Static and Dynamic Resting-State fMRI Connectivity Show Frequency-Resolved Associations With Intracranial EEG Connectivity.

Code ↔ Paper

11 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 11 matches · 4 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted

[1] § Materials and Methods › Functional Connectivity Analyses (icEEG) ↔ step05_compute_fc_eeg_hilbert_use_fmri_final_seeds.m, lines 1 to 53 · score 0.95 · 13–30 Hz, 31–50 Hz, 8–12 Hz, Band limited power, 51–100 Hz, 4–7 Hz · Report a mismatch
[2] § Results › Sensitivity and Robustness Analyses ↔ Fig_motion_plotting.m, the whole file · a weak match · score 0.69 · moderate motion, Motion robustness, EEG frequency bands, low motion, Dynamic EEG, fMRI · Report a mismatch
[3] § Materials and Methods › Functional Connectivity Analyses (icEEG) ↔ helpers/bandpass_hilbert_envelope.m, the whole file · a weak match · score 0.65 · Hilbert transform, Band limited power, BLP, signals, filtered, frequency bands · Report a mismatch
[4] § Materials and Methods › Sensitivity and Robustness Analyses ↔ step09_spatialStratified_windowLMM.m, lines 206 to 264 · score 0.65 · mesial temporal, frontal, LMT, RMT, LF, insula · Report a mismatch
[5] § Materials and Methods › Functional Connectivity Analyses (fMRI) ↔ step04_compute_fc_fmri.m, lines 5 to 53 · score 0.58 · hemodynamic delay, sliding window, Dynamic FC, shifted, bipolar, fMRI · Report a mismatch
[6] § Materials and Methods › Regions of Interest (ROI) and Electrode‐to‐fMRI Mapping ↔ helpers/extract_bipolar_fMRI_seedTS_excludeIED.m, lines 15 to 43 · score 0.58 · native space, IEDs, ROIs, epileptiform, filtered, Bipolar · Report a mismatch
[7] § Materials and Methods › Functional Connectivity Analyses (fMRI) ↔ step04_compute_fc_fmri.m, lines 5 to 53 · score 0.57 · hemodynamic delay, sliding window, shifted, overlap, bipolar, fMRI · Report a mismatch
[8] § Results › Exploratory Spatially Stratified Dynamic icEEG–fMRI Coupling ↔ step09_spatialStratified_windowLMM.m, lines 206 to 264 · score 0.57 · mesial temporal, spatially stratified, frontal, LMT, RMT, LF · Report a mismatch
[9] § Materials and Methods › Functional Connectivity Analyses (icEEG) ↔ helpers/bandpass_hilbert_envelope.m, the whole file · a weak match · score 0.57 · Hilbert transform, band limited power, BLP, signals, filtered, frequency bands · Report a mismatch
[10] § Materials and Methods › Linear Mixed‐Effects Modeling and Statistical Inference ↔ Step07_build_master_tables.m, lines 1 to 54 · score 0.53 · bipolar channel, dynamic FC, master, demographic, Fisher, covariates · Report a mismatch
[11] § Materials and Methods › Sensitivity and Robustness Analyses ↔ Fig_motion_plotting.m, the whole file · a weak match · score 0.53 · moderate motion, low motion, robustness, LMMs, fMRI, coupling · Report a mismatch

