OSCR

Regional sex differences in human cortical anatomy vary in their morphometric bases and overlap with sex chromosomal and gonadal influences.

Code ↔ Paper

10 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 10 matches
  1. [1] § Methods › Functional and molecular signatures of sex differences in cortical anatomy › Molecular pathways related to neurodevelopment ↔ figure3b_gsea.m, lines 154–202 · score 0.70 · fetal cells, SynGO, compartments, layers, pos, GSEA
  2. [2] § Results › Sex differences in regional cortical volume reflect spatially dissociable alterations of cortical surface area and thickness ↔ figure1_hcp.m, lines 187–217 · score 0.68 · log ratio, female biased regions, female biased CV, CV sex, surface, regional
  3. [3] § Methods › Functional and molecular signatures of sex differences in cortical anatomy › In vivo resting-state functional networks ↔ figure3a_functional_networks.m, lines 33–64 · score 0.67 · functional network, Yeo Krienen, Bonferroni correction, cortical features, spin, permutations
  4. [4] § Methods › Statistical analyses › Defining the effects of intact testicular hormone production on regional cortical anatomy ↔ figure4c_igd.m, lines 24–67 · score 0.65 · intact testicular hormone, IGD male, production, Global, Age
  5. [5] § Results › Regions of sex-biased SA and CT possess specific functional and molecular signatures ↔ figure3b_gsea.m, lines 154–202 · score 0.65 · SynGO, dashed boxes, solid boxes, ontologies, layer, GSEA
  6. [6] § Results › Regions of sex-biased SA and CT possess specific functional and molecular signatures ↔ figure3a_functional_networks.m, lines 33–64 · score 0.63 · functional network maps, Yeo Krienen, Bonferroni corrected, cortical features, empirical, tailed
  7. [7] § Results › Sex differences in regional cortical volume reflect spatially dissociable alterations of cortical surface area and thickness ↔ figure1_hcp.m, lines 187–217 · score 0.63 · log ratio, male biased CV, sex biased CV, female biases, squares, Figure 1
  8. [8] § Methods › Functional and molecular signatures of sex differences in cortical anatomy › Molecular pathways related to neurodevelopment ↔ figure3b_gsea.m, lines 40–99 · score 0.60 · gene expression, spatial correlation, male biased, transcriptomic, GSEA, female
  9. [9] § Results › Regions of sex-biased human cortical anatomy show congruent sensitivity to variations in X-chromosome dosage and testicular hormone production ↔ figure4c_igd.m, lines 24–67 · score 0.59 · intact testicular hormone, healthy male, production, IGD, sex
  10. [10] § Results › Regional sex differences in CV, SA, and CT show good within-sample reproducibility in a sample size dependent manner ↔ figure1_hcp.m, lines 219–267 · score 0.54 · opposite directions, Conjunction maps, cortical regions, insignificant, boxes, Bars

