High-frequency stimulation-induced secondary hyperalgesia shifts temporal order judgment and increases precuneus nodal degree in healthy adults.

Code ↔ Paper

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

[1] § Methods › Source-level connectivity and graph analysis ↔ custom_functions/recording_report.m, the whole file · a weak match · score 0.90 · amplitude envelope correlations, source space, band pass filtered, Functional connectivity, connectivity matrices, functional networks · Report a mismatch
[2] § Methods › Source-level connectivity and graph analysis ↔ custom_functions/pdf_report.m, the whole file · a weak match · score 0.89 · amplitude envelope correlations, source space, band pass filtered, Functional connectivity, connectivity matrices, functional networks · Report a mismatch
[3] § Methods › EEG preprocessing ↔ custom_functions/preprocessing_ICA.m, the whole file · a weak match · score 0.76 · IClabel, Independent component, EEGLAB, spherical, artifacts, rejected · Report a mismatch
[4] § Methods › EEG preprocessing ↔ custom_functions/recording_report.m, the whole file · a weak match · score 0.74 · ICLabel, Independent component, pass filter, rejected, segmented, classify · Report a mismatch

Paper

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The authors' code

MATLAB · 121 lines · 5.5 KB · CC-BY-4.0 · 2 matches

function recording_report(params,bidsID)
import mlreportgen.report.*
import mlreportgen.dom.*
f = strcat(bidsID, '_report');
rpt = Report(fullfile(params.ReportsPath, f), 'pdf');
% Title page
tp = TitlePage;
tp.Title = ['Report of ' bidsID];
append(rpt,tp);
%% Preprocessing report
ch1 = Chapter;
ch1.Title = 'Preprocessing report';
sec1 = Section;
sec1.Title = 'Bad channel rejection';
append(ch1,sec1);
para = Paragraph('Bad channels were automatically detected with clean_rawdata. By default flat channels, channels with high frequency noise and channels with poor predictability are rejected. In the following plot, bad channels are marked in blue.');
bc_plot = plot_badchannels_singlestudy(params, bidsID);
append(sec1,para);
add(sec1, Figure(bc_plot));
sec2 = Section;
sec2.Title = 'ICA. Independent component classification';
append(ch1,sec2);
para = Paragraph('Artefactual independent components were detected automatically with ICLabel. By default, components whose probability of being ''Muscle'' or ''Eye'' is higher than 80% are marked as artifactual and substracted from the data.');
append(sec2,para);
para = Paragraph(['In the current run, the threshold used was ' num2str(params.ICLabel(2,1)*100, '%d') '% for ''Muscle'' and ' num2str(params.ICLabel(3,1)*100, '%d') '% for ''Eye'' components.']);
append(sec2,para);
[IC_plot, ic_kept] = plot_ICs_singlestudy(params, bidsID);
add(sec2, Figure(IC_plot));
para = Paragraph([num2str(ic_kept*100, '%.2f') '% IC components were kept.']);
append(sec2,para);
sec3 = Section;
sec3.Title = 'Bad time segments rejection';
append(ch1,sec3);
[bs_plot, bs_length, recording_length] = plot_badtimesegments_singlestudy(params, bidsID);
para = Paragraph(['Bad time segments were detected automatically with ASR. In total ' num2str(bs_length, '%.1f') ' out of ' num2str(recording_length, '%.1f') ' seconds were rejected (' num2str((bs_length/recording_length)*100, '%.2f') ' % of the data).']);
append(sec3,para);
add(sec3, Figure(bs_plot));
append(rpt,ch1)
%% EEG features
ch2 = Chapter;
ch2.Title = 'EEG features report';
sec1 = Section;
sec1.Title = 'Power (sensor space)';
append(ch2,sec1);
para = Paragraph('Power spectrum was computed with dpss multitapers and a frequency smoothing of 1 Hz by default. Average power across all epochs and channels is depicted below. In the legend, the percentage of relative power for each frequency band is included in brackets.');
