OSCR

Reliability and signal comparison of OPM-MEG, fMRI & iEEG in a repeated movie viewing paradigm.

Code ↔ Paper

6 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 6 matches
  1. [1] § Methods › OPM › Amplitude of the analytic signal ↔ code/withinViewing_betweenSubs_OPM.m, lines 29–76 · score 0.91 · 12–28 Hz, 28–46 Hz, 55–70 Hz, 8–12 Hz, 0.5–116 Hz, 0.5–4 Hz
  2. [2] § Methods › OPM › Amplitude of the analytic signal ↔ code/MNE_betweenSubs_OPM.m, lines 29–76 · score 0.91 · 12–28 Hz, 28–46 Hz, 55–70 Hz, 8–12 Hz, 0.5–116 Hz, 0.5–4 Hz
  3. [3] § Results › Within-subject comparisons ↔ code/MNE_betweenSubs_OPM.m, lines 29–76 · score 0.85 · 12–28 Hz, 28–46 Hz, 55–70 Hz, 8–12 Hz, 0.5–116 Hz, 0.5–4 Hz
  4. [4] § Results › Within-subject comparisons ↔ code/MNE_withinSubs_OPM.m, lines 28–54 · score 0.85 · 12–28 Hz, 28–46 Hz, 55–70 Hz, 8–12 Hz, 0.5–116 Hz, 0.5–4 Hz
  5. [5] § Methods › Analyses › Signal-to-noise (SNR) comparisons ↔ code/SNR_OPM.m, lines 172–218 · score 0.54 · surrogate SNR, cluster sums, tails, OPM
  6. [6] § Methods › Analyses › Signal-to-noise (SNR) comparisons ↔ code/SNR_OPM-fMRI.m, lines 158–213 · score 0.53 · surrogate SNR, cluster sums, tails, OPM