Paper

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The authors' code

MATLAB · 202 lines · 7.9 KB · no license · 3 matches

  2. clear all; close all; clc;
  4. %% Prep
  6. % Load results
  7. load('matfiles/hcp_results.mat');
  9. % Load template
  10. load('fsaverage_7.1.0.mat');
  11. load('glasser_fsaverage_7.1.0.mat');
  12. load('Vasa_G360.mat');
  14. % Misc
  15. loadColorMaps;
  16. thresh = 0.05;
  17. nPerm = 10000;
  18. nTests = 6; % = 3 cortical features x 2 directions for sex bias
  20. % Set up paths
  21. figDir = 'gsea';
  23. %% Prep MAGICC
  25. % Load MAGICC-AHBA data mapped onto Glasser parcellation (Wagstyl et
  26. % al.,2023, eLife)
  27. load('matfiles/ahba_glasser.mat', 'GO_GE', 'GO_GE_ID', 'GE_Glasser', 'GE_ID');
  28. GE_Glasser = repmat(GE_Glasser, 2, 1);
  30. % Select cortex-coding genes
  31. indCortex = GO_GE(:,53) == 1;
  32. GE_Glasser = GE_Glasser(:,indCortex);
  33. GE_ID = GE_ID(indCortex);
  35. % Select relevent columns
  36. indCol = [2:52 54:57];
  37. GO_GE = GO_GE(indCortex,indCol) == 1;
  38. GO_GE_ID = GO_GE_ID(indCol);
  40. %% Compute correlation between sex difference and spatial transcripts (MAGICC)
  42. % Precompute indices for female and male biased regions
  43. indCV_F = HCP_CV_SEX.coef(2,:) > 0;
  44. indSA_F = HCP_SA_SEX.coef(2,:) > 0;
  45. indCT_F = HCP_CT_SEX.coef(2,:) > 0;
  46. indCV_M = HCP_CV_SEX.coef(2,:) < 0;
  47. indSA_M = HCP_SA_SEX.coef(2,:) < 0;
  48. indCT_M = HCP_CT_SEX.coef(2,:) < 0;
  50. % Compute imaging-transriptomic associations
  51. for i = 1:size(GE_Glasser,2) % For each gene
  52. % Show progress
  53. disp(['Iteration ' num2str(i)]);
  55. % Calculate spatial correlation
  56. [MAGICC_R_CV_F(i) ~] = corr(GE_Glasser(indCV_F,i), HCP_CV_SEX.coef(2,indCV_F)');
  57. [MAGICC_R_SA_F(i) ~] = corr(GE_Glasser(indSA_F,i), HCP_SA_SEX.coef(2,indSA_F)');
  58. [MAGICC_R_CT_F(i) ~] = corr(GE_Glasser(indCT_F,i), HCP_CT_SEX.coef(2,indCT_F)');
  59. [MAGICC_R_CV_M(i) ~] = corr(GE_Glasser(indCV_M,i), -HCP_CV_SEX.coef(2,indCV_M)');
  60. [MAGICC_R_SA_M(i) ~] = corr(GE_Glasser(indSA_M,i), -HCP_SA_SEX.coef(2,indSA_M)');
  61. [MAGICC_R_CT_M(i) ~] = corr(GE_Glasser(indCT_M,i), -HCP_CT_SEX.coef(2,indCT_M)');
  62. end
  64. % Indices for spin permutations
  65. indCV_F_Spin = HCP_CV_SEX_Spin > 0;
  66. indSA_F_Spin = HCP_SA_SEX_Spin > 0;
  67. indCT_F_Spin = HCP_CT_SEX_Spin > 0;
  68. indCV_M_Spin = HCP_CV_SEX_Spin < 0;
  69. indSA_M_Spin = HCP_SA_SEX_Spin < 0;
  70. indCT_M_Spin = HCP_CT_SEX_Spin < 0;
  72. parfor i = 1:size(GE_Glasser,2) % For each gene
  73. % Show progress
  74. disp(['Iteration ' num2str(i)]);
  76. for j = 1:nPerm
  77. % Calculate spatial correlation between gene expression and sex
  78. % difference
  79. [MAGICC_R_CV_F_Spin(i,j) ~] = corr(GE_Glasser(indCV_F,i), HCP_CV_SEX_Spin(indCV_F_Spin(:,j),j));
  80. [MAGICC_R_SA_F_Spin(i,j) ~] = corr(GE_Glasser(indSA_F,i), HCP_SA_SEX_Spin(indSA_F_Spin(:,j),j));
