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Measurement of the absolute value of the optical birefringence of myelin in primate brain tissue.

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Paper

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The authors' code

MATLAB · 160 lines · 5.7 KB · MIT

  1. %% read all .csv file in the folder, process fwhm for 25 ROIs in each file, ...
  2. % record all of the fwhm into a table, should delete other .csv file while
  3. % use
  5. clc;clear; close all;
  6. % Load data from CSV file
  7. files = dir('*.csv');
  8. fileNames = {files.name};
  10. % Extract ROI numbers using regex
  11. roiNumbers = cellfun(@(x) str2double(regexp(x, 'ROI_(\d+)_ROI', 'tokens', 'once')),fileNames);
  13. % Sort files based on ROI numbers
  14. [~, sortIdx] = sort(roiNumbers);
  15. sortedFiles = fileNames(sortIdx);
  17. % Initialize results matrix to store all FWHM
  18. % Rows: 25 FWHM for 1 image (25 total), Columns: number of images
  19. results = zeros(length(files),25);
  21. for fileIdx = 1:length(files)
  22. % Get current file info
  23. data = readmatrix(sortedFiles{fileIdx});
  25. % Separate the z-axis locations (first column) and intensity profiles (remaining columns)
  26. z_values = 0:0.3:0.3*(size(data,1)-1); % z-axis in microns,step is 0.3um
  27. intensity_profiles = data(:,2:end); % Each column is a z-profile
  29. % Initialize vector to store FWHM for each profile
  30. fwhm_values = zeros(1, size(intensity_profiles, 2));
  32. % Create a new figure
  33. figure;
  34. hold on; % Keep all profiles on the same plot
  35. colors = lines(size(intensity_profiles, 2)); % Generate unique colors
  37. % Loop through each z-profile column to fit to Gaussian, calculate FWHM, and plot
  38. for i = 1:size(intensity_profiles, 2)
  39. % Extract and background subtract each profile
  40. intensity_profile = intensity_profiles(:, i);
  41. z_values1 = z_values';
  42. intensity_profile2 = intensity_profile(~isnan(intensity_profile));
  44. % Continue only if there are data points left after removing NaNs
  45. if isempty(z_values1) || isempty(intensity_profile2)
  46. continue;
  47. end
  49. background = min(intensity_profile2);
  50. intensity_profile3 = intensity_profile2 - background;
  52. % Define Gaussian fit type
  53. gauss_fit = fittype('a * exp(-((x-b)^2) / (2*c^2))', 'independent', 'x', 'coefficients', {'a', 'b', 'c'});
  55. % Fit the Gaussian model to the data
  56. [fit_result, ~] = fit(z_values1, intensity_profile3, gauss_fit, 'StartPoint', [max(intensity_profile3), mean(z_values1), std(z_values1)]);
  58. % Calculate FWHM from the fitted parameter c (standard deviation)
  59. sigma = fit_result.c;
  60. fwhm_values(i) = 2 * sqrt(2 * log(2)) * sigma;
  62. %Plot the original profile and the fitted Gaussian curve
  63. % plot(z_values1, intensity_profile3, 'Color', colors(i, :), 'LineWidth', 1.5); % Original data
  64. plot(z_values1, fit_result(z_values1), '--', 'Color', colors(i, :), 'LineWidth', 1.2); % Fitted Gaussian
  65. end
  67. % Create legend with FWHM values
  68. legend_entries = arrayfun(@(x, y) sprintf('ROI %d (FWHM: %.2f um)', x, y), 1:size(intensity_profiles, 2), fwhm_values, 'UniformOutput', false);
  69. legend(legend_entries,'FontSize', 9);
  70. % legend('boxoff')
  72. % Display average thickness
  73. sample_thickness = mean(fwhm_values);
  74. results(fileIdx,:) = fwhm_values;
  76. % Add labels and title
  77. xlabel('Z Location (um)');
  78. ylabel('Intensity (background subtracted)');
  79. title(sprintf('Gaussian-Fitted Intensity Profiles Across Z with FWHM img %d', fileIdx));
  80. grid on;
  81. hold off;
  82. set(gca, 'LooseInset', [0,0,0,0]);
  83. set(gcf, 'position',[100,100,600,600],'PaperPositionMode', 'auto');
  84. exportgraphics(gcf, sprintf('img_%d.png', fileIdx),'BackgroundColor', 'none');
  86. end
  88. %% Create table
  89. % Create column names (fwhm labels)
  90. fwhmLabels = cell(1,25);
  91. for i = 1:25
  92. fwhmLabels{i} = sprintf('fwhm_%d', i);
  93. end
  95. % Create row names (images)
  96. imgLabels = cell(length(files),1);
  97. for i = 1:length(files)
  98. imgLabels{i} = sprintf('img_%d', i);
  99. end
  101. % Convert to table
  102. resultsTable = array2table(results,'VariableNames', fwhmLabels, ...
  103. 'RowNames', imgLabels);
  104. % Save results
  105. %%%%%%%%%%%%%%%%%%%%%%%%%
  106. % saveName = sprintf('49img_25ROI_analysis.csv');
  107. % writetable(resultsTable, fullfile(pwd, saveName), ...
  108. % 'WriteRowNames', true);
  110. %% plot boxplot for the table
  111. % Calculate the mean of each row
  112. row_means = mean(results, 2);
  114. % Create a box plot for the data
  115. figure;
  116. boxplot(results', 'Whisker', 1.5); % Transpose data so each row is treated as a group
  118. % Set x-axis labels to indicate rows
  119. xticks(1:size(results, 1)); % Set x-ticks at each boxplot position
  120. xticklabels(arrayfun(@(x) ['img ', num2str(x)], 1:size(results, 1), 'UniformOutput', false));
  122. % Annotate the mean values below each box plot
  123. hold on;
  124. for i = 1:size(results, 1)
  125. text(i, mean(results(:)) + 0.3, sprintf('Mean: %.3f', row_means(i)), ...
  126. 'HorizontalAlignment', 'center', 'FontSize', 10, 'Color', 'black','Rotation',45);
  127. end
  128. hold off;
  130. % Add labels and title
  131. ylabel('FWHM of each img');
  132. xlabel('Img');
  133. title('Boxplot with FWHM in img');
  134. ylim([min(results(:)) - 0.1, max(results(:)) + 0.1]); % Adjust y-axis for annotations
  135. grid on;
  137. %% plot boxplot of 7 img, where they are located at the right corner of the matrix.
  138. ... Their focus plane are more centered
  140. row_to_plot = 7:7:size(results,1);
  141. results7 = results(row_to_plot,:);
  142. means7 = mean(results7,2);
  144. % Create a box plot for the filtered data
  145. figure;
  146. boxplot(results7', 'Whisker', 1.5); % Transpose data for boxplot
  148. % Annotate the mean values below each box plot
  149. hold on;
  150. for i = 1:size(results7, 1)
  151. text(i, mean(results7(:)) + 0.2, sprintf('Mean: %.3f', means7(i)), ...
  152. 'HorizontalAlignment', 'center', 'FontSize', 10, 'Color', 'black','Rotation',45);
  153. end
  154. hold off;
  156. % Add labels and title
  157. ylabel('FWHM of each img');
  158. xlabel('Img');
  159. title('Boxplot with FWHM in img');
  160. grid on;

