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The individuality of single-frame functional brain connectivity.

Code ↔ Paper

1 match 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.

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  1. [1] § Methods › Static and dynamic functional networks ↔ LEiDA/LEiDA_func_noImg.m, the whole file · a weak match · score 0.76 · Hilbert transform, coherence matrices, BOLD Phase, edge, dynamic, nodes

Paper

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The authors' code

MATLAB · 93 lines · 2.5 KB · no license · 1 match

  1. function [Leading_Eig,Var_Eig,dFC] = LEiDA_func_noImg(ts)
  3. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  4. % LEADING EIGENVECTOR DYNAMICS ANALYSIS
  5. %
  6. % This function processes the data for LEiDA
  7. %
  8. % - Loads the BOLD data from all subjects in a matrix of Time x Brain areas
  9. % - Computes the BOLD phases via Hilbert transform
  10. % - Calculates the iFC (or instantaneous BOLD coherence matrix)
  11. % - Calculates the instantaneous Leading Eigenvector
  12. % - Calculates the instantaneous % of variance
  13. % - Calculates de time x time FCD matrices
  14. %
  15. % Saves into LEiDA_data.mat
  16. % Leading_Eig - Leading Eigenvector at each timepoint & each subject
  17. % Var_Eig - % of variance of the leading eigenvector
  18. % FCD_eig - Cosine similarity of eigenvectors over time
  19. %
  20. % Written by
  21. % Joana Cabral [email hidden]
  22. % Last edited May 2016
  23. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  25. ts = ts';
  27. % Set parameters and preallocate variables
  28. N = width(ts); % Number of nodes
  29. Tmax = height(ts); % Number of TRs
  31. Phases=zeros(N,Tmax);
  32. Leading_Eig=zeros(Tmax,N);
  33. Var_Eig=zeros(1,Tmax);
  34. FCD_eig =zeros(Tmax-2,Tmax-2);
  36. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  38. % Get the Phase of BOLD data
  39. for seed=1:N
  40. timeseriedata=demean(detrend(ts(:,seed)));
  41. Phases(seed,:) = angle(hilbert(timeseriedata));
  42. end
  44. % Get the iFC leading eigenvector at each time point
  45. iFC=zeros(N);
  47. % tic
  48. nEdges = N * (N-1) / 2;
  49. dFC = nan(Tmax,N,N);
  50. for t=1:Tmax
  51. for n=1:N
  52. for p=1:N
  53. iFC(n,p)=cos(adif(Phases(n,t),Phases(p,t)));
  54. end
  55. end
  57. % aij = iFC;
  58. % aij = triu(aij,1);
  59. % aij(aij == 0) = [];
  60. % dFC(t,:) = aij;
  61. dFC(t,:,:) = iFC;
  63. [eVec,eigVal]=eig(iFC);
  64. eVal=diag(eigVal);
  65. [val1, i_vec_1] = max(eVal);
  66. Leading_Eig(t,:)=eVec(:,i_vec_1);
  67. % Calculate the variance explained by the leading eigenvector
  68. Var_Eig(t)=val1/sum(eVal);
  70. % if t == 50
  71. % f = figure;
  72. % f.Position = [10 10 900 900];
  73. % imagesc(iFC);
  74. % colormap jet;
  75. % set(gca,'xtick',[]);
  76. % set(gca,'ytick',[]);
  77. %
  78. % f = figure;
  79. % f.Position = [10 10 1900 50];
  80. % imagesc(Leading_Eig(t,:));
  81. % colormap jet;
  82. % set(gca,'xtick',[]);
  83. % set(gca,'ytick',[]);
  84. %
  85. % f = figure;
  86. % f.Position = [10 10 900 900];
  87. % imagesc(Leading_Eig(t,:) .* Leading_Eig(t,:)');
  88. % colormap jet;
  89. % set(gca,'xtick',[]);
  90. % set(gca,'ytick',[]);
  91. % end
  92. end
  93. % toc

