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Layer-specific attentional modulation in the human primary somatosensory cortex.

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The authors' code

MATLAB · 203 lines · 5.4 KB · MIT

  1. % extract_qT1_laminar_profile.m
  2. %
  3. % Extract a laminar qT1 profile from a discrete LAYNII layer map (1..8)
  4. % within an ROI, while preserving the original analysis logic:
  5. % - optional ROI dilation
  6. % - layer-wise extraction using (layer == i)
  7. % - first derivative normalization
  8. % - second derivative computation
  9. %
  10. % This public-friendly version:
  11. % - removes hard-coded local paths
  12. % - saves outputs to ./out/
  13. % - keeps the original ROI dilation + layer==i logic unchanged
  14. %
  15. % Requirements:
  16. % - MATLAB
  17. % - Image Processing Toolbox (for imdilate)
  18. %
  19. % Important:
  20. % qT1, layer map, and ROI must be in the same voxel space and dimensions.
  21. %
  22. % Inputs to edit below:
  23. % - qT1_path
  24. % - layer_path
  25. % - roi_path
  26. % - subject_id
  27. %
  28. % Outputs:
  29. % ./out/<subject_id>_qT1profile.mat
  30. % ./out/<subject_id>_qT1profile.png
  32. clc;
  33. clear;
  34. close all;
  36. %% =========================
  37. % USER SETTINGS
  38. %% =========================
  39. subject_id = 'CYH_sb01'; % e.g., 'sub01'
  41. % Input files (edit these paths)
  42. qT1_path = '/path/to/qT1_map_new.nii';
  43. layer_path = '/path/to/rim_mask_layers_equidist.nii.gz'; % discrete LAYNII labels: 1..8
  44. roi_path = '/path/to/rh.S1_3b_in_qT1.nii.gz';
  46. % Number of cortical bins/layers
  47. n_layers = 8;
  49. % ROI dilation
  50. % Keep this identical to the original logic if you want to reproduce results.
  51. do_dilate = true;
  52. dilate_kernel = ones(3,3,3);
  54. % Flip control
  55. % In the original code, a double flip effectively resulted in no net flip.
  56. % To explicitly reproduce the original behavior, keep this false.
  57. % Set true only if you intentionally want reversed profile ordering.
  58. flip_profile = false;
  60. % Output directory
  61. out_dir = fullfile(pwd, 'out');
  63. %% =========================
  64. % CHECK INPUTS
  65. %% =========================
  66. assert(isfile(qT1_path), 'qT1 file not found: %s', qT1_path);
  67. assert(isfile(layer_path), 'Layer file not found: %s', layer_path);
  68. assert(isfile(roi_path), 'ROI file not found: %s', roi_path);
  70. if ~exist(out_dir, 'dir')
  71. mkdir(out_dir);
  72. end
  74. %% =========================
  75. % LOAD DATA
  76. %% =========================
  77. qT1 = double(niftiread(qT1_path));
  78. layer_map = double(niftiread(layer_path));
  79. roi = niftiread(roi_path) ~= 0;
  81. % Check dimensions
  82. assert(isequal(size(qT1), size(layer_map), size(roi)), ...
  83. 'qT1, layer map, and ROI must have identical dimensions.');
  85. % For discrete LAYNII layer maps, robustly enforce integer labels
  86. layer_lbl = round(layer_map);
  88. %% =========================
  89. % ROI DILATION
  90. %% =========================
  91. if do_dilate
  92. roi_dilated = imdilate(roi, dilate_kernel);
  93. else
  94. roi_dilated = roi;
  95. end