plot_intensity.m at commit 581b153, under MIT · at the source

Overview

Authors: Ting Xie1, Anna Novoseltseva2, Alexander J. Gray2, Mikayla Bradsby3, Irving J. Bigio1,2,3,4
ORCID iDs: Anna Novoseltseva, Alexander J. Gray, Irving J. Bigio
  1. Boston University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States
  2. Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
  3. Boston University, Department of Physics, Boston, Massachusetts, United States
  4. Boston University School of Medicine, Department of Medicine, Boston, Massachusetts, United States
Institutions: Boston University (United States)
Journal: Neurophotonics, volume 13, issue 2, article 025011
Dates: received 10 October 2025; accepted 20 May 2026; published online 16 June 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1117/1.nph.13.2.025011 · PMID 42326435 · PMCID PMC13276737 · OpenAlex W7165360014
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), cellular / molecular (subfield)
Methods: Graphs
Keywords: optical anisotropy, birefringence microscopy, neurodegenerative diseases, myelin quantification
Topic: Optical Imaging and Spectroscopy Techniques (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: NIH (R01AG075727); National Science Foundation (CBET 2215990)
Citations: not cited yet (Europe PMC); 38 references in the paper

Abstract

Significance: Researchers require quantitative biomarkers to accurately identify neurodegenerative diseases and quantitatively monitor disease progression. The structural anisotropy of myelin leads to strong optical birefringence, enabling quantitative imaging with polarized-light microscopy imaging for detailed myelin assessment in neurodegenerative disease states and aging studies.

