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Exploring Gray Matter Alterations in Post-Stroke Patients: A Structural Covariance Analysis.

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Paper

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The authors' code

MATLAB · 266 lines · 8.1 KB · GPL-3.0

  1. function BCCT
  2. clc;clear all;close all;
  3. [pat,nam,ext] = fileparts(which('BCCT.m'));
  4. % addpath([pat,filesep,'extendFunc']); % some immature ideas will add into this fold.
  5. addpath([pat,filesep,'supportFunc']); % add nifti IO and reslice data.
  6. % addpath([pat,filesep,'SomeTemplates']);
  7. TempExistPath = [pat,filesep,'SomeTemplates'];
  8. Hsize = get(0,'ScreenSize');
  9. Bsize = min(Hsize(3),Hsize(4));
  10. BsizeUsed = floor(Bsize*0.6);
  11. POS = [ceil((Hsize(3)-BsizeUsed)/2),ceil((Hsize(4)-BsizeUsed)/2),BsizeUsed,BsizeUsed];
  13. BCCT.TempExistPath = TempExistPath;
  14. BCCT.mainpath = pat;
  15. BCCT.fig = figure('Name','BCCT toolkit',...
  16. 'menubar','none',...
  17. 'numbertitle','off',...
  18. 'color',[0.95 0.95 0.95],...
  19. 'position',POS);
  20. movegui(BCCT.fig,'center');
  21. % BCCT.mainaxes = axes('parent',BCCT.fig,...
  22. % 'units','norm',...
  23. % 'pos',[0,0,1,1]);
  24. patmain = which('BCCT.m');
  25. [patfun,nam,ext] = fileparts(patmain);
  26. if ~isempty(dir(fullfile(patfun,'Shepherd_plotout')))
  27. addpath(fullfile(patfun,'Shepherd_plotout'));
  28. end
  29. % backgroundpictitle = fullfile(patfun,'hospital_bin.jpg');
  30. % [backbin backmap] = imread(backgroundpictitle);
  31. % backbinmax = max(backbin(:)); % 255
  32. % backbinmin = min(backbin(:)); % 0
  33. % backbinrange = backbinmax-backbinmin;
  34. % backbinranges = double(backbinrange);
  35. % backbin_t = (double(backbin)-double(backbinmin))/double(backbinranges); % norm to 0-1
  36. % ranges = 1/10;
  37. % backbin_t1 = backbin_t*ranges;
  38. % backbin_t2 = backbin_t1+(1-ranges);
  39. % backbin_t3 = backbin_t2*backbinranges;
  40. % backbin2 = uint8(backbin_t3);
  41. % colmap = [1-ranges:ranges/64:1;1-ranges:ranges/64:1;1-ranges:ranges/64:1]';
  42. % image(backbin2,'parent',BCCT.mainaxes,'CDataMapping','scaled');
  43. % axis(BCCT.mainaxes,'off');
  44. % freezeColors(GLMflex.des.mainwin);
  45. % colormap(BCCT.mainaxes,colmap)
  46. BCCT.NameExt = uicontrol('parent',BCCT.fig,...
  47. 'units','norm',...
  48. 'pos',[0.05,0.85,0.9,0.10],...
  49. 'style','text',...
  50. 'string',{'Brain Covariance Connectivity','Toolkit v2.1'},...
  51. 'fontunits', 'normalized',...
  52. 'fontsize',0.35,...
  53. 'fontweight','bold',...
  54. 'horizontalalign','center');
  55. BCCT.NameExt2 = uicontrol('parent',BCCT.fig,...
  56. 'units','norm',...
  57. 'pos',[0.05,0.75,0.9,0.08],...
  58. 'style','text',...
  59. 'string',{'Department of Radiology','Jinling Hospital, School of Medicine, Nanjing University'},...
  60. 'fontunits', 'normalized',...
  61. 'fontsize',0.2,...
  62. 'fontweight','bold',...
  63. 'horizontalalign','center',...
  64. 'foregroundcolor','r');
  67. BCCT.SCNax = axes('parent',BCCT.fig,...
  68. 'pos',[0.1,0.55,0.2,0.2]);
  69. BCCT.GCAax = axes('parent',BCCT.fig,...
