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Neuroplasticity and immune system are related to altered grey matter networks: a cohort study in sporadic Alzheimer's disease.

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] § Methods › Single-subject GM networks ↔ Extract_individual_GM_networks_v20131106/batch_extract_networks20131106.m, lines 1–66 · score 0.83 · GM network, standard deviation, clustering, MATLAB, angles, segmentation
  2. [2] § Methods › MRI acquisition and preprocessing ↔ Extract_individual_GM_networks_v20131106/batch_extract_networks20131106.m, lines 69–120 · score 0.60 · isotropic voxels, SPM, resliced, volumes, segment, scans

Paper

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The authors' code

MATLAB · 208 lines · 8.4 KB · MIT · 2 matches

  1. % Batch script: Extracts networks from grey matter segmentations.
  2. % Network analyses is done with other scripts.
  3. %
  4. % Depends on:
  5. % - the matlab nifti toolbox, download from: www/mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-image
  6. % - the output created with the 'create_cube_template.m script'.
  7. % - SPM5 or SPM8 imcalc function
  8. % - the functions that live in the "ALl_network_scripts" dir:
  9. % - determine_rois_with_minimum_nz : Determines which template extracts networks of minimum size.
  10. % - create_rrois : Permutes all the values in the scan.
  11. % - fast_cross_correlation: Computes the correlation matrices
  12. %
  13. % PLEASE ADJUST the following lines:
  14. % - line 43: add the path to the directory where you want to store the results
  15. % - line 46: add the full path to the directory were All_network_scripts/functions/ live.
  16. % - line 49: add the full path + file name of the txt file that contains the dir+names of all the scans (1 line per scan)
  17. % - line 52: add the full path of the directory were the resliced images need to be written to.
  18. % - line 55: add the full path of the directory where the spm canonical brain lives.
  19. % - line 58: add the full path of the directory where /bl_ind directory lives, this directory contains the cube templates (output from 'create_cube_template.m')
  20. %
  21. %
  22. %
  23. % Output (stored in the subject directory unless specified otherwise):
  24. % - iso2mm_s (subject number) .img : resliced images (this is stored in designated directory)
  25. % - creates a directory for each subject (1:n).
  26. % - Sa = 3D volume that contains the MRI data in a matrix form
  27. % - Va = header file that contains the scan info
  28. % - nz = number of cubes (i.e., size of the network)
  29. % - nancount = number of cubes that have standard deviation of 0. (these are excluded from further calculations)
  30. % - off_set = the template used to extract the minimum number of cubes.
  31. % - bind.mat (in single precision): this file contains the indices of the cubes that contain grey matter values
  32. % - rois.mat and rrois.mat (in single precision): these files contain the cubes
  33. % - lookup.mat and rlookup.mat : provide the link between the cubes from rois and rrois and the original scans (bind). These files are needed later when results are written in images (e.g., the degree or clustering values of the cubes).
  34. % - rotcorr.mat and rrcorr.mat (in single precision) are the correlation matrices maximised for with reflection over all angles and rotation with multiples of 45degrees.
  35. % - th = threshold to binarise the matrices: corresponds to the correlation value where in the random image
  36. % - fp = percentage of positive random correlations
  37. % - sp = sparsity of the matrix with threshold p_corrected = 0.05
  38. %
  39. % Author: Betty Tijms, 2011, version 12.08.2013
  40. % -------------------------------------------------------------------------------------------------------------------------------
  42. %cd /usr/local/MATLAB/R2011a/bin/
  43. matlab -nodesktop
  45. % --- ADJUST FOLLOWING ----%
  46. % Go to the directory where you want to store your output (i.e., networks)
  47. result_dir ='/ENTER_PATH_NAME_HERE/results';
  48. cd result_dir
  50. % FILL IN THE FILE NAME with full path that contains the text file with all the grey matter segmentations
  51. [CN_a1]=textread('/ENTER_PATH_NAME_HERE/grey_matter_segmentation_name_file.txt','%s');
  53. % FILL IN THE FULL PATH of the directory where the resliced images should live.
  54. reslice_dir = '/ENTER_PATH_NAME_HERE/reslice/';
  56. % Add the path to the Nifti toolbox (see read me file for download link)
  57. addpath /ENTER_PATH_NAME_HERE/ENTER_NAME_OF_NIFTI_TOOLBOX_HERE/
  59. % PATH where the canonical SPM image "avg305T1.nii´ can be found --> this is used to reslice the images
  60. P1= '/ENTER_PATH_TO_SPM/spm8/canonical/avg305T1.nii';
  62. % Please provide path to the folder Extract_individual_GM_networks
  63. all_network_scripts_path ='/ENTER_PATH_HERE/Extract_individual_GM_networks_v20131106/';
  65. % Please provide path to the folder bl_ind
  66. bl_dir = '/home/jagust/share/connect/networks_struct/Extract_individual_GM_networks_v20131106/bl_ind/';
