OSCR

Biophysical modeling of anatomically realistic prenatal cortical folding development.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Postprocessing analyses and quantitative metrics ↔ abaqus_script/surfReconstruction.m, lines 124–191 · score 0.60 · Quantitative metrics, sulcal depth, gyrification, node, reconstruct, curvature
  2. [2] § Methods › Postprocessing analyses and quantitative metrics ↔ symbolic regression/Pysr_growth_area.py, lines 135–220 · score 0.58 · predicted model, symbolic regression, growth ratio, metrics
  3. [3] § Methods › Postprocessing analyses and quantitative metrics ↔ symbolic regression/Pysr_growth_thickness.py, lines 127–215 · score 0.58 · predicted model, symbolic regression, growth ratio, metrics
  4. [4] § Results › Modeled folding patterns align with those observed in the developing brain ↔ figure scripts/fig4a.py, lines 57–75 · score 0.52 · gestational week, simulated brains, real brain, global, GI

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 283 lines · 15 KB · no license · 1 match

  1. % ---------------------------------------------------------
  2. % ------- Code for abaqus results postprocessing ----------
  3. % ---------------------------------------------------------
  5. clc;clear;
  7. addpath('E:\PhD\PINN\WholeBrain\Postprocessing\functionsMat'); % matlab functions
  8. addpath('E:\PhD\PINN\WholeBrain\Postprocessing\datafromAbaqus\connectivityFiles\smooth'); % face connectivity
  9. addpath('E:\PhD\PINN\WholeBrain\Postprocessing\datafromAbaqus\rawData\dataSmooth'); % raw data from simulation
  10. addpath('E:\PhD\PINN\WholeBrain\Mesh\SmoothSurface\inputFiles'); % reference vtk file
  12. caseId = {'S01'};
  13. idx = 1;
  15. coordinate_file_gray = sprintf('Data_%s_gray_smooth.xlsx', caseId{idx});
  16. coordinate_file_medium = sprintf('Data_%s_medium_smooth.xlsx',caseId{idx});
  17. coordinate_file_white = sprintf('Data_%s_white_smooth.xlsx',caseId{idx});
  18. connectivity_file = sprintf('Surface_connectivity_%s_smooth.xlsx', caseId{idx});
  20. reference_file_inner = ['22_lh.InnerSurf_', caseId{idx}, '.vtk'];
  21. reference_file_outer = ['22_lh.OuterSurf_', caseId{idx}, '.vtk'];
  23. dataStoragePath = 'E:\PhD\PINN\WholeBrain\Postprocessing\dataTemp\';
  24. fullFilePath = fullfile(dataStoragePath, ['Input_', caseId{idx}]);
  25. if ~exist(fullFilePath, 'dir')
  26. mkdir(fullFilePath);
  27. end
  28. casename = ['Brain_',caseId{idx}];
  29. max_frame = 100;
  30. num_frames= max_frame/5 + 1;
  31. %%
  32. % Set parameters
  33. iterations = 10;
  34. lambda = 0.3;
  35. batchSize = 1000;
  37. % Surface and connectivity sheets
  38. sheets = struct( ...
  39. 'gray', 'graysurf_connectivity_region19', ...
  40. 'medium', 'midsurf_connectivity_region19', ...
  41. 'white', 'whitesurf_connectivity_region19');
  43. % Read surface connectivity
  44. SurfConnectivity.gray = readmatrix(connectivity_file, 'Sheet', sheets.gray);
  45. SurfConnectivity.medium = readmatrix(connectivity_file, 'Sheet', sheets.medium);
  46. SurfConnectivity.white = readmatrix(connectivity_file, 'Sheet', sheets.white);
  48. % Read initial coordinates
  49. Nodes.gray = readmatrix(coordinate_file_gray, 'Sheet', 'Frame0');
  50. Nodes.medium = readmatrix(coordinate_file_medium, 'Sheet', 'Frame0');
