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Beta-band frequency shifts signal decisions in human prefrontal cortex

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  1. [1] § STAR★Methods › Quantification and statistical analysis › Burst analysis ↔ qmt4z/exp1_freqshift.m, lines 137–200 · score 0.59 · Spectral Events, findMethod, bursts, band

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The authors' code

MATLAB · 457 lines · 13 KB · no license · 1 match

  1. %% timelock and trial select
  3. % load ~epx1_instfreq_decdelay.mat'
  5. for si = 1:numel(decdelay_instfreq)
  7. trl = decdelay_instfreq{si}.trialinfo;
  9. % F1_trls = find(trl(:,5)==1 & trl(:,1)==1 & trl(:,4)==1); % correct short
  10. % F2_trls = find(trl(:,5)==1 & trl(:,1)==1 & trl(:,4)==2); % correct long
  12. F1_trls = find( (trl(:,3)==1 & trl(:,4)==1) | (trl(:,3)==2 & trl(:,4)==0) ); % subjective short
  13. F2_trls = find( (trl(:,3)==2 & trl(:,4)==1) | (trl(:,3)==1 & trl(:,4)==0) ); % correct long
  15. % F1_trls = find(trl(:,3)==1 & trl(:,4)==1); % correct short
  16. % F2_trls = find(trl(:,3)==2 & trl(:,4)==1); % correct long
  18. min_trls = min(numel(F1_trls), numel(F2_trls));
  20. F1 = randsample(F1_trls, min_trls);
  21. F2 = randsample(F2_trls, min_trls);
  23. % cfg = [];
  24. % cfg.demean = 'yes';
  25. % cfg.baselinewindow = [-.3 0];
  26. % decdelay_instfreqsi = ft_preprocessing(cfg, decdelay_instfreq{si})
  29. cfg = [];
  30. cfg.latency = [-.5 2];
  31. cfg.trials = F1;
  32. F1_if{si} = ft_timelockanalysis(cfg, decdelay_instfreq{si});
  33. cfg.trials = F2;
  34. F2_if{si} = ft_timelockanalysis(cfg, decdelay_instfreq{si});
  35. clear decdelay_instfreqsi
  36. % cfg = [];
  37. % cfg.baseline = [-.1 0];
  38. % F1_if{si} = ft_timelockbaseline(cfg, F1_if{si})
  39. % F2_if{si} = ft_timelockbaseline(cfg, F2_if{si})
  41. si
  42. end
  44. %% GA
  46. cfg = [];
  47. %cfg.keepindividual = 'yes';
  48. GAF1 = ft_timelockgrandaverage(cfg, F1_if{:});
  49. GAF2 = ft_timelockgrandaverage(cfg, F2_if{:});
  51. %% plot
  53. figure;
  54. plot(GAF1.time, GAF1.avg(2,:))
  55. hold on;
  56. plot(GAF2.time, GAF2.avg(2,:))
  57. xlim([-.3 2.2])
  59. %% stat
  61. label = F1_if{1}.label;
  63. % neighbours(1).label = label{1};
  64. % neighbours(1).neighblabel = {[label{2}, '; ' label{3}, '; ' label{4}]};
  65. % neighbours(2).label = label{2};
  66. % neighbours(2).neighblabel = {[label{1}, '; ' label{3}, '; ' label{4}]};
  67. % neighbours(3).label = label{3};
  68. % neighbours(3).neighblabel = {[label{1}, '; ' label{2}, '; ' label{4}]};
  69. % neighbours(4).label = label{4};
  70. % neighbours(4).neighblabel = {[label{1}, '; ' label{2}, '; ' label{3}]};
  72. neighbours(1).label = label{1};
  73. neighbours(1).neighblabel = label(3);
  74. neighbours(2).label = label{2};
  75. neighbours(2).neighblabel = label(4);
  76. neighbours(3).label = label{3};
  77. neighbours(3).neighblabel = label(1);
  78. neighbours(4).label = label{4};
  79. neighbours(4).neighblabel = label(2);
  81. %
  82. cfg = [];
  83. cfg.method = 'montecarlo';
  84. cfg.statistic = 'depsamplesT';
