Advertisement

Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann’s area 32 and area 21

  • Caroline MagnainEmail author
  • Jean C. Augustinack
  • Lee Tirrell
  • Morgan Fogarty
  • Matthew P. Frosch
  • David Boas
  • Bruce Fischl
  • Kathleen S. Rockland
Original Article
  • 151 Downloads

Abstract

Optical coherence tomography is an optical technique that uses backscattered light to highlight intrinsic structure, and when applied to brain tissue, it can resolve cortical layers and fiber bundles. Optical coherence microscopy (OCM) is higher resolution (i.e., 1.25 µm) and is capable of detecting neurons. In a previous report, we compared the correspondence of OCM acquired imaging of neurons with traditional Nissl stained histology in entorhinal cortex layer II. In the current method-oriented study, we aimed to determine the colocalization success rate between OCM and Nissl in other brain cortical areas with different laminar arrangements and cell packing density. We focused on two additional cortical areas: medial prefrontal, pre-genual Brodmann area (BA) 32 and lateral temporal BA 21. We present the data as colocalization matrices and as quantitative percentages. The overall average colocalization in OCM compared to Nissl was 67% for BA 32 (47% for Nissl colocalization) and 60% for BA 21 (52% for Nissl colocalization), but with a large variability across cases and layers. One source of variability and confounds could be ascribed to an obscuring effect from large and dense intracortical fiber bundles. Other technical challenges, including obstacles inherent to human brain tissue, are discussed. Despite limitations, OCM is a promising semi-high throughput tool for demonstrating detail at the neuronal level, and, with further development, has distinct potential for the automatic acquisition of large databases as are required for the human brain.

Keywords

Optical imaging Human brain Isocortex Limbic Neuron Tissue Validation 

Notes

Acknowledgements

The authors would like to thank the brain donors for their generous gift, Samantha Romano for help with histology and segmentation and Dr. Ender Konukoglu for the interaction non-linear registration tool.

Funding

We thank National Institutes of Health (NIH) for funding support: National Institute of Mental Health (MH107456), National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 1R01EB023281, R01EB006758, R21EB018907, R01EB019956), the National Institute on Aging (5R01AG008122, R01AG016495), the National Institute of Diabetes and Digestive and Kidney Diseases (1-R21-DK-108277-01), the National Institute for Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625), and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multi-institutional Human Connectome Project.

Compliance with ethical standards

Human/animal rights statement

No human participants or animals were used in this study. This study involved only de-identified post-mortem human tissue.

Conflict of interest

BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. BF’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies.