  51. Nodes.white = readmatrix(coordinate_file_white, 'Sheet', 'Frame0');
  53. % Extract node coordinates
  54. for type = ["gray", "medium", "white"]
  55. NodeCoord.(type) = Nodes.(type)(:, 1:3);
  56. triConn.(type) = quadToTriConnectivity(SurfConnectivity.(type), NodeCoord.(type));
  57. smoothedCoord.(type) = laplacianSmoothing(triConn.(type), NodeCoord.(type), iterations, lambda);
  58. end
  60. % Read parcellation info
  61. pvtk_inner = mvtk_read(reference_file_inner);
  62. pvtk_outer = mvtk_read(reference_file_outer);
  63. par_info_huang = pvtk_inner.par_huang;
  64. par_info_FS2009 = pvtk_inner.par_FS2009;
  66. % Compute parcellation nodes
  67. Nodes_par.inner = pvtk_inner.vertices;
  68. Nodes_par.outer = pvtk_outer.vertices;
  69. Nodes_par.medium = (Nodes_par.inner + Nodes_par.outer) / 2;
  71. % Create k-d trees
  72. for surface = ["inner", "medium", "outer"]
  73. kdtree.(surface) = createns(Nodes_par.(surface), 'NSMethod', 'kdtree');
  74. end
  76. % Assign parcellation
  77. par_simulation_huang.gray = assignParcellation(smoothedCoord.gray, kdtree.inner, par_info_huang, batchSize);
  78. par_simulation_FS2009.gray = assignParcellation(smoothedCoord.gray, kdtree.inner, par_info_FS2009, batchSize);
  79. par_simulation_huang.medium = assignParcellation(smoothedCoord.medium, kdtree.medium, par_info_huang, batchSize);
  80. par_simulation_FS2009.medium = assignParcellation(smoothedCoord.medium, kdtree.medium, par_info_FS2009, batchSize);
  81. par_simulation_huang.white = assignParcellation(smoothedCoord.white, kdtree.outer, par_info_huang, batchSize);
  82. par_simulation_FS2009.white = assignParcellation(smoothedCoord.white, kdtree.outer, par_info_FS2009, batchSize);
  84. %
  85. % ---------------------------------------------------------
  86. % --- Write vtk file of each frame into "Input" folder ----
  87. % ---------------------------------------------------------
  88. %
  89. for i =1:num_frames
  91. coordinate_sheet_deformed = ['Frame', num2str(5*(i-1))];
  92. RegionNodes_gray_deformed = readmatrix(coordinate_file_gray, 'Sheet', coordinate_sheet_deformed);
  93. RegionNodes_medium_deformed = readmatrix(coordinate_file_medium, 'Sheet', coordinate_sheet_deformed);
  94. RegionNodes_white_deformed = readmatrix(coordinate_file_white, 'Sheet', coordinate_sheet_deformed);
  96. NodeCoord_gray_deformed = RegionNodes_gray_deformed(:,1:3);
  97. NodeCoord_medium_deformed = RegionNodes_medium_deformed(:,1:3);
  98. NodeCoord_white_deformed = RegionNodes_white_deformed(:,1:3);
  100. triConnectivity_gray_deformed = triConn.gray;
  101. triConnectivity_medium_deformed = triConn.medium;
  102. triConnectivity_white_deformed = triConn.white;
  104. smoothedNodesCoord_gray_deformed = laplacianSmoothing(triConnectivity_gray_deformed, NodeCoord_gray_deformed, iterations, lambda);
  105. smoothedNodesCoord_medium_deformed = laplacianSmoothing(triConnectivity_medium_deformed, NodeCoord_medium_deformed, iterations, lambda);
  106. smoothedNodesCoord_white_deformed = laplacianSmoothing(triConnectivity_white_deformed, NodeCoord_white_deformed, iterations, lambda);
  108. vtkFilename_gray = [fullFilePath,'\gray_frame', num2str(5*(i-1)), '_',caseId{idx},'.vtk'];
  109. vtkFilename_medium = [fullFilePath,'\medium_frame', num2str(5*(i-1)), '_',caseId{idx},'.vtk'];
