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Declined connectivity of thalamus and dorsomedial prefrontal cortex in post-stroke cognitive impairment delineated by lesion network mapping.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › MRI acquisition ↔ code/peripheralFunctions/freesurfer/load_dicom_fl.m, lines 118 to 184 · score 0.58 · flip angle, echo, repetition, GE, Medical, position · Report a mismatch
  2. [2] § Materials and methods › MRI acquisition ↔ code/peripheralFunctions/freesurfer/MRIwrite.m, lines 29 to 115 · score 0.55 · flip angle, TE, eyes, TR, MRI, Hospital · Report a mismatch

Paper

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The authors' code

MATLAB · 197 lines · 5.4 KB · no license · 1 match

  1. function [vol, M, dcminfo, mr_parms] = load_dicom_fl(flist)
  2. % [vol, M, dcminfo, mr_parms] = load_dicom_fl(flist)
  3. %
  4. % Loads a volume from the dicom files in flist.
  5. %
  6. % The volume dimensions are arranged such that the
  7. % readout dimension is first, followed by the phase-encode,
  8. % followed by the slices (this is not implemented yet)
  9. %
  10. % M is the 4x4 vox2ras transform such that
  11. % vol(i1,i2,i3), xyz1 = M*[i1 i2 i3 1] where the
  12. % indicies are 0-based.
  13. %
  14. % mr_parms = [tr flipangle te ti]
  15. %
  16. % Does not handle multiple frames correctly yet.
  17. %
  20. %
  21. % load_dicom_fl.m
  22. %
  23. % Original Author: Doug Greve
  24. % CVS Revision Info:
  25. % $Author: nicks $
  26. % $Date: 2011/03/02 00:04:12 $
  27. % $Revision$
  28. %
  29. % Copyright ?? 2011 The General Hospital Corporation (Boston, MA) "MGH"
  30. %
  31. % Terms and conditions for use, reproduction, distribution and contribution
  32. % are found in the 'FreeSurfer Software License Agreement' contained
  33. % in the file 'LICENSE' found in the FreeSurfer distribution, and here:
  34. %
  35. % https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense
  36. %
  37. % Reporting: [email hidden]
  38. %
  41. vol=[];
  42. M=[];
  44. if(nargin ~= 1)
  45. fprintf('[vol, M] = load_dicom_fl(flist)\n');
  46. return;
  47. end
  49. nfiles = size(flist,1);
  51. tic
  52. fprintf('Loading dicom info foreach file \n');
  53. for n = 1:nfiles
  54. fname = deblank(flist(n,:));
  55. % fprintf('n = %d/%d, %s %g\n',n,nfiles,fname,toc);
  56. tmpinfo = dicominfo(fname);
  57. if(isempty(tmpinfo))
  58. fprintf('ERROR: reading %s\n',fname);
  59. return;
  60. end
  61. if(n > 1)
  62. % Check that the nth series number agrees with the first
  63. if(tmpinfo.SeriesNumber ~= dcminfo0(1).SeriesNumber)
  64. fprintf('ERROR: series number inconsistency (%s)\n',fname);
  65. return;
  66. end
  67. end
  68. tmpinfo.fname = fname;
  69. dcminfo0(n) = tmpinfo;
  70. end
  72. % Sort by slice location %
  73. dcminfo = sort_by_sliceloc(dcminfo0);
  75. % Slice direction cosine %
  76. sdc = dcminfo(nfiles).ImagePositionPatient-dcminfo(1).ImagePositionPatient;
  77. sdc = sdc /sqrt(sum(sdc.^2));
  79. % Distance between slices %
  80. dslice = sqrt(sum((dcminfo(2).ImagePositionPatient-dcminfo(1).ImagePositionPatient).^2));
  82. % Matrix of direction cosines %
  83. Mdc = zeros(3,3);
  84. Mdc(:,1) = dcminfo(1).ImageOrientationPatient(1:3);
  85. Mdc(:,2) = dcminfo(1).ImageOrientationPatient(4:6);
  86. Mdc(:,3) = sdc;
  88. % Voxel resolution %
  89. delta = [dcminfo(1).PixelSpacing; dslice];
  90. D = diag(delta);
  92. % XYZ of first voxel in first slice %
  93. P0 = dcminfo(1).ImagePositionPatient;
  95. % Change Siemens to be RAS %
  96. % GE is also LPS - change it to be RAS, too - ebeth %
  97. try
  98. Manufacturer = dcminfo(1).Manufacturer;
  99. catch
  100. Manufacturer = 'unknown';
  101. end
  102. if(strcmpi(Manufacturer,'Siemens') | strcmpi(Manufacturer,'ge medical systems'))
  103. % Change to RAS
  104. Mdc(1,:) = -Mdc(1,:);
  105. Mdc(2,:) = -Mdc(2,:);
  106. P0(1) = -P0(1);
  107. P0(2) = -P0(2);
  108. end
  110. % Compute vox2ras transform %
  111. M = [Mdc*D P0; 0 0 0 1];
  112. if (0&strcmpi(Manufacturer,'Siemens'))
  113. % Correcting for P0 being at corner of
  114. % the first voxel instead of at the center
  115. M = M*[[eye(3) [0.5 0.5 0]']; 0 0 0 1]; %'
  116. end
  118. % Pre-allocate vol. Note: column and row designations do
  119. % not really mean anything. The "column" is the fastest
  120. % dimension. The "row" is the next fastest, etc.
  121. ndim1 = dcminfo(1).Columns;
  122. ndim2 = dcminfo(1).Rows;
  123. ndim3 = nfiles;
  124. vol = zeros(ndim1,ndim2,ndim3);
  126. fprintf('Loading data from each file.\n');
  127. for n = 1:nfiles
  128. %fprintf('n = %d, %g\n',n,toc);
  129. fname = dcminfo(n).fname;
  130. x = dicomread(fname);
  131. if(isempty(x))
  132. fprintf('ERROR: could not load pixel data from %s\n',fname);
  133. return;
  134. end
  135. % Note: dicomread will transposed the image. This is supposed
  136. % to help. Anyway, to make the vox2ras transform agree with
  137. % the volume, the image is transposed back.
  138. vol(:,:,n) = x'; %'
  139. end
  141. % Reorder dimensions so that ReadOut dim is first %
  142. if(0 & ~strcmpi(dcminfo(1).PhaseEncodingDirection,'ROW'))
  143. % This does not work
  144. fprintf('INFO: permuting vol so that ReadOut is first dim\n');
  145. vol = permute(vol,[2 1 3]);
  146. Mtmp = M;
  147. M(:,1) = Mtmp(:,2);
  148. M(:,2) = Mtmp(:,1);
  149. end
  151. % Lines below correct for the Z-offset in GE machines - ebeth %
  152. % We're told ge machines recenter along superior/inferior axis but
  153. % don't update c_ras - but now c_s should be zero.
  154. if(strcmpi(Manufacturer,'ge medical systems'))
  155. % Lines below correct for the Z-offset in GE machines
  156. firstZ = dcminfo(1).ImagePositionPatient(3);
  157. lastXYZ = M*[size(vol)';1]; %'
  158. % size(imvol) = number of slices in all 3 dirs
  159. lastZ = lastXYZ(3);
  160. offsetZ = (lastZ + firstZ)/2.0;
  161. % Z0 = Z + offsetZ; [XYZ1]' = M*[CRS1]', need to add to M(3,4)(?)
  162. M(3,4) = M(3,4) - offsetZ;
  163. end
  165. % if(strcmpi(Manufacturer,'ge medical systems'))
  166. % M(3,4) = 0; % Wow - that was actually completely wrong!
  167. % end
  169. % Pull out some info from the header %
  170. if(isfield(dcminfo(1),'FlipAngle')) FlipAngle = pi*dcminfo(1).FlipAngle/180;
  171. else FlipAngle = 0;
  172. end
  173. if(isfield(dcminfo(1),'EchoTime')) EchoTime = dcminfo(1).EchoTime;
  174. else EchoTime = 0;
  175. end
  176. if(isfield(dcminfo(1),'RepetitionTime')) RepetitionTime = dcminfo(1).RepetitionTime;
  177. else RepetitionTime = 0;
  178. end
  179. InversionTime = 0;
  180. mr_parms = [RepetitionTime FlipAngle EchoTime InversionTime];
  182. return;
  184. %----------------------------------------------------------%
  185. function dcminfo2 = sort_by_sliceloc(dcminfo)
  186. nslices = length(dcminfo);
  187. sliceloc = zeros(nslices,1);
  188. for n = 1:nslices
  189. sliceloc(n) = dcminfo(n).SliceLocation;
  190. end
  192. [tmp ind] = sort(sliceloc);
  193. dcminfo2 = dcminfo(ind);
  194. return;
  196. %----------------------------------------------------------%

