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The "Brain's Traffic Map" Reveals Neural Pathways Linked to Coronary Microvascular Dysfunction in Women.

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The authors' code

MATLAB · 287 lines · 12 KB · no license

  1. clear;
  2. clc;
  4. % Constants
  5. num_aal = 90; % replace with appropriate number of brain regions specific to the atlas image that is aimed to be used
  6. listpath = fullfile('path_to_lists_directory'); % Replace with appropriate path for the subject list
  7. datapath = fullfile('path_to_fc_matrices'); % Replace with appropriate path for FC matrices
  8. outputpath = fullfile('path_to_output_directory'); % Replace with appropriate path
  9. % SC = structural connectivity
  10. % FC = functional connectivity
  12. % Load necessary data as .mat file
  13. load('AAL_ind_116to90.mat'); % update index (ind): AAL atlas compressed to 90 investigated regions such removing cerebellum and vermis from the atlas
  15. % Import subject IDs with both FC and SC matrices
  16. subjects_both = importSubjIDs(fullfile(listpath, 'HCP_1200_list_both_fmask.txt')); %replace with appropriate file name for subject list
  17. num_s = length(subjects_both);
  19. % Load or compute center of mass and distance matrices
  20. outfile1 = 'CMs_AAL_adult_90AAL.mat'; %replace with the atlas specific indices
  21. if ~exist(outfile1, 'file')
  22. AAL_data = niftiread('AAL.nii'); % replace with appropriate atlas image that is aimed to be used
  23. ROIs = unique(AAL_data);
  24. ROIs(ROIs == 0) = []; % remove background
  25. CMs = zeros(length(ROIs), 3);
  26. for idx = 1:length(ROIs)
  27. [x, y, z] = ind2sub(size(AAL_data), find(AAL_data == ROIs(idx)));
  28. CMs(idx, :) = [mean(x), mean(y), mean(z)];
  29. end
  30. CM_90 = CMs(ind, :);
  31. save(outfile1, 'CMs', 'CM_90', 'ind');
  32. end
  34. outfile2 = 'Distance_CMs_AAL_adult_90AAL.mat';
  35. if ~exist(outfile2, 'file')
  36. load(outfile1);
  37. Distance_90 = zeros(num_aal, num_aal); %replace with appropriate number of brain regions for a specific atlas
  38. for rr = 1:num_aal
  39. for rrr = rr:num_aal
  40. Distance_90(rr, rrr) = norm(CM_90(rr, :) - CM_90(rrr, :));
  41. Distance_90(rrr, rr) = Distance_90(rr, rrr);
  42. end
  43. end
  44. save(outfile2, 'Distance_90');
  45. end
  47. % Main computation loop
  48. for ss = 1:num_s
  49. fprintf('Calculating for subject %d\n', ss);
  50. subj = subjects_both{ss};
  52. % Define file paths
  53. finfile = fullfile(outputpath, 'FC_Matrices_FDR_corrected_90AAL', [subj '_Functional_Matrix_FDR_corrected.mat']);%replace with appropriate file name for FC
  54. sinfile = fullfile(outputpath, 'Structural_Matrices_individual_nothr_90AAL', [subj '_Structural_Matrix.mat']); %%replace with appropriate file name for SC
  55. outputdir_USFC = fullfile('path_to_output_USFC_directory'); % Replace with appropriate path