  70. 'pos',[0.4,0.55,0.2,0.2]);
  71. BCCT.WTAax = axes('parent',BCCT.fig,...
  72. 'pos',[0.7,0.55,0.2,0.2]);
  73. BCCT.Statax = axes('parent',BCCT.fig,...
  74. 'pos',[0.1,0.2,0.2,0.2]);
  75. BCCT.MODax = axes('parent',BCCT.fig,...
  76. 'pos',[0.4,0.2,0.2,0.2]);
  77. BCCT.SCNPb = uicontrol('parent',BCCT.fig,...
  78. 'units','norm',...
  79. 'pos',[0.1,0.425,0.2,0.1],...
  80. 'style','pushbutton',...
  81. 'string','SCN',...
  82. 'fontunits', 'normalized',...
  83. 'fontsize',0.35,...
  84. 'fontweight','bold',...
  85. 'horizontalalign','center');
  86. BCCT.GCAPb = uicontrol('parent',BCCT.fig,...
  87. 'units','norm',...
  88. 'pos',[0.4,0.425,0.2,0.1],...
  89. 'style','pushbutton',...
  90. 'string','CaSCN',...
  91. 'fontunits', 'normalized',...
  92. 'fontsize',0.35,...
  93. 'fontweight','bold',...
  94. 'horizontalalign','center');
  95. BCCT.WTAPb = uicontrol('parent',BCCT.fig,...
  96. 'units','norm',...
  97. 'pos',[0.7,0.425,0.2,0.1],...
  98. 'style','pushbutton',...
  99. 'string','WTA',...
  100. 'fontunits', 'normalized',...
  101. 'fontsize',0.35,...
  102. 'fontweight','bold',...
  103. 'horizontalalign','center');
  105. BCCT.MODPb = uicontrol('parent',BCCT.fig,...
  106. 'units','norm',...
  107. 'pos',[0.4,0.075,0.2,0.1],...
  108. 'style','pushbutton',...
  109. 'string','Modulate',...
  110. 'fontunits', 'normalized',...
  111. 'fontsize',0.35,...
  112. 'fontweight','bold',...
  113. 'horizontalalign','center');
  114. axis(BCCT.SCNax,'off')
  115. axis(BCCT.GCAax,'off')
  116. axis(BCCT.WTAax,'off')
  117. axis(BCCT.Statax,'off')
  118. axis(BCCT.MODax,'off')
  119. BCCT.Exit = uicontrol('parent',BCCT.fig,...
  120. 'units','norm',...
  121. 'pos',[0.7,0.075,0.2,0.1],...
  122. 'style','pushbutton',...
  123. 'string','Exit',...
  124. 'fontunits', 'normalized',...
  125. 'fontsize',0.35,...
  126. 'fontweight','bold',...
  127. 'horizontalalign','center');
  129. BCCT.StatPb = uicontrol('parent',BCCT.fig,...
  130. 'units','norm',...
  131. 'pos',[0.1,0.075,0.2,0.1],...
  132. 'style','popupmenu',...
  133. 'string',{'Statistic','SCN','WTA','CaSCN','Modulate'},...
  134. 'fontunits', 'normalized',...
  135. 'fontsize',0.35,...
  136. 'fontweight','bold',...
  137. 'horizontalalign','center',...
  138. 'Tooltipstring','Stastical analysis for two groups');
  140. BCCT.Utils = uicontrol('parent',BCCT.fig,...
  141. 'units','norm',...
  142. 'pos',[0.7,0.25,0.2,0.1],...
  143. 'style','popupmenu',...
  144. 'string',{'Utilites','Make Final Mask','help','Viewer','View for Surf'},...
  145. 'fontunits', 'normalized',...
  146. 'fontsize',0.35,...
  147. 'fontweight','bold',...
  148. 'horizontalalign','center');
  149. STATPICPAT = fullfile(patfun,'stat.png');
  150. [STATPIC STATMAP] = imread(STATPICPAT);
  151. imshow(STATPIC,STATMAP,'parent',BCCT.Statax)
  153. mapmodPAT = fullfile(patfun,'mapmod.bmp');
  154. [mapPIC mapMAP] = imread(mapmodPAT);
  155. imshow(mapPIC,mapMAP,'parent',BCCT.SCNax)
  157. matmodPAT = fullfile(patfun,'Modulate.bmp');
  158. [matPIC matMAP] = imread(matmodPAT);
  159. imshow(matPIC,matMAP,'parent',BCCT.MODax)
  161. WTAPAT = fullfile(patfun,'WTAmod.bmp');