load_dicom_fl.m at commit 464553a, no license · at the source

Overview

Authors: Ting Liu1,2, Yingjie Tang3, Yu Wang3, Sisi Jiang3, Junxia Chen3, Lin Ma1,2, Lijuan Li1,2, Cheng Luo1,3,4, Qifu Li1,2
ORCID iDs: Cheng Luo, Qifu Li
  1. Department of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou 570100, Hainan Province, China
  2. Key Laboratory of Brain Science Research & Transformation in Tropical Environment of Hainan Province, Hainan Medical University, Haikou 570100, China
  3. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
  4. Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
Institutions: Hainan Medical University (China); University of Electronic Science and Technology of China (China); Chinese Academy of Medical Sciences & Peking Union Medical College (China)
Journal: Brain communications, volume 8, issue 2, article fcag112
Dates: received 20 April 2025; accepted 26 March 2025; published online 28 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag112 · PMID 41971052 · PMCID PMC13062370 · OpenAlex W7141877059
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: Alzheimer's / dementia (population), stroke (population), systems (subfield)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging, Machine learning, Smoothing, state filtering, decompositions
Keywords: post-stroke cognitive impairment, lesion network mapping, thalamus, memory, dorsomedial prefrontal cortex
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Natural Science Foundation of Hainan Province (821QN0987); Epilepsy Research Science Innovation Group of Hainan Medical University; Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province (QRCBT202121); Hainan Province Clinical Medical Center
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Post-stroke cognitive impairment (PSCI) is a common complication of stroke. Stroke events can often cause functional changes in distant brain regions beyond the lesion, which may be the underlying cause of PSCI. However, there are still no consistent imaging features with specificity for PSCI. In the present study, we utilized lesion network mapping analysis to address this question. The left thalamus was the only lesion of interest that we converged on, and it mapped the cognitive impairment circuit of PSCI. Patients with severe cognitive impairment had a higher proportion of lesions falling into the cognitive impairment circuit than those with mild cognitive impairment. From the perspectives of brain function, neurotransmitters and molecular genetics, we found that spatial regions of the circuit are strongly associated with memory-related functions. Interestingly, the cognitive impairment circuit from PSCI does share high spatial similarity with the published memory circuit. Besides, reduced functional connectivity in the left thalamus and left dorsomedial prefrontal cortex may be a crucial reason for cognitive decline in PSCI. Overall, our study identifies a cognitive impairment circuit in PSCI, which impairment may be an important mechanism for memory disorders in PSCI. Connectivity of the left thalamus and dorsomedial prefrontal cortex is expected to serve as a biomarker and new target of neuromodulation therapy.