  69. %% -----Finished with the adjustments ---%
  71. % add path to the functions in All_network_scripts folder
  72. addpath(strcat(all_network_scripts_path,'functions'))
  74. % Get content of all_dirs.txt files ( this file contains all the dirs of the cube template for all possible off set values)
  75. CN_a2 =textread(strcat(bl_dir, 'all_dirs.txt'),'%s');
  77. % end of loop
  78. numimages=size(CN_a1,1);
  80. % number of dimensions
  81. n=3;
  82. s=n^3;
  84. % Loop through all the grey matter segmentations listed in CN_a1 and extract network. Takes ~25 minutes per scan.
  86. for im=1:numimages
  87. % Get this scan and do the following loop to threshold and then reslice it
  88. this_scan=char(CN_a1(im));
  90. % make the directory for this subject
  91. mkdir(strcat('s', int2str(im),'/data/rotation/'))
  92. mkdir(strcat('s', int2str(im),'/images/'))
  94. % Go to this directory
  95. cd(strcat('s', int2str(im)))
  97. % which directory are we now?
  98. t_result_dir = strcat(result_dir, '/s',int2str(im));
  100. % %% Next reslice the scan to 2x2x2 mm isotropic voxels to reduce amount of data.
  101. P = strvcat(P1, this_scan);
  102. Q = strcat(reslice_dir,'/iso2mm_s', num2str(im), '.nii');
  103. f = 'i2';
  104. flags = {[],[],[],[]};
  105. Q = spm_imcalc_ui(P,Q,f,flags);
  106. %
  107. % %close the figure window
  108. close
  110. % %% Now extract Sa and Va --> Va contains all the info from the .hrd file, Sa is the actual image, it contains the grey matter intensity values.
  111. Va=spm_vol(strcat(reslice_dir, '/iso2mm_s', num2str(im), '.nii'));
  112. % Va=spm_vol(this_scan);
  113. Sa=spm_read_vols(Va);
  115. % Save them in the current directory
  116. save Va.mat Va
  117. save Sa.mat Sa
  119. % clear variables that aren't needed anymore
  120. clear this_scan P Q f flags
  122. %% Get the off_set bl_ind (this corresponds to the indices that make up the 3d cubes) with the minimum number if cubes (min. nz).
  123. [nz, nan_count, off_set] = determine_rois_with_minimumNZ(CN_a2, bl_dir, n, Sa, t_result_dir);
  125. % Store nz.m : the number of cubes, this is the size of the network
  126. save data/nz.mat nz
  128. % nan_count = number of cubes that have a variance of 0, these are excluded because cannot compute correlation coefficient for these cubes.
  129. save data/nan_count.mat nan_count
  130. % off_set indicates which specific template was used to get the cubes.
  131. save data/off_set.mat off_set
  133. % Get bind and store bind too. Bind contains the indices of the cubes, so we can efficiently do computations later. It is a long vector of which every 27 consecutive voxels are a cube.
  134. %tt=strcat(bl_dir, off_set, '/data/bind.m');
  135. load data/bind.m
  137. % Convert to single, for memory reasons
  138. bind=single(bind);
  139. delete data/bind.m
  140. save data/bind.mat bind
  142. % Now create:
  143. % - the rois: This is a matrix of which each column corresponds to a cube, we will compute use this to compute the correlations
  144. % - lookup table --> to lookup the cubes that correspond to the correlations.
  146. lb=length(bind); % End point for loop
  147. col=1; % iteration counter
  149. rois=zeros(s,nz, 'single'); % Create variable to store the rois (i.e., cubes)
  150. lookup=zeros(s,nz, 'single'); % Create variable to store the lookup table --> this table links the cubes to the indices in the original iso2mm image.
  152. % The next loop gos through bind for indices of voxels that belong to each ROI (i.e. cube)
  153. % lb is the last voxel that belongs to a roi
  154. %lookup is lookup table to go from corr index to bind to Sa
  156. for i=1:s:lb
  157. rois(:,col)=Sa(bind(i:(i+(s-1))));
  158. lookup(:,col)=i:(i+(s-1));
  159. col=col+1;
  160. end
  163. % Save the rois and lookup table
  164. save data/rois.mat rois
  165. % look up table --> to go from corr index to bind to Sa
  166. save data/lookup.mat lookup
  168. %clear unneeded variables
  169. clear lookup lb
  171. %% Randomise ROIS: Create a 'random brain' to estimate the threshold with for later stages
  172. [rrois, rlookup]= create_rrois (rois, n, Sa, Va, off_set, bind, bl_dir, nz);
  174. % save rrois and rlookup
  175. save data/rrois.mat rrois
  176. save data/rlookup.mat rlookup
  178. % Remove all variables that take space and aren't needed anymore
  179. clear bind lookup rlookup Sa Va
  181. % Set the following variable to 2 when correlation is maximised for rotation with angle multiples of 45degrees.
  182. forty=2;
  184. [rotcorr, rrcorr] = fast_cross_correlation(rois, rrois, n,forty);
  186. % Save it
  187. save data/rotation/rotcorr.mat rotcorr
  188. save data/rotation/rrcorr.mat rrcorr
  190. %% Now get the threshold and save it
  191. [th, fp, sp] = auto_threshold(rotcorr, rrcorr, nz);
  193. % Add this threshold to all_th
  194. %all_th(im,:)=[th,fp,sp];
  195. %save this and get later
  196. save data/th.mat th
  197. save data/fp.mat fp
  198. save data/sp.mat sp
  200. % Clear all variables
  201. clear rotcorr rrcorr th fp sp
  204. cd ..
  205. im
  207. end

