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Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation.

Code ↔ Paper

1 match 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.

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  1. [1] § Methods › Experimental model validation › Flow-mediated dilation test ↔ InVivo_Experiments/Run_FMD_experiment.m, lines 1–58 · score 0.60 · shear stress, Shear rate, mmHg, diameter

Paper

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The authors' code

MATLAB · 257 lines · 7.1 KB · MIT · 1 match

  1. %% This script uses data from - which can be obtain via reasonable request to Philip Ainslie:
  2. % Carr, Jay MJR, et al. "Internal carotid and brachial artery
  3. % shear‐dependent vasodilator function in young healthy humans."
  4. % The Journal of physiology 598.23 (2020): 5333-5350. Figure 2B
  6. j = 1; %for indexing if you are going through more than 1 data sets
  7. endot= [20]; %endothelial response time (tau_endo)
  8. cuttime = [150]
  9. dat = readtable(['cFMD-Table 1.csv'], 'ReadRowNames',false, 'ReadVariableNames', true);
  11. CBFv1 = dat.ShearRate_1_s_;
  12. diameter = dat.ContinuousDiameter_cm_;
  13. time = dat.Time;
  16. %Smooth and interpolate:
  17. CBFv = movmean(CBFv1, 10, 'omitmissing');
  18. diam = movmean(diameter, 10, 'omitmissing');
  19. CBF(:,1) = time;
  21. cPerc = (CBFv)/CBFv(1)-1;
  22. dPerc = diam/diam(1)-1;
  24. %convert shear stress to CBF
  25. %convert diam to radius of 0.1:
  26. sc = diam(1,1)/0.1;
  27. CBF(:,2) = CBFv.*pi.*(diam./sc).^2;%Divide by 4 to convert to radius of 0.1
  28. CBF(:,2) = CBF(:,2);%+0.6*rand(size(CBF(:,2)));
  30. %remove nans:
  31. cut = find(isnan(CBF(:,2)));
  32. time(cut) = [];
  33. CBF(cut,:) = [];
  34. diam(cut) = [];
  35. CBFv(cut) = [];
  37. cut = find(isnan(CBF(:,1)));
  38. time(cut) = [];
  39. CBF(cut,:) = [];
  40. diam(cut) = [];
  41. CBFv(cut) = [];
  43. cut = find(time > cuttime(j))
  44. time(cut) = [];
  45. CBF(cut,:) = [];
  46. diam(cut) = [];
  47. CBFv(cut) = [];
  49. cut = find(time < cutstart(j))
  50. time(cut) = [];
  51. CBF(cut,:) = [];
  52. diam(cut) = [];
  53. CBFv(cut) = [];
  56. Press = 70.*ones(size(CBF)); %Assume pressure is normal CPP = 70 mmHg
  58. load("FakeData/FMD/Paramvals.mat")
  60. %%
  61. paramvals(7) = 1; %endothelial mechanism
  62. paramvals(4) = endot(j);
  63. paramvals(8) = 1;
  64. paramvals(6) = 1;
  65. paramvals(5) = 1;
  66. paramvals(2) = CBF(1,2);
  67. try
  68. paramvals(10) = ECO2(1,2);
  69. catch
  70. paramvals(10) = 40;
  71. end
  72. %
  73. % paramvals(7) = 1;
  74. % paramvals(1) = 0.1;
  75. % paramvals(3) =1.5;
  76. % paramvals(14) = 6;
  77. % paramvals(4) = 35
  80. IC = [paramvals(2), paramvals(1), 0, 0, 0, 0, 0, 0, 0];
  81. %figure(2), plot(time, CBF(:,2)), ylabel('Flow'), yyaxis right, plot(time, diam), ylabel('Diam')
  82. for i = 1:5
  83. if i == 1
  84. paramvals(6:8) = [1,1,1]
  85. tit ='Metabolic, Myogenic, and Endothelial Mechanisms'
  86. ct = 5
  87. elseif i == 2
  88. paramvals(6:8) = [0,0,0]
  89. tit ='No CVTR'
  90. ct = 1
  91. elseif i == 3
  92. paramvals(6:8) = [0,1,1]
  93. tit ='Endothelial and Metabolic Mechanisms'
  94. ct = 3
  95. elseif i == 4
  96. paramvals(6:8) = [1,1,0]
  97. tit ='Endothelial and Myogenic Mechanisms'
  98. ct = 4
  99. elseif i == 5
  100. paramvals(6:8) = [0,1,0]
  101. tit ='Endothelial Mechanism Only'
  102. ct = 2
  103. end
  104. [t,y] = ode23(@(t, y) cerebralbloodflow_changeshearstress(t,y,paramvals,[time, Press/75], CBF, [], [], []), time(4:end)', IC);
  106. %scale diameters:
  107. if i == 1
  108. b1 = [ones(size(y(:,2))) y(:,2)]\diam(4:end);
  110. %or do just standard scaling:
  111. %b1 = [1;1];
  113. if length(find(isnan(b1))>1)
  114. b1 = [ones(size(y(:,2))) y(:,2)]\diam(1:end-3);
  115. end
  116. end
  117. sim_diam = [ones(size(y(:,2))) y(:,2)]*(b1);%if it's really bad sometimes the slope is -
  119. if 1 %turn on to plot
  120. fig = figure, nexttile,
  121. fig.Position = [-209 1482 370 364];
  122. fig.Units = 'pixels'
  123. plot(t, sim_diam, 'k', 'linewidth',3), hold on, plot(time, diam, 'k:', 'linewidth',3) %don't have to convert because radius doesn't change with CVTR off
  124. ylabel('Diameter (mm)')
  126. set(gca, 'box','off')
  127. set(gcf,'color','white')
