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Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data.

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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] § Results › Jointly Informed fMRI and EEG Components ↔ GroupICAT/icatb/icatb_batch_files/input_neuromark_fmri_2.2.m, lines 94–129 · score 0.66 · EH, OT, CE, FR, SA, OC
  2. [2] § Results › Reproducibility and Stability Analysis ↔ GroupICAT/icatb/toolbox/icasso122/icassoShow.m, lines 1–145 · score 0.58 · cluster quality, independent component, Iq, ICASSO, partitions, Himberg

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 130 lines · 4.5 KB · no license · 1 match

  1. %% GIFT Batch Template (generic)
  2. % Fill in the USER SETTINGS section, then run:
  3. % icatb_batch_file_run('input_this_file.m');
  4. % Date 2/10/2026
  6. %% -----------------------------
  7. % USER SETTINGS
  8. % -----------------------------
  10. % Modality: 'fMRI', 'sMRI', or 'EEG'
  11. modalityType = 'fMRI';
  13. % TR in seconds (scalar or 1 x nSubjects vector)
  14. TR = 2;
  16. % Output
  17. outputDir = 'C:\Users\user\study\output';
  18. prefix = 'nmark2p2';
  20. % Data selection method (1/2/3/4). This template uses Method 4.
  21. dataSelectionMethod = 4;
  23. % Method 4: list subject files (rows = subjects, cols = sessions)
  24. % Example: 2 subjects, 1 session
  25. input_data_file_patterns = {
  26. 'C:\Users\user\study\input\subject1.nii'
  27. 'C:\Users\user\study\input\subject2.nii'
  28. };
  30. % Optional: per-subject design matrices (only used for certain keyword_designMatrix settings)
  31. % for each subject i.e., if you have selected 'diff_sub_diff_sess' for variable keyword_designMatrix.
  32. input_design_matrices = {};
  34. % Dummy scans to drop
  35. dummy_scans = 0;
  37. % Mask: [] for default, or full path, or special strings (if your lab uses them)
  38. maskFile = 'default&icv'; % or [] / 'C:\path\mask.nii'
  40. % Preprocessing:
  41. % 1 Remove mean per time point
  42. % 2 Remove mean per voxel
  43. % 3 Intensity normalization
  44. % 4 Variance normalization
  45. preproc_type = 1;
  47. % Scaling:
  48. % 0 none, 1 percent signal change, 2 Z-scores
  49. scaleType = 2;
  51. % ICA algorithm (string name or numeric, depending on your GIFT version)
  52. % Examples: 'infomax', 'fastica', 'moo-icar', ...
  53. algoType = 'moo-icar';
  55. % Spatial reference template for constrained / Neuromark-style ICA
  56. % (only used by certain algorithms like 'moo-icar' / constrained spatial ICA)
  57. % fMRI templates: Neuromark_fMRI_1.0.nii, Neuromark_fMRI_2.0_modelorder-175.nii,
  58. % Neuromark_fMRI_2.0_modelorder-25.nii, Neuromark_fMRI_2.1_modelorder-multi.nii,
  59. % Neuromark_fMRI_2.2_modelorder-multi.nii, Neuromark_fMRI_3.0_aging_modelorder-100.nii
  60. % Neuromark_fMRI_3.0_development_modelorder-100.nii, Neuromark_fMRI_3.0_infant_modelorder-100.nii
  61. % Neuromark_fMRI_WM_2.2_modelorder-multi.nii
  62. % Templates for sMRI Neuromark: Neuromark_sMRI_1.0_modelorder-30_2x2x2.nii
  63. % Neuromark_sMRI_3.0_modelorder-100_3x3x3.nii, Neuromark_dMRI_3.0_modelorder-100_3x3x3.nii
  64. % Neuromark_PET-FBP_1.0_modelorder-40_2x2x2.nii
  65. refFiles = which('Neuromark_fMRI_2.2_modelorder-multi.nii');
  67. %% -----------------------------
  68. % PERFORMANCE / PARALLEL SETTINGS
  69. % -----------------------------
  71. % Performance type:
  72. % 1 Maximize performance
  73. % 2 Less memory usage
  74. % 3 User specified settings
  75. perfType = 1;
  77. % Parallel execution
  78. % mode: 'serial' or 'parallel'
  79. parallel_info.mode = 'serial';
  80. parallel_info.num_workers = 4;
  82. %% -----------------------------
  83. % REPORT / DISPLAY SETTINGS (fmri and smri only)
  84. % -----------------------------
  86. display_results.formatName = 'html';
  87. display_results.slices_in_mm = (-40:4:72);
  88. display_results.convert_to_zscores = 'yes';
  89. display_results.threshold = 1.0;
  90. display_results.image_values = 'positive';
  91. display_results.slice_plane = 'axial';
  92. display_results.anatomical_file = which('ch2bet_3x3x3.nii');
  94. %% Network summary (fMRI; especially useful with Neuromark templates)
  95. display_results.network_summary_opts = struct();
  96. display_results.network_summary_opts.comp_network_names = { ...
  97. 'CB', (1:13); ...
  98. 'VI-OT', (14:19); ...
  99. 'VI-OC', (20:25); ...
  100. 'PL', (26:36); ...
  101. 'SC-EH', (37:39); ...
  102. 'SC-ET', (40:45); ...
  103. 'SC-BG', (46:54); ...
  104. 'SM', (55:68); ...
  105. 'HC-IT', (69:75); ...
  106. 'HC-TP', (76:80); ...
  107. 'HC-FR', (81:90); ...
  108. 'TN-CE', (91:93); ...
  109. 'TN-DM', (94:101); ...
  110. 'TN-SA', (102:105) ...
  111. };
  112. display_results.network_summary_opts.outputDir = fullfile(outputDir, 'network_summary');
  113. display_results.network_summary_opts.prefix = [prefix, '_network_summary'];
  114. display_results.network_summary_opts.structFile = which('ch2bet_3x3x3.nii');
  115. display_results.network_summary_opts.image_values = 'positive';
  116. display_results.network_summary_opts.threshold = 2;
  117. display_results.network_summary_opts.convert_to_z = 'yes';
  119. % Other network summary options
  120. %display_results.network_summary_opts.conn_threshold = 0.2;
  121. %display_results.network_summary_opts.fnc_colorbar_label = 'Corr';
  122. %options are 'slices' and 'render'
  123. %display_results.network_summary_opts.display_type = 'slices';
  124. %display_results.network_summary_opts.slice_plane = 'axial';
  125. %colormap of the correlations
  126. %display_results.network_summary_opts.cmap = jet(64);
  127. %CLIM - range of the data values in [min_value, max_value] format
  129. %display_results.network_summary_opts.CLIM=CLIM;