batch_extract_networks20131106.m at commit 183d3c1, under MIT · at the source

Overview

Authors: Diederick M de Leeuw1,2, Flora H Duits1,2, Ellen Dicks1,3, Eleonora M Vromen1,2, Charlotte E Teunissen2,4, Frederik Barkhof5,6, Wiesje M van der Flier1,2, Pieter Jelle Visser1,2, Betty M Tijms1,2
ORCID iDs: Diederick M de Leeuw, Charlotte E Teunissen, Frederik Barkhof, Wiesje M van der Flier, Betty M Tijms
  1. Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, 1081HV Amsterdam, The Netherlands
  2. Amsterdam Neuroscience, Neurodegeneration, 1081HV Amsterdam, The Netherlands
  3. Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
  4. Department of Laboratory Medicine, Neurochemistry Lab, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081HV Amsterdam, The Netherlands
  5. Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, 1081HV Amsterdam, The Netherlands
  6. Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, WC1N 3BG London, UK
Institutions: Amsterdam Neuroscience (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Amsterdam UMC Location Vrije Universiteit Amsterdam (Netherlands); Mayo Clinic (United States); Amsterdam University Medical Centers (Netherlands); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom)
Journal: Brain communications, volume 8, issue 4, article fcag257
Dates: received 7 August 2025; accepted 25 February 2026; published online 8 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag257 · PMID 42465730 · PMCID PMC13373788 · OpenAlex W7167672644
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population)
Methods: Statistics, Connectivity, Graphs
Keywords: CSF proteomics, Alzheimer’s disease, grey matter networks
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: ZonMw (733050824, 09150171910068); NIA NIH HHS (U01 AG024904)
Citations: not cited yet (Europe PMC); 83 references in the paper

Abstract

Grey matter network topology is altered in Alzheimer’s disease and these alterations are related to cognitive decline. Understanding the biological underpinnings of loss of brain connectivity may provide insights into mechanisms related to developing Alzheimer’s dementia (i.e. dementia A+). We investigated which biological processes as measured in CSF proteomics were associated with loss of brain connections across the Alzheimer’s disease continuum. We included 347 individuals with abnormal CSF amyloid [mean age ± standard deviation (SD) 66 ± 8; 98 cognitively unimpaired—A+, 88 mild cognitive impairment—A+, 161 dementia A+] and 146 cognitively unimpaired individuals with normal CSF amyloid (mean age ± SD 62 ± 8) and available T1w MRI-scans and CSF proteomic data (3097 proteins using tandem mass tag spectrometry) from the Amsterdam Dementia Cohort. We used an automated pipeline to construct grey matter networks from 3D-T1 sequences and for each network, calculated the small-worldness coefficient, which we previously found to be robustly related to cognitive decline. Linear models were applied to test associations between CSF protein levels and connectivity measures using an interaction term for clinical stage while controlling for connectivity density, age and sex. We validated our results in data from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Pathway enrichment analysis was performed for proteins associated with loss of brain connectivity (P < 0.05) using the Gene Ontology database. Individuals across the Alzheimer’s disease continuum had lower small-worldness coefficients compared with controls (ANOVA P < 0.001). In amyloid positive individuals, higher levels of 222 proteins and lower levels of 482 proteins were associated with lower small-worldness coefficients and were enriched for innate immune system and neuroplasticity pathways, respectively. Stratified for disease stage, most protein associations with lower small-worldness coefficients were found in mild cognitive impairment A+ (n = 527 proteins) and dementia A+ (n = 799 proteins) with considerable overlap (n = 239 proteins). Proteins in these stages were enriched for complement activation and synaptic integrity. In cognitive unimpairment A+, we found proteins enriched for processes involved in apoptosis. We did not find any enriched biological processes in controls. Repeating analyses in ADNI indicated that similar biological processes were associated with altered grey matter network connectivity. Higher CSF levels of proteins involved in immune responses and lower levels of proteins related to neuroplasticity were associated with lower small-worldness coefficients across the Alzheimer’s disease continuum. This suggests that preserving cognitive function in the presence of amyloid and prevention of dementia A+ may require therapies that strengthen synapses and targets the innate immune system in addition to amyloid and tau.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

