolk RP, Henry RM, Heine RJ, Bouter LM, Stehouwer CD, Polak BC (2006) Are retinal microvascular abnormalities associated with large artery endothelial dysfunction and intima-media thickness? The Hoorn Study. Clin Sci (Lond) 110:597–604

    Article  Google Scholar 

  56. Stanton AV, Wasan B, Cerutti A et al (1995) Vascular network changes in the retina with age and hypertension. J Hypertens 13(12 Pt 2):1724–1728

    CAS  PubMed  Google Scholar 

  57. Van den Born BJ, Hulsman CA, Hoekstra JB, Schlingemann RO, van Montfrans GA (2005) Value of routine funduscopy in patients with hypertension: systematic review. BMJ 331:73

    Article  PubMed Central  PubMed  Google Scholar 

  58. Landa G, Jangi AA, Garcia PM, Rosen RB (2012) Initial report of quantification of retinal blood flow velocity in normal human subjects using the retinal functional imager (RFI). Int Ophthalmol 32:211–215

    Article  PubMed  Google Scholar 

  59. Burgansky-Eliash Z, Lowenstein A, Neuderfer M, Kesler A, Barash H, Nelson DA, Grinvald A, Barak A (2013) Retinal function imager measurements of retinal blood flow velocity and their relationship to various physiological parameters. Ophthalmic Surg Lasers Imaging Retina 44:51–58

    Article  PubMed  Google Scholar 

  60. Burgansky-Eliash Z, Barash H, Nelson DA et al (2012) Retinal function imager measurements of retinal blood flow velocity in patients with early diabetes mellitus. Retina 32:112–119

    Article  PubMed  Google Scholar 

  61. Barak A, Burgansky-Eliash Z, Barash H, Nelson DA, Izhaky D, Barak A, Lowenstein A, Neuderfer M, Kesler A, Grinvald A (2012) The effect of intravitreal bevacizumab (Avastin) injection on retinal blood flow velocity in patients with choroidal neovascularization. Eur J Opthtalmol 22:423–430

    Article  Google Scholar 

  62. Beutelspacher SC, Serbecic N, Barash H, Burgansky-Eliash Z, Grinvald A, Jonas JB (2011) Central serous chorioretinopathy shows reduced retinal circulation in retinal function imaging (RFI). Acta Ophthalmol 23:1755–3768

    Google Scholar 

  63. Beutelspacher SC, Barash H, Burgansky-Eliash Z, Grinvald A, Serbecic N, Jost J (2011) Retinal blood flow velocity measured by retinal function imaging in retinitis pigmentosa. Graefes Arch Clin Exp Ophthalmol 249:1855–1858

    Article  PubMed  Google Scholar 

  64. Landa G, Amde W, Haileselassie Y, Rosen RB (2011) Cilioretinal arteries in diabetic eyes are associated with increased retinal blood flow velocity and occurrence of diabetic macular edema. Retina 31:304–311

    Article  PubMed  Google Scholar 

  65. Landa G, Garcia PM, Rosen RB (2009) Correlation between retina blood flow velocity assessed by retinal function imager and retina thickness estimated by scanning laser ophthalmoscopy/optical coherence tomography. Ophthalmologica 223:155–161

    Article  PubMed  Google Scholar 

  66. Nelson DA, Amit R, Oaknin J, Burgansky-Eliash Z, Barash H, Izhaky D, Lowenstein A, Barak A, Bartov E, Rock T, Grinvald A (2011) Wide-field high-resolution imaging of perfused capillaries without the use of contrast agent. Clin Ophthalmol 5:1095–1106

    PubMed Central  PubMed  Google Scholar 

  67. Kwan AS, Barry C, McAllister IL, Constable I (2006) Fluorescein angiography and adverse drug reactions revisited: the Lions Eye experience. Clin Experiment Ophthalmol 34:33–38

    Article  PubMed  Google Scholar 

  68. Watkin AJ, Alshareef RA, Rezeq SS, Sampat KM, Chhablani J, Bartsch DU, Freeman WR, Haller JA, Garg SJ (2012) Comparative analysis of the retinal microvasculature visualized with fluorescein angiography and the retinal function imager. Am J Opthtalmol 154:901–907

    Article  Google Scholar 

  69. Stefansson E, Landers MB 3rd, Wolbarsht ML (1983) Oxygenation and vasodilatation in relation to diabetic and other proliferative retinopathies. Ophthalmic Surg 14:209–226

    CAS  PubMed  Google Scholar 

  70. Stefansson E, Machemer R, de Juan E, Jr MCBW 2nd, Peterson J (1992) Retinal oxygenation and laser treatment in patients with diabetic retinopathy. Am J Ophthalmol 113:36–38