input_neuromark_fmri_2.2.m at commit a1c9161, no license · at the source

Overview

Authors: Souvik Phadikar1, Mahshid Fouladivanda1, Cyrus Eierud1, Armin Iraji1, Lei Wu1, Martin P Paulus2, Rayus Kuplicki2, Masaya Misaki2, Vince D Calhoun1
ORCID iDs: Souvik Phadikar, Mahshid Fouladivanda, Cyrus Eierud, Armin Iraji, Lei Wu, Martin P Paulus, Rayus Kuplicki, Masaya Misaki, Vince D Calhoun
  1. Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
  2. Laureate Institute for Brain Research (LIBR), Tulsa, Oklahoma, 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); Laureate Institute for Brain Research (United States)
Journal: Human brain mapping, volume 47, issue 10, article e70599
Dates: received 6 November 2025; accepted 24 June 2026; published online 12 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/hbm.70599 · PMID 42438078 · PMCID PMC13358026 · OpenAlex W7168127286
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), fMRI (modality), human (organism), depression (population), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Evoked potentials, Preprocessing
Keywords: depression, EEG‐fMRI, group joint ICA, joint FNC, multimodal fusion
MeSH: Brain*, Brain Mapping*, Depression*, Electroencephalography*, Magnetic Resonance Imaging*, Multimodal Imaging*, Adult, Female, Humans, Image Processing, Computer-Assisted, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (2316421); National Institutes of Health (R01EB006841); NIBIB NIH HHS (R01 EB006841); NIH HHS (R01EB006841)
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