bettytijms/Single_Subject_Grey_Matter_Networks

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 183d3c1d7eaddfb62c056b4b32e9cc1b4ee93e8f, 4 June 2020
Languages: MATLAB (13)
Size: 20 files, 13 scripts
Software Heritage: not archived
Found in: the text, “Single-subject GM networks”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
15 files
At the source: github.com/bettytijms/Single_Subject_Grey_Matter_Networks

sites.google.com/site/bctnet

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Single-subject GM networks”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: sites.google.com/site/bctnet/

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;
  • 13 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

The data from the ADC cohort that support the findings of this study are available from the corresponding author, upon reasonable request. The data from the ADNI cohort can be requested online at https://adni.loni.usc.edu/. All analyses were performed in R using packages lme4, lmerTest and emmeans.

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, 9 authors, 3 keywords, 2 funders, 83 references.

Cite

This paper

de Leeuw, D. M., Duits, F. H., Dicks, E., Vromen, E. M., Teunissen, C. E., Barkhof, F., van der Flier, W. M., Visser, P. J., & Tijms, B. M. (2026). Neuroplasticity and immune system are related to altered grey matter networks: a cohort study in sporadic Alzheimer's disease. Brain communications, 8(4), fcag257. https://doi.org/10.1093/braincomms/fcag257

BibTeX

@article{deleeuw2026neuroplasticity,
author = {de Leeuw, Diederick M and Duits, Flora H and Dicks, Ellen and Vromen, Eleonora M and Teunissen, Charlotte E and Barkhof, Frederik and van der Flier, Wiesje M and Visser, Pieter Jelle and Tijms, Betty M},
title = {{Neuroplasticity and immune system are related to altered grey matter networks: a cohort study in sporadic Alzheimer's disease}},
journal = {Brain communications},
year = {2026},
month = jul,
volume = {8},
number = {4},
pages = {fcag257},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag257},
url = {https://doi.org/10.1093/braincomms/fcag257},
pmid = {42465730},
pmcid = {PMC13373788}
}

RIS

TY - JOUR
AU - de Leeuw, Diederick M
AU - Duits, Flora H
AU - Dicks, Ellen
AU - Vromen, Eleonora M
AU - Teunissen, Charlotte E
AU - Barkhof, Frederik
AU - van der Flier, Wiesje M
AU - Visser, Pieter Jelle
AU - Tijms, Betty M
TI - Neuroplasticity and immune system are related to altered grey matter networks: a cohort study in sporadic Alzheimer's disease
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/07/08
VL - 8
IS - 4
SP - fcag257
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag257
UR - https://doi.org/10.1093/braincomms/fcag257
LA - en
ER -

CSL-JSON

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"id": "10.1093/braincomms/fcag257",
"type": "article-journal",
"title": "Neuroplasticity and immune system are related to altered grey matter networks: a cohort study in sporadic Alzheimer's disease",
"container-title": "Brain communications",
"author": [
{
"family": "de Leeuw",
"given": "Diederick M"
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{
"family": "Duits",
"given": "Flora H"
},
{
"family": "Dicks",
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{
"family": "Vromen",
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{
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{
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{
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"given": "Wiesje M"
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{
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"given": "Pieter Jelle"
},
{
"family": "Tijms",
"given": "Betty M"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "4",
"page": "fcag257",
"DOI": "10.1093/braincomms/fcag257",
"PMID": "42465730",
"PMCID": "PMC13373788",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag257",
"language": "en",
"issued": {
"date-parts": [
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8
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}
}

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