Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann’s area 32 and area 21
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Author(s)
Magnain, Caroline
Augustinack, Jean C.
Tirrell, Lee
Fogarty, Morgan
Frosch, Matthew P.
Boas, David A
Fischl, Bruce
Rockland, Kathleen S.
Date Issued
October 2018
Journal
Brain Structure and Function
Publisher
Springer Science and Business Media LLC
Citation
Magnain, C. et al. "Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann’s area 32 and area 21." Brain Structure and Function 224, 1 (October 2018): 351–362 © 2018 Springer-Verlag GmbH Germany, part of Springer Nature
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Author's final manuscript
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.
MIT Department
MIT-IBM Watson AI Lab
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard University--MIT Division of Health Sciences and Technology
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DOI of Published Version
https://doi.org/10.1007/s00429-018-1777-z