  81. [MAGICC_R_CT_F_Spin(i,j) ~] = corr(GE_Glasser(indCT_F,i), HCP_CT_SEX_Spin(indCT_F_Spin(:,j),j));
  82. [MAGICC_R_CV_M_Spin(i,j) ~] = corr(GE_Glasser(indCV_M,i), -HCP_CV_SEX_Spin(indCV_M_Spin(:,j),j));
  83. [MAGICC_R_SA_M_Spin(i,j) ~] = corr(GE_Glasser(indSA_M,i), -HCP_SA_SEX_Spin(indSA_M_Spin(:,j),j));
  84. [MAGICC_R_CT_M_Spin(i,j) ~] = corr(GE_Glasser(indCT_M,i), -HCP_CT_SEX_Spin(indCT_M_Spin(:,j),j));
  85. end
  86. end
  88. % Write out the spin permuted results
  89. for i = 1:nPerm
  90. % Show progress
  91. disp(['Iteration ' num2str(i)]);
  93. writematrix(MAGICC_R_CV_F_Spin(:,i), fullfile(figDir, ['/GSEA_R/input/MAGICC_R_CV_F_Spin_BS' num2str(i) '.txt']));
  94. writematrix(MAGICC_R_SA_F_Spin(:,i), fullfile(figDir, ['/GSEA_R/input/MAGICC_R_SA_F_Spin_BS' num2str(i) '.txt']));
  95. writematrix(MAGICC_R_CT_F_Spin(:,i), fullfile(figDir, ['/GSEA_R/input/MAGICC_R_CT_F_Spin_BS' num2str(i) '.txt']));
  96. writematrix(MAGICC_R_CV_M_Spin(:,i), fullfile(figDir, ['/GSEA_R/input/MAGICC_R_CV_M_Spin_BS' num2str(i) '.txt']));
  97. writematrix(MAGICC_R_SA_M_Spin(:,i), fullfile(figDir, ['/GSEA_R/input/MAGICC_R_SA_M_Spin_BS' num2str(i) '.txt']));
  98. writematrix(MAGICC_R_CT_M_Spin(:,i), fullfile(figDir, ['/GSEA_R/input/MAGICC_R_CT_M_Spin_BS' num2str(i) '.txt']));
  99. end
  101. %% Perform GSEA in R (see the accompanying R scripts)
  104. %% Read the GSEA results done in R
  106. % Pathways
  107. MAGICC_terms = sort(GO_GE_ID);
  109. % Read GSEA results from R
  110. MAGICC_FCV_pos = xlsread(['gsea/output_main/MAGICC_GSEA_F_CV_pos.xlsx']);
  111. MAGICC_FSA_pos = xlsread(['gsea/output_main/MAGICC_GSEA_F_SA_pos.xlsx']);
  112. MAGICC_FCT_pos = xlsread(['gsea/output_main/MAGICC_GSEA_F_CT_pos.xlsx']);
  113. MAGICC_MCV_pos = xlsread(['gsea/output_main/MAGICC_GSEA_M_CV_pos.xlsx']);
  114. MAGICC_MSA_pos = xlsread(['gsea/output_main/MAGICC_GSEA_M_SA_pos.xlsx']);
  115. MAGICC_MCT_pos = xlsread(['gsea/output_main/MAGICC_GSEA_M_CT_pos.xlsx']);
  117. % Read the results based on spin-permuted HCP sex difference maps from R
  118. parfor iBS = 1:nPerm
  119. MAGICC_FCV_pos_bs(:,:,iBS) = xlsread(['gsea/output_bs/MAGICC_GSEA_F_CV_pos_BS' num2str(iBS) '.xlsx']);
  120. MAGICC_FSA_pos_bs(:,:,iBS) = xlsread(['gsea/output_bs/MAGICC_GSEA_F_SA_pos_BS' num2str(iBS) '.xlsx']);
  121. MAGICC_FCT_pos_bs(:,:,iBS) = xlsread(['gsea/output_bs/MAGICC_GSEA_F_CT_pos_BS' num2str(iBS) '.xlsx']);
  122. MAGICC_MCV_pos_bs(:,:,iBS) = xlsread(['gsea/output_bs/MAGICC_GSEA_M_CV_pos_BS' num2str(iBS) '.xlsx']);
  123. MAGICC_MSA_pos_bs(:,:,iBS) = xlsread(['gsea/output_bs/MAGICC_GSEA_M_SA_pos_BS' num2str(iBS) '.xlsx']);
  124. MAGICC_MCT_pos_bs(:,:,iBS) = xlsread(['gsea/output_bs/MAGICC_GSEA_M_CT_pos_BS' num2str(iBS) '.xlsx']);
  125. end
  126. save('matfiles/MAGICC_BS.mat', 'MAGICC_FCV_pos_bs', 'MAGICC_FSA_pos_bs', 'MAGICC_FCT_pos_bs', ...