    CAS  PubMed  Google Scholar 

  71. Tiedeman JS, Kirk SE, Srinivas S, Beach JM (1998) Retinal oxygen consumption during hyperglycemia in patients with diabetes without retinopathy. Ophthalmology 105:31–36

    Article  CAS  PubMed  Google Scholar 

  72. Sebag J, Delori FC, Feke GT, Weiter JJ (1989) Effects of optic atrophy on retinal blood flow and oxygen saturation in humans. Arch Ophthalmol 107:222–226

    Article  CAS  PubMed  Google Scholar 

  73. Grinvald A, Sharon D, Slovin H, Vanzetta I (2005) Intrinsic signal imaging in the neocortex; implications for hemodynamic based functional imaging. In: Yuste R, Konnerth A (eds) Imaging in neuroscience and development: a laboratory manual. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, Chapter 89

    Google Scholar 

  74. Hill DK, Keynes RD (1949) Opacity changes in stimulated nerve. J Physiol 108:278–281

    PubMed Central  Google Scholar 

  75. Cohen LB, Keynes RD, Hille B (1968) Light scattering and birefringence changes during nerve activity. Nature 218:438–441

    Article  CAS  PubMed  Google Scholar 

  76. Cohen LB (1973) Changes in neuron structure during action potential propagation and synaptic transmission. Physiol Rev 53:373–418

    CAS  PubMed  Google Scholar 

  77. Malonek D, Grinvald A (1996) Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science 272:551–554

    Article  CAS  PubMed  Google Scholar 

  78. Tsunoda K, Oguchi Y, Hanazono G, Tanifuji M (2004) Mapping cone- and rod-induced retinal responsiveness in macaque retina by optical imaging. Invest Ophthalmol Vis Sci 45:3820–3826

    Article  PubMed  Google Scholar 

  79. Abramoff MD, Kwon YH, Ts’o D, Soliz P, Zimmerman B, Pokorny J, Kardon R (2006) Visual stimulus-induced changes in human near-infrared fundus reflectance. Invest Ophthalmol Vis Sci 47:715–721

    Article  PubMed Central  PubMed  Google Scholar 

  80. Hanazono G, Tsunoda K, Shinoda K, Tsubota K, Miyake Y, Tanifuji M (2007) Intrinsic signal imaging in macaque retina reveals different types of flash-induced light reflectance changes of different origins. Invest Ophthalmol Vis Sci 48:2903–2912

    Article  PubMed  Google Scholar 

  81. Hanazono G, Tsunoda K, Kazato Y, Tsubota K, Tanifuji M (2008) Evaluating neural activity of retinal ganglion cells by flash-evoked intrinsic signal imaging in macaque retina. Invest Ophthalmol Vis Sci 49:4655–4663

    Article  PubMed  Google Scholar 

  82. Grieve K, Roorda A (2008) Intrinsic signals from human cone photoreceptors. Invest Ophthalmol Vis Sci 49:713–719

    Article  PubMed  Google Scholar 

  83. Srinivasan VJ, Chen Y, Duker JS, Fujimoto JG (2009) In vivo functional imaging of intrinsic scattering changes in the human retina with high-speed ultrahigh resolution OCT. Opt Express 17:3861–3877

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  84. Jiang H, Ye Y, DeBuc DC, Lam BL, Rundek T, Tao A, Shao Y, Wang J (2013) Human conjunctival microvasculature assessed with a retinal function imager (RFI). Microvasc Res 85:134–137

    Article  PubMed Central  PubMed  Google Scholar 

  85. Shtoyerman E, Arieli A, Slovin H, Vanzetta I, Grinvald A (2000) Long-term optical imaging and spectroscopy reveal mechanisms underlying the intrinsic signal and stability of cortical maps in V1 of behaving monkeys. J Neurosci 20:8111–8121

    CAS  PubMed  Google Scholar 

  86. Arieli A, Grinvald A, Slovin H (2002) Dural substitute for long-term imaging of cortical activity in behaving monkeys and its clinical implications. J Neurosci Methods 114:119–133

    Article  PubMed  Google Scholar 

  87. Ratzlaff EH, Grinvald A (1991) A tandem-lens epifluorescence macroscope: hundred-fold brightness advantage for wide-field imaging. J Neurosci Methods 36:127–137

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by CNRS and Ministère de la Recherche (ACI Neuroscience), by a CNRS PICS grant to IV, by the Foundation NRJ. Part of this work was supported by the European Union (Integrated Project BrainScales, IST-FET-2010-269921), by an ANR grant to IV (French-Hungarian international project “Multiscalefunim”), and by a grant from the Grodetsky and Dominic centers to AG. The work on the retina was supported by the Weizmann Institute of Science and Optical Imaging Ltd. grants to AG. Sub-parts of the text have been reprinted from [29], Copyright (2012), with permission from Elsevier, and from [8] copyright (2009) with permission from Springer.