  57. if ~exist(outputdir_USFC, 'dir')
  58. mkdir(outputdir_USFC);
  59. end
  61. % Check if output files already exist
  62. outfile_all = fullfile(outputdir_USFC, [subj '_Cost_Route_all_Matrix.mat']);
  63. if ~exist(outfile_all, 'file')
  64. load(finfile);
  65. load(sinfile);
  66. % Make the economical assumption to find the most efficient segments up to 4 steps
  67. Cost_M = Distance_90 ./ Structural_M;
  68. Min_cost_M = inf(num_aal, num_aal);
  69. Route_M = cell(num_aal, num_aal);
  71. for rr = 1:(num_aal - 1)
  72. for rrr = (rr + 1):num_aal
  73. if Functional_M(rr, rrr) ~= 0
  74. % Find minimum cost routes (up to 4 steps)
  75. min_routes = cell(4, 1);
  76. min_costs = zeros(4, 1);
  77. min_costs(1) = Cost_M(rr, rrr);
  78. min_routes{1} = [rr, rrr];
  80. % 2 steps
  81. [min_cost, I1] = min(Cost_M(rr, :) + Cost_M(:, rrr)');
  82. min_costs(2) = min_cost;
  83. min_routes{2} = [rr, I1, rrr];
  85. % 3 steps
  86. costs = arrayfun(@(x, y) Cost_M(rr, x) + Cost_M(x, y) + Cost_M(y, rrr), 1:num_aal, 1:num_aal);
  87. [min_cost, I] = min(costs(:));
  88. [I1, I2] = ind2sub(size(costs), I);
  89. min_costs(3) = min_cost;
  90. min_routes{3} = [rr, I1, I2, rrr];
  92. % 4 steps
  93. costs = arrayfun(@(x, y, z) Cost_M(rr, x) + Cost_M(x, y) + Cost_M(y, z) + Cost_M(z, rrr), 1:num_aal, 1:num_aal, 1:num_aal);
  94. [min_cost, I] = min(costs(:));
  95. [I1, I2, I3] = ind2sub(size(costs), I);
  96. min_costs(4) = min_cost;
  97. min_routes{4} = [rr, I1, I2, I3, rrr];
  99. % Select the minimum route
  100. [All_min_cost, All_I] = min(min_costs);
  101. Min_cost_M(rr, rrr) = All_min_cost;
  102. if ~isinf(All_min_cost)
  103. Route_M{rr, rrr} = min_routes{All_I};
  104. end
  105. end
  106. end
  107. end
  109. % Symmetrize matrices
  110. It = logical(tril(ones(num_aal, num_aal), -1));
  111. Min_cost_M(It) = Min_cost_M';
  112. Route_M(It) = Route_M';
  114. % Save the results
  115. save(outfile_all, 'Cost_M', 'Min_cost_M', 'Route_M');
  116. end
  118. % Route-specific matrices
  119. Route_M1_eff = cell(num_aal, num_aal);
  120. Route_M2_eff = cell(num_aal, num_aal);
  121. Route_M3_eff = cell(num_aal, num_aal);
  122. Route_M4_eff = cell(num_aal, num_aal);
  124. for rr = 1:(num_aal-1)
  125. for rrr = rr+1:num_aal
  126. route_len = length(Route_M{rr, rrr});
  127. switch route_len
  128. case 2
  129. Route_M1_eff{rr, rrr} = Route_M{rr, rrr};
  130. case 3
  131. Route_M2_eff{rr, rrr} = Route_M{rr, rrr};
  132. case 4
  133. Route_M3_eff{rr, rrr} = Route_M{rr, rrr};
  134. case 5
  135. Route_M4_eff{rr, rrr} = Route_M{rr, rrr};
  136. end
  137. end
  138. end
  140. % Symmetrize the route matrices
  141. It = logical(tril(ones(num_aal,num_aal),-1));
  142. Route_M1_eff(It) = Route_M1_eff';
  143. Route_M2_eff(It) = Route_M2_eff';
  144. Route_M3_eff(It) = Route_M3_eff';
  145. Route_M4_eff(It) = Route_M4_eff';