  127. 'MAGICC_MCV_pos_bs', 'MAGICC_MSA_pos_bs', 'MAGICC_MCT_pos_bs', '-v7.3');
  129. % Extract the normalize enrichment scores and p-values
  130. MAGICC_NES = [MAGICC_FCV_pos(:,5)'; ...
  131. MAGICC_MCV_pos(:,5)'; ...
  132. zeros(1,55);
  133. MAGICC_FSA_pos(:,5)'; ...
  134. MAGICC_MSA_pos(:,5)'; ...
  135. zeros(1,55);
  136. MAGICC_FCT_pos(:,5)'; ...
  137. MAGICC_MCT_pos(:,5)'];
  139. % Compute the p-values for normalized enrichment scores against the ones
  140. % from the null distribution (derived from spin permutations of HCP sex
  141. % differences)
  142. MAGICC_P_PERM = [sum(MAGICC_FCV_pos(:,1) > squeeze(MAGICC_FCV_pos_bs(:,1,:)),2)'; ...
  143. sum(MAGICC_MCV_pos(:,1) > squeeze(MAGICC_MCV_pos_bs(:,1,:)),2)'; ...
  144. zeros(1,55);
  145. sum(MAGICC_FSA_pos(:,1) > squeeze(MAGICC_FSA_pos_bs(:,1,:)),2)'; ...
  146. sum(MAGICC_MSA_pos(:,1) > squeeze(MAGICC_MSA_pos_bs(:,1,:)),2)'; ...
  147. zeros(1,55);
  148. sum(MAGICC_FCT_pos(:,1) > squeeze(MAGICC_FCT_pos_bs(:,1,:)),2)'; ...
  149. sum(MAGICC_MCT_pos(:,1) > squeeze(MAGICC_MCT_pos_bs(:,1,:)),2)'] ./ nPerm;
  151. % Bonf. correction
  152. MAGICC_Q_PERM = MAGICC_P_PERM .* nTests;
  154. %% Visualize the GSEA results
  156. % Misc
  157. MAGICC_NES(:,end+1) = 0; % Dummy column
  158. MAGICC_P_PERM(:,end+1) = 0; % Dummy column
  159. MAGICC_Q_PERM(:,end+1) = 0; % Dummy column
  161. % Orders of ontological terms
  162. % 1:3 Epochs
  163. % 4:10 Cells
  164. % 11:21 Cell compartment
  165. % 22:28 Fetal layers
  166. % 29:44 Fetal cells
  167. % 45:50 Layers
  168. % 51:54 Rare diseases
  169. % 55: SynGO
  170. MAGICC_terms{56} = '';
  172. % Plot order
  173. ordind = [1:3 56 22:28 56 29:44 56 45:50 56 4:10 56 11:21 55 56];
  175. % Visualize
  176. h = figure;
  177. imagesc(abs(MAGICC_NES(:,ordind)), [0 3.5]); hold on;
  178. colormap(cmap.Purples);
  179. xticks(1:length(ordind));
  180. xticklabels(strrep(MAGICC_terms(ordind), '_', ' '));
  181. xticklabels({}); yticklabels({});
  182. xticks([]); yticks([]);
  183. ax = gca;
  184. ax.TickDir = 'out';
  185. ax.Box = 'off';
  186. h.Position = [0 0 3000 1000];
  187. for i = ordind
  188. for j = [1:2 4:5 7:8] % only pos enrichments
  189. if i < 56
  190. if MAGICC_Q_PERM(j,i) < thresh % Dashed box
  191. vertices = 0.5 + [find(ordind==i)-1 j-1; find(ordind==i) j-1; find(ordind==i) j; find(ordind==i)-1 j];
  192. patch('Faces', 1:4, 'Vertices', vertices, 'FaceColor', 'none', 'LineWidth', 6, 'EdgeColor', [0.7 0.7 0]);
  193. elseif MAGICC_P_PERM(j,i) < thresh % Solid box
  194. vertices = 0.5 + [find(ordind==i)-1 j-1; find(ordind==i) j-1; find(ordind==i) j; find(ordind==i)-1 j];
  195. patch('Faces', 1:4, 'Vertices', vertices, 'FaceColor', 'none', 'LineWidth', 6, 'EdgeColor', [0.7 0.7 0], 'LineStyle', ':');
  196. end
  197. end
  198. end
  199. end
  200. exportgraphics(h, fullfile(figDir, 'MAGICC_GSEA_unipos_BS_pPerm.eps'), 'ContentType', 'vector');
  202. close all;