append(sec1,para);
[power_plot, topo_plot] = plot_power(params,bidsID);
add(sec1,Figure(power_plot));
para = Paragraph('Spatial distribution of power per frequency band. Average power across epochs is depicted for each frequency band.');
append(sec1,para);
add(sec1,Figure(topo_plot));
sec2 = Section;
sec2.Title = 'Peak frequency (sensor space)';
append(ch2,sec2);
para = Paragraph('Peak frequency was computed in the PSD averaged across epochs and channels in the alpha range with two different methods: frequency at the maximum of the peak and the center of gravity of the power spectrum in the alpha range.');
append(sec2,para);
pf_plot = plot_peakfrequency(params,bidsID);
add(sec2,Figure(pf_plot));
sec3 = Section;
sec3.Title = 'Power (source space)';
append(ch2,sec3);
para = Paragraph('Power at the 400 source ROIs was estimated by projecting the frequency-band specific spatial filter to the sensor-space band-pass filtered data. For visualization the 400 ROIs were interpolated to a standard cortical surface.');
append(sec3,para);
power_source_plot = plot_power_source(params,bidsID);
add(sec3,Figure(power_source_plot));
sec4 = Section;
sec4.Title = 'Connectivity (source space)';
append(ch2,sec4);
para = Paragraph('Functional connectivity was estimated on the 400 source ROIs with two measures: debiased weighted PLI (phase-based) and orthogonalised Amplitude Envelope Correlation (amplitude-based).');
append(sec4,para);
para = Paragraph('Connectivity matrices are organized per functional network. The left (up) network submodule corresponds to ROIs of that network located on the left brain hemisphere. The right (down) network submodule corresponds to ROIs of that network located on the right hemisphere.');
append(sec4,para);
dwpli_plot = plot_connectivity(params,bidsID, 'dwpli');
aec_plot = plot_connectivity(params,bidsID, 'aec');
sec41 = Section;
sec41.Title = 'debiased weighted Phase Lag Index';
append(sec4,sec41);
add(sec41,Figure(dwpli_plot));
sec42 = Section;
sec42.Title = 'Amplitude Envelope Correlation';
append(sec4,sec42);
add(sec42,Figure(aec_plot));
sec5 = Section;
sec5.Title = 'Network characterization - graph theory measures based on connectivity';
append(ch2,sec5);
para = Paragraph('For each connectivity measure and frequency band, 2 local graph measures (degree and global clustering coefficient) and 3 global graph measures (global clustering coefficient, global efficiency and smallworldness) were computed.');
append(sec5,para);
para = Paragraph('By default the connectivity matrix is thresholded keeping the 20% of strongest connections and binarized for computing the graph measures.');
append(sec5,para);
sec51 = Section;
sec51.Title = 'dwPLI - Network characterization';
append(sec5,sec51);
[degree_plot, cc_plot, global_plot] = plot_graph_measures(params,bidsID,'dwpli');
add(sec51,Figure(degree_plot));
add(sec51,Figure(cc_plot));
add(sec51,Figure(global_plot));
sec52 = Section;
sec52.Title = 'AEC - Network characterization';
append(sec5,sec52);
[degree_plot, cc_plot, global_plot] = plot_graph_measures(params,bidsID,'aec');
add(sec52,Figure(degree_plot));
add(sec52,Figure(cc_plot));
add(sec52,Figure(global_plot));
append(rpt,ch2);
close(rpt);
% rptview(rpt);
end

recording_report.m at commit b200603, under CC-BY-4.0 · at the source

Overview

Authors: Yuki Morikawa^1,2, Michihiro Osumi^2,3

ORCID iDs: Yuki Morikawa, Michihiro Osumi

Department of Rehabilitation, Heisei Memorial Hospital, Shijo-cho, Kashihara-city, Nara, Japan
Graduate School of Health Sciences, Kio University, Umaminaka, Koryo-cho, Kitakatsuragi-gun, Nara, Japan
Neurorehabilitation Research Center, Kio University, Umaminaka, Koryo-cho, Kitakatsuragi-gun, Nara, Japan