  162. [WTAPIC WTAMAP] = imread(WTAPAT);
  163. imshow(WTAPIC,WTAMAP,'parent',BCCT.WTAax)
  165. GCAPAT = fullfile(patfun,'GCA.bmp');
  166. [GCAPIC GCAMAP] = imread(GCAPAT);
  167. imshow(GCAPIC,GCAMAP,'parent',BCCT.GCAax)
  169. set(BCCT.SCNPb,'callback',{@ASFCSCN,BCCT});
  170. set(BCCT.MODPb,'callback',{@ASFCMOD,BCCT});
  171. set(BCCT.WTAPb,'callback',{@ASFCWTA,BCCT});
  172. set(BCCT.StatPb,'callback',{@ASFCStatistic,BCCT});
  173. set(BCCT.Exit,'callback',{@ASFCEXIT,BCCT});
  174. set(BCCT.Utils,'callback',{@ASFCUtilti,BCCT});
  175. set(BCCT.GCAPb,'callback',{@ASFCGCA,BCCT});
  176. end
  177. function ASFCGCA(varargin)
  178. BCCT = varargin{3};
  179. close(BCCT.fig)
  180. patmainfun = which('BCCT.m');
  181. [path nam ext] = fileparts(patmainfun);
  182. addpath(fullfile(path,'BCCT_CaSCN'));
  183. BCCT_CaSCN_GUI;
  184. % BCCT_GCA_GUI
  185. end
  186. function ASFCSCN(varargin)
  187. BCCT = varargin{3};
  188. close(BCCT.fig)
  189. patmainfun = which('BCCT.m');
  190. [path nam ext] = fileparts(patmainfun);
  191. addpath(fullfile(path,'BCCT_SCN'));
  192. BCCT_SCN_GUI;
  193. % BCCT_CON_MAP_GUI
  194. end
  195. function ASFCMOD(varargin)
  196. BCCT = varargin{3};
  197. close(BCCT.fig)
  198. patmainfun = which('BCCT.m');
  199. [path nam ext] = fileparts(patmainfun);
  200. addpath(fullfile(path,'BCCT_Modulate'));
  201. BCCT_Modulate_GUI;
  202. end
  203. function ASFCWTA(varargin)
  204. BCCT = varargin{3};
  205. close(BCCT.fig)
  206. patmainfun = which('BCCT.m');
  207. [path nam ext] = fileparts(patmainfun);
  208. addpath(fullfile(path,'BCCTcorr_WTA'));
  209. BCCT_WTA_GUI;
  210. end
  211. function ASFCStatistic(varargin)
  212. BCCT = varargin{3};
  213. patmainfun = which('BCCT.m');
  214. [path nam ext] = fileparts(patmainfun);
  215. addpath(fullfile(path,'BCCT_Statistic'));
  216. valst = get(BCCT.StatPb,'val');
  217. if valst==2
  218. % BCCT_STAT_GUI;
  219. BCCT_SCN_statGUI;
  220. close(BCCT.fig)
  221. end
  222. if valst==3
  223. BCCT_WTA_statGUI;
  224. close(BCCT.fig)
  225. end
  226. if valst==4
  227. BCCT_CaSCN_statGUI;
  228. end
  229. if valst==5
  230. BCCT_Modulate_statGUI;
  231. end
  232. end
  233. function ASFCEXIT(varargin)
  234. BCCT = varargin{3};
  235. close(BCCT.fig);
  236. end
  237. function ASFCUtilti(varargin)
  238. BCCT = varargin{3};
  239. VALutil = get(BCCT.Utils,'val');
  241. patmainfun = which('BCCT.m');
  242. [path nam ext] = fileparts(patmainfun);
  243. addpath(fullfile(path,'BCCT_Util'));
  244. if VALutil==2 % make final mask
  245. % addpath([BCCT.mainpath,filesep,'extendFunc',filesep,'MakeFinalMask']);
  246. BCCT_makefinalMask
  247. end
  248. if VALutil==3
  249. web('BCCT+manuVer1.mht')
  250. end
  251. if VALutil==4
  252. patmainfun = which('BCCT.m');
  253. [path nam ext] = fileparts(patmainfun);
  254. addpath(fullfile(path,'BCCT_ShowRes'));
  255. % BCCT_VIEW_simple;
  256. BCCT_VIEWmain;
  257. end
  258. if VALutil==5
  259. patmainfun = which('BCCT.m');
  260. [path nam ext] = fileparts(patmainfun);
  261. addpath(fullfile(path,'BCCT_ShowRes'));
  262. % BCCT_VIEW_simple;
  263. BCCT_VIEW_surfsimp;
  264. end
  265. end