figure3b_gsea.m at commit 0566cf1, no license · at the source

Overview

Authors: Hyo M. Lee1, Siyuan Liu1, Elisa Guma1,2, Elizabeth Levitis1,3,4, Rebecca Shafee1, Gabrielle Dugan1, François M. Lalonde1, Liv Clasen1, Alex DeCasien1,5, M. Mallar Chakravarty6,7, Jason P. Lerch8,9,10,11, Konrad Wagstyl12,13, Angela Delaney14,15, Armin Raznahan1
ORCID iDs: Hyo M. Lee, Siyuan Liu, Rebecca Shafee, François M. Lalonde, M. Mallar Chakravarty, Konrad Wagstyl, Angela Delaney, Armin Raznahan
15 affiliations
  1. Section on Developmental Neurogenomics, National Institute of Mental Health,Bethesda, MD USA
  2. Department of Pediatrics, Massachusetts General Hospital,Boston, MA USA
  3. Center for Medical Image Computing, Department of Computer Science, University College London,London, UK
  4. Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia,Philadelphia, PA USA
  5. Laboratory of Neurogenetics, National Institute on Aging,Bethesda, MD USA
  6. Department of Psychiatry, McGill University,Montreal, QC Canada
  7. Cerebral Imaging Centre, Douglas Research Centre,Montreal, QC Canada
  8. Mouse Imaging Centre, Toronto, ON Canada
  9. The Hospital for Sick Children,Toronto, ON Canada
  10. Department of Medical Biophysics, University of Toronto,Toronto, ON Canada
  11. Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford,Oxford, UK
  12. School of Biomedical Engineering & Imaging Sciences, King’s College London,London, UK
  13. UCL Great Ormond Street Institute of Child Health,London, UK
  14. Division of Endocrinology, Department of Pediatric Medicine, St. Jude Children’s Research Hospital,Memphis, TN USA
  15. Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital,Memphis, TN USA
Institutions: National Institute of Mental Health (United States); Massachusetts General Hospital (United States); University College London (United Kingdom); Children's Hospital of Philadelphia (United States); National Institute on Aging (United States); McGill University (Canada); Douglas Mental Health University Institute (Canada); Hospital for Sick Children (Canada); University of Toronto (Canada); University of Oxford (United Kingdom); King's College London (United Kingdom); St. Jude Children's Research Hospital (United States)
Journal: Nature communications, volume 17, issue 1, article 7503
Dates: received 19 September 2025; accepted 1 June 2026; published online 13 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74274-8 · PMID 42288478 · PMCID PMC13408908 · OpenAlex W7164664419
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Statistics, Connectivity
Keywords: Development of the nervous system, Computational neuroscience
MeSH: Cerebral Cortex*, Chromosomes, Human, X*, Sex Characteristics*, Sex Chromosomes*, Adult, Aneuploidy, Female, Gonadotropin-Releasing Hormone, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Young Adult (* major topic)
Topic: Genetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute of Mental Health (1ZIAMH002949-09)
Citations: not cited yet (Europe PMC); 104 references in the paper

Abstract

Humans show reproducible sex differences in regional cortical volume (CV), but it remains unclear how these relate to the two biologically dissociable determinants of CV – surface area (SA) and cortical thickness (CT) – or to potentially causal sex chromosomal and gonadal effects. Here, we analyze neuroimaging data in two independent human cohorts (N = 1754) to identify and interrelate highly reproducible sex differences in regional CV, SA and CT – as well as their alignment with distinct functional networks and histomolecular signatures. Integrating neuroimaging data from clinical cohorts with sex chromosome aneuploidy and isolated gonadotropin-releasing hormone deficiency (N = 313) establishes that regions of sex-biased cortical anatomy are enriched for congruent effects of X-chromosome dosage (e.g., primary sensory and insular cortices) and gonadal hormones (e.g., dorsal parietal regions and angular gyrus). This work refines maps of sex-biased human cortical organization and helps to narrow hypotheses regarding their potential genetic and endocrine causes.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

TheHyoLee/sex_differences_in_human_cortical_anatomy

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0566cf1c33276748e5c51d11fdcc4c3b4e474630, 13 May 2026
Languages: MATLAB (7), R (2)
Size: 9 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files), fdr_bh (Benjamini-Hochberg FDR) (4 files), clusterProfiler (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/TheHyoLee/sex_differences_in_human_cortical_anatomy

Code availability

The code used for the analyses can be found at https://github.com/TheHyoLee/sex_differences_in_human_cortical_anatomy.