Paper

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The authors' code

MATLAB · 69 lines · 2.1 KB · no license · 2 matches

%% Supplementary figure: Motion robustness of EEG–fMRI coupling
% One-panel plot: beta(ΔEEG FC) across bands
% Low-motion vs moderate-motion with 95% CI
% -------- Paths --------
inCSV = "/Volumes/MIND/ICE/Tara/Kristina_paper/master_tables/GROUP_MASTER/group_stats_outputs_motion/MotionRobustness_LMM_byGroup_thr0p5.csv";
outFig = "/Volumes/MIND/ICE/Tara/Kristina_paper/master_tables/GROUP_MASTER/group_stats_outputs_motion/Fig_Supp_MotionRobustness.png";
% -------- Load data --------
T = readtable(inCSV);
% Ensure correct ordering of bands
bandOrder = ["delta","theta","alpha","beta","low_gamma","high_gamma"];
bandLabels = ["\delta","\theta","\alpha","\beta","low \gamma","high \gamma"];
% Keep only dynamic term (already isolated in table)
T.band = categorical(T.band, bandOrder, "Ordinal", true);
% Split by motion group
Tlow = T(T.motion_group=="low", :);
Tmod = T(T.motion_group=="moderate", :);
% -------- Prepare figure --------
figure("Color","w","Position",[100 100 850 420]);
hold on;
x = 1:numel(bandOrder);
offset = 0.12;
% ---- LOW MOTION ----
y_low = Tlow.beta_dev;
errL_low = y_low - Tlow.ci_low_dev;
errU_low = Tlow.ci_high_dev - y_low;
errorbar(x-offset, y_low, errL_low, errU_low, ...
"o-", "LineWidth",1.6, "MarkerSize",7, ...
"CapSize",10, "DisplayName","Low motion");
% ---- MODERATE MOTION ----
y_mod = Tmod.beta_dev;
errL_mod = y_mod - Tmod.ci_low_dev;
errU_mod = Tmod.ci_high_dev - y_mod;
errorbar(x+offset, y_mod, errL_mod, errU_mod, ...
"s--", "LineWidth",1.6, "MarkerSize",7, ...
"CapSize",10, "DisplayName","Moderate motion");
% -------- Axes & labels --------
set(gca,"XTick",x,"XTickLabel",bandLabels,"FontSize",11);
ylabel("\beta (\Delta EEG FC)","FontWeight","bold");
xlabel("EEG frequency band","FontWeight","bold");
yline(0,"k:","LineWidth",1);
legend(["Low motion","Moderate motion"], ...
"Location","northwest","Box","off");
title("Motion robustness of dynamic EEG–fMRI coupling","FontWeight","bold");
xlim([0.5 numel(x)+0.5]);
box off;
grid on;
% -------- Save --------
exportgraphics(gcf, outFig, "Resolution",300);
close(gcf);
fprintf("[OK] Saved motion robustness figure:\n%s\n", outFig);

Fig_motion_plotting.m at commit 07a5ed8, no license · at the source

Overview

Authors: Tahereh Rashnavadi^1,2, Kristina Sabaroedin^1,2,3, William Wilson^2,4, Daniel J Pittman^2,4, Paolo Federico^1,2,4, Pierre Levan^1,2,5,6

ORCID iDs: Tahereh Rashnavadi, Paolo Federico

Department of Radiology, University of Calgary, Calgary, Canada
Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
Department of Paediatrics, University of Calgary, Calgary, Canada
Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
Seaman Family MR Research Centre, University of Calgary, Calgary, Canada
Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada

Institutions: University of Calgary (Canada); Hotchkiss Brain Institute (Canada); Alberta Children's Hospital Research Institute (Canada)

Journal: Human brain mapping, volume 47, issue 14, article e70646

Dates: received 29 March 2026; accepted 8 September 2026; published online 30 September 2026; in print October 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/hbm.70646 · PMID 42813515 · PMCID PMC13625044 · OpenAlex W7214941582

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: fMRI (modality), intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), epilepsy (population), systems (subfield)

Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Physiology & signal measures

Keywords: BOLD fluctuations, cross‐modal brain imaging, dynamic functional connectivity, intracranial EEG‐fMRI, neurovascular coupling, resting‐state functional connectivity, static functional connectivity

MeSH: Brain*, Brain Waves*, Connectome*, Electrocorticography*, Epilepsies, Partial*, Magnetic Resonance Imaging*, Nerve Net*, Adult, Female, Humans, Male, Middle Aged, Neural Pathways, Rest, Young Adult (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Canadian Institutes of Health Research (CIHR) (PJT-183825); 2026 Canadian Neuroanalytics Scholars (CNS) Program; Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN-2021-02797); HBI Inspires Postdoctoral Fellowship in Neuroscience; Hotchkiss Brain Institute, University of Calgary; Alberta Children’s Hospital Research Institute

Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Resting‐state functional connectivity (FC) derived from functional MRI (fMRI) is widely used to characterize brain functional organization, yet its electrophysiological basis remains incompletely understood. Here, we investigated how electrophysiological connectivity relates to both static and dynamic fMRI connectivity using simultaneous intracranial EEG (icEEG)–fMRI recordings from 48 adults with focal epilepsy. Linear mixed‐effects models were applied to sampled electrode‐pair connections to examine frequency‐resolved associations between band‐limited icEEG connectivity and corresponding fMRI FC within non‐epileptogenic brain regions. Time‐averaged icEEG connectivity was robustly associated with spatial variation in static fMRI connectivity across sampled connections and frequency bands, with strongest effects in the beta and low‐gamma ranges. At the window level, dynamic fMRI connectivity showed significant positive associations with window‐specific icEEG FC deviations in the alpha, beta, and low‐gamma bands. Exploratory spatial stratification suggested that cross‐modal coupling varied across clinically sampled frontal, temporal, and insular region pairs, and the main findings remained robust after motion‐control analyses. Together, these findings indicate that resting‐state fMRI connectivity is associated with both average electrophysiological coupling across sampled connections and window‐level fluctuations in electrophysiological connectivity, with the strongest primary band‐wise evidence observed in the alpha, beta, and low‐gamma ranges.