References

  1. Amunts K, Lepage C, Borgeat L, Mohlberg H, Dickscheid T, Rousseau ME, Bludau S, Bazin PL, Lewis LB, Oros-Peusquens AM, Shah NJ, Lippert T, Zilles K, Evans AC (2013) BigBrain: an ultrahigh-resolution 3D human brain model. Science 340(6139):1472–1475.  https://doi.org/10.1126/science.1235381 CrossRefPubMedGoogle Scholar
  2. An L, Li P, Shen TT, Wang R (2011) High speed spectral domain optical coherence tomography for retinal imaging at 500,000 A-lines per second. Biomed Opt Express 2(10):2770–2783CrossRefGoogle Scholar
  3. Ashburner J (2012) SPM: a history. Neuroimage 62(2):791–800.  https://doi.org/10.1016/j.neuroimage.2011.10.025 CrossRefPubMedGoogle Scholar
  4. Assayag O, Grieve K, Devaux B, Harms F, Pallud J, Chretien F, Boccara C, Varlet P (2013) Imaging of non-tumorous and tumorous human brain tissues with full-field optical coherence tomography. Neuroimage Clin 2:549–557.  https://doi.org/10.1016/j.nicl.2013.04.005 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Axer M, Strohmer S, Grassel D, Bucker O, Dohmen M, Reckfort J, Zilles K, Amunts K (2016) Estimating fiber orientation distribution functions in 3D-polarized light imaging. Front Neuroanat 10:40.  https://doi.org/10.3389/fnana.2016.00040 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Baumann B, Woehrer A, Ricken G, Augustin M, Mitter C, Pircher M, Kovacs GG, Hitzenberger CK (2017) Visualization of neuritic plaques in Alzheimer’s disease by polarization-sensitive optical coherence microscopy. Sci Rep 7:43477.  https://doi.org/10.1038/srep43477 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bookstein FL (1989) Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Anal Mach Intell 11(6):567–585.  https://doi.org/10.1109/34.24792 CrossRefGoogle Scholar
  8. Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82(4):239–259CrossRefGoogle Scholar
  9. Brodmann K (1909) Vergleichende Lokalisationslehre der Grosshirnrinde. Johann Ambrosius Barth, LeipzigGoogle Scholar
  10. Chung K, Wallace J, Kim SY, Kalyanasundaram S, Andalman AS, Davidson TJ, Mirzabekov JJ, Zalocusky KA, Mattis J, Denisin AK, Pak S, Bernstein H, Ramakrishnan C, Grosenick L, Gradinaru V, Deisseroth K (2013) Structural and molecular interrogation of intact biological systems. Nature 497(7449):332–337.  https://doi.org/10.1038/nature12107 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Coupe P, Catheline G, Lanuza E, Manjon JV (2017) Towards a unified analysis of brain maturation and aging across the entire lifespan: a MRI analysis. Hum Brain Mapp 38(11):5501–5518.  https://doi.org/10.1002/hbm.23743 CrossRefPubMedGoogle Scholar
  12. Da X, Toledo JB, Zee J, Wolk DA, Xie SX, Ou Y, Shacklett A, Parmpi P, Shaw L, Trojanowski JQ, Davatzikos C (2014) Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. Neuroimage Clin 4:164–173.  https://doi.org/10.1016/j.nicl.2013.11.010 CrossRefPubMedGoogle Scholar
  13. Datta G, Colasanti A, Rabiner EA, Gunn RN, Malik O, Ciccarelli O, Nicholas R, Van Vlierberghe E, Van Hecke W, Searle G, Santos-Ribeiro A, Matthews PM (2017) Neuroinflammation and its relationship to changes in brain volume and white matter lesions in multiple sclerosis. Brain 140(11):2927–2938.  https://doi.org/10.1093/brain/awx228 CrossRefPubMedGoogle Scholar
  14. Ding SL, Royall JJ, Sunkin SM, Ng L, Facer BA, Lesnar P, Guillozet-Bongaarts A, McMurray B, Szafer A, Dolbeare TA, Stevens A, Tirrell L, Benner T, Caldejon S, Dalley RA, Dee N, Lau C, Nyhus J, Reding M, Riley ZL, Sandman D, Shen E, van der Kouwe A, Varjabedian A, Write M, Zollei L, Dang C, Knowles JA, Koch C, Phillips JW, Sestan N, Wohnoutka P, Zielke HR, Hohmann JG, Jones AR, Bernard A, Hawrylycz MJ, Hof PR, Fischl B, Lein ES (2016) Comprehensive cellular-resolution atlas of the adult human brain. J Comp Neurol 524(16):3127–3481.  https://doi.org/10.1002/cne.24080 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Economo C, Koskinas GN (1925) Die Cytoarchitektonik der Hirnrinde des erwachsenen MenschenGoogle Scholar