Paper

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

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The authors' code

MATLAB · 213 lines · 8 KB · no license · 2 matches

  1. clear;
  2. clc;
  4. % -------------------------------------------------------------------------------------------------------------------
  5. % TAKE CARE OF FILENAMES/PATHS & TOOLBOXES
  6. % -------------------------------------------------------------------------------------------------------------------
  7. mydir = pwd;
  8. idcs = strfind(mydir, filesep);
  10. bsbr_opm_path = fullfile(mydir(1:idcs(end-1)), 'OPM_reliability', 'derivatives', ...
  11. 'bsbr', 'OPM');
  12. save_path = fullfile(mydir(1:idcs(end-1)), 'OPM_reliability', 'derivatives', ...
  13. 'bsbr', 'OPM', 'GA_bsbr');
  14. fig_save_path = fullfile(mydir(1:idcs(end-1)), 'OPM_reliability', 'derivatives', ...
  15. 'bsbr', 'OPM', 'bsbr_figs');
  16. nii_path = fullfile(mydir(1:idcs(end-1)), 'OPM_reliability', 'derivatives', ...
  17. 'bsbr', 'OPM', 'bsbr_nifti');
  18. if ~exist(save_path, 'dir'); mkdir(save_path); end
  19. if ~exist(fig_save_path, 'dir'); mkdir(fig_save_path); end
  20. if ~exist(nii_path, 'dir'); mkdir(nii_path); end
  22. % add fieltrip to path
  23. toolpth_ = fullfile(mydir(1:idcs(end-1)), 'OPM_reliability', 'toolboxes');
  24. addpath(fullfile(toolpth_, 'fieldtrip'));
  25. addpath(genpath(fullfile(toolpth_, 'fieldtrip', 'external', 'spm12')));
  26. ft_defaults;
  29. %% -------------------------------------------------------------------------------------------------------------------
  30. % START WITH OPM
  31. % -------------------------------------------------------------------------------------------------------------------
  32. % Load raw correlations
  33. load(fullfile(bsbr_opm_path, 'real/bsbr_OPM_1_OLD.mat'));
  34. load(fullfile(bsbr_opm_path, 'real/bsbr_OPM_2_OLD.mat'));
  36. % Get gen vars
  37. num_sub = size(bsbr_opm_1, 2);
  38. num_vox = size(bsbr_opm_1, 1);
  39. frequencies = {'delta', 'theta', 'alpha', 'beta', 'gamma1', 'gamma2', 'hf', 'broad'};
  40. num_freq = size(frequencies, 2);
  41. num_surr = 100;
  43. delta_band = [.5 4]; % added
  44. theta_band = [4 8];
  45. alpha_band = [8 12];
  46. beta_band = [12 28];
  47. gamma1_band = [28 46];
  48. gamma2_band = [55 70];
  49. hf_band = [64 116];
  50. broad_band = [.5 116];
  52. freq_Hz = {delta_band, theta_band,alpha_band, beta_band,...
  53. gamma1_band, gamma2_band, hf_band, broad_band};
  55. % Compute raw avg correlations
  56. bsbr_opm = nan([num_vox, num_sub, num_freq]);
  57. for freq = 1:num_freq
  58. for sub = 1:num_sub
  59. temp_real = nanmean([(squeeze(nanmean(bsbr_opm_1(:, sub, :, freq), 3))), (squeeze(nanmean(bsbr_opm_2(:, sub, :, freq), 3)))],2);
  60. bsbr_opm(:, sub, freq) = temp_real;
  61. end
  62. end
  63. bsbr_opm_avg = squeeze(nanmean(bsbr_opm, 2));
  66. % Compute z avg correlations
  67. load(fullfile(bsbr_opm_path, 'real/bsbr_z_OPM_1_OLD.mat'));
  68. load(fullfile(bsbr_opm_path, 'real/bsbr_z_OPM_2_OLD.mat'));
  69. bsbr_z_opm = nan([num_vox, num_sub, num_freq]);
  70. for freq = 1:num_freq
  71. for sub = 1:num_sub
  72. temp_real = nanmean([(squeeze(nanmean(bsbr_z_opm_1(:, sub, :, freq), 3))), (squeeze(nanmean(bsbr_z_opm_2(:, sub, :, freq), 3)))],2);
  73. bsbr_z_opm(:, sub, freq) = temp_real;
  74. end
  75. end
  76. bsbr_z_opm_avg = squeeze(nanmean(bsbr_z_opm, 2));
  79. %% initialize
  80. num_dist = 10000;
  81. surr_dist_temp = nan([num_vox, num_dist]); % Preallocate for storing results
  82. bsbr_rand_surr = nan([num_vox, num_sub]);
  83. surr_dist = nan([num_vox, num_dist, num_freq]);% Preallocate the temporary matrix
  85. %% Compute surrogate distriution
  86. bsbr_z_surr_opm = nan([num_vox, num_surr, num_sub, num_freq]);
  87. for freq = 1:num_freq
  88. freq_name = frequencies{freq};
  89. for sub = 1:num_sub
  90. load(fullfile(bsbr_opm_path, 'surr', sprintf('sub_%03d_surr_bsbr_1_z_%s_OLD.mat', sub, freq_name)));
  91. load(fullfile(bsbr_opm_path, 'surr', sprintf('sub_%03d_surr_bsbr_2_z_%s_OLD.mat', sub, freq_name)));
  92. bsbr_z_surr_opm(:, :, sub, freq) = nanmean(cat(3, squeeze(nanmean(sub_surr_1_z(:, :, :), 3)), squeeze(nanmean(sub_surr_2_z(:, :, :), 3))), 3);
  93. end
  94. end
  95. fprintf('Surrogate dataset for %s done\n', freq_name)
  97. for freq = 1:num_freq
  98. freq_name = frequencies{freq};
  99. parfor i = 1:num_dist % Parallelized loop over num_dist