  110. vtkFilename_white = [fullFilePath,'\white_frame', num2str(5*(i-1)),'_',caseId{idx},'.vtk'];
  112. dataVTK_gray=struct('vertices',smoothedNodesCoord_gray_deformed,'faces',triConnectivity_gray_deformed,'par_huang',par_simulation_huang.gray,'par_FS2009',par_simulation_FS2009.gray);
  113. dataVTK_medium=struct('vertices',smoothedNodesCoord_medium_deformed,'faces',triConnectivity_medium_deformed,'par_huang',par_simulation_huang.medium,'par_FS2009',par_simulation_FS2009.medium);
  114. dataVTK_white=struct('vertices',smoothedNodesCoord_white_deformed,'faces',triConnectivity_white_deformed,'par_huang',par_simulation_huang.white,'par_FS2009',par_simulation_FS2009.white);
  116. mvtk_write(dataVTK_gray,vtkFilename_gray,'legacy');
  117. mvtk_write(dataVTK_medium,vtkFilename_medium,'legacy');
  118. mvtk_write(dataVTK_white,vtkFilename_white,'legacy');
  119. disp(coordinate_sheet_deformed);
  120. end
  122. disp('All vtk files have been written into "Input" folder.');
  124. %%
  125. % -------------------------------------------------------------------------------------------------------
  126. % --- Calculating the quantitative metrics: MC, Cortical thickness,sulcal depth, local and global GI ----
  127. % -------------------------------------------------------------------------------------------------------
  128. %
  130. GI = zeros(num_frames,1);
  131. GI_alpha = zeros(num_frames,1);
  133. tic;
  134. for i=7:9
  135. last_time=toc;
  136. pvtk_gray=mvtk_read([fullFilePath,'\gray_frame', num2str(5*(i-1)), '_',caseId{idx},'.vtk']);
  137. pvtk_medium=mvtk_read([fullFilePath,'\medium_frame', num2str(5*(i-1)), '_',caseId{idx},'.vtk']);
  138. pvtk_white=mvtk_read([fullFilePath,'\white_frame', num2str(5*(i-1)), '_',caseId{idx},'.vtk']);
  140. Nodes_gray=pvtk_gray.vertices;
  141. Nodes_medium = pvtk_medium.vertices;
  142. Nodes_white=pvtk_white.vertices;
  143. connectivity_gray=double(pvtk_gray.faces);
  144. par_simu_huang = pvtk_gray.par_huang;
  145. par_simu_FS2009 = pvtk_gray.par_FS2009;
  147. txtFiles = dir([dataStoragePath, 'lgi\', sprintf('gray_frame%d_%s*.txt',5*(i-1), caseId{idx})]);
  149. if isempty(txtFiles)
  150. warning('No matching file found for gray_frame%d', 5 * (i-1));
  151. continue;
  152. end
  153. lgiFilePath = fullfile([dataStoragePath, 'lgi\'], txtFiles(1).name);
  154. lgi = readmatrix(lgiFilePath);
  156. Cthickness1 = corticalThickness(Nodes_gray, Nodes_white);
  157. Cthickness2 = corticalThickness(Nodes_gray, Nodes_medium);
  159. [GC, MC_dimensionless, MC, k_max, k_min, shapeIndex]= Curvatures(connectivity_gray,Nodes_gray(:,1),Nodes_gray(:,2),Nodes_gray(:,3)); % calculate the mean curvature
  160. data_to_smooth = [GC, MC_dimensionless, MC, k_max, k_min, shapeIndex];
  161. smoothed_data = dataSmoothing(Nodes_gray, connectivity_gray, data_to_smooth, 10, 0.5);
  162. GC_corr = smoothed_data(:,1);
  163. MC_corr = smoothed_data(:,2);
  164. MC_dimensionless_corr = smoothed_data(:,3);
  165. k_max_corr = smoothed_data(:,4);
  166. k_min_corr = smoothed_data(:,5);
  167. shapeIndex_corr = smoothed_data(:,6);
  169. [Connectivity_hull, vertices_hull, GI(i)]= GyrificationIndex(connectivity_gray,Nodes_gray(:,1),Nodes_gray(:,2),Nodes_gray(:,3)); % calculate the global GI