  147. % Save route-specific matrices
  148. save(fullfile(outputdir_USFC, [subj '_Route1_Matrix.mat']), 'Route_M1_eff');
  149. save(fullfile(outputdir_USFC, [subj '_Route2_Matrix.mat']), 'Route_M2_eff');
  150. save(fullfile(outputdir_USFC, [subj '_Route3_Matrix.mat']), 'Route_M3_eff');
  151. save(fullfile(outputdir_USFC, [subj '_Route4_Matrix.mat']), 'Route_M4_eff');
  153. % Further processing and saving FC and SC matrices
  154. outfile_FC_M1 = fullfile(outputdir_USFC, [subj '_FC_R1_Matrix.mat']);
  155. outfile_FC_M2 = fullfile(outputdir_USFC, [subj '_FC_R2_Matrix.mat']);
  156. outfile_FC_M3 = fullfile(outputdir_USFC, [subj '_FC_R3_Matrix.mat']);
  157. outfile_FC_M4 = fullfile(outputdir_USFC, [subj '_FC_R4_Matrix.mat']);
  158. outfile_USFC_all = fullfile(outputdir_USFC, [subj '_USFC_Matrix.mat']);
  159. outfile_USFC_all_abs = fullfile(outputdir_USFC, [subj '_USFC_Matrix_abs.mat']);
  160. outfile_Route_count = fullfile(outputdir_USFC, [subj '_USFC_Route_count_Matrix.mat']);
  162. if ~exist(outfile_FC_M1, 'file')
  163. load(finfile);
  164. load(sinfile);
  166. SC_M1 = zeros(num_aal, num_aal);
  167. SC_M2 = zeros(num_aal, num_aal);
  168. SC_M3 = zeros(num_aal, num_aal);
  169. SC_M4 = zeros(num_aal, num_aal);
  170. FC_M1 = zeros(num_aal, num_aal);
  171. FC_M2 = zeros(num_aal, num_aal);
  172. FC_M3 = zeros(num_aal, num_aal);
  173. FC_M4 = zeros(num_aal, num_aal);
  175. % Construct the SC and FC for each step
  176. for rr = 1:num_aal
  177. for rrr = 1:num_aal
  178. Route = Route_M{rr, rrr};
  179. if ~isempty(Route)
  180. n_steps = length(Route);
  181. switch n_steps
  182. case 2
  183. SC_M1(rr, rrr) = Structural_M(Route(1), Route(2));
  184. FC_M1(rr, rrr) = Functional_M(Route(1), Route(2));
  185. case 3
  186. SC_M2(rr, rrr) = (Structural_M(Route(1), Route(2)) + Structural_M(Route(2), Route(3))) / 2;
  187. FC_M2(rr, rrr) = Functional_M(Route(1), Route(3));
  188. case 4
  189. SC_M3(rr, rrr) = (Structural_M(Route(1), Route(2)) + Structural_M(Route(2), Route(3)) + Structural_M(Route(3), Route(4))) / 3;
  190. FC_M3(rr, rrr) = Functional_M(Route(1), Route(4));
  191. case 5
  192. SC_M4(rr, rrr) = (Structural_M(Route(1), Route(2)) + Structural_M(Route(2), Route(3)) + Structural_M(Route(3), Route(4)) + Structural_M(Route(4), Route(5))) / 4;
  193. FC_M4(rr, rrr) = Functional_M(Route(1), Route(5));
  194. end
  195. end
  196. end
  197. end
  199. % Symmetrize matrices
  200. SC_M1(It) = SC_M1';
  201. SC_M2(It) = SC_M2';
  202. SC_M3(It) = SC_M3';
  203. SC_M4(It) = SC_M4';
  204. FC_M1(It) = FC_M1';
  205. FC_M2(It) = FC_M2';
  206. FC_M3(It) = FC_M3';
  207. FC_M4(It) = FC_M4';
  209. % Save the results
  210. save(outfile_FC_M1, 'FC_M1');
  211. save(outfile_FC_M2, 'FC_M2');
  212. save(outfile_FC_M3, 'FC_M3');
  213. save(outfile_FC_M4, 'FC_M4');
  214. end
  216. % Calculate and save USFC matrices