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 11 matches between paragraphs and lines of code.

rashnavadi/Static-and-dynamic-resting-state-fMRI-icEEG-FC

License: none: the authors keep all their rights

State: the link answers, verified on 12 November 2026

Evidence: files inventoried

Commit: 07a5ed8fc3471830dc324d6334d02e2a9479bdae, 20 February 2026

Languages: MATLAB (46), Python (12)

Size: 71 files, 58 scripts

Software Heritage: not archived

Found in: “Data Availability Statement”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NiBabel (12 files), NumPy (12 files), pandas (12 files), Matplotlib (10 files), Statistics and Machine Learning Toolbox (8 files), Nilearn (7 files), FSL (4 files), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file)

Availability: 2 checks, the latest on 12 November 2026: the link answers

12 November 2026: the link answers
3 October 2026: the link answers

59 files

Fig_ICE017_Run1a_plot_ordered_heatmap_EEG_Hilbert_bands.m, MATLAB, 398 lines
Fig_ICE017_Run1a_plot_ordered_heatmap_fMRI.m, MATLAB, 146 lines
Fig_ICE060_Run1_plot_ordered_heatmap_EEG_Hilbert_bands.m, MATLAB, 445 lines
Fig_ICE060_Run1_plot_ordered_heatmap_fMRI.m, MATLAB, 194 lines
Fig_motion_plotting.m, MATLAB, 69 lines, 2 matches
Fig_pairLevel_trueStatic_vs_windowAvg_staticFC.m, MATLAB, 128 lines
Fig_window_level_DeltaEEG_randomSlope_dynamicFC.m, MATLAB, 141 lines
ICE060_on_brain.py, Python, 202 lines
ICE060_on_brain2.py, Python, 176 lines
ICE060_on_brain_far_edges.py, Python, 230 lines
Step07_build_master_tables.m, MATLAB, 649 lines, 1 match
Step11_compute_ICC_table.m, MATLAB, 165 lines
SupTable_compare_beta2_Model1_vs_Model2.m, MATLAB, 91 lines
batch_runner_extract_fMRI_TS.m, MATLAB, 151 lines
debugging_subj.m, MATLAB, 30 lines
electrode_localization.py, Python, 320 lines
export_EEG_run_to_csv.m, MATLAB, 67 lines
export_FMRI_run_to_csv.m, MATLAB, 53 lines
final_table.m, MATLAB, 38 lines
helpers/bandpass_hilbert_envelope.m, MATLAB, 49 lines, 2 matches
helpers/build_bipolar_signals_from_monopolar.m, MATLAB, 66 lines
helpers/canonical_bipolar_name.m, MATLAB, 77 lines
helpers/extract_bipolar_fMRI_seedTS_excludeIED.m, MATLAB, 374 lines, 1 match
helpers/find_eeg_bin_file.m, MATLAB, 40 lines
helpers/load_eeg_bin_with_labels.m, MATLAB, 111 lines
helpers/main_channels_map.m, MATLAB, 317 lines
helpers/not_used_new_convert_MNI_to_fMRI_voxel_coords.m, MATLAB, 225 lines
helpers/sliding_window_indices.m, MATLAB, 38 lines
helpers/uppertri_vec.m, MATLAB, 13 lines
mni_to_func_overlay_elec.py, Python, 295 lines
mni_to_nativeAnat_overlay.py, Python, 303 lines
node_edge_FC_func_space.py, Python, 312 lines
node_edge_FC_highres_space.py, Python, 344 lines
plot_BLUP_allBands_oneFigure.m, MATLAB, 196 lines
plot_dynamic_FC.m, MATLAB, 185 lines
plot_dynamic_FC_columnWise.m, MATLAB, 274 lines
run_pipeline_all_subjects.m, MATLAB, 59 lines
run_step07_all_subjects.m, MATLAB, 45 lines
run_step07_one_subject.m, MATLAB, 40 lines
select_most_rep_subj_static_EEG_fMRI.m, MATLAB, 347 lines
step00_config.m, MATLAB, 114 lines
step01_make_segment_table.m, MATLAB, 48 lines
step02_load_fmri_seedTS.m, MATLAB, 84 lines
step03_load_eeg_and_segment.m, MATLAB, 117 lines
step04_compute_fc_fmri.m, MATLAB, 269 lines, 2 matches
step05_compute_fc_eeg_hilbert_use_fmri_final_seeds.m, MATLAB, 196 lines, 1 match
step08A_build_windowLevel_LMM_parts_fromMasterTables_v2.m, MATLAB, 149 lines
step08_fit_LMM_pairLevel_trueStatic_windowAvg.m, MATLAB, 279 lines
step08_fit_LMM_windowLevel_DEV_fromParts.m, MATLAB, 540 lines
step09_make_spatial_figures_fromCSVs_v2.m, MATLAB, 138 lines
step09_make_spatial_figures_fromCSVs_v2_ROIsreordered.m, MATLAB, 209 lines
step09_make_spatial_tables_longform.m, MATLAB, 111 lines
step09_spatialStratified_windowLMM.m, MATLAB, 278 lines, 2 matches
step10_motionRobustness_windowLMM.m, MATLAB, 419 lines
test.py, Python, 227 lines
test2.py, Python, 173 lines
test3.py, Python, 37 lines
verify_vox_or_mm.py, Python, 17 lines
README.md, Text, 67 lines

