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Parallel multilink group joint ICA (pmg-jICA): Fusion of 3D structural and 4D functional data across multiple resting fMRI networks.

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § METHODS › Data and Preprocessing ↔ FIT/ica_fuse/ica_fuse_spm_files/ica_fuse_spm_smoothkern.m, the whole file · a weak match · score 0.73 · Gaussian smoothing, full width, SPM, kernel, FWHM, variance
  2. [2] § METHODS › Data and Preprocessing ↔ FIT/ica_fuse/ica_fuse_spm_files/ica_fuse_spm_reslice.m, lines 1–140 · score 0.59 · resliced images, SPM, isotropic, fMRI, variance, voxel

Paper

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The authors' code

MATLAB · 55 lines · 2 KB · no license · 1 match

  1. function krn = ica_fuse_spm_smoothkern(fwhm,x,t)
  2. % Generate a Gaussian smoothing kernel
  3. % FORMAT krn = spm_smoothkern(fwhm,x,t)
  4. % fwhm - full width at half maximum
  5. % x - position
  6. % t - either 0 (nearest neighbour) or 1 (linear).
  7. % [Default: 1]
  8. %
  9. % krn - value of kernel at position x
  10. %__________________________________________________________________________
  11. %
  12. % For smoothing images, one should really convolve a Gaussian with a sinc
  13. % function. For smoothing histograms, the kernel should be a Gaussian
  14. % convolved with the histogram basis function used. This function returns
  15. % a Gaussian convolved with a triangular (1st degree B-spline) basis
  16. % function (by default). A Gaussian convolved with a hat function (0th
  17. % degree B-spline) can also be returned.
  18. %__________________________________________________________________________
  19. % Copyright (C) 2005-2011 Wellcome Trust Centre for Neuroimaging
  21. % John Ashburner
  22. % $Id: spm_smoothkern.m 4419 2011-08-03 18:42:35Z guillaume $
  25. if nargin<3, t = 1; end
  27. % Variance from FWHM
  28. s = (fwhm/sqrt(8*log(2)))^2+eps;
  30. % The simple way to do it. Not good for small FWHM
  31. % krn = (1/sqrt(2*pi*s))*exp(-(x.^2)/(2*s));
  33. if t==0
  34. % Gaussian convolved with 0th degree B-spline
  35. % int(exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t= -0.5..0.5)
  36. w1 = 1/sqrt(2*s);
  37. krn = 0.5*(erf(w1*(x+0.5))-erf(w1*(x-0.5)));
  38. krn(krn<0) = 0;
  40. elseif t==1
  41. % Gaussian convolved with 1st degree B-spline
  42. % int((1-t)*exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t= 0..1)
  43. % +int((t+1)*exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t=-1..0)
  44. w1 = 0.5*sqrt(2/s);
  45. w2 = -0.5/s;
  46. w3 = sqrt(s/2/pi);
  47. krn = 0.5*(erf(w1*(x+1)).*(x+1) + erf(w1*(x-1)).*(x-1) - 2*erf(w1*x ).* x)...
  48. +w3*(exp(w2*(x+1).^2) + exp(w2*(x-1).^2) - 2*exp(w2*x.^2));
  49. krn(krn<0) = 0;
  51. else
  52. error('Only defined for nearest neighbour and linear interpolation.');
  53. % If anyone knows a nice formula for a sinc function convolved with a
  54. % a Gaussian, then that could be quite useful.
  55. end

ica_fuse_spm_smoothkern.m at commit 29f72fa, no license · at the source

Overview

Authors: K M Ibrahim Khalilullah1, Souvik Phadikar1, Oktay Agcaoglu1, Jing Sui1, Marlena Duda1, Tülay Adali2, Vince D Calhoun1
ORCID iDs: K M Ibrahim Khalilullah, Souvik Phadikar, Oktay Agcaoglu, Marlena Duda, Tülay Adali, Vince D Calhoun
  1. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
  2. Department of Electrical and Computer Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA
Institutions: Georgia Institute of Technology (United States); Emory University (United States); Georgia State University (United States); Center for Translational Research in Neuroimaging and Data Science (United States); University of Maryland, Baltimore County (United States)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 3, pages 787-808
Dates: received 5 September 2025; accepted 1 April 2026; published online 25 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.575 · PMID 42730199 · PMCID PMC13569335 · OpenAlex W7155192139
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), systems (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, fMRI & imaging
Keywords: Multimodal fusion, Group ICA, Joint ICA, pmg-jICA, Gray matter, fMRI, Intrinsic connectivity network, Joint spatial maps and subject loadings
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIA NIH HHS (RF1 AG063153)
Citations: not cited yet (Europe PMC); 49 references in the paper

