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A Replicable NeuroMark Template for Whole-Brain SPECT Reveals Data-Driven Perfusion Networks and Their Alterations in Schizophrenia.

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  1. [1] § Methods ↔ demo/cfg/Input_spatial_ica_bids.m, lines 64–70 · score 0.50 · MOO ICAR, Blind ICA, algorithm

Paper

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The authors' code

MATLAB · 111 lines · 4.5 KB · CC-BY-4.0 · 1 match

  1. % Enter the values for the variables required for the ICA analysis.
  2. % Variables are on the left and the values are on the right.
  3. % Characters must be enterd in single quotes
  4. %
  5. % After entering the parameters, use icatb_batch_file_run(inputFile);
  7. %% Modality. Options are fMRI and EEG
  8. modalityType = 'fMRI';
  10. %% Output directory
  11. outputDir = evalin('base', 'GICA_OUTPUTDIR');
  14. %% Enter TR in seconds. If TRs vary across subjects, TR must be a row vector of length equal to the number of subjects.
  15. TR = 2;
  17. %% Parallel info
  18. % enter mode serial or parallel. If parallel, enter number of
  19. % sessions/workers to do job in parallel
  20. parallel_info.mode = 'serial';
  21. parallel_info.num_workers = 4;
  23. %% Group PCA performance settings. Best setting for each option will be selected based on variable MAX_AVAILABLE_RAM in icatb_defaults.m.
  24. % If you have selected option 3 (user specified settings) you need to manually set the PCA options. See manual or other
  25. % templates (icatb/icatb_batch_files/Input_data_subjects_1.m) for more information to set PCA options
  26. %
  27. % Options are:
  28. % 1 - Maximize Performance
  29. % 2 - Less Memory Usage
  30. % 3 - User Specified Settings
  31. perfType = 1;
  33. % Input for design matrices will be used only if you have a design matrix
  34. % for each subject i.e., if you have selected 'diff_sub_diff_sess' for
  35. % variable keyword_designMatrix.
  36. input_design_matrices = {};
  38. % Enter no. of dummy scans to exclude from the group ICA analysis. If you have no dummy scans leave it as 0.
  39. dummy_scans = 0;
  41. %% Enter Name (Prefix) Of Output Files
  42. prefix = 'neuromark';
  44. %% Enter location (full file path) of the image file to use as mask
  45. % or use Default mask which is []
  46. maskFile = 'default&icv';
  49. %% Data Pre-processing options
  50. % 1 - Remove mean per time point
  51. % 2 - Remove mean per voxel
  52. % 3 - Intensity normalization
  53. % 4 - Variance normalization
  54. preproc_type = 1;
  57. %% Scale the Results. Options are 0, 1, 2
  58. % 0 - Don't scale
  59. % 1 - Scale to Percent signal change
  60. % 2 - Scale to Z scores
  61. scaleType = 2;
  64. %% 'Which ICA Algorithm Do You Want To Use';
  65. % see icatb_icaAlgorithm for details or type icatb_icaAlgorithm at the
  66. % command prompt.
  67. % Note: Use only one subject and one session for Semi-blind ICA. Also specify atmost two reference function names
  69. % 1 means infomax, 2 means fastICA, etc.
  70. algoType = 'moo-icar';
  72. %% Specify spatial reference files for constrained ICA (spatial) or moo-icar.
  73. refFiles = which('Neuromark_fMRI_1.0.nii');
  75. %% Report generator (fmri and smri only)
  76. display_results.formatName = 'html';
  77. display_results.slices_in_mm = (-40:4:72);
  78. display_results.convert_to_zscores = 'yes';
  79. display_results.threshold = 1.0;
  80. display_results.image_values = 'positive and negative';
  81. display_results.slice_plane = 'axial';
  82. display_results.anatomical_file = which('ch2bet_3x3x3.nii');
  84. %% Network summary options
  85. %Network names and components are used in the plots (only fmri). If you are using
  86. %moo-icar or constrained ica (spatial), you can specify network names and
  87. %components within each network. Below is an example from neuromark
  88. %template labels
  89. display_results.network_summary_opts.comp_network_names = { 'SC', (1:5);
  90. 'AU', (6:7);
  91. 'SM', (8:16);
  92. 'VI', (17:25);
  93. 'CC', (26:42);
  94. 'DM', (43:49);
  95. 'CB', (50:53)};
  96. display_results.network_summary_opts.outputDir = fullfile(outputDir, 'network_summary');
  97. display_results.network_summary_opts.prefix = [prefix, '_network_summary'];
  98. display_results.network_summary_opts.structFile = which('ch2bet_3x3x3.nii');
  99. display_results.network_summary_opts.image_values = 'positive and negative';
  100. display_results.network_summary_opts.threshold = 2;
  101. display_results.network_summary_opts.convert_to_z = 'yes';
  102. %some more network summary options
  103. %display_results.network_summary_opts.conn_threshold = 0.2;
  104. %display_results.network_summary_opts.fnc_colorbar_label = 'Corr';
  105. %options are 'slices' and 'render'
  106. %display_results.network_summary_opts.display_type = 'slices';
  107. %display_results.network_summary_opts.slice_plane = 'axial';
  108. %colormap of the correlations
  109. %display_results.network_summary_opts.cmap = jet(64);
  110. %CLIM - range of the data values in [min_value, max_value] format
  111. %display_results.network_summary_opts.CLIM=CLIM;