  97. % Restrict analysis to valid layers inside the dilated ROI
  98. layered_roi = (layer_lbl > 0) & roi_dilated;
  100. %% =========================
  101. % EXTRACT qT1 PROFILE
  102. %% =========================
  103. qT1_profile = nan(n_layers, 1);
  104. nvox_layer = zeros(n_layers, 1);
  106. for i = 1:n_layers
  107. mask = (layer_lbl == i) & layered_roi;
  108. nvox_layer(i) = nnz(mask);
  110. if nvox_layer(i) > 0
  111. qT1_profile(i) = mean(qT1(mask), 'omitnan');
  112. end
  113. end
  115. % Optional explicit flip
  116. if flip_profile
  117. qT1_profile = flipud(qT1_profile);
  118. nvox_layer = flipud(nvox_layer);
  119. end
  121. %% =========================
  122. % DERIVATIVES
  123. %% =========================
  124. % Match original style:
  125. % 1) first derivative from qT1_profile
  126. % 2) normalize by max absolute value
  127. % 3) second derivative from normalized first derivative
  128. dqT1 = gradient(qT1_profile);
  130. m = max(abs(dqT1));
  131. if isfinite(m) && m > 0
  132. dqT1_normalized = dqT1 / m;
  133. else
  134. dqT1_normalized = dqT1;
  135. end
  137. ddqT1 = gradient(dqT1_normalized);
  139. % Bin-center depth coordinates
  140. depth_centers = linspace(0.5/n_layers, 1 - 0.5/n_layers, n_layers);
  142. %% =========================
  143. % PLOT
  144. %% =========================
  145. fig = figure('Color', 'w', 'Position', [200 200 650 750]);
  147. subplot(3,1,1);
  148. plot(depth_centers, qT1_profile, 'k-o', 'LineWidth', 2);
  149. title('qT1 Profile', 'Interpreter', 'none');
  150. xlabel('Normalized depth (bin centers)');
  151. ylabel('qT1');
  152. grid on;
  154. subplot(3,1,2);
  155. plot(depth_centers, dqT1_normalized, 'b-o', 'LineWidth', 2);
  156. title('First Derivative (normalized)', 'Interpreter', 'none');
  157. xlabel('Normalized depth (bin centers)');
  158. ylabel('dqT1 (normalized)');
  159. grid on;
  161. subplot(3,1,3);
  162. plot(depth_centers, ddqT1, 'r-o', 'LineWidth', 2);
  163. title('Second Derivative', 'Interpreter', 'none');
  164. xlabel('Normalized depth (bin centers)');
  165. ylabel('ddqT1');
  166. grid on;
  168. sgtitle(sprintf('%s | voxels/layer: %s', ...
  169. subject_id, strjoin(string(nvox_layer'), ' ')), ...
  170. 'Interpreter', 'none');
  172. png_path = fullfile(out_dir, sprintf('%s_qT1profile.png', subject_id));
  173. saveas(fig, png_path);
  175. %% =========================
  176. % SAVE OUTPUT
  177. %% =========================
  178. out = struct();
  179. out.subject_id = subject_id;
  180. out.qT1_path = qT1_path;
  181. out.layer_path = layer_path;
  182. out.roi_path = roi_path;
  183. out.n_layers = n_layers;
  184. out.do_dilate = do_dilate;
  185. out.dilate_kernel = dilate_kernel;
  186. out.flip_profile = flip_profile;
  188. out.nvox_layer = nvox_layer(:);
  189. out.depth_centers = depth_centers(:);
  191. out.qT1_profile = qT1_profile(:);
  192. out.dqT1 = dqT1(:);
  193. out.dqT1_normalized = dqT1_normalized(:);
  194. out.ddqT1 = ddqT1(:);
  196. out.figure_png = png_path;
  198. mat_path = fullfile(out_dir, sprintf('%s_qT1profile.mat', subject_id));
  199. save(mat_path, 'out', '-v7.3');
  201. fprintf('\nSaved outputs:\n');
  202. fprintf(' %s\n', mat_path);
  203. fprintf(' %s\n\n', png_path);