  217. if ~exist(outfile_USFC_all, 'file')
  218. USFC_M = zeros(num_aal, num_aal);
  219. USFC_M_abs = zeros(num_aal, num_aal);
  220. RouteCounts_M = zeros(num_aal, num_aal);
  222. for rr = 1:num_aal
  223. for rrr = 1:num_aal
  224. Route = Route_M{rr, rrr};
  225. if ~isempty(Route)
  226. n_steps = length(Route) - 1;
  227. for nn = 1:n_steps
  228. step_i = Route(nn);
  229. step_j = Route(nn+1);
  230. if step_i < step_j
  231. USFC_M(step_i, step_j) = USFC_M(step_i, step_j) + Functional_M(rr, rrr);
  232. USFC_M_abs(step_i, step_j) = USFC_M_abs(step_i, step_j) + abs(Functional_M(rr, rrr));
  233. RouteCounts_M(step_i, step_j) = RouteCounts_M(step_i, step_j) + 1;
  234. elseif step_j < step_i
  235. USFC_M(step_j, step_i) = USFC_M(step_j, step_i) + Functional_M(rrr, rr);
  236. USFC_M_abs(step_j, step_i) = USFC_M_abs(step_j, step_i) + abs(Functional_M(rrr, rr));
  237. RouteCounts_M(step_j, step_i) = RouteCounts_M(step_j, step_i) + 1;
  238. end
  239. end
  240. end
  241. end
  242. end
  244. % Symmetrize matrices
  245. USFC_M(It) = USFC_M';
  246. USFC_M_abs(It) = USFC_M_abs';
  247. RouteCounts_M(It) = RouteCounts_M';
  249. % Save the results
  250. save(outfile_USFC_all, 'USFC_M');
  251. save(outfile_USFC_all_abs, 'USFC_M_abs');
  252. save(outfile_Route_count, 'RouteCounts_M');
  253. end
  255. % Save the SC and FC matrices as edge files for further analysis and plotting (replace with appropriate paths)
  257. baseFolder_SC_M = fullfile('path_to_SC_M_directory');
  258. if ~exist(baseFolder_SC_M, 'dir')
  259. mkdir(baseFolder_SC_M);
  260. end
  261. saveAsEdge(SC_M1, fullfile(baseFolder_SC_M, [subj, '_SC_M1.edge']));
  262. saveAsEdge(SC_M2, fullfile(baseFolder_SC_M, [subj, '_SC_M2.edge']));
  263. saveAsEdge(SC_M3, fullfile(baseFolder_SC_M, [subj, '_SC_M3.edge']));
  264. saveAsEdge(SC_M4, fullfile(baseFolder_SC_M, [subj, '_SC_M4.edge']));
  266. baseFolder_FC_M = fullfile('path_to_FC_M_directory');
  267. if ~exist(baseFolder_FC_M, 'dir')
  268. mkdir(baseFolder_FC_M);
  269. end
  270. saveAsEdge(FC_M1, fullfile(baseFolder_FC_M, [subj, '_FC_M1.edge']));
  271. saveAsEdge(FC_M2, fullfile(baseFolder_FC_M, [subj, '_FC_M2.edge']));
  272. saveAsEdge(FC_M3, fullfile(baseFolder_FC_M, [subj, '_FC_M3.edge']));
  273. saveAsEdge(FC_M4, fullfile(baseFolder_FC_M, [subj, '_FC_M4.edge']));
  275. baseFolder_USFC_M = fullfile('path_to_USFC_M_directory');
  276. if ~exist(baseFolder_USFC_M, 'dir')
  277. mkdir(baseFolder_USFC_M);
  278. end
  279. saveAsEdge(USFC_M, fullfile(baseFolder_USFC_M, [subj, '_USFC.edge']));
  280. saveAsEdge(USFC_M_abs, fullfile(baseFolder_USFC_M, [subj, '_USFC_abs.edge']));
  282. baseFolder_Route = fullfile('path_to_Route_directory');
  283. if ~exist(baseFolder_Route, 'dir')
  284. mkdir(baseFolder_Route);
  285. end
  286. saveAsEdge(RouteCounts_M, fullfile(baseFolder_Route, [subj, '_Route_counted.edge']));
  287. end