  170. SulcDepth = SulcalDepth(Connectivity_hull,vertices_hull,Nodes_gray(:,1),Nodes_gray(:,2),Nodes_gray(:,3)); % calculate the sulcal depth
  172. [Connectivity_hull_alpha, vertices_hull_alpha, GI_alpha(i)]= GyrificationIndex_alphashape(connectivity_gray,Nodes_gray(:,1),Nodes_gray(:,2),Nodes_gray(:,3)); % calculate the global GI
  173. SulcDepth_alpha = SulcalDepth_alphashape(Connectivity_hull_alpha,vertices_hull_alpha,Nodes_gray(:,1),Nodes_gray(:,2),Nodes_gray(:,3)); % calculate the sulcal depth
  174. SulcDepth_alpha_shrink = SulcalDepth_alphashape_shrink(Connectivity_hull_alpha,vertices_hull_alpha,Nodes_gray(:,1),Nodes_gray(:,2),Nodes_gray(:,3)); % calculate the sulcal depth
  176. data_to_smooth1= [SulcDepth,SulcDepth_alpha,SulcDepth_alpha_shrink];
  177. smoothed_data1 = dataSmoothing(Nodes_gray, connectivity_gray, data_to_smooth1, 2, 1);
  179. SulcDepth_corr = smoothed_data1(:,1);
  180. SulcDepth_alpha_corr = smoothed_data1(:,2);
  181. SulcDepth_alpha_shrink_corr = smoothed_data1(:,3);
  183. vtkFilename = [dataStoragePath,'DataSummary\',caseId{idx},'_data_frame', num2str(5 * (i-1)), '.vtk'];
  185. dataVTK=struct('vertices',Nodes_gray,'faces',connectivity_gray, ...
  186. 'MC',MC_corr,'MC_dimensionless',MC_dimensionless_corr, 'GC',GC_corr,'k_max',k_max_corr,'k_min',k_min_corr,'shapeIndex',shapeIndex_corr,...
  187. 'par_huang',par_simu_huang,'par_FS2009',par_simu_FS2009,'SulcDepth',SulcDepth_corr,'SulcDepth_alpha',SulcDepth_alpha_corr,'SulcDepth_alpha_shrink',SulcDepth_alpha_shrink_corr,...
  188. 'CorticalThickness_GW',Cthickness1,'CorticalThickness_GM',Cthickness2, 'Lgi', lgi, 'GI',GI(i)*ones(length(MC_corr),1),'GI_alpha',GI_alpha(i)*ones(length(MC_corr),1));
  189. mvtk_write(dataVTK,vtkFilename,'legacy');
  190. fprintf('Frame %d, finished within %f s\n',5*(i-1), toc);
  191. end
  193. %%
  194. % --------------------------------------------------------------------------------------------
  195. % --- Collecting results by averaging those quantitative metrics throughtout each regions ----
  196. % --------------------------------------------------------------------------------------------
  197. %
  198. num_regions=length(unique(par_simu_huang));
  200. MC_aver = zeros(num_regions,num_frames);
  201. MC_dimensionless_aver = zeros(num_regions,num_frames);
  202. GC_aver = zeros(num_regions,num_frames);
  203. k_max_aver = zeros(num_regions,num_frames);
  204. k_min_aver = zeros(num_regions,num_frames);
  205. shapeIndex_aver = zeros(num_regions,num_frames);
  206. CThickness_GW_aver = zeros(num_regions,num_frames);
  207. CThickness_GM_aver = zeros(num_regions,num_frames);
  208. SulcDepth_aver = zeros(num_regions,num_frames);
  209. SulcDepth_alpha_aver = zeros(num_regions,num_frames);
  210. SulcDepth_alpha_shrink_aver = zeros(num_regions,num_frames);
  211. Lgi_aver = zeros(num_regions,num_frames);
  212. GI_aver = zeros(num_regions,num_frames);
  213. GI_alpha_aver = zeros(num_regions,num_frames);
  214. %
  215. for i=1:num_frames
  216. pvtk = mvtk_read([dataStoragePath,'DataSummary\',caseId{idx},'_data_frame', num2str(5 * (i-1)), '.vtk']);
  217. for j = 1:num_regions
  218. MC_aver(j,i)=mean(abs(pvtk.MC(pvtk.par_huang==j-1)));
  219. MC_dimensionless_aver(j,i)=mean(abs(pvtk.MC_dimensionless(pvtk.par_huang==j-1)));
  220. GC_aver(j,i) = mean(abs(pvtk.GC(pvtk.par_huang==j-1)));