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Appendix: Surgery, Optical Imaging Data Acquisition Stimulation Parameters

Appendix: Surgery, Optical Imaging Data Acquisition Stimulation Parameters

1.1 Surgery

Details for the surgeries in all three preparations (rat, cat, monkey) can be found elsewhere [29, 30, 85]. Briefly: in all three cases, a recording chamber was implanted on the skull—either in an acute procedure (rat, cat) or chronically (monkey). A cranial window was opened above the cortical area of interest and the dura was either left in place (rat) or removed (cat, monkey), and in the awake monkey case, replaced by an artificial transparent dural substitute [86]. The chamber was filled with saline (rat), silicon oil (cat), or agar (monkey) to stabilize the cortex against movements (heartbeat, respiration…) and closed with a cover-glass to provide a flat optical interface and, in the case of the monkey, a sterile seal. Anesthesia was urethane in the rat, sodium pentobarbital for the cat and isofluorane for the monkey (in this case for surgery only). All surgical and maintenance procedures were in agreement with NIH guidelines.

1.2 Stimulation Protocol

In the rat case, a spreading depression was elicited by carefully touching the cortex with a needle about 2 mm away from the border of the imaged area (for details, see [29]). In the alert monkey case, the experimental paradigm was a simple fixation task. A trial started when the monkey began fixating on a fixation point (0.1°), displayed on a CRT screen. After 200 ms, a high luminance contrast drifting square grating (0.125–0.25 cycles/deg, 8–32°/s) appeared for 3 s, except in the “blank” conditions (no grating). The stimulus was then turned off, and the monkey had to continue to fixate until the fixation point disappeared, for a total fixation period of 6.5 s. An isoluminant, uniformly gray-screen inter-trial interval of ~10 s followed. Data from trials where the monkey broke fixation were rejected.

1.3 Optical Imaging

The light source was a tungsten-halogen lamp (100 or 150 W, Zeiss, Germany) from which specific wavelengths can be selected using adequate interference filters (Omega Optical, Brattleboro, USA). The cortex was illuminated using light guides. To maximize RBC contrast and thus blood vessel contrast in general, we used green light, since, in this part of the spectrum, hemoglobin (Hb) absorption is large.

To reach a satisfactory illumination level despite small exposure times corresponding to high frame-rate collection (100 Hz in the rat, 200 Hz in the monkey) and high magnifications, we used an interference filter with a rather large transmission window (30 nm), centered at 540 nm in the case of the rat, and a more narrow one (10 nm) centered at the isosbestic point of 570 nm in the case of the monkey. Images were acquired using a commercial CCD-based imaging system (Imager 3001, Optical Imaging Inc., Germantown, USA). A macroscope [87] was mounted onto the camera. It was composed of a 50 mm lens and an inverted 135 mm lens, yielding a magnification of 2.7, such that our spatial resolution was 9 × 9 μm/pixel, in the case of the rat. In the case of the monkey, we used an upper lens of 100 mm, yielding a magnification of 2 and a spatial resolution of ~12 × 12 μm/pixel. To obtain an optimal identification of vascular activity, the optical system was focused onto the cortical vasculature.

In the case of the monkey, data acquisition lasted the time of a trial (~6 s). Data were written onto the hard disk after each trial. In the rat case, data acquisition lasted 7.5 min and was split into 1 s long periods of continuous 100 Hz acquisition (100 frames), interleaved with 20 ms pauses necessary to store the data onto the hard disk, as required by the data acquisition software we used for this purpose (Optical Imaging Inc., Germantown, USA).

Veins and arteries were first identified by visual inspection (morphology, pulsation, color of vessels…) through a microscope and through the camera. This classification was then integrated with oximetric information obtained from an “oximetric image” of the vasculature [30]: Two images of the cortical surface were taken, one at 560 and the other at 540 nm illumination (peaks of deoxy- and oxyhemoglobin absorption, respectively). One image was then divided by the other and the gray level scale of the resulting “oximetric image” was shifted, such that the parenchyma, which contains mainly capillaries, appeared to be in the middle of it (gray). With this procedure, the various vessel types in the image were thus automatically shifted to the bright or the dark half of the grayscale according to their different degree of oxygenation, allowing for a further identification of veins and venules (dark), as well as of arteries and arterioles (bright).

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Vanzetta, I., Deneux, T., Grinvald, A. (2014). High-Resolution Wide-Field Optical Imaging of Microvascular Characteristics: From the Neocortex to the Eye. In: Zhao, M., Ma, H., Schwartz, T. (eds) Neurovascular Coupling Methods. Neuromethods, vol 88. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0724-3_7

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  • DOI: https://doi.org/10.1007/978-1-4939-0724-3_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0723-6

  • Online ISBN: 978-1-4939-0724-3

  • eBook Packages: Springer Protocols

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