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Stable insular spectral patterns underlie dynamic pain encoding in chronic neuropathic pain.

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 · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Spectral and functional connectivity analysis › Spectral analysis ↔ compute_psd_metrics.m, lines 1–54 · score 0.88 · Power spectral density, 13–30 Hz, 8–13 Hz, 1–4 Hz, 4–8 Hz, PSD
  2. [2] § Materials and methods › Spectral and functional connectivity analysis › Spectral analysis ↔ build_connectivity_longtable.m, lines 42–95 · score 0.76 · 13–30 Hz, 8–13 Hz, 1–4 Hz, 4–8 Hz, Welch, delta
  3. [3] § Materials and methods › Statistical analysis ↔ run_connectivity_lme_models.m, lines 1–66 · score 0.58 · pair day, Fisher, logit, aggregated, transformed, ipsilateral
  4. [4] § Materials and methods › Study participants ↔ run_all_analysis.m, the whole file · a weak match · score 0.57 · iEEG, chronic neuropathic pain, insular, day

Paper

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

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The authors' code

MATLAB · 174 lines · 4.6 KB · MIT · 1 match

  1. function status = compute_epoch_psd_metrics(rootPath, subjectIDs, opts)
  2. % compute_epoch_psd_metrics
  3. %
  4. % Compute epoch-level power spectral density (PSD) metrics from
  5. % *_BipolarEEG_QC_Post.mat files.
  6. %
  7. % INPUT
  8. % rootPath : root data directory
  9. % subjectIDs : cell array of subject IDs
  10. %
  11. % OPTIONAL (opts)
  12. % opts.epochLenSec (default = 5)
  13. % opts.maxEpochs (default = 60)
  14. % opts.welch struct with fields:
  15. % .win, .noverlap, .nfft, .fmax
  16. % opts.writeOutputs (default = true)
  17. %
  18. % OUTPUT
  19. % Per subject:
  20. % *_PSD_PerChannel.csv
  21. % *_PSD_ByRegion_Day.csv
  22. % *_PSD_EpochLong.csv
  23. %
  24. % NOTES
  25. % Requires Signal Processing Toolbox (pwelch)
  27. % ---------------- defaults ----------------
  28. if nargin < 1 || isempty(rootPath)
  29. error('rootPath is required.');
  30. end
  31. if nargin < 2 || isempty(subjectIDs)
  32. error('subjectIDs must be provided.');
  33. end
  34. if nargin < 3 || isempty(opts)
  35. opts = struct();
  36. end
  38. if ~isfield(opts,'epochLenSec'), opts.epochLenSec = 5; end
  39. if ~isfield(opts,'maxEpochs'), opts.maxEpochs = 60; end
  40. if ~isfield(opts,'welch')
  41. opts.welch = struct('win',2048,'noverlap',1024,'nfft',8192,'fmax',55);
  42. end
  43. if ~isfield(opts,'writeOutputs'), opts.writeOutputs = true; end
  45. bands = struct( ...
  46. 'delta',[1 4], ...
  47. 'theta',[4 8], ...
  48. 'alpha',[8 13], ...
  49. 'beta',[13 30], ...
  50. 'gamma',[30 min(55,opts.welch.fmax)]);
  52. bandNames = fieldnames(bands)';
  54. status = struct('Subject',{},'Days',{},'EpochRows',{},'Warnings',{});
  56. % ================================================================
  57. for s = 1:numel(subjectIDs)
  59. subj = string(subjectIDs{s});
  60. inDir = fullfile(rootPath, subj, subj + "_setfiles");
  61. qcFile = fullfile(inDir, subj + "_BipolarEEG_QC_Post.mat");
  63. if exist(qcFile,'file') ~= 2
  64. warning('Missing QC file for %s', subj);
  65. continue;
  66. end
  68. L = load(qcFile);
  69. if ~isfield(L,'EEG_bipolar_QC') || isempty(L.EEG_bipolar_QC)
  70. warning('No bipolar QC data for %s', subj);
  71. continue;
  72. end
  74. EEGq = L.EEG_bipolar_QC;
  76. outDir = fullfile(rootPath, subj, 'PSD_Epochs');
  77. if opts.writeOutputs && ~exist(outDir,'dir')
  78. mkdir(outDir);