Abstract

Integrating neuroimaging data enhances our understanding of the brain. Structural magnetic resonance imaging (sMRI) offers high-resolution anatomical detail, while functional MRI (fMRI) captures dynamic neural activity. Combining these modalities can reveal significant structure–function relationships in the brain. However, existing approaches typically link sMRI to only a single fMRI network, overlooking the spatial complexity of multiple networks and thereby missing distributed structure–function relationships. To address this limitation, we present parallel multilink group joint ICA (pmg-jICA), a data-driven framework that fuses gray matter images from sMRI with multiple intrinsic fMRI networks within a single model. pmg-jICA captures cross-network structure–function coupling, preserves subject-specific variability, and enables robust group-level statistical analyses. To demonstrate the approach, we applied pmg-jICA to an Alzheimer’s disease (AD) dataset, recovering linked structural and functional components for 53 brain networks. Notably, patients with AD exhibited alterations in subcortical, cognitive control and visual regions. Importantly, the subject loadings enabled the computation of functional network connectivity, revealing additional alterations in subcortical, visual, and cognitive control systems. Overall, our results demonstrate that pmg-jICA overcomes key limitations of existing multimodal fusion techniques, yielding deeper insights into structure–function disruptions in AD and potentially offering a flexible framework for studying other neurological and psychiatric disorders.

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.

trendscenter/fit

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 29f72fa7363b954a780c45945a9dc14a7d66d809, 20 August 2026
Languages: MATLAB (542), C (36), C/C++ (6), Python (2)
Size: 796 files, 586 scripts
Software Heritage: archived
Found in: “DATA AND CODE AVAILABILITY”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: GIFT (23 files), Statistics and Machine Learning Toolbox (9 files), FieldTrip (4 files), GIfTI library for MATLAB (4 files), SPM (4 files), NumPy (2 files), scikit-learn (2 files), SciPy (2 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
587 files
At the source: github.com/trendscenter/fit

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;
  • 586 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 and code availability

The data are used from the OASIS-3 and can be access via the following website: https://www.oasis-brains.org. The pmg-jICA method and example run code can be found in the FIT toolbox at https://github.com/trendscenter/fit.

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, 7 authors, 8 keywords, 1 funder, 49 references.

Cite

This paper

Khalilullah, K. M. I., Phadikar, S., Agcaoglu, O., Sui, J., Duda, M., Adali, T., & Calhoun, V. D. (2026). Parallel multilink group joint ICA (pmg-jICA): Fusion of 3D structural and 4D functional data across multiple resting fMRI networks. Network neuroscience (Cambridge, Mass.), 10(3), 787-808. https://doi.org/10.1162/netn.a.575

BibTeX

@article{khalilullah2026parallel,
author = {Khalilullah, K M Ibrahim and Phadikar, Souvik and Agcaoglu, Oktay and Sui, Jing and Duda, Marlena and Adali, Tülay and Calhoun, Vince D},
title = {{Parallel multilink group joint ICA (pmg-jICA): Fusion of 3D structural and 4D functional data across multiple resting fMRI networks}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {10},
number = {3},
pages = {787--808},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.575},
url = {https://doi.org/10.1162/netn.a.575},
pmid = {42730199},
pmcid = {PMC13569335}
}

RIS

TY - JOUR
AU - Khalilullah, K M Ibrahim
AU - Phadikar, Souvik
AU - Agcaoglu, Oktay
AU - Sui, Jing
AU - Duda, Marlena
AU - Adali, Tülay
AU - Calhoun, Vince D
TI - Parallel multilink group joint ICA (pmg-jICA): Fusion of 3D structural and 4D functional data across multiple resting fMRI networks
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/08/25
VL - 10
IS - 3
SP - 787
EP - 808
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.575
UR - https://doi.org/10.1162/netn.a.575
LA - en
ER -

CSL-JSON

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"container-title": "Network neuroscience (Cambridge, Mass.)",
"author": [
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"family": "Khalilullah",
"given": "K M Ibrahim"
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}

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

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