Institutions: Kio University (Japan); Heisei Memorial Hospital (Japan)

Journal: PloS one, volume 21, issue 9, article e0358552

Dates: received 19 February 2026; accepted 2 September 2026; published online 25 September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0358552 · PMID 42789585 · PMCID PMC13614642 · OpenAlex W7214385881

Open access: gold, a free copy (OpenAlex)

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

Categories: EEG (modality), human (organism), pain (population), cognitive (subfield)

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

MeSH: Hyperalgesia*, Judgment*, Parietal Lobe*, Adult, Attention, Electric Stimulation, Electroencephalography, Female, Healthy Volunteers, Humans, Male, Young Adult (* major topic)

Journal subjects: Biology and Life Sciences, Neuroscience, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Psychology, Social Sciences, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Physiology, Electrophysiology, Neurophysiology, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Signs and Symptoms, Pain, Sensory Physiology, Somatosensory System, Pain Sensation, Sensory Systems, Anatomy, Body Limbs, Arms, Forearms, Pain Management, Hyperalgesia, Behavior, Surgical and Invasive Medical Procedures, Functional Electrical Stimulation

Topic: Pain Mechanisms and Treatments (Physiology (medical), Medicine), according to OpenAlex

Funding: Japan Society for the Promotion of Science (23K10442)

Citations: not cited yet (Europe PMC); 40 references in the paper

Abstract

Pain can influence attention and other higher-order cognitive processes. In experimental settings, acute pain has been linked to attentional prioritization of the painful body region. However, conventional experimental pain models often rely on brief, phasic nociceptive stimuli and are therefore less suited to examining spatial attention during a sustained, experimentally induced sensitized state. In this study, we examined whether high-frequency stimulation (HFS)-induced secondary hyperalgesia, as a model of acute experimental sensitization, was associated with changes in temporal order judgment (TOJ) and resting-state EEG-derived measures in healthy adults. Sixteen healthy adults (mean age, 28.5 years; seven women) underwent HFS of the forearm to induce secondary hyperalgesia. Spatial attention was assessed using a TOJ task, and resting-state EEG data were analyzed using source-level graph-theoretical measures. HFS increased pinprick pain sensitivity in the surrounding skin adjacent to the stimulation site. TOJ performance showed a significant shift in the point of subjective simultaneity (PSS) toward the HFS-treated side, indicating an attentional bias independent of subjective pain intensity. EEG analysis revealed a significant increase in nodal degree in the bilateral precuneus in the theta band, and this change was positively correlated with the PSS shift (ρ = 0.53, p = 0.036). These findings suggest that both behavioral attentional bias and short-term changes in resting-state EEG-derived measures can accompany acute experimentally induced sensitization. The present results should be interpreted within the scope of the HFS model as reflecting short-term modulation during an acute sensitized state rather than chronic pain-like reorganization.

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 4 matches between paragraphs and lines of code.

MariusKlug/zapline-plus

License: GPL-3.0

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: f4628a32f4ec53cebfb07b1f6a8f465f44f1377a, 14 April 2023

Languages: MATLAB (26)

Size: 32 files, 26 scripts

Software Heritage: not archived

Found in: the text, “EEG preprocessing”