  86. cfg.correctm = 'cluster';
  87. cfg.clusteralpha = 0.05;
  89. %cfg.frequency = [4 30];
  90. cfg.latency = [0 2];
  92. cfg.clusterstatistic = 'wcm';
  93. cfg.tail = 0; % -1, 1 or 0 (default = 0); one-sided or two-sided test
  94. cfg.clustertail = 0;
  95. cfg.alpha = 0.05; % alpha level of the permutation test
  96. cfg.numrandomization = 5000;
  98. % design
  99. ll = numel(F1_if);
  100. design = [];
  101. design = repmat(1:ll, 1, 2);
  102. design(2,:) = [repmat(1, 1, ll) repmat(2, 1, ll)];
  103. cfg.design = design;
  104. %cfg.minnbchan = 1;
  106. cfg.ivar = 2;
  107. cfg.uvar = 1;
  108. cfg.neighbours = neighbours;
  110. cfg.channel = [1:4];
  112. stat = ft_timelockstatistics(cfg, F1_if{:}, F2_if{:}) % negclu
  113. %stat = ft_timelockstatistics(cfg, GAF1, GAF2)
  114. % subj decision:
  115. % p=4e-4
  116. %
  117. %% get cluster values
  119. clustertimes = stat.time(stat.negclusterslabelmat(2,:)==1);
  120. start_idx = find(stat.time==clustertimes(1));
  121. end_idx = find(stat.time==clustertimes(end));
  123. for si=1:numel(F1_if)
  125. ind_diff(si) = mean(F1_if{si}.avg(2, start_idx:end_idx))-mean(F2_if{si}.avg(2, start_idx:end_idx));
  127. end
  128. %% plot
  129. timevec = [-2:1/250:1.996];
  130. figure;
  131. plot(timevec, squeeze(nanmean(long_correct_if(:,4,:),1)))
  132. hold on;
  133. plot(timevec, squeeze(nanmean(short_correct_if(:,4,:),1)))
  134. xlim([-.5 1.8])
  137. %% burst detection
  138. addpath /project/3035003.01/JURIQUILLA/toolbox/SpectralEvents-master/SpectralEvents-master/
  139. tic
  140. clear
  141. src_data_folder = '/project/3015079.02/categorization EEG/elie/svs_data_500Hz/';
  142. src_data_files = dir(fullfile(src_data_folder, '*mat'));
  144. d=1
  145. for si = [1:numel(src_data_files)] % 7 is nan
  147. load([src_data_folder src_data_files(si).name])
  149. % cfg = [];
  150. % cfg.derivative = 'yes';
  151. % svs_data = ft_preprocessing(cfg, svs_data);
  153. trl = []; trl = svs_data.trialinfo;
  155. F1_trls = find(trl(:,3)==1 & trl(:,4)==1); % correct short
  156. F2_trls = find(trl(:,3)==2 & trl(:,4)==1); % correct long
  158. % min_trls = min(numel(F1_trls), numel(F2_trls));
  159. %
  160. % F1 = randsample(F1_trls, min_trls);
  161. % F2 = randsample(F2_trls, min_trls);
  163. cfg = [];
  164. cfg.latency = [0 2];
  165. cfg.channel = 2;
  166. %cfg.trials = [F1 F2];
  167. %cfg.avgoverchan = 'yes';
  168. data = ft_selectdata(cfg, svs_data);
  170. % update trl
  171. %trl = data.trialinfo;
  173. match = zeros(size(trl,1),1);
  174. match(trl(:,3)==1 & trl(:,4)==1)=1;
  175. match(trl(:,3)==2 & trl(:,4)==1)=2;
  177. % put data in time x trials matrix
  178. for ti = 1:numel(data.trial)
  180. x{d}(ti, :) = cell2mat(data.trial(ti));
  182. end
  184. x{d} = x{d}';
  185. % long vs short
  186. classLabels{d} = match; clear match
  187. %classLabels{1} = 1;
  189. d = d+1;
  190. end
  192. eventBand = [13,35]; % freq range of bursts
  193. fVec = 12:.5:36; % freqs for TFR
  194. Fs = 500; % sampling rate
  195. findMethod = 1;
  196. vis = false; % visualize