  16. Falahati F, Ferreira D, Muehlboeck JS, Eriksdotter M, Simmons A, Wahlund LO, Westman E (2017) Monitoring disease progression in mild cognitive impairment: associations between atrophy patterns, cognition, APOE and amyloid. Neuroimage Clin 16:418–428.  https://doi.org/10.1016/j.nicl.2017.08.014 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM (2004) Sequence-independent segmentation of magnetic resonance images. Neuroimage 23(Suppl 1):S69–S84.  https://doi.org/10.1016/j.neuroimage.2004.07.016 CrossRefGoogle Scholar
  18. Gabbott PL, Warner TA, Jays PR, Bacon SJ (2003) Areal and synaptic interconnectivity of prelimbic (area 32), infralimbic (area 25) and insular cortices in the rat. Brain Res 993(1–2):59–71CrossRefGoogle Scholar
  19. Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, Hee MR, Flotte T, Gregory K, Puliafito CA et al (1991) Optical coherence tomography. Science 254(5035):1178–1181CrossRefGoogle Scholar
  20. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) Fsl. Neuroimage 62(2):782–790.  https://doi.org/10.1016/j.neuroimage.2011.09.015 CrossRefPubMedGoogle Scholar
  21. Lee KS, Hur H, Bae JY, Kim IJ, Kim DU, Nam KH, Kim G-H, Chang KS (2018) High speed parallel spectral-domain OCT using spectrally encoded line-field illumination. Appl Phys Lett 112(4):041102CrossRefGoogle Scholar
  22. Lichtenegger A, Harper DJ, Augustin M, Eugui P, Muck M, Gesperger J, Hitzenberger CK, Woehrer A, Baumann B (2017) Spectroscopic imaging with spectral domain visible light optical coherence microscopy in Alzheimer’s disease brain samples. Biomed Opt Express 8(9):4007–4025.  https://doi.org/10.1364/BOE.8.004007 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Lu CD, Waheed NK, Witkin A, Baumal CR, Liu JJ, Potsaid B, Duker JS (2018) Microscope-integrated intraoperative ultrahigh-speed swept-source optical coherence tomography for widefield retinal and anterior segment imaging. Ophthalmic Surg Lasers Imaging Retina 49(2):94–102CrossRefGoogle Scholar
  24. Magnain C, Augustinack JC, Reuter M, Wachinger C, Frosch MP, Ragan T, Akkin T, Wedeen VJ, Boas DA, Fischl B (2014) Blockface histology with optical coherence tomography: a comparison with Nissl staining. Neuroimage 84:524–533.  https://doi.org/10.1016/j.neuroimage.2013.08.072 CrossRefPubMedGoogle Scholar
  25. Magnain C, Augustinack JC, Konukoglu E, Frosch MP, Sakadzic S, Varjabedian A, Garcia N, Wedeen VJ, Boas DA, Fischl B (2015) Optical coherence tomography visualizes neurons in human entorhinal cortex. Neurophotonics 2(1):015004.  https://doi.org/10.1117/1.NPh.2.1.015004 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Magnain C, Wang H, Sakadzic S, Fischl B, Boas DA (2016) En face speckle reduction in optical coherence microscopy by frequency compounding. Opt Lett 41(9):1925–1928.  https://doi.org/10.1364/OL.41.001925 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Mai JK, Paxinos G (eds) (2011) The human nervous system. Academic Press, Cambridge, United StatesGoogle Scholar
  28. Palomero-Gallagher N, Mohlberg H, Zilles K, Vogt B (2008) Cytology and receptor architecture of human anterior cingulate cortex. J Comp Neurol 508(6):906–926.  https://doi.org/10.1002/cne.21684 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Palomero-Gallagher N, Zilles K, Schleicher A, Vogt BA (2013) Cyto- and receptor architecture of area 32 in human and macaque brains. J Comp Neurol 521(14):3272–3286.  https://doi.org/10.1002/cne.23346 CrossRefPubMedGoogle Scholar
  30. Pircher M, Götzinger E, Leitgeb RA, Fercher AF, Hitzenberger CK (2003) Speckle reduction in optical coherence tomography by frequency compounding. J Biomed Opt 8(3):565–570CrossRefGoogle Scholar
  31. Potsaid B, Gorczynska I, Srinivasan VJ, Chen Y, Jiang J, Cable A, Fujimoto JG (2008) Ultrahigh speed spectral/Fourier domain OCT ophthalmic imaging at 70,000 to 312,500 axial scans per second. Opt Express 16(19):15149–15169CrossRefGoogle Scholar
  32. Preibisch S, Saalfeld S, Tomancak P (2009) Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25(11):1463–1465.  https://doi.org/10.1093/bioinformatics/btp184 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Ragan T, Kadiri LR, Venkataraju KU, Bahlmann K, Sutin J, Taranda J, Arganda-Carreras I, Kim Y, Seung HS, Osten P (2012) Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat Methods 9(3):255–258.  https://doi.org/10.1038/nmeth.1854 CrossRefPubMedPubMedCentralGoogle Scholar