BCCT.m at commit d0e9b68, under GPL-3.0 · at the source

Overview

Authors: Juan‐Juan Lu1, Xiang‐Xin Xing1, Jiao Qu2, Jia‐Jia Wu1, Jie Ma1, Mou‐Xiong Zheng3, Xu‐Yun Hua4, Jian‐Guang Xu1,5
  1. Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine Shanghai University of Traditional Chinese Medicine Shanghai China
  2. Department of Radiology Shanghai Songjiang District Central Hospital Shanghai China
  3. Department of Traumatology and Orthopedics, Shuguang Hospital Shanghai University of Traditional Chinese Medicine Shanghai China
  4. Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine Shanghai University of Traditional Chinese Medicine Shanghai China
  5. Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of Education Shanghai China
Institutions: Shanghai University of Traditional Chinese Medicine (China); Yueyang Hospital (China); Songjiang District Central Hospital (China); Shuguang Hospital (China)
Journal: Brain and behavior, volume 16, issue 8, article e71667
Dates: received 26 September 2025; accepted 25 July 2026; published online 15 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/brb3.71667 · PMID 42603270 · PMCID PMC13477228 · OpenAlex W7203558365
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), stroke (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Preprocessing
Keywords: magnetic resonance imaging, source‐based morphometry, stroke, structural covariance network, volume
MeSH: Brain*, Gray Matter*, Stroke*, Adult, Aged, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82272583, 82172554, 82272589, 82302870); National Key R&D Program of China (2018YFC2001600, 2018YFC2001604); Shanghai Health Care Commission (2022JC026); Shanghai Science and Technology Committee (22010504200); Shanghai Rising-Star Program (23QA1409200); Shanghai Youth Top Talent Development Plan; Shanghai "Rising Stars of Medical Talent" - Distinguished Young Medical Talent Program; Shanghai Talent Development Fund (2021074); High-level Chinese Medicine Key Discipline Construction Project (Integrative Chinese and Western Medicine Clinic) of National Administration of TCM (zyyzdxk‐2023065); Science & Technology Development Fund of Shanghai University of Traditional Chinese Medicine (23KFL112)
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Background: While structural covariance analysis is adept at assessing morphometric correlations among broadly distributed brain regions, limited research has focused on alterations in structural covariance networks post‐stroke. This study aims to identify significant brain gray matter changes and distinct structural covariance patterns in stroke patients.

Methods: Nineteen post‐stroke patients (17 males, aged 50.79 ± 11.98 years) and 19 healthy controls (11 males, 44.32 ± 13.34 years) underwent structural magnetic resonance imaging. Source‐based morphometry (SBM) analysis was utilized to explore alterations in independent components representing gray matter structural networks. Between‐group comparisons were performed while controlling for age and gender. Structural covariance networks were constructed to explore changes in these patterns among stroke patients.

Results: SBM analysis identified 13 independent components (ICs). Compared to controls, post‐stroke patients exhibited significant alterations in gray matter in the subcortical network (IC 4, IC 8, and IC 10). Additionally, in post‐stroke patients, there was a reduction in structural covariance between the subcortical‐cerebellar networks, accompanied by an increase between the subcortical‐prefrontal networks.