USFC.m at commit b39b850, no license · at the source

Overview

Authors: Arzu C Has Silemek PhD1,2, Jeffrey C Wertheimer MD3, Janet Wei MD4, Yibin Xie2, Mitzi Gonzales PhD1, Debiao Li PhD2, Oana Dumitrascu MD5, Sarah Kremen MD1, Zaldy S Tan MD6, Micheal D Nelson PhD4,7, C Noel Bairey Merz MD4, Pascal Sati PhD1,2, Wei Gao PhD1
ORCID iDs: Arzu C Has Silemek PhD, Jeffrey C Wertheimer MD, Janet Wei MD, Yibin Xie, Mitzi Gonzales PhD, Debiao Li PhD, Oana Dumitrascu MD, Sarah Kremen MD, Zaldy S Tan MD, Micheal D Nelson PhD, C Noel Bairey Merz MD, Pascal Sati PhD, Wei Gao PhD
  1. Department of Neurology, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  2. Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute (BIRI), Cedars‐Sinai Medical Center, Los Angeles, California, USA
  3. Department of Physical Medicine and Rehabilitation, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  4. Barbra Streisand Women's Heart Center, Smidt Heart Institute, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  5. Department of Neurology, Mayo Clinic College of Medicine and Science, Scottsdale, Arizona, USA
  6. Departments of Neurology and Medicine, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  7. Department of Kinesiology, College of Nursing and Health Innovation, The University of Texas at Arlington, Arlington, Texas, USA
Institutions: Cedars-Sinai Medical Center (United States); Cedars-Sinai Smidt Heart Institute (United States); Mayo Clinic (United States); Mayo Clinic in Arizona (United States); The University of Texas at Arlington (United States)
Journal: Brain and behavior, volume 16, issue 6, article e71559
Dates: received 31 January 2026; accepted 4 June 2026; published online 25 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/brb3.71559 · PMID 42348298 · PMCID PMC13296822 · OpenAlex W7165872490
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: brain–heart axis, cognition, coronary microvascular dysfunction, neuroimaging, Unified Structural and Functional Connectivity, women's cardiovascular health
MeSH: Brain*, Coronary Artery Disease*, Aged, Cognition, Female, Humans, Magnetic Resonance Imaging, Microvessels, Middle Aged, Neural Pathways (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Heart, Lung and Blood Institutes (1R03 AG032631, N01-HV-68164, U0164829, K23HL151867 T32 HL69751, N01-HV-68161, PR150224P1 (CDMRP-DoD), MO1-RR00425, N01-HV-68162, R01HL124649, U01 HL649241, U54AG065141, U54AG094168, N01-HV-68163, U01 HL649141); NHLBI NIH HHS (R01 HL146158, UL1TR000124, R01 HL090957, K23 HL151867, K23 HL125941, K23 HL105787, R01 HL124649, K23 HL127262, T32 HL069751); Barbra Streisand Women's Cardiovascular Research and Education Program, Cedars-Sinai Medical Center; Edythe L. Broad and the Constance Austin Women's Heart Research Fellowships, Cedars-Sinai Medical Center; NIA NIH HHS (U54 AG094168, R03 AG032631, U54 AG065141); Erika Glazer Women's Heart Health Project; National Center for Advancing Translational Sciences; National Center for Research Resources; NCATS NIH HHS (UL1 TR000124); National Heart, Lung, and Blood Institute (UL1TR000124); Adelson Family Foundation, Cedars-Sinai Medical Center; NCRR NIH HHS (M01 RR000425); Linda Joy Pollin Women's Heart Health Program
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Background: The brain–heart axis is central to vascular health, yet no imaging biomarkers capture integrated dysfunction across neural and coronary microvascular networks. Although coronary microvascular dysfunction links to cognitive decline, neural correlates connecting cerebral efficiency with coronary physiology remain unclear.

Objectives: To determine whether the Unified Structural and Functional Connectivity (USFC)—a multimodal magnetic resonance imaging (MRI) “traffic map” of brain network efficiency—predicts coronary endothelial function and cognition in women with ischemia and no obstructive coronary artery disease (INOCA).

Methods: Thirty‐three women with suspected INOCA from the Women's Ischemia Syndrome Evaluation (WISE) study (NCT03876223) underwent invasive coronary function testing, cardiac MRI, cognitive evaluation, and multimodal brain MRI. USFC, structural connectivity (SC), and functional connectivity (FC) were computed for predefined 10 backbone pathways. Support vector regression and logistic classification assessed predictive performance.

Results: USFC explained 16%–20% more variance in coronary endothelial function, myocardial perfusion reserve, and cognition than SC or FC alone (p < 0.05). Connectivity between the left caudate–superior medial orbital gyrus and right calcarine–inferior occipital gyrus emerged as robust predictors of crystallized cognition (r = –0.78, p FDR < 0.05) and coronary endothelial function (r = 0.70, p FDR < 0.05), respectively. USFC also best discriminated low versus high coronary blood flow (area under the ROC curve [AUC]: USFC 0.622 vs. SC 0.349 and FC 0.425; p < 0.05).

Conclusions: USFC identifies neuro–cardiac pathways linking cerebral efficiency with coronary endothelial function. These results introduce a sensitive biomarker of systemic vulnerability, highlighting occipital and frontostriatal pathways as shared substrates of dysfunction. USFC offers a mechanistic framework for detecting vascular risk across metabolically demanding tissues.