Holds: README, license file

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

Tools: Signal Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

28 files

clean_data_with_zapline_plus.m, MATLAB, 1,052 lines
clean_data_with_zapline_plus_eeglab_wrapper.m, MATLAB, 28 lines
eegplugin_zapline_plus.m, MATLAB, 36 lines
find_next_noisefreq.m, MATLAB, 113 lines
iterative_outlier_removal.m, MATLAB, 41 lines
nt_bias_fft.m, MATLAB, 58 lines
nt_cov.m, MATLAB, 93 lines
nt_demean.m, MATLAB, 74 lines
nt_dss0.m, MATLAB, 61 lines
nt_fold.m, MATLAB, 21 lines
nt_greetings.m, MATLAB, 17 lines
nt_mmat.m, MATLAB, 54 lines
nt_multishift.m, MATLAB, 46 lines
nt_normcol.m, MATLAB, 102 lines
nt_pca.m, MATLAB, 61 lines
nt_pcarot.m, MATLAB, 40 lines
nt_regcov.m, MATLAB, 44 lines
nt_smooth.m, MATLAB, 52 lines
nt_tsr.m, MATLAB, 151 lines
nt_unfold.m, MATLAB, 21 lines
nt_vecadd.m, MATLAB, 43 lines
nt_vecmult.m, MATLAB, 53 lines
nt_version.m, MATLAB, 2 lines
nt_xcov.m, MATLAB, 55 lines
nt_zapline_plus.m, MATLAB, 171 lines
pop_zapline_plus.m, MATLAB, 37 lines
LICENSE, License, 674 lines
README.md, Text, 29 lines

At the source: github.com/MariusKlug/zapline-plus

crisglav/discover-eeg

License: CC-BY-4.0

State: the link answers, verified on 1 November 2026

Evidence: files inventoried

Commit: b200603c73566746db41e20c178cae89cdd4a6c7, 24 October 2025

Languages: MATLAB (47)

Size: 70 files, 47 scripts

Software Heritage: not archived

Found in: the text, “Source-level connectivity and graph analysis”

Holds: README, license file, 3 notebooks

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

Tools: FieldTrip (21 files), EEGLAB (5 files), Statistics and Machine Learning Toolbox (4 files), Signal Processing Toolbox (3 files), Brain Connectivity Toolbox (1 file), ICLabel (1 file), Parallel Computing Toolbox (1 file)

Availability: 2 checks, the latest on 1 November 2026: the link answers

1 November 2026: the link answers
28 September 2026: the link answers

49 files

custom_functions/aecConnectivity.m, MATLAB, 36 lines
custom_functions/aecConnectivity_brainstorm.m, MATLAB, 48 lines
custom_functions/compute_aec.m, MATLAB, 29 lines
custom_functions/compute_dwpli.m, MATLAB, 53 lines
custom_functions/compute_graph_measures.m, MATLAB, 50 lines
custom_functions/compute_peakfrequency.m, MATLAB, 33 lines
custom_functions/compute_power.m, MATLAB, 20 lines
custom_functions/compute_spatial_filter.m, MATLAB, 71 lines
custom_functions/load_preprocessed_data.m, MATLAB, 137 lines
custom_functions/pdf_report.m, MATLAB, 101 lines, 1 match
custom_functions/plot_ICs.m, MATLAB, 103 lines
custom_functions/plot_ICs_singlestudy.m, MATLAB, 62 lines
custom_functions/plot_atlasregions.m, MATLAB, 31 lines
custom_functions/plot_badchannels.m, MATLAB, 71 lines
custom_functions/plot_badchannels_singlestudy.m, MATLAB, 34 lines
custom_functions/plot_badtimesegments.m, MATLAB, 105 lines
custom_functions/plot_badtimesegments_singlestudy.m, MATLAB, 53 lines
custom_functions/plot_connectivity.m, MATLAB, 53 lines
custom_functions/plot_electrodesandsources.m, MATLAB, 31 lines
custom_functions/plot_epochs.m, MATLAB, 50 lines
custom_functions/plot_graph_measures.m, MATLAB, 97 lines
custom_functions/plot_peakfrequency.m, MATLAB, 30 lines
custom_functions/plot_power.m, MATLAB, 62 lines
custom_functions/plot_power_source.m, MATLAB, 33 lines
custom_functions/preprocessing_ICA.m, MATLAB, 56 lines, 1 match
custom_functions/preprocessing_select_ICA_rep.m, MATLAB, 14 lines
custom_functions/recording_report.m, MATLAB, 121 lines, 2 matches
define_params.m, MATLAB, 430 lines
examples/closed_8min.m, MATLAB, 25 lines
examples/fig23_discover_outcome.m, MATLAB, 83 lines
examples/fig4_datasets.m, MATLAB, 325 lines
examples/fig4_datasets_cp.m, MATLAB, 195 lines
examples/fig4_power_ec_eo.m, MATLAB, 245 lines
examples/fig5_lemon_young_old.m, MATLAB, 479 lines
examples/lemon2bids.m, MATLAB, 63 lines
examples/lemon8min_fieldtrip.m, MATLAB, 81 lines
external_functions/fdr.m, MATLAB, 138 lines
external_functions/raincloudplots/rm_raincloud.m, MATLAB, 176 lines
external_functions/raincloudplots/rm_raincloud_cg.m, MATLAB, 310 lines
external_functions/spider_plot-master/spider_plot.m, MATLAB, 1,571 lines
external_functions/spider_plot-master/spider_plot_R2019b.m, MATLAB, 1,372 lines
external_functions/spider_plot-master/spider_plot_R2019b_examples.mlx, MATLAB, to read at its source
external_functions/spider_plot-master/spider_plot_class.m, MATLAB, 2,186 lines
external_functions/spider_plot-master/spider_plot_class_examples.mlx, MATLAB, to read at its source
external_functions/spider_plot-master/spider_plot_examples.mlx, MATLAB, to read at its source
external_functions/stdshade.m, MATLAB, 67 lines
main_pipeline.m, MATLAB, 434 lines
LICENSE.txt, License, 395 lines
README.md, Text, 134 lines