Input_spatial_ica_bids.m at commit 0dd81ed, under CC-BY-4.0 · at the source

Overview

Authors: Amritha Harikumar1,2, Bradley Baker1, Daniel Amen3,4, David Keator5,3,4, Vince D. Calhoun1
ORCID iDs: Amritha Harikumar
  1. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech,Emory, Atlanta, GA USA
  2. The Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology/Emory University, Amritha Harikumar,55 Park Place NE, Atlanta, GA 30303 USA
  3. Change Your Brain Change Your Life Foundation, Costa Mesa, CA USA
  4. Amen Clinics Inc,Costa Mesa, CA USA
  5. Psychiatry and Human Behavior, University of California,Irvine, CA USA
Institutions: Center for Translational Research in Neuroimaging and Data Science (United States); Emory University (United States); Amen Clinics (United States); University of California, Irvine (United States)
Journal: Neuroinformatics, volume 24, issue 3, article 41
Dates: received 15 April 2026; accepted 30 June 2026; published online 10 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12021-026-09798-x · PMID 42426349 · PMCID PMC13350202 · OpenAlex W7167835319
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), PET / SPECT (modality), human (organism), stroke (population), schizophrenia / psychosis (population)
Methods: Smoothing, state filtering, decompositions, Preprocessing
Keywords: SPECT, Brain networks, Schizophrenia, FMRI, Brain imaging, NeuroMark
MeSH: Brain*, Cerebrovascular Circulation*, Schizophrenia*, Tomography, Emission-Computed, Single-Photon*, Adult, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Male (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (R01MH123600)
Citations: cited by 1 paper (Europe PMC); 33 references in the paper
Research resources: RRID:SCR_004757

Abstract

Single photon emission computed tomography (SPECT) is a highly specialized imaging modality that enables measurement of regional cerebral perfusion and, in particular, regional cerebral blood flow (rCBF). Recent technological advances have improved SPECT quantification and reliability, making it increasingly useful for studying rCBF abnormalities and perfusion–network alterations in psychiatric and neurological disorders. To characterize large-scale functional organization in SPECT data, data-driven decomposition methods such as independent component analysis (ICA) have been used to extract covarying perfusion patterns that map onto interpretable brain networks. Blind ICA provides a data-driven approach to estimate these networks without strong prior assumptions. More recently, a hybrid approach that leverages spatial priors to guide a spatially constrained ICA (sc-ICA) have been used to fully automate the ICA analysis while also providing participant-specific network estimates. While this has been reliably demonstrated in fMRI with the NeuroMark template, there is currently no comparable SPECT template. A SPECT template would enable automatic estimation of functional SPECT networks with participant-specific expressions that correspond across participants and studies. The current study introduces a new replicable NeuroMark SPECT template for estimating canonical perfusion covariance patterns (networks). We first identify replicable SPECT networks using blind ICA applied to two large sample SPECT datasets. We then demonstrate the use of the resulting template by applying sc-ICA to an independent schizophrenia dataset. In sum, this work presents and shares the first NeuroMark SPECT template and demonstrating its utility in an independent cohort, providing a scalable and robust framework for network-based analyses.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12021-026-09798-x.