  100. bsbr_rand_surr_local = nan([num_vox, num_sub]); % Declare local variable to avoid broadcast issues
  101. bsbr_rand_surr_avg_local = nan(num_vox, 1); % Local version for storing the average
  103. for vox = 1:num_vox
  104. for sub = 1:num_sub
  105. surr_idx = randi([1,100]);
  106. bsbr_rand_surr_local(vox, sub) = squeeze(bsbr_z_surr_opm(vox, surr_idx, sub, freq));
  107. end
  108. bsbr_rand_surr_avg_local(vox) = nanmean(bsbr_rand_surr_local(vox, :), 2); % Compute average for each voxel
  109. end
  111. surr_dist_temp(:, i) = bsbr_rand_surr_avg_local; % Store the result for this iteration
  112. fprintf('Permutation %d done\n', i)
  113. end
  114. % Add the data to the big df and clear vars
  115. surr_dist(:, :, freq) = surr_dist_temp;
  116. fprintf('%s added to dataframe\n', freq_name)
  118. end
  120. % SAVE IT
  121. outfilename = fullfile(save_path, 'bsbr_surr_dist_OPM_OLD.mat');
  122. save(outfilename, 'surr_dist');
  123. fprintf('Permutation for bsbr saved\n')
  125. %% Compute z-scores & p vals
  126. % load(fullfile(save_path, 'bsbr_surr_dist_OPM_OLD.mat'));
  128. GAp_bsbr_opm = nan([num_vox, num_freq]);
  129. GAz_bsbr_opm = nan([num_vox, num_freq]);
  130. for vox = 1:num_vox
  131. for freq = 1:num_freq
  132. rd_avg = squeeze(bsbr_z_opm_avg(vox, freq));
  133. gt_count = sum(surr_dist(vox, :, freq) > rd_avg);
  134. p_val = gt_count/num_dist;
  135. z_score = (rd_avg - nanmean(surr_dist(vox, :, freq)))./nanstd(surr_dist(vox, :, freq));
  136. GAp_bsbr_opm(vox, freq) = p_val;
  137. GAz_bsbr_opm(vox, freq) = z_score;
  138. end
  139. end
  141. %% Compute FDR correction on p-vals
  142. fdr_z_scores = nan(size(GAz_bsbr_opm));
  143. fdr_p_vals = false(size(GAp_bsbr_opm)); % Logical array to store FDR mask
  145. % Loop over each frequency and apply FDR correction individually
  146. for freq = 1:num_freq
  147. % Extract p-values for the current frequency
  148. p_vals = GAp_bsbr_opm(:, freq);
  150. % Perform FDR correction on p-values for this frequency
  151. [~, crit_p, ~, adj_p] = fdr_bh(p_vals, 0.05);
  153. % Mask the p-values based on the FDR threshold
  154. fdr_p_vals(:, freq) = p_vals <= crit_p;
  156. % Store the corrected z-scores for the significant values
  157. fdr_z_scores(fdr_p_vals(:, freq), freq) = GAz_bsbr_opm(fdr_p_vals(:, freq), freq);
  158. end
  160. %% SAVE THE STATS
  161. bsbr_opm_stats = struct();
  162. bsbr_opm_stats.raw_atanh = bsbr_z_opm;
  163. bsbr_opm_stats.raw_atanh_avg = bsbr_z_opm_avg;
  164. bsbr_opm_stats.raw_corrs = bsbr_opm;
  165. bsbr_opm_stats.raw_corrs_avg = bsbr_opm_avg;
  166. bsbr_opm_stats.z_scores = GAz_bsbr_opm;
  167. bsbr_opm_stats.p_vals = GAp_bsbr_opm;
  168. bsbr_opm_stats.z_scores_fdr = fdr_z_scores;
  169. bsbr_opm_stats.crit_p = crit_p;
  171. % mask the actual correlation values based on those that survive FDR
  172. bsbr_opm_stats.corrs_fdr = nan(size(bsbr_opm_stats.raw_corrs_avg));
  173. bsbr_opm_stats.corrs_fdr(~isnan(bsbr_opm_stats.z_scores_fdr)) = bsbr_opm_stats.raw_corrs_avg(~isnan(bsbr_opm_stats.z_scores_fdr));
  175. outfilename = fullfile(save_path, 'bsbr_OPM_stats_OLD.mat');
  176. save(outfilename, "bsbr_opm_stats");
  178. %% -------------------------------------------------------------------------------------------------------------------
  179. % PLOT
  180. % -------------------------------------------------------------------------------------------------------------------
  181. % first load tempalte
  182. load(fullfile(toolpth_, '/fieldtrip/template/headmodel/livs_template_grid.mat'));
  183. load(fullfile(toolpth_, '/fieldtrip/template/headmodel/standard_mri.mat'));
  185. base_file_names = {'z_OLD.nii', 'r_OLD.nii'};
  186. data_sets = {bsbr_opm_stats.z_scores_fdr, bsbr_opm_stats.corrs_fdr};
  189. % Plot it
  190. for freq = 1:num_freq
  191. freq_name = frequencies{freq};
  193. % Loop over different data sets for plotting
  194. for data = 1:length(data_sets)
  195. temp_data = data_sets{data};
  196. template_grid.data = nan(size(template_grid.inside));
  197. template_grid.data(template_grid.inside) = temp_data(:, freq);
  200. % Interpolate for .nii
  201. cfg = [];
  202. cfg.parameter = 'data';
  203. interp = ft_sourceinterpolate(cfg, template_grid, mri);
  205. % Write to disk with frequency band in the filename
  206. cfg = [];
  207. cfg.parameter = 'data';
  208. cfg.filetype = 'nifti';
  209. cfg.filename = fullfile(nii_path, [freq_name, '_' base_file_names{data}]);
  210. ft_volumewrite(cfg, interp);
  211. end
  212. end