At the source: github.com/crisglav/discover-eeg

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;
73 scripts, each with its path and the digest of its content;
4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

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Data

Datasets cited

figshare:33318534, at figshare; found in “Data Availability”

Data Availability

All data underlying the findings described in this manuscript are fully available without restriction from figshare: https://doi.org/10.6084/m9.figshare.33318534. The deposited dataset is released under a CC BY 4.0 license. No directly identifying participant information is included.

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, issue, pages, dates, 2 authors, 12 MeSH terms, 1 funder, 40 references.

Cite

This paper

Morikawa, Y., & Osumi, M. (2026). High-frequency stimulation-induced secondary hyperalgesia shifts temporal order judgment and increases precuneus nodal degree in healthy adults. PloS one, 21(9), e0358552. https://doi.org/10.1371/journal.pone.0358552

BibTeX

@article{morikawa2026high, author = {Morikawa, Yuki and Osumi, Michihiro}, title = {{High-frequency stimulation-induced secondary hyperalgesia shifts temporal order judgment and increases precuneus nodal degree in healthy adults}}, journal = {PloS one}, year = {2026}, month = sep, volume = {21}, number = {9}, pages = {e0358552}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0358552}, url = {https://doi.org/10.1371/journal.pone.0358552}, pmid = {42789585}, pmcid = {PMC13614642} }

RIS

TY - JOUR AU - Morikawa, Yuki AU - Osumi, Michihiro TI - High-frequency stimulation-induced secondary hyperalgesia shifts temporal order judgment and increases precuneus nodal degree in healthy adults T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/09/25 VL - 21 IS - 9 SP - e0358552 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0358552 UR - https://doi.org/10.1371/journal.pone.0358552 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pone.0358552", "type": "article-journal", "title": "High-frequency stimulation-induced secondary hyperalgesia shifts temporal order judgment and increases precuneus nodal degree in healthy adults", "container-title": "PloS one", "author": [ { "family": "Morikawa", "given": "Yuki" }, { "family": "Osumi", "given": "Michihiro" } ], "container-title-short": "PLoS One", "volume": "21", "issue": "9", "page": "e0358552", "DOI": "10.1371/journal.pone.0358552", "PMID": "42789585", "PMCID": "PMC13614642", "ISSN": "1932-6203", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pone.0358552", "language": "en", "issued": { "date-parts": [ [ 2026, 9, 25 ] ] } }

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