This title appears in the Scientific Report :
2022
Please use the identifier:
http://hdl.handle.net/2128/31061 in citations.
Please use the identifier: http://dx.doi.org/10.1038/s41598-022-08140-0 in citations.
Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue
Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue
The method 3D polarised light imaging (3D-PLI) measures the birefringence of histological brain sections to determine the spatial course of nerve fibres (myelinated axons). While the in-plane fibre directions can be determined with high accuracy, the computation of the out-of-plane fibre inclination...
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Personal Name(s): | Menzel, Miriam (Corresponding author) |
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Reuter, Jan A. / Gräßel, David / Costantini, Irene / Amunts, Katrin / Axer, Markus | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 |
Published in: | Scientific reports, 12 (2022) 1, S. 4328 |
Imprint: |
[London]
Macmillan Publishers Limited, part of Springer Nature
2022
|
DOI: |
10.1038/s41598-022-08140-0 |
Document Type: |
Journal Article |
Research Program: |
JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) Human Brain Project Specific Grant Agreement 3 Neuroscientific Data Analytics and AI |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1038/s41598-022-08140-0 in citations.
The method 3D polarised light imaging (3D-PLI) measures the birefringence of histological brain sections to determine the spatial course of nerve fibres (myelinated axons). While the in-plane fibre directions can be determined with high accuracy, the computation of the out-of-plane fibre inclinations is more challenging because they are derived from the amplitude of the birefringence signals, which depends e.g. on the amount of nerve fibres. One possibility to improve the accuracy is to consider the average transmitted light intensity (transmittance weighting). The current procedure requires effortful manual adjustment of parameters and anatomical knowledge. Here, we introduce an automated, optimised computation of the fibre inclinations, allowing for a much faster, reproducible determination of fibre orientations in 3D-PLI. Depending on the degree of myelination, the algorithm uses different models (transmittance-weighted, unweighted, or a linear combination), allowing to account for regionally specific behaviour. As the algorithm is parallelised and GPU optimised, it can be applied to large data sets. Moreover, it only uses images from standard 3D-PLI measurements without tilting, and can therefore be applied to existing data sets from previous measurements. The functionality is demonstrated on unstained coronal and sagittal histological sections of vervet monkey and rat brains. |