  128. set(gca, 'fontsize',15)
  129. xlabel('Time (s)')
  131. title(tit)
  132. if i == 1
  133. title(["Metabolic, Myogenic,"; "and Endothelial Mechanisms"])
  134. ax = gca
  135. ylims = ax.YLim
  136. elseif i == 2
  137. legend('Scaled Diameter_{sim}','Diameter_{data}');%, 'CBF_{data}')%, title(['Radius = ', num2str(paramvals(1))])
  138. end
  139. ylim(ylims);
  142. if j == 1
  143. if i == 3 || i == 5
  144. axes('Position',[0.55, 0.6, 0.35, 0.3])
  145. plot(t, sim_diam, 'k', 'linewidth',3), hold on, plot(time, diam, 'k:', 'linewidth',3) %don't have to convert because radius doesn't change with CVTR off
  146. ylabel('Diameter')
  147. xlabel(' Time')
  148. set(gca, 'XTick', [])
  149. set(gca, 'YColor', 'k')
  150. set(gca, 'box','off')
  151. end
  152. elseif j == 2
  153. if i == 3 || i == 5
  154. axes('Position', [0.6500 0.2000 0.2500 0.3000])
  155. plot(t, sim_diam, 'k', 'linewidth',3), hold on, plot(time, diam, 'k:', 'linewidth',3) %don't have to convert because radius doesn't change with CVTR off
  156. ylabel('Diameter')
  157. xlabel('Time')
  158. set(gca, 'XTick', [])
  159. set(gca, 'YColor', 'k')
  160. set(gca, 'box','off')
  161. end
  162. elseif j == 3
  163. if i == 3 || i == 5
  164. axes('Position', [0.2200 0.6400 0.2500 0.2500])
  165. yyaxis right
  166. plot(t, sim_diam, 'k', 'linewidth',3), hold on, plot(time, diam, 'k:', 'linewidth',3) %don't have to convert because radius doesn't change with CVTR off
  167. ylabel('Diameter')
  168. xlabel('Time')
  169. set(gca, 'XTick', [])
  170. set(gca, 'YColor', 'k')
  171. set(gca, 'box','off')
  172. yyaxis left
  173. set(gca, 'YColor', 'none')
  174. end
  175. elseif j == 4
  176. if i == 3 || i == 5
  177. axes('Position', [0.6500 0.2000 0.2500 0.2500])
  178. plot(t, sim_diam, 'k', 'linewidth',3), hold on, plot(time, diam, 'k:', 'linewidth',3) %don't have to convert because radius doesn't change with CVTR off
  179. ylabel('Diameter')
  180. xlabel('Time')
  181. set(gca, 'XTick', [])
  182. set(gca, 'YColor', 'k')
  183. set(gca, 'box','off')
  184. end
  185. elseif j == 5
  186. if i == 3 || i == 5
  187. axes('Position', [0.2500 0.6400 0.2500 0.2500])
  188. yyaxis right
  189. plot(t, sim_diam, 'k', 'linewidth',3), hold on, plot(time, diam, 'k:', 'linewidth',3) %don't have to convert because radius doesn't change with CVTR off
  190. ylabel('Diameter')
  191. xlabel('Time')
  192. set(gca, 'XTick', [])
  193. set(gca, 'YColor', 'k')
  194. set(gca, 'box','off')
  195. yyaxis left
  196. set(gca, 'YColor', 'none')
  197. end
  198. end
  199. % saveas(gcf, [filename, '_', strrep(tit, ' ', ''), '.fig'])
  200. % saveas(gcf, [filename, '_', strrep(tit, ' ', ''), '.png'])
  203. %plot Radius
  204. fig = figure,
  205. fig.Position = [-209 1482 370 364];
  206. fig.Units = 'pixels'
  207. yyaxis right, plot(t-t(1), y(:,3:5), 'linewidth',3)
  208. if i == 2
  209. l = legend('\phi_{myo}', '\phi_{endo}', '\phi_{meta}')%, title(['Radius = ', num2str(paramvals(1))])
  210. %keyboard
  211. end
  212. set(gca, 'box','off')
  213. set(gcf,'color','white')
  214. set(gca, 'fontsize',15)
  215. xlabel('Time (s)')
  216. %tit = input("Input Title and Save Name ", "s")
  217. title(tit)
  218. if i == 1
  219. title(["Metabolic, Myogenic,"; "and Endothelial Mechanisms"])
  220. yyaxis right
  221. ax = gca
  222. yylimR = ax.YLim
  223. end
  224. yyaxis left
  225. ax2 = gca
  226. set(ax2.YAxis(1), 'Visible', 'off')
  227. yyaxis right
  228. ylim(yylimR)
  229. ylabel('Force From Mechansims')
  230. % saveas(gcf, [filename, '_', strrep(tit, ' ', ''), 'Radius.fig'])
  231. % saveas(gcf, [filename, '_', strrep(tit, ' ', ''), 'Radius.png'])
  234. if i == 1
  235. %plot pressure:
  236. fig = figure,
  237. fig.Position = [-209 1482 370 364];
  238. fig.Units = 'pixels'
  239. plot(time, CBFv, 'Color', [190, 94, 121]./215, 'linewidth',3)
  240. ylabel('Shear Rate (1/s)')
  241. xlabel('Time (s)')
  242. set(gca, 'box','off')
  243. set(gcf,'color','white')
  244. set(gca, 'fontsize',15)
  245. % saveas(fig, [filename, '_Shear.fig'])
  246. % saveas(fig, [filename, '_Shear.png'])
  247. end
  248. end
  250. corelation = corr(sim_diam, diam(4:end))
  251. mse = mean((sim_diam-diam(4:end)).^2)
  254. mse_all(ct) = [mse];
  255. corr_all(ct)= [corelation];
  257. end

