Brain Structure and Function

, Volume 224, Issue 1, pp 351–362 | Cite as

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


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.


Optical imaging Human brain Isocortex Limbic Neuron Tissue Validation 



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.


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.


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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

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