OSCR

Cross-Night Modulation of Change Detection ERPs to Foreign Speech Sound Features During N2 Sleep.

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] § Method › Statistics for Sleep Exposure Data ↔ SL06_RandomEffectsComparison.m, lines 1–36 · score 0.82 · likelihood ratio, linear mixed, random intercept, ERP models, model comparisons, exposure
  2. [2] § Method › Statistics for Sleep Exposure Data ↔ SL05ExposureDurationLMM.m, lines 276–320 · score 0.68 · likelihood ratio, Exposure duration, model comparisons, fit, Night
  3. [3] § Method › Statistics for Sleep Exposure Data ↔ SL06LmmTtestFigure2B.m, lines 1–34 · score 0.67 · night pairwise comparisons, Bonferroni correction, omnibus, model, exposure, ERP
  4. [4] § Method › Preprocessing and Statistical Analysis of Sleep EEG Data ↔ SL05descriptivestat.m, lines 30–44 · score 0.61 · stimulus onset, small deviants, large deviants, epochs
  5. [5] § Method › Preprocessing and Statistical Analysis of Sleep EEG Data ↔ SL04Epoch.m, lines 2–15 · score 0.57 · 100–700 ms, EEGLAB, baseline, epochs, filtered, 100 ms
  6. [6] § Method › Statistics for Sleep Exposure Data ↔ SL06_RandomEffectsComparison.m, lines 1–36 · score 0.56 · linear mixed, random intercept, models, latency, P450, amplitude