Aim: Our aim is to measure the absolute refractive index difference (birefringence) of the myelin sheath of primate brain tissue using birefringence microscopy (BRM).

Approach: Three-micron cryo-sectioned samples from the paraformaldehyde-fixed corpus callosum of a 22-year-old male rhesus macaque were analyzed using a BRM system. To quantify the absolute birefringence, transversely oriented axons were imaged with a 40× objective under red light-emitting diode illumination (λ=625 nm). Measurements focused on large myelinated axons (diameters ∼2 to 8 μm) mounted in 85% glycerol, ensuring high-resolution characterization of thick myelin sheaths.

Results: The myelin birefringence was determined to be Δn=0.012±0.001, representing the first absolute measurement of this optical property for myelin in primate brain tissue.

Conclusions: This absolute birefringence value enables quantification of myelin volume fraction and assessment of myelin loss in ex vivo measurements, providing an accurate optical biomarker for neurodegenerative diseases and aging studies through polarized light microscopic imaging.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

Ting24k/Myelin-Birefringence-measurement

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 581b15373a77c59d87d6de467dc56c0706a08d2d, 22 April 2026
Languages: MATLAB (5)
Size: 63 files, 5 scripts
Software Heritage: not archived
Found in: “Code and Data Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Curve Fitting Toolbox (2 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
7 files
At the source: github.com/Ting24k/Myelin-Birefringence-measurement.git

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Code and Data Availability

All code used for the acquisition and analysis of qBRM data can be found at https://github.com/Ting24k/Myelin-Birefringence-measurement.git. All imaging data are available at the BioImage Archive under accession S-BSST2953: https://www.ebi.ac.uk/biostudies/studies/S-BSST2953.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 2 funders, 34 references.

Cite

This paper

Xie, T., Novoseltseva, A., Gray, A. J., Bradsby, M., & Bigio, I. J. (2026). Measurement of the absolute value of the optical birefringence of myelin in primate brain tissue. Neurophotonics, 13(2), 025011. https://doi.org/10.1117/1.nph.13.2.025011

BibTeX

@article{xie2026measurement,
author = {Xie, Ting and Novoseltseva, Anna and Gray, Alexander J. and Bradsby, Mikayla and Bigio, Irving J.},
title = {{Measurement of the absolute value of the optical birefringence of myelin in primate brain tissue}},
journal = {Neurophotonics},
year = {2026},
month = apr,
volume = {13},
number = {2},
pages = {025011},
publisher = {Society of Photo-Optical Instrumentation Engineers},
issn = {2329-423X},
doi = {10.1117/1.nph.13.2.025011},
url = {https://doi.org/10.1117/1.nph.13.2.025011},
pmid = {42326435},
pmcid = {PMC13276737}
}

RIS

TY - JOUR
AU - Xie, Ting
AU - Novoseltseva, Anna
AU - Gray, Alexander J.
AU - Bradsby, Mikayla
AU - Bigio, Irving J.
TI - Measurement of the absolute value of the optical birefringence of myelin in primate brain tissue
T2 - Neurophotonics
J2 - Neurophotonics
PY - 2026
DA - 2026/04/01
VL - 13
IS - 2
SP - 025011
SN - 2329-423X
PB - Society of Photo-Optical Instrumentation Engineers
DO - 10.1117/1.nph.13.2.025011
UR - https://doi.org/10.1117/1.nph.13.2.025011
LA - en
ER -

CSL-JSON

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"id": "10.1117/1.nph.13.2.025011",
"type": "article-journal",
"title": "Measurement of the absolute value of the optical birefringence of myelin in primate brain tissue",
"container-title": "Neurophotonics",
"author": [
{
"family": "Xie",
"given": "Ting"
},
{
"family": "Novoseltseva",
"given": "Anna"
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{
"family": "Gray",
"given": "Alexander J."
},
{
"family": "Bradsby",
"given": "Mikayla"
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],
"container-title-short": "Neurophotonics",
"volume": "13",
"issue": "2",
"page": "025011",
"DOI": "10.1117/1.nph.13.2.025011",
"PMID": "42326435",
"PMCID": "PMC13276737",
"ISSN": "2329-423X",
"publisher": "Society of Photo-Optical Instrumentation Engineers",
"URL": "https://doi.org/10.1117/1.nph.13.2.025011",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

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