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Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI.

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Paper

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The authors' code

MATLAB · 52 lines · 1.2 KB · no license

  1. function [thresholded,numl_FC,numl_FC_sel] = Matrix_thresholding_justFC(Input,thresh_sel)
  2. %%Initial thresholds
  3. thresh_FC = 0.1:0.1:4;
  4. % thresh_SC = 0.1:0.1:4;
  5. num_IC=10;
  6. %% U * A * S
  7. S = Input.S;
  8. A = Input.A;
  9. U = Input.U;
  10. UA = U(1:num_IC,1:num_IC) * A;
  11. Y = UA*S;
  13. size_S=size(S,2);
  15. Y_FC = Y(:,1:size_S);
  16. % Y_SC = Y(:,8256+1:end);
  17. S_FC = S(:,1:size_S);
  18. % S_SC = S(:,8256+1:end);
  20. %% Main of Code -> FC
  21. NumofConn = size(Y_FC,2);
  22. for jj = 1:NumofConn
  23. temp = Y_FC(2:num_IC,jj);
  24. temp = temp .^2;
  25. YY_FC(jj) = sqrt(sum(temp));
  26. end
  28. for ind = 1:numel(thresh_FC)
  29. for row = 1:num_IC
  30. Coef_FC = max(abs(UA(2:num_IC,row)))./ YY_FC ;
  31. Mat_FC(row,:) = S_FC(row,:) .* Coef_FC;
  32. check_FC(row,:) = abs(Mat_FC(row,:)) > thresh_FC(ind);
  33. end
  35. for row = 1:10
  36. numl_FC(ind,row) = round(numel(find(check_FC(row,:) == 1))./NumofConn.*100);
  37. end
  39. if ind == thresh_sel
  40. check_FC_sel = check_FC;
  41. numl_FC_sel = numl_FC(ind,:);
  42. end
  43. end
  45. %% Thresholded
  46. thresholded_FC = S_FC;
  47. % thresholded_SC = S_SC;
  48. thresholded_FC(find(~check_FC_sel)) = 0;
  49. % thresholded_SC(find(~check_SC_sel)) = 0;
  50. thresholded = [thresholded_FC];

Edge_pruning.m at commit fc7430d, no license · at the source

Overview

Authors: Farzaneh Keyvanfard1, Abbas Nasiraei-Moghaddam2,3
ORCID iDs: Farzaneh Keyvanfard
  1. Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
  2. Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
  3. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
Institutions: K. N. Toosi University of Technology (Iran); Amirkabir University of Technology (Iran); Institute for Research in Fundamental Sciences (Iran)
Journal: Biomedical engineering and computational biology, volume 17, article 11795972251404254
Dates: received 2 September 2025; accepted 14 November 2025; published online 15 May 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1177/11795972251404254 · PMID 42153007 · PMCID PMC13180186 · OpenAlex W7161299382
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), Alzheimer's / dementia (population), computational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, Graphs, fMRI & imaging
Keywords: blind analysis, brain subnetwork, functional connectivity, Alzheimer, rs-fMRI
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 67 references in the paper

Abstract

Introduction: Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to examine functional connectivity (FC) alterations in neurological disorders such as Alzheimer’s disease (AD). Traditional studies either employ whole-brain analyses or focus on specific regions, yet the vast number of FCs and their interrelations complicate interpretation. This study adopts a data-driven, hypothesis-free approach to detect altered functional subnetworks in AD.

Methods: Independent component analysis (ICA) was applied to FC matrices from 34 AD patients and 49 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). After pruning, significant subnetworks distinguishing AD from HC were identified. Graph theoretical parameters were computed for each subnetwork, and their associations with Mini-Mental State Examination (MMSE) scores were assessed.

Results: Three subnetworks effectively differentiated AD patients from HCs. One subnetwork showed significant group differences in network strength, clustering coefficient, and local efficiency, despite no whole-brain differences. Abnormal functional lateralization also emerged within subnetworks. Moreover, FC weights in the identified subnetworks positively correlated with MMSE scores, linking cognitive performance to subnetwork connectivity.

Conclusion: These results demonstrate the utility of a data-driven approach in detecting AD-specific altered subnetworks. By providing a modular perspective, this method facilitates targeted examination of connectivity changes, improves interpretability, and deepens understanding of functional disruptions in AD.

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

Repository

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

ICA-RAICAR-Pruning/Code

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: fc7430dc71d899cc1d55584bcad706e168694a65, 8 December 2025
Languages: MATLAB (2)
Size: 4 files, 2 scripts
Software Heritage: not archived
Found in: the end of the paper
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2 files
At the source: github.com/ICA-RAICAR-Pruning/Code

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;
  • 2 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.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 5 keywords, 62 references.

Cite

This paper

Keyvanfard, F., & Nasiraei-Moghaddam, A. (2026). Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI. Biomedical engineering and computational biology, 17, 11795972251404254. https://doi.org/10.1177/11795972251404254

BibTeX

@article{keyvanfard2026blind,
author = {Keyvanfard, Farzaneh and Nasiraei-Moghaddam, Abbas},
title = {{Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI}},
journal = {Biomedical engineering and computational biology},
year = {2026},
month = may,
volume = {17},
pages = {11795972251404254},
publisher = {SAGE Publications},
issn = {1179-5972},
doi = {10.1177/11795972251404254},
url = {https://doi.org/10.1177/11795972251404254},
pmid = {42153007},
pmcid = {PMC13180186}
}

RIS

TY - JOUR
AU - Keyvanfard, Farzaneh
AU - Nasiraei-Moghaddam, Abbas
TI - Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI
T2 - Biomedical engineering and computational biology
J2 - Biomed Eng Comput Biol
PY - 2026
DA - 2026/05/15
VL - 17
SP - 11795972251404254
SN - 1179-5972
PB - SAGE Publications
DO - 10.1177/11795972251404254
UR - https://doi.org/10.1177/11795972251404254
LA - en
ER -

CSL-JSON

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"id": "10.1177/11795972251404254",
"type": "article-journal",
"title": "Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI",
"container-title": "Biomedical engineering and computational biology",
"author": [
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"family": "Keyvanfard",
"given": "Farzaneh"
},
{
"family": "Nasiraei-Moghaddam",
"given": "Abbas"
}
],
"container-title-short": "Biomed Eng Comput Biol",
"volume": "17",
"page": "11795972251404254",
"DOI": "10.1177/11795972251404254",
"PMID": "42153007",
"PMCID": "PMC13180186",
"ISSN": "1179-5972",
"publisher": "SAGE Publications",
"URL": "https://doi.org/10.1177/11795972251404254",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
15
]
]
}
}

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