  221. k_max_aver(j,i) = mean(abs(pvtk.k_max(pvtk.par_huang==j-1)));
  222. k_min_aver(j,i) = mean(abs(pvtk.k_min(pvtk.par_huang==j-1)));
  223. shapeIndex_aver(j,i) = mean(abs(pvtk.shapeIndex(pvtk.par_huang==j-1)));
  224. CThickness_GW_aver(j,i)=mean(pvtk.CorticalThickness_GW(pvtk.par_huang==j-1));
  225. CThickness_GM_aver(j,i)=mean(pvtk.CorticalThickness_GM(pvtk.par_huang==j-1));
  226. SulcDepth_aver(j,i) = mean(abs(pvtk.SulcDepth(pvtk.par_huang==j-1)));
  227. SulcDepth_alpha_aver(j,i) = mean(abs(pvtk.SulcDepth_alpha(pvtk.par_huang==j-1)));
  228. SulcDepth_alpha_shrink_aver(j,i) = mean(abs(pvtk.SulcDepth_alpha_shrink(pvtk.par_huang==j-1)));
  229. Lgi_aver(j,i)=mean(pvtk.Lgi(pvtk.par_huang==j-1));
  230. GI_aver(j,i)=mean(pvtk.GI(pvtk.par_huang==j-1));
  231. GI_alpha_aver(j,i)=mean(pvtk.GI_alpha(pvtk.par_huang==j-1));
  232. end
  233. end
  235. excel_file = [dataStoragePath,'dataSummary\Data_summary_',caseId{idx},'_L.xlsx'];
  236. time_values = (0.05 * (1:num_frames) - 0.05); % Compute time values
  237. variable_names = {'MC_aver', 'MC_dimensionless_aver', 'GC_aver','k_max_aver','k_min_aver','shapeIndex_aver','SulcDepth_aver','SulcDepth_alpha_aver',...
  238. 'SulcDepth_alpha_shrink_aver','CThickness_GW_aver','CThickness_GM_aver','Lgi_aver','GI_aver','GI_alpha_aver'};
  240. % Write data to Excel
  241. for region = 1:num_regions
  242. % Initialize data matrix for the region
  243. data_matrix = [(time_values)', ... % Frame numbers
  244. MC_aver(region, 1:num_frames)', ...
  245. MC_dimensionless_aver(region, 1:num_frames)', ...
  246. GC_aver(region, 1:num_frames)', ...
  247. k_max_aver(region, 1:num_frames)', ...
  248. k_min_aver(region, 1:num_frames)', ...
  249. shapeIndex_aver(region, 1:num_frames)', ...
  250. SulcDepth_aver(region, 1:num_frames)', ...
  251. SulcDepth_alpha_aver(region, 1:num_frames)', ...
  252. SulcDepth_alpha_shrink_aver(region, 1:num_frames)', ...
  253. CThickness_GW_aver(region, 1:num_frames)',...
  254. CThickness_GM_aver(region, 1:num_frames)',...
  255. Lgi_aver(region, 1:num_frames)',...
  256. GI_aver(region, 1:num_frames)',...
  257. GI_alpha_aver(region, 1:num_frames)',...
  258. ];
  259. % Create headers
  260. headers = {'Time', 'MC', 'MC_dimensionless','GC','k_max','k_min','shapeIndex','SulcDepth','SulcDepth_alpha','SulcDepth_alpha_shrink', 'CThickness_GW','CThickness_GM','LGI','GI','GI_alpha'};
  262. % Write to Excel
  263. sheet_name = sprintf('Region_%d', region - 1);
  264. writecell([headers; num2cell(data_matrix)], excel_file, 'Sheet', sheet_name);
  265. end
  266. disp('Data successfully written to Excel file.');
  267. %
  268. save([dataStoragePath,'dataSummary\',casename,'.mat']);
  271. % Helper function to assign parcellation labels in parallel
  272. function par_cell = assignParcellation(nodes, kdtree, par_info, batchSize)
  273. numPoints = size(nodes, 1);
  274. numBatches = ceil(numPoints / batchSize);
  275. par_cell = cell(numBatches, 1);
  276. parfor batch = 1:numBatches
  277. idxRange = (batch - 1) * batchSize + 1 : min(batch * batchSize, numPoints);
  278. batchPoints = nodes(idxRange, :);
  279. idx = knnsearch(kdtree, batchPoints);
  280. par_cell{batch} = par_info(idx);
  281. end
  282. par_cell = vertcat(par_cell{:});
  283. end