LEiDA_func_noImg.m at commit aa94df2, no license · at the source

Overview

Authors: Clayton C. McIntyre1, Heather M. Shappell2, Mohsen Bahrami3, Robert G. Lyday3, Paul J. Laurienti3
ORCID iDs: Clayton C. McIntyre
  1. Neuroscience Graduate Program, Wake Forest Graduate School of Arts and Sciences, Winston-Salem, NC, United States
  2. Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, United States
  3. Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
Institutions: Wake Forest University (United States); Wake Forest University Health Sciences (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1254
Dates: received 14 July 2025; accepted 21 April 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1254 · PMID 42326560 · PMCID PMC13277782 · OpenAlex W7160332451
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, fMRI & imaging
Keywords: dynamic functional connectivity, LEiDA, resting-state, fingerprinting, fMRI, development
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute on Alcohol Abuse and Alcoholism (P50AA026117, F31AA032409); U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) (K25EB032903); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (K25AG090707, RO1AG052419, RO1AG052419-05S1, P30AG021332); U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS) (UL1TR001420); Translational Science Center, Wake Forest University
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Converging evidence from studies on brain network “fingerprinting” and precision functional mapping suggest that brain networks are highly individualized in functionally meaningful ways. Concurrently with a growth in studies on this topic, there has been a rise in interest on dynamics (approximately second-to-second changes) in brain networks within scan sessions. While analyses of traditional static networks have increasingly grown towards emphasizing the importance of individual differences in brain network topology, studies of dynamic networks typically follow methodology that require brain states to be considered at a group level. Recent studies have begun to assess the individuality of recurring dynamic brain “states”. In this work, we extend this recent work by exploring the extent to which functional connectivity fingerprinting is feasible at single-frame temporal resolution. We estimate connectivity at individual volumes using phase coherence. We find that the identity of participants can be classified based on single volumes given sufficient database scan data and that having more highly parcellated atlases facilitates identification. Finally, we find that tasks can be identified more readily within subjects than between subjects. We conclude that participant identity may be an important driver of observed single-volume connectivity patterns. Further, the single-volume neural correlates of a task appear to be more consistent within subjects than between subjects. This highlights the importance of considering individual variability in studies of brain network dynamics.

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 1 match between paragraphs and lines of code.

ccmcinty/The-individuality-of-single-frame-functional-brain-connectivity-

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: aa94df2a39e7c286ade97dc1f05d19cdb072e069, 5 January 2026
Languages: MATLAB (32)
Size: 248 files, 32 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (11 files), Parallel Computing Toolbox (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
33 files
At the source: github.com/ccmcinty/The-individuality-of-single-frame-functional-brain-connectivity-

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 32 scripts, each with its path and the digest of its content;
  • 1 match 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 and Code Availability

Data in this work came from the MSC, NCANDA, and BNET studies. MSC is publicly available here: https://openneuro.org/datasets/ds000224/versions/1.0.4. NCANDA data are available following an application process beginning here: https://ncanda.org/datasharing.php. BNET data are not publicly available as they were collected prior to mandatory public sharing of NIH-funded fMRI data acquisition, and participants therefore did not provide data release agreements. The BNET data can be made available upon request to the authors with appropriate Institutional Review Board approval and data use agreements. MATLAB scripts were written to perform all analyses in this work and are available on github: https://github.com/ccmcinty/The-individuality-of-single-frame-functional-brain-connectivity-.

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, pages, dates, 5 authors, 6 keywords, 5 funders, 55 references.

Cite

This paper

McIntyre, C. C., Shappell, H. M., Bahrami, M., Lyday, R. G., & Laurienti, P. J. (2026). The individuality of single-frame functional brain connectivity. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1254. https://doi.org/10.1162/imag.a.1254

BibTeX

@article{mcintyre2026individuality,
author = {McIntyre, Clayton C. and Shappell, Heather M. and Bahrami, Mohsen and Lyday, Robert G. and Laurienti, Paul J.},
title = {{The individuality of single-frame functional brain connectivity}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1254},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1254},
url = {https://doi.org/10.1162/imag.a.1254},
pmid = {42326560},
pmcid = {PMC13277782}
}

RIS

TY - JOUR
AU - McIntyre, Clayton C.
AU - Shappell, Heather M.
AU - Bahrami, Mohsen
AU - Lyday, Robert G.
AU - Laurienti, Paul J.
TI - The individuality of single-frame functional brain connectivity
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/06/17
VL - 4
SP - IMAG.a.1254
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1254
UR - https://doi.org/10.1162/imag.a.1254
LA - en
ER -

CSL-JSON

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"family": "McIntyre",
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"volume": "4",
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"PMID": "42326560",
"PMCID": "PMC13277782",
"ISSN": "2837-6056",
"publisher": "MIT Press",
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"language": "en",
"issued": {
"date-parts": [
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