Conclusion: The present findings highlight structural abnormalities within the subcortical network and significant alterations in the structural interactions between the subcortical, cerebellar, and prefrontal networks in stroke patients. The observed findings suggest large‐scale anatomical reorganization following stroke and provide insight into network‐level neuroanatomical alterations associated with post‐stroke functional deficits.

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

Repository

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

JLhos-fmri/BrainCovarianceConnectToolkitV2.1

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: d0e9b68f721f766bfa6424b80f488964f4a1869b, 28 May 2021
Languages: MATLAB (187)
Size: 252 files, 187 scripts
Software Heritage: not archived
Found in: the text, “SCN Analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (14 files), FreeSurfer (6 files), SPM (6 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
189 files
At the source: github.com/JLhos-fmri/BrainCovarianceConnectToolkitV2.1

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;
  • 187 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 data supporting this study's findings are available from the corresponding author upon reasonable request.

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, 8 authors, 5 keywords, 10 MeSH terms, 10 funders, 43 references.

Cite

This paper

Lu, J., Xing, X., Qu, J., Wu, J., Ma, J., Zheng, M., Hua, X., & Xu, J. (2026). Exploring Gray Matter Alterations in Post-Stroke Patients: A Structural Covariance Analysis. Brain and behavior, 16(8), e71667. https://doi.org/10.1002/brb3.71667

BibTeX

@article{lu2026exploring,
author = {Lu, Juan‐Juan and Xing, Xiang‐Xin and Qu, Jiao and Wu, Jia‐Jia and Ma, Jie and Zheng, Mou‐Xiong and Hua, Xu‐Yun and Xu, Jian‐Guang},
title = {{Exploring Gray Matter Alterations in Post-Stroke Patients: A Structural Covariance Analysis}},
journal = {Brain and behavior},
year = {2026},
month = aug,
volume = {16},
number = {8},
pages = {e71667},
publisher = {Wiley},
issn = {2162-3279},
doi = {10.1002/brb3.71667},
url = {https://doi.org/10.1002/brb3.71667},
pmid = {42603270},
pmcid = {PMC13477228}
}

RIS

TY - JOUR
AU - Lu, Juan‐Juan
AU - Xing, Xiang‐Xin
AU - Qu, Jiao
AU - Wu, Jia‐Jia
AU - Ma, Jie
AU - Zheng, Mou‐Xiong
AU - Hua, Xu‐Yun
AU - Xu, Jian‐Guang
TI - Exploring Gray Matter Alterations in Post-Stroke Patients: A Structural Covariance Analysis
T2 - Brain and behavior
J2 - Brain Behav
PY - 2026
DA - 2026/08/01
VL - 16
IS - 8
SP - e71667
SN - 2162-3279
PB - Wiley
DO - 10.1002/brb3.71667
UR - https://doi.org/10.1002/brb3.71667
LA - en
ER -

CSL-JSON

{
"id": "10.1002/brb3.71667",
"type": "article-journal",
"title": "Exploring Gray Matter Alterations in Post-Stroke Patients: A Structural Covariance Analysis",
"container-title": "Brain and behavior",
"author": [
{
"family": "Lu",
"given": "Juan‐Juan"
},
{
"family": "Xing",
"given": "Xiang‐Xin"
},
{
"family": "Qu",
"given": "Jiao"
},
{
"family": "Wu",
"given": "Jia‐Jia"
},
{
"family": "Ma",
"given": "Jie"
},
{
"family": "Zheng",
"given": "Mou‐Xiong"
},
{
"family": "Hua",
"given": "Xu‐Yun"
},
{
"family": "Xu",
"given": "Jian‐Guang"
}
],
"container-title-short": "Brain Behav",
"volume": "16",
"issue": "8",
"page": "e71667",
"DOI": "10.1002/brb3.71667",
"PMID": "42603270",
"PMCID": "PMC13477228",
"ISSN": "2162-3279",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/brb3.71667",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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