Paper

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

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The authors' code

MATLAB · 354 lines · 12 KB · no license · 2 matches

  1. %% ============================================================
  2. % SL06_ModelComparisonOnly.m
  3. %
  4. % Purpose:
  5. % Compare candidate linear mixed-effects models for four ERP outcomes:
  6. % P2 amplitude, P450 amplitude, P2 latency, and P450 latency.
  7. %
  8. % Step 1: Compare random-effects structures using REML while keeping
  9. % the fixed-effects structure identical.
  10. %
  11. % Random-intercept model:
  12. % Y ~ Days * DeviantType + Exposure + (1 | Subjects)
  13. %
  14. % Random-slope model:
  15. % Y ~ Days * DeviantType + Exposure + (1 + Days | Subjects)
  16. %
  17. % Step 2: Using the selected random-effects structure, compare models
  18. % with versus without Exposure using ML.
  19. %
  20. % Without Exposure:
  21. % Y ~ Days * DeviantType + selected random-effects structure
  22. %
  23. % With Exposure:
  24. % Y ~ Days * DeviantType + Exposure + selected random-effects structure
  25. %
  26. % Model-selection rule:
  27. % A likelihood-ratio-test p value < .05 favors the more complex model.
  28. % AIC and BIC differences are also reported for transparency.
  29. %
  30. % Output:
  31. % ERP_ModelComparisonOnly.xlsx
  32. % Sheet 1: RandomEffectsComparison
  33. % Sheet 2: ExposureComparison
  34. % ============================================================
  36. clc; clear; close all;
  38. %% -------------------- Basic settings --------------------
  39. nDaysExpected = 4;
  40. nSubExpected = 20;
  42. dayLevels = arrayfun(@num2str, 1:nDaysExpected, 'UniformOutput', false);
  43. devLevels = {'Small','Large'};
  44. measureNames = {'Amplitude','Latency'};
  45. dummyCoding = 'effects';
  47. %% -------------------- File paths --------------------
  48. resultDir = 'G:\study2\002\sleep\2ndanalysis\results';
  49. cd(resultDir);
  51. matFile = fullfile(resultDir, 'erp_statisticsdata_simple.mat');
  52. exposureFile = 'G:\study2\002\sleep\2ndanalysis\exposure_dur.xlsx';
  53. outFile = fullfile(resultDir, 'ERP_ModelComparisonOnly.xlsx');
  55. %% -------------------- Load ERP data --------------------
  56. load(matFile, 'amplitude_mmn', 'amplitude_p3', 'latency_mmn', 'latency_p3');
  58. %% -------------------- Load exposure duration --------------------
  59. expTbl = readtable(exposureFile);
  60. subj_exp = expTbl{:,1};
  61. expMat = expTbl{:,2:end};
  63. if size(expMat,2) ~= nDaysExpected
  64. error('The exposure file must contain %d day/night columns; %d were found.', ...
  65. nDaysExpected, size(expMat,2));
  66. end
  68. nSub_exp = size(expMat,1);
  69. if nSub_exp ~= nSubExpected
  70. warning('The exposure file contains %d participants rather than the expected %d.', ...
  71. nSub_exp, nSubExpected);
  72. end
  74. %% -------------------- Check ERP dimensions --------------------
  75. [nNight1, nSub1, nDev1] = size(amplitude_mmn);
  76. [nNight2, nSub2, nDev2] = size(amplitude_p3);
  77. [nNight3, nSub3, nDev3] = size(latency_mmn);
  78. [nNight4, nSub4, nDev4] = size(latency_p3);
  80. if ~(nNight1 == nDaysExpected && nNight2 == nDaysExpected && ...
  81. nNight3 == nDaysExpected && nNight4 == nDaysExpected)
  82. error('All ERP arrays must contain %d nights.', nDaysExpected);
  83. end
  85. if ~(nSub1 == nSub_exp && nSub2 == nSub_exp && ...
  86. nSub3 == nSub_exp && nSub4 == nSub_exp)
  87. error('The number of participants differs between the ERP and exposure data.');
  88. end
  90. if ~(nDev1 == 2 && nDev2 == 2 && nDev3 == 2 && nDev4 == 2)
  91. error('The third ERP dimension must contain two deviant types: Small and Large.');
  92. end
  94. %% -------------------- Correct latency origin --------------------
  95. % Convert latency values from epoch-relative timing to stimulus-relative
  96. % timing when the epoch begins 0.2 s before stimulus onset.
  97. latency_mmn = latency_mmn - 0.2;
  98. latency_p3 = latency_p3 - 0.2;
  100. %% -------------------- Build long-format tables --------------------
  101. T_amp = build_long_table_with_deviant( ...
  102. amplitude_mmn, amplitude_p3, subj_exp, expMat, ...
  103. 'Amplitude', nDaysExpected, dayLevels, devLevels);
  105. T_lat = build_long_table_with_deviant( ...
  106. latency_mmn, latency_p3, subj_exp, expMat, ...
  107. 'Latency', nDaysExpected, dayLevels, devLevels);
  109. T_all = {T_amp, T_lat};
  111. %% -------------------- Model comparisons --------------------
  112. randomRows = {};
  113. exposureRows = {};
  114. rRandom = 1;
  115. rExposure = 1;
  117. for m = 1:numel(T_all)
  119. Tm = T_all{m};
  120. comps = categories(Tm.Component);
  122. for ic = 1:numel(comps)
  124. thisComp = comps{ic};
  125. Tc = Tm(Tm.Component == thisComp, :);
  127. Tc.Subjects = categorical(Tc.Subjects);
  128. Tc.Days = categorical(Tc.Days, dayLevels);
  129. Tc.DeviantType = categorical(Tc.DeviantType, devLevels);
  131. % Both candidate models must be fitted to exactly the same rows.
  132. Tc = Tc(~isnan(Tc.Y) & ~isnan(Tc.Exposure), :);
  134. if height(Tc) < 10
  135. warning('%s | %s: too few complete observations; comparison skipped.', ...
  136. measureNames{m}, thisComp);
  137. continue;
  138. end
  140. fprintf('\n====================================================\n');
  141. fprintf('%s | %s\n', measureNames{m}, thisComp);
  143. %% Step 1: Random-effects structure comparison using REML
  144. formula_RI = 'Y ~ Days * DeviantType + Exposure + (1 | Subjects)';
  145. formula_RS = 'Y ~ Days * DeviantType + Exposure + (1 + Days | Subjects)';
  147. selectedRandom = 'RandomInterceptOnly';
  148. randomTerm = '(1 | Subjects)';
  149. randomStatus = 'OK';
  151. pRandom = NaN;
  152. logLik_RI = NaN;
  153. logLik_RS = NaN;
  154. AIC_RI = NaN;
  155. AIC_RS = NaN;
  156. BIC_RI = NaN;
  157. BIC_RS = NaN;
  159. try
  160. lme_RI = fitlme(Tc, formula_RI, ...
  161. 'FitMethod', 'REML', 'DummyVarCoding', dummyCoding);
  163. logLik_RI = lme_RI.LogLikelihood;
  164. AIC_RI = lme_RI.ModelCriterion.AIC;
  165. BIC_RI = lme_RI.ModelCriterion.BIC;