surfReconstruction.m at commit 57164fd, no license · at the source

Overview

Authors: Jixin Hou1, Zhengwang Wu2, Kun Jiang1, Taotao Wu3, Lu Zhang4, Dajiang Zhu5, Wei Gao6, Mir Jalil Razavi7, Tianming Liu8, Ellen Kuhl9, Gang Li2, Xianqiao Wang1
ORCID iDs: Zhengwang Wu, Kun Jiang, Wei Gao, Mir Jalil Razavi, Ellen Kuhl, Gang Li, Xianqiao Wang
  1. School of ECAM, College of Engineering, University of Georgia,Athens, GA USA
  2. Department of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill,Chapel Hill, NC USA
  3. School of Chemical, Materials, and Biomedical Engineering, College of Engineering, University of Georgia,Athens, GA USA
  4. Department of Computer Science, Indiana University-Indianapolis,Indianapolis, IN USA
  5. Department of Computer Science, University of Texas at Arlington,Arlington, TX USA
  6. Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center,Los Angeles, CA USA
  7. Department of Mechanical Engineering, Binghamton University,Binghamton, NY USA
  8. School of Computing, University of Georgia,Athens, GA USA
  9. Department of Mechanical Engineering, Stanford University,Stanford, CA USA
Institutions: University of Georgia (United States); University of North Carolina at Chapel Hill (United States); Indiana University – Purdue University Indianapolis (United States); The University of Texas at Arlington (United States); Cedars-Sinai Medical Center (United States); Binghamton University (United States); Stanford University (United States)
Journal: Nature communications, volume 17, issue 1, article 8711
Dates: received 25 November 2025; accepted 23 June 2026; published online 16 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-75582-9 · PMID 42463653 · PMCID PMC13490475 · OpenAlex W7169191766
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Statistics, Machine learning, Preprocessing
Keywords: Computational biophysics, Biophysical models, Biomedical engineering
MeSH: Cerebral Cortex*, Female, Fetus, Humans, Lissencephaly, Magnetic Resonance Imaging, Morphogenesis, Neurodevelopment, Polymicrogyria, Pregnancy (* major topic)
Topic: Fetal and Pediatric Neurological Disorders (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: NSF (IIS-2011369); NIH (1R01NS135574-01); National Institutes of Health (EB037388)
Citations: cited by 1 paper (Europe PMC); 77 references in the paper

Abstract

Cortical folds encode the architecture of human cognition, yet the mechanisms that transform the smooth fetal cortex into its convoluted geometry remain elusive. Biophysical modeling enables mechanistic insight into cortical morphogenesis, but existing models often lack anatomical realism and fail to capture key hallmarks and morphometrics of dynamic cortical folding in the developing human brain. Here, we introduce a whole-brain developmental framework that integrates region-specific, data-driven growth laws with anatomically realistic cortical geometry to enable biologically interpretable modeling of cortical morphogenesis during gestation. Growth fields derived from large-scale prenatal magnetic resonance imaging data capture spatiotemporal variations in cortical expansion and thickness across parcellated regions. Incorporating heterogeneous growth yields folding patterns that match key anatomical landmarks and quantitative morphometrics from human imaging. Systematic perturbations of geometry and growth attributes delineate control parameters that produce realistic morphological variability and replicate clinically atypical brain phenotypes consistent with lissencephaly, pachygyria, and polymicrogyria. This framework provides a quantitative foundation for elucidating the mechanisms of typical and atypical fetal brain development.