  79. end
  81. epochLong = [];
  83. for d = 1:numel(EEGq)
  85. E = EEGq{d};
  86. if isempty(E) || ~isfield(E,'data')
  87. continue;
  88. end
  90. X = double(E.data);
  91. fs = E.srate;
  92. [nChan, nSamp] = size(X);
  94. if nSamp < fs
  95. continue;
  96. end
  98. Nepoch = round(opts.epochLenSec * fs);
  99. nMax = min(opts.maxEpochs, floor(nSamp / Nepoch));
  100. if nMax < 1
  101. continue;
  102. end
  104. X = X(:,1:(Nepoch*nMax));
  106. % Welch setup
  107. w = opts.welch;
  108. wlen = min(w.win, Nepoch);
  109. ovlp = min(w.noverlap, floor(wlen/2));
  110. nfft = max(w.nfft, 2^nextpow2(wlen));
  111. fmax = min(w.fmax, fs/2);
  113. [~, fHz] = pwelch(zeros(Nepoch,1), hamming(wlen), ovlp, nfft, fs);
  114. useK = (fHz >= 0 & fHz <= fmax);
  115. fUse = fHz(useK);
  117. for c = 1:nChan
  119. xe = X(c,:) - mean(X(c,:), 'omitnan');
  120. xe = reshape(xe, Nepoch, nMax);
  122. for e = 1:nMax
  124. x = detrend(xe(:,e));
  125. [pxx,~] = pwelch(x, hamming(wlen), ovlp, nfft, fs);
  127. pUse = pxx(useK);
  128. pUse(pUse<=0) = eps;
  130. for b = bandNames
  131. band = b{1};
  132. fr = bands.(band);
  133. mask = fUse >= fr(1) & fUse < fr(2);
  135. if nnz(mask) < 2
  136. val = NaN;
  137. else
  138. val = 10*log10(trapz(fUse(mask), pUse(mask)));
  139. end
  141. row = struct();
  142. row.Subject = subj;
  143. row.DayIndex = d;
  144. row.Channel = c;
  145. row.Epoch = e;
  146. row.Band = band;
  147. row.Value = val;
  149. epochLong = [epochLong; row]; %#ok<AGROW>
  150. end
  151. end
  152. end
  153. end
  155. if isempty(epochLong)
  156. continue;
  157. end
  159. Tlong = struct2table(epochLong);
  161. if opts.writeOutputs
  162. outFile = fullfile(outDir, subj + "_PSD_EpochLong.csv");
  163. writetable(Tlong, outFile);
  164. fprintf('Saved PSD for %s\n', subj);
  165. end
  167. status(end+1) = struct( ...
  168. 'Subject', subj, ...
  169. 'Days', numel(EEGq), ...
  170. 'EpochRows', height(Tlong), ...
  171. 'Warnings', "" ); %#ok<AGROW>
  172. end
  174. end

compute_psd_metrics.m at commit a9e7743, under MIT · at the source

Overview

Authors: Chang-Chia Liu1, Mark Quigg2, Patrick H Finan3, Shayan Moosa1, W Jeffrey Elias1
ORCID iDs: Chang-Chia Liu
  1. Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
  2. Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
  3. Department of Anesthesiology, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
Institutions: University of Virginia (United States)
Journal: Brain communications, volume 8, issue 4, article fcag299
Dates: received 16 March 2026; accepted 27 July 2026; published online 3 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag299 · PMID 42582622 · PMCID PMC13457931 · OpenAlex W7172318494
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), pain (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics
Keywords: Insula, Chronic neuropathic pain, Intracranial EEG, Pain intensity, Pain unpleasantness
Topic: Pain Mechanisms and Treatments (Physiology, Medicine), according to OpenAlex
Funding: NINDS NIH HHS (UH3 NS123308)
Citations: not cited yet (Europe PMC); 21 references in the paper

Abstract

Chronic neuropathic pain persists for years yet fluctuates markedly from day to day, raising a fundamental question: how do stable neural patterns relate to dynamic pain experience? The insula is a central hub for pain processing, but it remains unclear whether ongoing pain is associated with enduring baseline neural patterns within the insula or day-to-day variation in neural activity.