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

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;
  • 9 scripts, each with its path and the digest of its content;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

No dataset and no data link were found in the paper.

Data availability

Source Data - Sex Differences in Human Cortical Anatomy.xlsx contains: the group-level statistical parameters (standardized effect sizes, nominal p values and p values adjusted for multiple comparison) for the effects of sex, sex chromosome dosage, and testicular hormone production; the enrichments for resting-state functional networks; and the ranked gene lists for the gene set enrichment analysis of cortical sex difference maps. The HCP S1200 data release (March 2017) is publicly available at https://db.humanconnectome.org/. The UK Biobank data release (2020) is publicly available at https://www.ukbiobank.ac.uk. For the HCP and UKB datasets, the data access agreements and privacy policies prohibit sharing participant-level data such as neuroimaging-derived variables. Source data are provided with this paper.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 2 keywords, 14 MeSH terms, 1 funder, 102 references.

Cite

This paper

Lee, H. M., Liu, S., Guma, E., Levitis, E., Shafee, R., Dugan, G., Lalonde, F. M., Clasen, L., DeCasien, A., Chakravarty, M. M., Lerch, J. P., Wagstyl, K., Delaney, A., & Raznahan, A. (2026). Regional sex differences in human cortical anatomy vary in their morphometric bases and overlap with sex chromosomal and gonadal influences. Nature communications, 17(1), 7503. https://doi.org/10.1038/s41467-026-74274-8

BibTeX

@article{lee2026regional,
author = {Lee, Hyo M. and Liu, Siyuan and Guma, Elisa and Levitis, Elizabeth and Shafee, Rebecca and Dugan, Gabrielle and Lalonde, François M. and Clasen, Liv and DeCasien, Alex and Chakravarty, M. Mallar and Lerch, Jason P. and Wagstyl, Konrad and Delaney, Angela and Raznahan, Armin},
title = {{Regional sex differences in human cortical anatomy vary in their morphometric bases and overlap with sex chromosomal and gonadal influences}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7503},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74274-8},
url = {https://doi.org/10.1038/s41467-026-74274-8},
pmid = {42288478},
pmcid = {PMC13408908}
}

RIS

TY - JOUR
AU - Lee, Hyo M.
AU - Liu, Siyuan
AU - Guma, Elisa
AU - Levitis, Elizabeth
AU - Shafee, Rebecca
AU - Dugan, Gabrielle
AU - Lalonde, François M.
AU - Clasen, Liv
AU - DeCasien, Alex
AU - Chakravarty, M. Mallar
AU - Lerch, Jason P.
AU - Wagstyl, Konrad
AU - Delaney, Angela
AU - Raznahan, Armin
TI - Regional sex differences in human cortical anatomy vary in their morphometric bases and overlap with sex chromosomal and gonadal influences
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/13
VL - 17
IS - 1
SP - 7503
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74274-8
UR - https://doi.org/10.1038/s41467-026-74274-8
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74274-8",
"type": "article-journal",
"title": "Regional sex differences in human cortical anatomy vary in their morphometric bases and overlap with sex chromosomal and gonadal influences",
"container-title": "Nature communications",
"author": [
{
"family": "Lee",
"given": "Hyo M."
},
{
"family": "Liu",
"given": "Siyuan"
},
{
"family": "Guma",
"given": "Elisa"
},
{
"family": "Levitis",
"given": "Elizabeth"
},
{
"family": "Shafee",
"given": "Rebecca"
},
{
"family": "Dugan",
"given": "Gabrielle"
},
{
"family": "Lalonde",
"given": "François M."
},
{
"family": "Clasen",
"given": "Liv"
},
{
"family": "DeCasien",
"given": "Alex"
},
{
"family": "Chakravarty",
"given": "M. Mallar"
},
{
"family": "Lerch",
"given": "Jason P."
},
{
"family": "Wagstyl",
"given": "Konrad"
},
{
"family": "Delaney",
"given": "Angela"
},
{
"family": "Raznahan",
"given": "Armin"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7503",
"DOI": "10.1038/s41467-026-74274-8",
"PMID": "42288478",
"PMCID": "PMC13408908",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74274-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
13
]
]
}
}

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