The integration of EEG and fMRI offers a powerful method for exploring the brain's spatial and temporal dynamics. However, existing approaches typically summarize both EEG and fMRI, often removing temporal information before combining the modalities. Our novel approach brings together group ICA of fMRI, group ICA of EEG, and joint ICA to propose a multimodal data fusion approach, named group joint ICA (gjICA), that links simultaneous EEG‐fMRI data from multiple subjects. The proposed framework enables group‐level mapping and provides single‐subject estimates via back‐reconstruction, facilitating a comprehensive picture of brain networks. The gjICA also introduces joint functional network connectivity (jFNC), which provides connectivity between fMRI networks as well as between EEG components, hence linking temporal and spatial information. When applied to concurrent resting EEG‐fMRI data from 121 participants, we identified 63 multimodal brain components. The statistical analyses of these components further revealed that depression is characterized by widespread multimodal connectivity, with visual and higher cognition networks acting as hubs in these alterations. Further, joint histogram analysis revealed that depression is broadly associated with reduced EEG component expression with increased fMRI component expression in cerebellar, visual, subcortical, and sensorimotor networks, suggesting altered neurovascular coupling and modality‐specific dysfunctions. Overall, the proposed gjICA approach provides a robust framework for fusing EEG and fMRI data while preserving the spatiotemporal information in both modalities, enabling the identification of a more comprehensive picture of multimodal neural relationships, enhancing our understanding of brain dynamics and providing new insights into complex brain disorders such as depression.

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, with 2 matches between paragraphs and lines of code.

trendscenter/gift

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a1c91618b3603d07fc134d4857badcdc89796325, 23 September 2026
Languages: MATLAB (946), C (58), C/C++ (20), C++ (8), Shell (5), Python (4), JavaScript (2), Java (1)
Size: 1,739 files, 1,044 scripts
Software Heritage: archived
Found in: the text, “Introduction”
Holds: README, environment (GroupICAT/icatb/toolbox/autolabeller/requirements.txt, GroupICAT/icatb/nipype-0.10.0/nipype/interfaces/gift/setup.py), tests, documentation
Not found: license file, CITATION.cff, continuous integration
Tools: GIFT (479 files), Statistics and Machine Learning Toolbox (26 files), SPM (17 files), EEGLAB (7 files), Signal Processing Toolbox (7 files), GIfTI library for MATLAB (6 files), Parallel Computing Toolbox (5 files), Image Processing Toolbox (4 files), Optimization Toolbox (3 files), export_fig (2 files), FieldTrip (2 files), CAT12 (1 file), FSL (1 file), Nipype (1 file), NumPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
1,045 files
At the source: github.com/trendscenter/gift

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;
  • 1,044 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 Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY-NC), 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, 5 keywords, 12 MeSH terms, 4 funders, 47 references.

Cite

This paper

Phadikar, S., Fouladivanda, M., Eierud, C., Iraji, A., Wu, L., Paulus, M. P., Kuplicki, R., Misaki, M., & Calhoun, V. D. (2026). Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data. Human brain mapping, 47(10), e70599. https://doi.org/10.1002/hbm.70599

BibTeX

@article{phadikar2026group,
author = {Phadikar, Souvik and Fouladivanda, Mahshid and Eierud, Cyrus and Iraji, Armin and Wu, Lei and Paulus, Martin P and Kuplicki, Rayus and Misaki, Masaya and Calhoun, Vince D},
title = {{Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data}},
journal = {Human brain mapping},
year = {2026},
month = jul,
volume = {47},
number = {10},
pages = {e70599},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70599},
url = {https://doi.org/10.1002/hbm.70599},
pmid = {42438078},
pmcid = {PMC13358026}
}

RIS

TY - JOUR
AU - Phadikar, Souvik
AU - Fouladivanda, Mahshid
AU - Eierud, Cyrus
AU - Iraji, Armin
AU - Wu, Lei
AU - Paulus, Martin P
AU - Kuplicki, Rayus
AU - Misaki, Masaya
AU - Calhoun, Vince D
TI - Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/07/01
VL - 47
IS - 10
SP - e70599
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70599
UR - https://doi.org/10.1002/hbm.70599
LA - en
ER -

CSL-JSON

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