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 2 matches between paragraphs and lines of code.

BMHLab/AHBAprocessing

License: none: the authors keep all their rights
State: the link answers, verified on 27 October 2026
Evidence: files inventoried
Commit: 464553aa7882a260547f14d26e869ff314c7dfa5, 23 November 2021
Languages: MATLAB (238), C/C++ (89), C++ (50), C (7), Shell (1)
Size: 450 files, 385 scripts
Software Heritage: not archived
Found in: the text, “Gene expression data processing”
Holds: README, tests
Not found: license file, CITATION.cff, environment file, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (15 files), FreeSurfer (7 files), Curve Fitting Toolbox (1 file), Image Processing Toolbox (1 file)
Availability: 2 checks, the latest on 27 October 2026: the link answers
  • 27 October 2026: the link answers
  • 26 September 2026: the link answers
386 files
At the source: github.com/BMHLab/AHBAprocessing

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 385 scripts, each with its path and the digest of its content;
  • 2 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

Due to privacy and ethical constraints, they are not available to the public. The data for this study are available from the corresponding authors upon reasonable request. The codes for Lesion Network Mapping and Dominance Analysis in this manuscript can be found in the Supplementary methods 3–4. The codes for other methods are clearly stated in the methods section of the manuscript.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 4 funders, 56 references.

Cite

This paper

Liu, T., Tang, Y., Wang, Y., Jiang, S., Chen, J., Ma, L., Li, L., Luo, C., & Li, Q. (2026). Declined connectivity of thalamus and dorsomedial prefrontal cortex in post-stroke cognitive impairment delineated by lesion network mapping. Brain communications, 8(2), fcag112. https://doi.org/10.1093/braincomms/fcag112

BibTeX

@article{liu2026declined,
author = {Liu, Ting and Tang, Yingjie and Wang, Yu and Jiang, Sisi and Chen, Junxia and Ma, Lin and Li, Lijuan and Luo, Cheng and Li, Qifu},
title = {{Declined connectivity of thalamus and dorsomedial prefrontal cortex in post-stroke cognitive impairment delineated by lesion network mapping}},
journal = {Brain communications},
year = {2026},
month = mar,
volume = {8},
number = {2},
pages = {fcag112},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag112},
url = {https://doi.org/10.1093/braincomms/fcag112},
pmid = {41971052},
pmcid = {PMC13062370}
}

RIS

TY - JOUR
AU - Liu, Ting
AU - Tang, Yingjie
AU - Wang, Yu
AU - Jiang, Sisi
AU - Chen, Junxia
AU - Ma, Lin
AU - Li, Lijuan
AU - Luo, Cheng
AU - Li, Qifu
TI - Declined connectivity of thalamus and dorsomedial prefrontal cortex in post-stroke cognitive impairment delineated by lesion network mapping
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/03/28
VL - 8
IS - 2
SP - fcag112
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag112
UR - https://doi.org/10.1093/braincomms/fcag112
LA - en
ER -

CSL-JSON

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"id": "10.1093/braincomms/fcag112",
"type": "article-journal",
"title": "Declined connectivity of thalamus and dorsomedial prefrontal cortex in post-stroke cognitive impairment delineated by lesion network mapping",
"container-title": "Brain communications",
"author": [
{
"family": "Liu",
"given": "Ting"
},
{
"family": "Tang",
"given": "Yingjie"
},
{
"family": "Wang",
"given": "Yu"
},
{
"family": "Jiang",
"given": "Sisi"
},
{
"family": "Chen",
"given": "Junxia"
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{
"family": "Ma",
"given": "Lin"
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{
"family": "Li",
"given": "Lijuan"
},
{
"family": "Luo",
"given": "Cheng"
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{
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"given": "Qifu"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "2",
"page": "fcag112",
"DOI": "10.1093/braincomms/fcag112",
"PMID": "41971052",
"PMCID": "PMC13062370",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag112",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
28
]
]
}
}

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