At the source: github.com/rashnavadi/Static-and-dynamic-resting-state-fMRI-icEEG-FC

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;
58 scripts, each with its path and the digest of its content;
11 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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

Data Availability Statement

The data supporting this study are not publicly available due to patient privacy and ethical restrictions. All analysis code required to reproduce the results and figures is publicly available at: https://github.com/rashnavadi/Static‐and‐dynamic‐resting‐state‐fMRI‐icEEG‐FC (https://github.com/rashnavadi/Static-and-dynamic-resting-state-fMRI-icEEG-FC).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 3 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 keywords, 15 MeSH terms, 6 funders, 82 references.

Cite

This paper

Rashnavadi, T., Sabaroedin, K., Wilson, W., Pittman, D. J., Federico, P., & Levan, P. (2026). Static and Dynamic Resting-State fMRI Connectivity Show Frequency-Resolved Associations With Intracranial EEG Connectivity. Human brain mapping, 47(14), e70646. https://doi.org/10.1002/hbm.70646

BibTeX

@article{rashnavadi2026static, author = {Rashnavadi, Tahereh and Sabaroedin, Kristina and Wilson, William and Pittman, Daniel J and Federico, Paolo and Levan, Pierre}, title = {{Static and Dynamic Resting-State fMRI Connectivity Show Frequency-Resolved Associations With Intracranial EEG Connectivity}}, journal = {Human brain mapping}, year = {2026}, month = oct, volume = {47}, number = {14}, pages = {e70646}, publisher = {Wiley}, issn = {1065-9471}, doi = {10.1002/hbm.70646}, url = {https://doi.org/10.1002/hbm.70646}, pmid = {42813515}, pmcid = {PMC13625044} }

RIS

TY - JOUR AU - Rashnavadi, Tahereh AU - Sabaroedin, Kristina AU - Wilson, William AU - Pittman, Daniel J AU - Federico, Paolo AU - Levan, Pierre TI - Static and Dynamic Resting-State fMRI Connectivity Show Frequency-Resolved Associations With Intracranial EEG Connectivity T2 - Human brain mapping J2 - Hum Brain Mapp PY - 2026 DA - 2026/10/01 VL - 47 IS - 14 SP - e70646 SN - 1065-9471 PB - Wiley DO - 10.1002/hbm.70646 UR - https://doi.org/10.1002/hbm.70646 LA - en ER -

CSL-JSON

{ "id": "10.1002/hbm.70646", "type": "article-journal", "title": "Static and Dynamic Resting-State fMRI Connectivity Show Frequency-Resolved Associations With Intracranial EEG Connectivity", "container-title": "Human brain mapping", "author": [ { "family": "Rashnavadi", "given": "Tahereh" }, { "family": "Sabaroedin", "given": "Kristina" }, { "family": "Wilson", "given": "William" }, { "family": "Pittman", "given": "Daniel J" }, { "family": "Federico", "given": "Paolo" }, { "family": "Levan", "given": "Pierre" } ], "container-title-short": "Hum Brain Mapp", "volume": "47", "issue": "14", "page": "e70646", "DOI": "10.1002/hbm.70646", "PMID": "42813515", "PMCID": "PMC13625044", "ISSN": "1065-9471", "publisher": "Wiley", "URL": "https://doi.org/10.1002/hbm.70646", "language": "en", "issued": { "date-parts": [ [ 2026, 10, 1 ] ] } }

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