Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue
Abstract 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 in...
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| Format: | Article |
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Nature Portfolio
2022-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-08140-0 |
| _version_ | 1797865094507397120 |
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| author | Miriam Menzel Jan A. Reuter David Gräßel Irene Costantini Katrin Amunts Markus Axer |
| author_facet | Miriam Menzel Jan A. Reuter David Gräßel Irene Costantini Katrin Amunts Markus Axer |
| author_sort | Miriam Menzel |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T23:02:31Z |
| format | Article |
| id | doaj.art-1ff30ad9df514a18aaeba0bb789af4e4 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T23:02:31Z |
| publishDate | 2022-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1ff30ad9df514a18aaeba0bb789af4e42023-03-22T10:51:04ZengNature PortfolioScientific Reports2045-23222022-03-0112111410.1038/s41598-022-08140-0Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissueMiriam Menzel0Jan A. Reuter1David Gräßel2Irene Costantini3Katrin Amunts4Markus Axer5Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHEuropean Laboratory for Non-Linear Spectroscopy, University of FlorenceInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbHAbstract 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.https://doi.org/10.1038/s41598-022-08140-0 |
| spellingShingle | Miriam Menzel Jan A. Reuter David Gräßel Irene Costantini Katrin Amunts Markus Axer Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue Scientific Reports |
| title | Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue |
| title_full | Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue |
| title_fullStr | Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue |
| title_full_unstemmed | Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue |
| title_short | Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue |
| title_sort | automated computation of nerve fibre inclinations from 3d polarised light imaging measurements of brain tissue |
| url | https://doi.org/10.1038/s41598-022-08140-0 |
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