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 1 match between paragraphs and lines of code.

esalman/autolabeller

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d184924a90b6130b09a68fd2628b3f229e3c8a16, 18 November 2022
Languages: MATLAB (68)
Size: 145 files, 68 scripts
Software Heritage: archived
Found in: the text, “Blind ICA Approach”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: SPM (9 files), GIFT (5 files), Statistics and Machine Learning Toolbox (5 files), export_fig (2 files), Image Processing Toolbox (2 files), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
70 files
At the source: github.com/esalman/autolabeller

trendscenter/gift-bids

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0dd81ed375f01917777ad0c3497d15c545d11779, 6 April 2026
Languages: Shell (24), MATLAB (9), R (1)
Size: 72 files, 34 scripts
Software Heritage: archived
Found in: “Data Availability”
Holds: README, license file, environment (Dockerfile, misc/Legacy/4.0c/Dockerfile)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: FSL (6 files), GIFT (4 files), fMRIPrep (3 files), FreeSurfer (3 files), dcm2niix (2 files), BIDS Validator (1 file), broom (1 file), ggpubr (1 file), Statistics and Machine Learning Toolbox (1 file), pandas (1 file), SPM (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
36 files
At the source: github.com/trendscenter/gift-bids/tree/main/misc/spect/proj/march2024

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 102 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

No dataset and no data link were found in the paper.

Data Availability

Due to the sensitive nature of this clinical data, the SPECT data has not been posted publicly but the anonymized demographic and other clinical data used in this study is freely available for research by request to Dr. Keator. The code in MATLAB and other associated files are available on Github: (https://github.com/trendscenter/gift-bids/tree/main/misc/spect/proj/march2024).

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 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 10 MeSH terms, 1 funder, 31 references, 1 RRID.

Cite

This paper

Harikumar, A., Baker, B., Amen, D., Keator, D., & Calhoun, V. D. (2026). A Replicable NeuroMark Template for Whole-Brain SPECT Reveals Data-Driven Perfusion Networks and Their Alterations in Schizophrenia. Neuroinformatics, 24(3), 41. https://doi.org/10.1007/s12021-026-09798-x

BibTeX

@article{harikumar2026replicable,
author = {Harikumar, Amritha and Baker, Bradley and Amen, Daniel and Keator, David and Calhoun, Vince D.},
title = {{A Replicable NeuroMark Template for Whole-Brain SPECT Reveals Data-Driven Perfusion Networks and Their Alterations in Schizophrenia}},
journal = {Neuroinformatics},
year = {2026},
month = jul,
volume = {24},
number = {3},
pages = {41},
publisher = {Springer Science+Business Media},
issn = {1539-2791},
doi = {10.1007/s12021-026-09798-x},
url = {https://doi.org/10.1007/s12021-026-09798-x},
pmid = {42426349},
pmcid = {PMC13350202}
}

RIS

TY - JOUR
AU - Harikumar, Amritha
AU - Baker, Bradley
AU - Amen, Daniel
AU - Keator, David
AU - Calhoun, Vince D.
TI - A Replicable NeuroMark Template for Whole-Brain SPECT Reveals Data-Driven Perfusion Networks and Their Alterations in Schizophrenia
T2 - Neuroinformatics
J2 - Neuroinformatics
PY - 2026
DA - 2026/07/10
VL - 24
IS - 3
SP - 41
SN - 1539-2791
PB - Springer Science+Business Media
DO - 10.1007/s12021-026-09798-x
UR - https://doi.org/10.1007/s12021-026-09798-x
LA - en
ER -

CSL-JSON

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"title": "A Replicable NeuroMark Template for Whole-Brain SPECT Reveals Data-Driven Perfusion Networks and Their Alterations in Schizophrenia",
"container-title": "Neuroinformatics",
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