  167. lme_RS = fitlme(Tc, formula_RS, ...
  168. 'FitMethod', 'REML', 'DummyVarCoding', dummyCoding);
  170. logLik_RS = lme_RS.LogLikelihood;
  171. AIC_RS = lme_RS.ModelCriterion.AIC;
  172. BIC_RS = lme_RS.ModelCriterion.BIC;
  174. cmpRandom = compare(lme_RI, lme_RS);
  175. pRandom = get_compare_pvalue(cmpRandom);
  177. if ~isnan(pRandom) && pRandom < 0.05
  178. selectedRandom = 'RandomInterceptPlusDaysSlope';
  179. randomTerm = '(1 + Days | Subjects)';
  180. end
  182. catch ME
  183. randomStatus = ['ComparisonFailed: ' ME.message];
  184. selectedRandom = 'RandomInterceptOnly';
  185. randomTerm = '(1 | Subjects)';
  186. end
  188. deltaAIC_RSminusRI = AIC_RS - AIC_RI;
  189. deltaBIC_RSminusRI = BIC_RS - BIC_RI;
  191. randomRows(rRandom,:) = { ...
  192. measureNames{m}, thisComp, height(Tc), ...
  193. formula_RI, formula_RS, ...
  194. logLik_RI, logLik_RS, ...
  195. AIC_RI, AIC_RS, deltaAIC_RSminusRI, ...
  196. BIC_RI, BIC_RS, deltaBIC_RSminusRI, ...
  197. pRandom, selectedRandom, randomStatus};
  198. rRandom = rRandom + 1;
  200. fprintf('Random-effects comparison: p = %.4f; selected = %s\n', ...
  201. pRandom, selectedRandom);
  203. %% Step 2: Exposure comparison using ML
  204. formula_noExp = sprintf('Y ~ Days * DeviantType + %s', randomTerm);
  205. formula_Exp = sprintf('Y ~ Days * DeviantType + Exposure + %s', randomTerm);
  207. exposureStatus = 'OK';
  208. selectedExposure = 'NoExposure';
  210. pExposure = NaN;
  211. logLik_noExp = NaN;
  212. logLik_Exp = NaN;
  213. AIC_noExp = NaN;
  214. AIC_Exp = NaN;
  215. BIC_noExp = NaN;
  216. BIC_Exp = NaN;
  218. try
  219. lme_noExp = fitlme(Tc, formula_noExp, ...
  220. 'FitMethod', 'ML', 'DummyVarCoding', dummyCoding);
  222. lme_Exp = fitlme(Tc, formula_Exp, ...
  223. 'FitMethod', 'ML', 'DummyVarCoding', dummyCoding);
  225. logLik_noExp = lme_noExp.LogLikelihood;
  226. logLik_Exp = lme_Exp.LogLikelihood;
  227. AIC_noExp = lme_noExp.ModelCriterion.AIC;
  228. AIC_Exp = lme_Exp.ModelCriterion.AIC;
  229. BIC_noExp = lme_noExp.ModelCriterion.BIC;
  230. BIC_Exp = lme_Exp.ModelCriterion.BIC;
  232. cmpExposure = compare(lme_noExp, lme_Exp);
  233. pExposure = get_compare_pvalue(cmpExposure);
  235. if ~isnan(pExposure) && pExposure < 0.05
  236. selectedExposure = 'WithExposure';
  237. end
  239. catch ME
  240. exposureStatus = ['ComparisonFailed: ' ME.message];
  241. end
  243. deltaAIC_ExpMinusNoExp = AIC_Exp - AIC_noExp;
  244. deltaBIC_ExpMinusNoExp = BIC_Exp - BIC_noExp;
  246. exposureRows(rExposure,:) = { ...
  247. measureNames{m}, thisComp, height(Tc), selectedRandom, ...
  248. formula_noExp, formula_Exp, ...
  249. logLik_noExp, logLik_Exp, ...
  250. AIC_noExp, AIC_Exp, deltaAIC_ExpMinusNoExp, ...
  251. BIC_noExp, BIC_Exp, deltaBIC_ExpMinusNoExp, ...
  252. pExposure, selectedExposure, exposureStatus};
  253. rExposure = rExposure + 1;
  255. fprintf('Exposure comparison: p = %.4f; selected = %s\n', ...
  256. pExposure, selectedExposure);
  257. end
  258. end
  260. %% -------------------- Convert results to tables --------------------
  261. T_random = cell2table(randomRows, 'VariableNames', { ...
  262. 'Measure','Component','NRows', ...
  263. 'RandomInterceptFormula','RandomSlopeFormula', ...
  264. 'LogLik_RI','LogLik_RS', ...
  265. 'AIC_RI','AIC_RS','DeltaAIC_RSminusRI', ...
  266. 'BIC_RI','BIC_RS','DeltaBIC_RSminusRI', ...
  267. 'LRT_p','SelectedRandomEffects','Status'});
  269. T_exposure = cell2table(exposureRows, 'VariableNames', { ...
  270. 'Measure','Component','NRows','SelectedRandomEffects', ...
  271. 'NoExposureFormula','ExposureFormula', ...
  272. 'LogLik_NoExposure','LogLik_Exposure', ...
  273. 'AIC_NoExposure','AIC_Exposure','DeltaAIC_ExpMinusNoExp', ...
  274. 'BIC_NoExposure','BIC_Exposure','DeltaBIC_ExpMinusNoExp', ...
  275. 'LRT_p','SelectedExposureModel','Status'});
  277. %% -------------------- Export results --------------------
  278. writetable(T_random, outFile, 'Sheet', 'RandomEffectsComparison');
  279. writetable(T_exposure, outFile, 'Sheet', 'ExposureComparison');
  281. fprintf('\nModel-comparison results saved to:\n%s\n', outFile);
  283. %% ============================================================
  284. % Local functions
  285. % ============================================================
  287. function T = build_long_table_with_deviant(A_p2, A_p450, subj_exp, expMat, ...
  288. measureName, nDays, dayLevels, devLevels)
  290. rows = {};
  291. r = 1;
  293. nSub = size(A_p2,2);
  294. nDev = size(A_p2,3);
  296. for isub = 1:nSub
  297. for iday = 1:nDays
  299. dayStr = num2str(iday);
  300. expVal = expMat(isub, iday);
  302. for idev = 1:nDev
  304. devName = devLevels{idev};
  306. rows(r,:) = { ...
  307. subj_exp(isub), dayStr, expVal, ...
  308. A_p2(iday,isub,idev), measureName, 'P2', devName};
  309. r = r + 1;
  311. rows(r,:) = { ...
  312. subj_exp(isub), dayStr, expVal, ...
  313. A_p450(iday,isub,idev), measureName, 'P450', devName};
  314. r = r + 1;
  315. end
  316. end
  317. end
  319. T = cell2table(rows, 'VariableNames', ...
  320. {'Subjects','Days','Exposure','Y','Measure','Component','DeviantType'});
  322. T.Subjects = categorical(T.Subjects);
  323. T.Days = categorical(T.Days, dayLevels);
  324. T.Component = categorical(T.Component);
  325. T.Measure = categorical(T.Measure);
  326. T.DeviantType = categorical(T.DeviantType, devLevels);
  328. % Remove rows with missing outcome values. Exposure missingness is handled
  329. % before model fitting so that both candidate models use identical rows.
  330. T = T(~isnan(T.Y), :);
  332. end
  335. function p = get_compare_pvalue(cmpTbl)
  337. p = NaN;
  338. varNames = cmpTbl.Properties.VariableNames;
  340. if any(strcmp(varNames, 'pValue'))
  341. vals = cmpTbl.pValue;
  342. p = vals(end);
  343. elseif any(strcmp(varNames, 'pValue_LRT'))
  344. vals = cmpTbl.pValue_LRT;
  345. p = vals(end);
  346. else
  347. idx = find(contains(lower(string(varNames)), 'pvalue'), 1);
  348. if ~isempty(idx)
  349. vals = cmpTbl.(varNames{idx});
  350. p = vals(end);
  351. end
  352. end
  354. end