  34. Reuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4):1402–1418.  https://doi.org/10.1016/j.neuroimage.2012.02.084 CrossRefPubMedPubMedCentralGoogle Scholar
  35. Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, Morris JC, Dale AM, Fischl B (2004) Thinning of the cerebral cortex in aging. Cereb Cortex 14(7):721–730.  https://doi.org/10.1093/cercor/bhh032 CrossRefPubMedGoogle Scholar
  36. Schmitt JM, Xiang SH, Yung KM (1999) Speckle in optical coherence tomography: an overview. In: Saratov fall meeting'98: light scattering technologies for mechanics, biomedicine, and material science, vol 3726, International society for optics and photonics, pp 450–462Google Scholar
  37. van Soest G, Regar E, van der Steen AF, Villiger ML, Tearney GJ, Bouma BE (2012) Frequency domain multiplexing for speckle reduction in optical coherence tomography. J Biomed Opt 17(7):076018PubMedGoogle Scholar
  38. Srinivasan VJ, Radhakrishnan H, Jiang JY, Barry S, Cable AE (2012) Optical coherence microscopy for deep tissue imaging of the cerebral cortex with intrinsic contrast. Opt Express 20(3):2220–2239.  https://doi.org/10.1364/OE.20.002220 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Tomer R, Ye L, Hsueh B, Deisseroth K (2014) Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat Protoc 9(7):1682–1697.  https://doi.org/10.1038/nprot.2014.123 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Tsai TH, Potsaid B, Tao YK, Jayaraman V, Jiang J, Heim PJ, Kraus MF, Zhou C, Hornegger J, Mashimo H, Cable AE, Fujimoto JG (2013) Ultrahigh speed endoscopic optical coherence tomography using micromotor imaging catheter and VCSEL technology. Biomed Opt Express 4(7):1119–1132CrossRefGoogle Scholar
  41. Van Essen DC (2012) Cortical cartography and Caret software. Neuroimage 62(2):757–764.  https://doi.org/10.1016/j.neuroimage.2011.10.077 CrossRefPubMedGoogle Scholar
  42. Vogt BA, Hof PR, Zilles K, Vogt LJ, Herold C, Palomero-Gallagher N (2013) Cingulate area 32 homologies in mouse, rat, macaque and human: cytoarchitecture and receptor architecture. J Comp Neurol 521(18):4189–4204.  https://doi.org/10.1002/cne.23409 CrossRefPubMedGoogle Scholar
  43. Wang H, Zhu J, Reuter M, Vinke LN, Yendiki A, Boas DA, Fischl B, Akkin T (2014) Cross-validation of serial optical coherence scanning and diffusion tensor imaging: a study on neural fiber maps in human medulla oblongata. Neuroimage 100:395–404.  https://doi.org/10.1016/j.neuroimage.2014.06.032 CrossRefPubMedPubMedCentralGoogle Scholar
  44. Wang H, Akkin T, Magnain C, Wang R, Dubb J, Kostis WJ, Yaseen MA, Cramer A, Sakadzic S, Boas D (2016) Polarization sensitive optical coherence microscopy for brain imaging. Opt Lett 41(10):2213–2216.  https://doi.org/10.1364/OL.41.002213 CrossRefPubMedPubMedCentralGoogle Scholar
  45. Wang H, Magnain C, Wang R, Dubb J, Varjabedian A, Tirrell LS, Stevens A, Augustinack JC, Konukoglu E, Aganj I, Frosch MP, Schmahmann JD, Fischl B, Boas DA (2017a) as-PSOCT: volumetric microscopic imaging of human brain architecture and connectivity. Neuroimage 165:56–68.  https://doi.org/10.1016/j.neuroimage.2017.10.012 CrossRefPubMedGoogle Scholar
  46. Wang H, Magnain C, Sakadžić S, Fischl B, Boas DA (2017b) Characterizing the optical properties of human brain tissue with high numerical aperture optical coherence tomography. Biomed Opt Express 8(12):5617–5636CrossRefGoogle Scholar
  47. Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxas D, Whitaker R (2002) Engineering and algorithm design for an image processing API: a technical report on ITK—the insight toolkit. Stud Health Technol Inform 85:586–592PubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Radiology, Athinoula A Martinos CenterMassachusetts General HospitalCharlestownUSA
  2. 2.C.S. Kubik Laboratory for Neuropathology, Pathology ServiceMGHBostonUSA
  3. 3.Department of Electrical and Computer EngineeringBoston University School of MedicineBostonUSA
  4. 4.MIT Computer Science and AI LabCambridgeUSA
  5. 5.Department of Anatomy and NeurobiologyBoston University School of MedicineBostonUSA

Personalised recommendations