Run_FMD_experiment.m at commit e540c0a, under MIT · at the source

Overview

Authors: Jennifer K. Briggs1, J. N. Stroh2, Soojin Park3,4, Brandon Foreman5, Michael M. Tymko6,7, Jay Carr8,7, Melike Sirlanci2, Philip N. Ainslie7, Richard K. P. Benninger1,9, Tellen D. Bennett2,10, David J. Albers1,2,3
ORCID iDs: J. N. Stroh
  1. Department of Biomedical Engineering, University of Colorado Denver | Anschutz,Aurora, CO USA
  2. Department of Biomedical Informatics, University of Colorado School of Medicine,Aurora, CO USA
  3. Department of Biomedical Informatics, Columbia University Irving Medical Center,New York, NY USA
  4. Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons,New York, NY USA
  5. Department of Neurology, University of Cincinnati College of Medicine,Cincinnati, OH USA
  6. Integrative Cerebrovascular and Environmental Physiology (ICEP) SB Laboratory, University of Guelph,Guelph, ON Canada
  7. Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia—Okanagan,Kelowna, BC Canada
  8. Institute of Mountain Emergency Medicine, Eurac Research,Bolzano, Italy
  9. Barbara Davis Center for Diabetes, University of Colorado Anschutz,Aurora, CO USA
  10. Department of Pediatrics (Critical Care Medicine), University of Colorado School of Medicine,Aurora, CO USA
Institutions: University of Colorado Denver (United States); University of Colorado Anschutz (United States); Columbia University Irving Medical Center (United States); Columbia University (United States); University of Cincinnati (United States); University of Guelph (Canada); University of British Columbia (Canada); Eurac Research (Italy)
Journal: NPJ digital medicine, volume 9, issue 1, article 428
Dates: received 3 July 2025; accepted 23 March 2026; published online 6 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41746-026-02600-x · PMID 41942716 · PMCID PMC13234139 · OpenAlex W7150830012
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), clinical / translational (subfield)
Methods: Smoothing, state filtering, decompositions, Statistics
Keywords: Computational biology and bioinformatics, Engineering, Neurology, Neuroscience
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (DGE-1938058Briggs); National Institutes of Health (R01 NS131606, R01 DK102950, K24 HL168225, R01 LM006910); Natural Sciences and Engineering Research Council of Canada (40165)
Citations: not cited yet (Europe PMC); 68 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, with 1 match between paragraphs and lines of code.