SL06_RandomEffectsComparison.m at commit 96d2a93, no license · at the source

Overview

Authors: Qin Li1, Jari L. O. Kurkela1,2, Jarmo A. Hämäläinen1, Xueqiao Li1, Piia Astikainen1
ORCID iDs: Jarmo A. Hämäläinen, Xueqiao Li, Piia Astikainen
  1. Department of Psychology University of Jyväskylä Jyväskylä Finland
  2. Faculty of Social Sciences/Psychology Tampere University Tampere Finland
Institutions: University of Jyväskylä (Finland); Tampere University (Finland)
Journal: The European journal of neuroscience, volume 64, issue 6, article e70670
Dates: received 10 August 2026; accepted 25 August 2026; published online 16 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/ejn.70670 · PMID 42746800 · PMCID PMC13579495 · OpenAlex W7213356647
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Evoked potentials
Keywords: auditory oddball, event‐related potentials, sleep study
MeSH: Brain*, Evoked Potentials*, Evoked Potentials, Auditory*, Sleep*, Sleep Stages*, Speech Perception*, Acoustic Stimulation, Adult, Electroencephalography, Female, Humans, Male, Phonetics, Young Adult (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Research Council of Finland (351009); China Scholarship Council
Citations: not cited yet (Europe PMC); 59 references in the paper