MNE_betweenSubs_OPM.m, no license · at the source

Overview

Authors: Olivia R. Christiano1, Sebastian Michelmann1
ORCID iDs: Olivia R. Christiano, Sebastian Michelmann
  1. Department of Psychology, New York University, New York, NY, United States
Institutions: New York University (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1218
Dates: received 14 July 2025; accepted 20 March 2026; published online 8 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1218 · PMID 42125202 · PMCID PMC13159027 · OpenAlex W7148998135
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), MEG (modality), intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: OPM, MEG, fMRI, iEEG, reliability, intersubject correlation, cross-modal correlation
Topic: Atomic and Subatomic Physics Research (Atomic and Molecular Physics, and Optics, Physics and Astronomy), according to OpenAlex
Citations: not cited yet (Europe PMC); 78 references in the paper

Abstract

Optically pumped magnetometers (OPMs) offer a promising advancement in noninvasive neuroimaging via magnetoencephalography (MEG), but establishing their reliability and comparability to existing methods remains an ongoing endeavor. Here, we evaluated OPM recordings by assessing their test-retest reliability and comparing them to functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (iEEG) recordings. Data were collected from three independent participant groups during repeated viewings of a movie segment. In 7 canonical frequency bands (δ: 0.5–4 Hz, θ: 4–8 Hz, α: 8–12 Hz, β: 12–28 Hz, γ1: 28–46 Hz, γ2: 55–70 Hz, and HF: 64–116 Hz), as well as a broadband (BB: 0.5–116 Hz) signal, we quantified the signal consistency (1) within individuals, (2) across subjects, and (3) across modalities. OPM exhibited widespread reliability, particularly in lower frequency bands; spatial patterns resembled those of fMRI and iEEG in visual and auditory regions. Cross-modal analyses revealed robust correspondence between OPM and both fMRI and iEEG, including inverse correlations at low frequencies and positive correlations at higher frequencies in the OPM-fMRI comparison, consistent with known relationships between oscillatory power and BOLD responses. Comparisons of signal-to-noise (SNR) estimates further revealed that in some regions, the SNR of cross-modal alignment exceeded within-modality reliability, suggesting that bridging between modalities can sometimes enhance SNR by attenuating reliably shared noise. Our findings demonstrate that OPM consistently captures stimulus-driven neural dynamics that converge with established modalities.

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 6 matches between paragraphs and lines of code.

OSF urjvb

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (20)
Size: 20 files, 20 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (20 files), fdr_bh (Benjamini-Hochberg FDR) (19 files), Statistics and Machine Learning Toolbox (19 files), Image Processing Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
20 files
At the source: osf.io/urjvb

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;
  • 20 scripts, each with its path and the digest of its content;
  • 6 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

Datasets cited

Data and Code Availability

Data and analysis code underlying the conclusions of the manuscript are available at the Open Science Framework (OSF): https://osf.io/urjvb. The raw OPM data from Rier et al. (2023) are available on Zenodo: https://doi.org/10.5281/zenodo.7525341

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 7 keywords, 77 references.

Cite

This paper

Christiano, O. R., & Michelmann, S. (2026). Reliability and signal comparison of OPM-MEG, fMRI &amp; iEEG in a repeated movie viewing paradigm. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1218. https://doi.org/10.1162/imag.a.1218

BibTeX

@article{christiano2026reliability,
author = {Christiano, Olivia R. and Michelmann, Sebastian},
title = {{Reliability and signal comparison of OPM-MEG, fMRI \&amp; iEEG in a repeated movie viewing paradigm}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {4},
pages = {IMAG.a.1218},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1218},
url = {https://doi.org/10.1162/imag.a.1218},
pmid = {42125202},
pmcid = {PMC13159027}
}

RIS

TY - JOUR
AU - Christiano, Olivia R.
AU - Michelmann, Sebastian
TI - Reliability and signal comparison of OPM-MEG, fMRI &amp; iEEG in a repeated movie viewing paradigm
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/05/08
VL - 4
SP - IMAG.a.1218
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1218
UR - https://doi.org/10.1162/imag.a.1218
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1218",
"type": "article-journal",
"title": "Reliability and signal comparison of OPM-MEG, fMRI &amp; iEEG in a repeated movie viewing paradigm",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Christiano",
"given": "Olivia R."
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{
"family": "Michelmann",
"given": "Sebastian"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1218",
"DOI": "10.1162/imag.a.1218",
"PMID": "42125202",
"PMCID": "PMC13159027",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1218",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
8
]
]
}
}

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