We analysed multiday bilateral intracranial electroencephalography recordings from six individuals with chronic unilateral neuropathic pain, comprising 34 days of resting-state data. Spectral power, peak-derived spectral metrics and intra-insular functional connectivity (coherence and phase-locking value) were quantified across anterior and posterior insular subregions. Linear mixed-effects models were used to distinguish stable within-cohort neural patterns from within-subject, day-to-day pain-related fluctuations in pain intensity and unpleasantness, with explicit control for longitudinal temporal drift.

The insula exhibited stable within-cohort spectral organization characterized by an anterior–posterior gradient with lower posterior fast-band power and spectral slowing, together with greater posterior spectral power in the hemisphere contralateral to the chronic pain side. Superimposed on this stable pattern, daily pain intensity was most strongly associated with increased contralateral posterior alpha power, accounting for approximately 33% of within-subject variance. Pain unpleasantness showed its strongest association with increased contralateral posterior beta power, accounting for approximately 19% of within-subject variance. Peak-derived spectral metrics and intra-insular connectivity largely reflected stable baseline patterns, with comparatively modest day-to-day pain-related variation.

These exploratory findings suggest that chronic neuropathic pain is associated with stable insular spectral patterns upon which frequency-specific oscillatory variation relates to daily pain experience. Posterior insular alpha and beta oscillations showed partially distinct associations with pain intensity and unpleasantness, respectively, whereas intra-insular connectivity primarily reflected stable network structure. Posterior insular spectral power may therefore provide a candidate physiological signal for tracking pain states.

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 4 matches between paragraphs and lines of code.

UVA-LIU/UVA-LIU-uva-insular-iEEG-neuropathic-pain-analysis

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: a9e7743c02a909f149875feb810ba3a0ca7bf5c3, 28 April 2026
Languages: MATLAB (7)
Size: 9 files, 7 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
9 files
At the source: github.com/UVA-LIU/UVA-LIU-uva-insular-iEEG-neuropathic-pain-analysis

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;
  • 7 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.

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

Data are available from the corresponding author upon reasonable request. The dataset is currently being prepared for public release via the DABI repository. Code used for preprocessing, spectral analysis, connectivity analysis, aperiodic residual analysis and statistical modelling is available at https://github.com/UVA-LIU/UVA-LIU-uva-insular-iEEG-neuropathic-pain-analysis.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 1 funder, 21 references.

Cite

This paper

Liu, C.-C., Quigg, M., Finan, P. H., Moosa, S., & Elias, W. J. (2026). Stable insular spectral patterns underlie dynamic pain encoding in chronic neuropathic pain. Brain communications, 8(4), fcag299. https://doi.org/10.1093/braincomms/fcag299

BibTeX

@article{liu2026stable,
author = {Liu, Chang-Chia and Quigg, Mark and Finan, Patrick H and Moosa, Shayan and Elias, W Jeffrey},
title = {{Stable insular spectral patterns underlie dynamic pain encoding in chronic neuropathic pain}},
journal = {Brain communications},
year = {2026},
month = aug,
volume = {8},
number = {4},
pages = {fcag299},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag299},
url = {https://doi.org/10.1093/braincomms/fcag299},
pmid = {42582622},
pmcid = {PMC13457931}
}

RIS

TY - JOUR
AU - Liu, Chang-Chia
AU - Quigg, Mark
AU - Finan, Patrick H
AU - Moosa, Shayan
AU - Elias, W Jeffrey
TI - Stable insular spectral patterns underlie dynamic pain encoding in chronic neuropathic pain
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/08/03
VL - 8
IS - 4
SP - fcag299
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag299
UR - https://doi.org/10.1093/braincomms/fcag299
LA - en
ER -

CSL-JSON

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"family": "Liu",
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"page": "fcag299",
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"PMID": "42582622",
"PMCID": "PMC13457931",
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