Abstract

The sleeping brain can discriminate speech sounds, but evidence for cross‐night changes in neural responses to foreign speech in adults remains limited. Here, Finnish‐speaking adults naïve to tonal languages were exposed during Stage N2 of non‐rapid eye movement (NREM) sleep to small and large deviations in tone (pitch contour) embedded in vowel /a/ across four consecutive nights, while electroencephalography was recorded. Event‐related potentials (ERPs) P2 and P450 revealed robust change detection responses to tone deviations during sleep, and importantly, P450 amplitude decreased from night 1 to night 4. No evidence of perceptual learning was found in waking change detection ERPs or in behavioural performance measured with the same stimuli before and after the four‐night sleep exposure. In conclusion, the sleeping brain remained sensitive to deviations in vowel tone, and the amplitude reduction of the late ERP deflection suggests a change in auditory processing across nights of exposure. However, in the absence of transfer effects to wakeful measures, these findings do not provide evidence for phonetic learning‐related plasticity, and the extent to which the effects are speech‐specific remains unclear. These findings provide new insight into the dynamics of auditory processing during sleep and contribute to broader neuroscientific understanding of sensory processing in altered states of consciousness.

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.

AsophiliaChance/Sleep_exposure

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 96d2a937290f3ef28ed423bb462402664383eaba, 1 September 2026
Languages: MATLAB (11)
Size: 13 files, 11 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: EEGLAB (7 files), FieldTrip (4 files), Statistics and Machine Learning Toolbox (4 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
12 files
At the source: github.com/AsophiliaChance/Sleep_exposure

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;
  • 11 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

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

Data Availability Statement

The raw data are not publicly available due to legal restrictions. The data that support the findings of this study are available upon reasonable request from Piia Astikainen (). The codes used in the analysis are published in GitHub ‐ AsophiliaChance/Sleep_exposure (https://github.com/AsophiliaChance/Sleep_exposure).

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 3, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 14 MeSH terms, 2 funders, 55 references.

Cite

This paper

Li, Q., Kurkela, J. L. O., Hämäläinen, J. A., Li, X., & Astikainen, P. (2026). Cross-Night Modulation of Change Detection ERPs to Foreign Speech Sound Features During N2 Sleep. The European journal of neuroscience, 64(6), e70670. https://doi.org/10.1111/ejn.70670

BibTeX

@article{li2026cross,
author = {Li, Qin and Kurkela, Jari L. O. and Hämäläinen, Jarmo A. and Li, Xueqiao and Astikainen, Piia},
title = {{Cross-Night Modulation of Change Detection ERPs to Foreign Speech Sound Features During N2 Sleep}},
journal = {The European journal of neuroscience},
year = {2026},
month = sep,
volume = {64},
number = {6},
pages = {e70670},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70670},
url = {https://doi.org/10.1111/ejn.70670},
pmid = {42746800},
pmcid = {PMC13579495}
}

RIS

TY - JOUR
AU - Li, Qin
AU - Kurkela, Jari L. O.
AU - Hämäläinen, Jarmo A.
AU - Li, Xueqiao
AU - Astikainen, Piia
TI - Cross-Night Modulation of Change Detection ERPs to Foreign Speech Sound Features During N2 Sleep
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/09/01
VL - 64
IS - 6
SP - e70670
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70670
UR - https://doi.org/10.1111/ejn.70670
LA - en
ER -

CSL-JSON

{
"id": "10.1111/ejn.70670",
"type": "article-journal",
"title": "Cross-Night Modulation of Change Detection ERPs to Foreign Speech Sound Features During N2 Sleep",
"container-title": "The European journal of neuroscience",
"author": [
{
"family": "Li",
"given": "Qin"
},
{
"family": "Kurkela",
"given": "Jari L. O."
},
{
"family": "Hämäläinen",
"given": "Jarmo A."
},
{
"family": "Li",
"given": "Xueqiao"
},
{
"family": "Astikainen",
"given": "Piia"
}
],
"container-title-short": "Eur J Neurosci",
"volume": "64",
"issue": "6",
"page": "e70670",
"DOI": "10.1111/ejn.70670",
"PMID": "42746800",
"PMCID": "PMC13579495",
"ISSN": "0953-816X",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/ejn.70670",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
1
]
]
}
}

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[10] doi:10.7554/elife.103956 [code]
Human brain-wide activation of sleep rhythms.
Journal: eLife
In common: EEG, 4 references

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