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 4 matches between paragraphs and lines of code.

BioDMX-UGA/BiophysicalCorticalMorphogenesis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 57164fd28c24933ab895b9334fec3190ae78123b, 16 July 2026
Languages: MATLAB (19), Python (16)
Size: 41 files, 35 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (15 files), pandas (15 files), NumPy (14 files), SymPy (3 files), seaborn (2 files), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
36 files
At the source: github.com/BioDMX-UGA/BiophysicalCorticalMorphogenesis

Code availability

The code supporting this study, including scripts for growth model characterization, computational model construction, simulation postprocessing, data analysis and figure creation, is available on GitHub at (https://github.com/BioDMX-UGA/BiophysicalCorticalMorphogenesis).

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 4 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

Datasets cited

Data availability

The data supporting these findings, including model files and the regional surface area and cortical thickness data used for growth model characterization have been deposited in the Figshare repository71 and are available at (10.6084/m9.figshare.31866178). The MRI data obtained from the dHCP dataset are publicly available through the Developing Human Connectome Project repository at (https://www.developingconnectome.org). Source data are provided with this paper.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 10 MeSH terms, 3 funders, 74 references.

Cite

This paper

Hou, J., Wu, Z., Jiang, K., Wu, T., Zhang, L., Zhu, D., Gao, W., Razavi, M. J., Liu, T., Kuhl, E., Li, G., & Wang, X. (2026). Biophysical modeling of anatomically realistic prenatal cortical folding development. Nature communications, 17(1), 8711. https://doi.org/10.1038/s41467-026-75582-9

BibTeX

@article{hou2026biophysical,
author = {Hou, Jixin and Wu, Zhengwang and Jiang, Kun and Wu, Taotao and Zhang, Lu and Zhu, Dajiang and Gao, Wei and Razavi, Mir Jalil and Liu, Tianming and Kuhl, Ellen and Li, Gang and Wang, Xianqiao},
title = {{Biophysical modeling of anatomically realistic prenatal cortical folding development}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8711},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75582-9},
url = {https://doi.org/10.1038/s41467-026-75582-9},
pmid = {42463653},
pmcid = {PMC13490475}
}

RIS

TY - JOUR
AU - Hou, Jixin
AU - Wu, Zhengwang
AU - Jiang, Kun
AU - Wu, Taotao
AU - Zhang, Lu
AU - Zhu, Dajiang
AU - Gao, Wei
AU - Razavi, Mir Jalil
AU - Liu, Tianming
AU - Kuhl, Ellen
AU - Li, Gang
AU - Wang, Xianqiao
TI - Biophysical modeling of anatomically realistic prenatal cortical folding development
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/16
VL - 17
IS - 1
SP - 8711
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75582-9
UR - https://doi.org/10.1038/s41467-026-75582-9
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
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{
"family": "Hou",
"given": "Jixin"
},
{
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{
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{
"family": "Wu",
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{
"family": "Zhang",
"given": "Lu"
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{
"family": "Zhu",
"given": "Dajiang"
},
{
"family": "Gao",
"given": "Wei"
},
{
"family": "Razavi",
"given": "Mir Jalil"
},
{
"family": "Liu",
"given": "Tianming"
},
{
"family": "Kuhl",
"given": "Ellen"
},
{
"family": "Li",
"given": "Gang"
},
{
"family": "Wang",
"given": "Xianqiao"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8711",
"DOI": "10.1038/s41467-026-75582-9",
"PMID": "42463653",
"PMCID": "PMC13490475",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75582-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
16
]
]
}
}

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