Trial Registration: ClinicalTrials.gov identifier: NCT03876223

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

Repositories

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

ArzuHas/USFC

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b39b8501beb2b42bb7930556a5286e8be3c27a64, 10 October 2024
Languages: MATLAB (3)
Size: 8 files, 3 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/ArzuHas/USFC.git

Zenodo 13997197

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source:

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 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

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

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. A custom MATLAB code for construction of Unified Structural and Functional Connectivity is available online (Has Silemek 2024) at: https://github.com/ArzuHas/USFC.git.

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, 13 authors, 6 keywords, 10 MeSH terms, 13 funders, 46 references.

Cite

This paper

Has Silemek, A. C., Wertheimer, J. C., Wei, J., Xie, Y., Gonzales, M., Li, D., Dumitrascu, O., Kremen, S., Tan, Z. S., Nelson, M. D., Bairey Merz, C. N., Sati, P., & Gao, W. (2026). The "Brain's Traffic Map" Reveals Neural Pathways Linked to Coronary Microvascular Dysfunction in Women. Brain and behavior, 16(6), e71559. https://doi.org/10.1002/brb3.71559

BibTeX

@article{hassilemek2026brain,
author = {Has Silemek, Arzu C and Wertheimer, Jeffrey C and Wei, Janet and Xie, Yibin and Gonzales, Mitzi and Li, Debiao and Dumitrascu, Oana and Kremen, Sarah and Tan, Zaldy S and Nelson, Micheal D and Bairey Merz, C Noel and Sati, Pascal and Gao, Wei},
title = {{The "Brain's Traffic Map" Reveals Neural Pathways Linked to Coronary Microvascular Dysfunction in Women}},
journal = {Brain and behavior},
year = {2026},
month = jun,
volume = {16},
number = {6},
pages = {e71559},
publisher = {Wiley},
issn = {2162-3279},
doi = {10.1002/brb3.71559},
url = {https://doi.org/10.1002/brb3.71559},
pmid = {42348298},
pmcid = {PMC13296822}
}

RIS

TY - JOUR
AU - Has Silemek, Arzu C
AU - Wertheimer, Jeffrey C
AU - Wei, Janet
AU - Xie, Yibin
AU - Gonzales, Mitzi
AU - Li, Debiao
AU - Dumitrascu, Oana
AU - Kremen, Sarah
AU - Tan, Zaldy S
AU - Nelson, Micheal D
AU - Bairey Merz, C Noel
AU - Sati, Pascal
AU - Gao, Wei
TI - The "Brain's Traffic Map" Reveals Neural Pathways Linked to Coronary Microvascular Dysfunction in Women
T2 - Brain and behavior
J2 - Brain Behav
PY - 2026
DA - 2026/06/01
VL - 16
IS - 6
SP - e71559
SN - 2162-3279
PB - Wiley
DO - 10.1002/brb3.71559
UR - https://doi.org/10.1002/brb3.71559
LA - en
ER -

CSL-JSON

{
"id": "10.1002/brb3.71559",
"type": "article-journal",
"title": "The \"Brain's Traffic Map\" Reveals Neural Pathways Linked to Coronary Microvascular Dysfunction in Women",
"container-title": "Brain and behavior",
"author": [
{
"family": "Has Silemek",
"given": "Arzu C"
},
{
"family": "Wertheimer",
"given": "Jeffrey C"
},
{
"family": "Wei",
"given": "Janet"
},
{
"family": "Xie",
"given": "Yibin"
},
{
"family": "Gonzales",
"given": "Mitzi"
},
{
"family": "Li",
"given": "Debiao"
},
{
"family": "Dumitrascu",
"given": "Oana"
},
{
"family": "Kremen",
"given": "Sarah"
},
{
"family": "Tan",
"given": "Zaldy S"
},
{
"family": "Nelson",
"given": "Micheal D"
},
{
"family": "Bairey Merz",
"given": "C Noel"
},
{
"family": "Sati",
"given": "Pascal"
},
{
"family": "Gao",
"given": "Wei"
}
],
"container-title-short": "Brain Behav",
"volume": "16",
"issue": "6",
"page": "e71559",
"DOI": "10.1002/brb3.71559",
"PMID": "42348298",
"PMCID": "PMC13296822",
"ISSN": "2162-3279",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/brb3.71559",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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