jenniferkbriggs/CereBRLSIM

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: e540c0a9c83411cab76f0f5a908dd624289572dd, 31 July 2025
Languages: MATLAB (14)
Size: 26 files, 14 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (4 files), Statistics and Machine Learning Toolbox (4 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
16 files
At the source: github.com/jenniferkbriggs/CereBRLSIM

Code availability statement

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Read it in the paper: doi.org/10.1038/s41746-026-02600-x.

Tracing map

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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;
  • 14 scripts, each with its path and the digest of its content;
  • 1 match 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

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Code and data availability statement

The paper has a code and 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/s41746-026-02600-x.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 4 keywords, 3 funders, 58 references.

Cite

This paper

Briggs, J. K., Stroh, J. N., Park, S., Foreman, B., Tymko, M. M., Carr, J., Sirlanci, M., Ainslie, P. N., Benninger, R. K. P., Bennett, T. D., & Albers, D. J. (2026). Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation. NPJ digital medicine, 9(1), 428. https://doi.org/10.1038/s41746-026-02600-x

BibTeX

@article{briggs2026towards,
author = {Briggs, Jennifer K. and Stroh, J. N. and Park, Soojin and Foreman, Brandon and Tymko, Michael M. and Carr, Jay and Sirlanci, Melike and Ainslie, Philip N. and Benninger, Richard K. P. and Bennett, Tellen D. and Albers, David J.},
title = {{Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation}},
journal = {NPJ digital medicine},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {428},
publisher = {Nature Publishing Group},
issn = {2398-6352},
doi = {10.1038/s41746-026-02600-x},
url = {https://doi.org/10.1038/s41746-026-02600-x},
pmid = {41942716},
pmcid = {PMC13234139}
}

RIS

TY - JOUR
AU - Briggs, Jennifer K.
AU - Stroh, J. N.
AU - Park, Soojin
AU - Foreman, Brandon
AU - Tymko, Michael M.
AU - Carr, Jay
AU - Sirlanci, Melike
AU - Ainslie, Philip N.
AU - Benninger, Richard K. P.
AU - Bennett, Tellen D.
AU - Albers, David J.
TI - Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation
T2 - NPJ digital medicine
J2 - NPJ Digit Med
PY - 2026
DA - 2026/04/06
VL - 9
IS - 1
SP - 428
SN - 2398-6352
PB - Nature Publishing Group
DO - 10.1038/s41746-026-02600-x
UR - https://doi.org/10.1038/s41746-026-02600-x
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41746-026-02600-x",
"type": "article-journal",
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"container-title": "NPJ digital medicine",
"author": [
{
"family": "Briggs",
"given": "Jennifer K."
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{
"family": "Stroh",
"given": "J. N."
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{
"family": "Park",
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{
"family": "Foreman",
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},
{
"family": "Tymko",
"given": "Michael M."
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{
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{
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"given": "Melike"
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{
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"given": "Philip N."
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{
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"given": "Richard K. P."
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{
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"given": "Tellen D."
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"container-title-short": "NPJ Digit Med",
"volume": "9",
"issue": "1",
"page": "428",
"DOI": "10.1038/s41746-026-02600-x",
"PMID": "41942716",
"PMCID": "PMC13234139",
"ISSN": "2398-6352",
"publisher": "Nature Publishing Group",
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"issued": {
"date-parts": [
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}

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