extract_qT1_laminar_profile.m at commit 9586b39, under MIT · at the source

Overview

Authors: Dongho Kim1,2, SoHyun Han3, Seongyun Kim1, Seulgi Eun4, Min-Suk Kang1,2,5, Choong-Wan Woo1,2,6,7, Seong-Gi Kim1,2,6
ORCID iDs: Seulgi Eun, Min-Suk Kang, Choong-Wan Woo, Seong-Gi Kim
  1. Center for Neuroscience Imaging Research, Institute for Basic Science,Suwon, South Korea
  2. Department of Brain Science and Engineering, Sungkyunkwan University,Suwon, South Korea
  3. Center for Bio-imaging and Translational Research, Korea Basic Science Institute,Cheongju, South Korea
  4. Division of KM Science Research, Korea Institute of Oriental Medicine (KIOM),Daejeon, South Korea
  5. Department of Psychology, Sungkyunkwan University,Seoul, South Korea
  6. Department of Biomedical Engineering, Sungkyunkwan University,Suwon, South Korea
  7. Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University,Suwon, South Korea
Institutions: Institute for Basic Science (South Korea); Sungkyunkwan University (South Korea); Korea Basic Science Institute (South Korea); Korea Institute of Oriental Medicine (South Korea)
Journal: Nature communications, volume 17, issue 1, article 5163
Dates: received 27 December 2024; accepted 29 March 2026; published online 13 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71842-w · PMID 41974711 · PMCID PMC13249840 · OpenAlex W7154076634
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging
Keywords: Sensory processing, Cognitive neuroscience
MeSH: Attention*, Somatosensory Cortex*, Adult, Brain Mapping, Female, Fingers, Humans, Magnetic Resonance Imaging, Male, Wrist, Young Adult (* major topic)
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: Institute for Basic Science, Korea,IBS-R015-D2
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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

dhkim15/S1-laminar-attention-SEBOLD

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 9586b39c5c0c88ab964daeeb1adfc4f96d47acc2, 9 March 2026
Languages: MATLAB (3)
Size: 6 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files), shadedErrorBar (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
5 files
At the source: github.com/dhkim15/S1-laminar-attention-SEBOLD

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-71842-w.

Tracing map

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Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-71842-w.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 11 MeSH terms, 1 funder, 83 references.

Cite

This paper

Kim, D., Han, S., Kim, S., Eun, S., Kang, M.-S., Woo, C.-W., & Kim, S.-G. (2026). Layer-specific attentional modulation in the human primary somatosensory cortex. Nature communications, 17(1), 5163. https://doi.org/10.1038/s41467-026-71842-w

BibTeX

@article{kim2026layer,
author = {Kim, Dongho and Han, SoHyun and Kim, Seongyun and Eun, Seulgi and Kang, Min-Suk and Woo, Choong-Wan and Kim, Seong-Gi},
title = {{Layer-specific attentional modulation in the human primary somatosensory cortex}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5163},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71842-w},
url = {https://doi.org/10.1038/s41467-026-71842-w},
pmid = {41974711},
pmcid = {PMC13249840}
}

RIS

TY - JOUR
AU - Kim, Dongho
AU - Han, SoHyun
AU - Kim, Seongyun
AU - Eun, Seulgi
AU - Kang, Min-Suk
AU - Woo, Choong-Wan
AU - Kim, Seong-Gi
TI - Layer-specific attentional modulation in the human primary somatosensory cortex
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/13
VL - 17
IS - 1
SP - 5163
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71842-w
UR - https://doi.org/10.1038/s41467-026-71842-w
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71842-w",
"type": "article-journal",
"title": "Layer-specific attentional modulation in the human primary somatosensory cortex",
"container-title": "Nature communications",
"author": [
{
"family": "Kim",
"given": "Dongho"
},
{
"family": "Han",
"given": "SoHyun"
},
{
"family": "Kim",
"given": "Seongyun"
},
{
"family": "Eun",
"given": "Seulgi"
},
{
"family": "Kang",
"given": "Min-Suk"
},
{
"family": "Woo",
"given": "Choong-Wan"
},
{
"family": "Kim",
"given": "Seong-Gi"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5163",
"DOI": "10.1038/s41467-026-71842-w",
"PMID": "41974711",
"PMCID": "PMC13249840",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71842-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
13
]
]
}
}

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