  198. [specEvents, TFRs, timeseries] = spectralevents(eventBand, fVec, Fs, findMethod, vis, x, classLabels);
  200. toc
  201. %% extract params
  203. for fi = 1:numel(specEvents)
  205. maxfreq_short(fi) = mean(specEvents(fi).Events.Events.maximafreq(specEvents(fi).Events.Events.classLabels==1));
  206. maxfreq_long(fi) = mean(specEvents(fi).Events.Events.maximafreq(specEvents(fi).Events.Events.classLabels==2));
  208. burstrate_short(fi) = sum(specEvents(fi).Events.Events.classLabels==1) / sum(specEvents(fi).TrialSummary.TrialSummary.classLabels==1);
  209. burstrate_long(fi) = sum(specEvents(fi).Events.Events.classLabels==2) / sum(specEvents(fi).TrialSummary.TrialSummary.classLabels==2);
  211. Fspan_short(fi) = mean(specEvents(fi).Events.Events.Fspan(specEvents(fi).Events.Events.classLabels==1));
  212. Fspan_long(fi) = mean(specEvents(fi).Events.Events.Fspan(specEvents(fi).Events.Events.classLabels==2));
  214. maxtiming_short(fi) = mean(specEvents(fi).Events.Events.maximatiming(specEvents(fi).Events.Events.classLabels==1));
  215. maxtiming_long(fi) = mean(specEvents(fi).Events.Events.maximatiming(specEvents(fi).Events.Events.classLabels==2));
  217. duration_short(fi) = mean(specEvents(fi).Events.Events.duration(specEvents(fi).Events.Events.classLabels==1));
  218. duration_long(fi) = mean(specEvents(fi).Events.Events.duration(specEvents(fi).Events.Events.classLabels==2));
  220. maxpow_short(fi) = mean(specEvents(fi).Events.Events.maximapower(specEvents(fi).Events.Events.classLabels==1));
  221. maxpow_long(fi) = mean(specEvents(fi).Events.Events.maximapower(specEvents(fi).Events.Events.classLabels==2));
  223. % numevents_short(fi) = mean(specEvents(fi).TrialSummary.TrialSummary.eventnumber(specEvents(fi).TrialSummary.TrialSummary.classLabels==1));
  224. % numevents_long(fi) = mean(specEvents(fi).TrialSummary.TrialSummary.eventnumber(specEvents(fi).TrialSummary.TrialSummary.classLabels==2));
  225. % this was same as burst rate
  226. end
  228. % maxfreq is significant with findMethod=1
  229. % even moreso with findMethod = 2 (T=4)
  230. % less so but stsill sig with method 3 (T=2.3, p=.03)
  232. % to report for revision
  233. % mean burst rate short 3.00 +/- .300 (T=48.9, p=0)
  234. % mean burst rate long 2.87 +/- .384 (T=36.7, p=0)
  235. %% tfr
  237. clear
  238. src_data_folder = '/project/3015079.02/categorization EEG/elie/svs_data/';
  239. src_data_files = dir(fullfile(src_data_folder, '*mat'));
  241. for fi = 1:numel(src_data_files)
  243. load([src_data_folder src_data_files(fi).name])
  245. trl = svs_data.trialinfo;
  247. trl = []; trl = svs_data.trialinfo;
  249. F1_trls = find(trl(:,3)==1 & trl(:,4)==1); % correct match
  250. F2_trls = find(trl(:,3)==2 & trl(:,4)==1); % correct mismatch
  252. min_trls = min(numel(F1_trls), numel(F2_trls));
  254. F1 = randsample(F1_trls, min_trls);
  255. F2 = randsample(F2_trls, min_trls);
  257. cfg = [];
  258. cfg.latency = [-.1 2];
  259. data = ft_selectdata(cfg, svs_data)
  261. cfg = [];
  262. cfg.method = 'wavelet';
  263. % cfg.output = 'fractal';
  264. % cfg.taper = 'hanning';
  265. cfg.foi = [4:36];
  266. cfg.pad = 3;
  267. cfg.toi = 0:.1:2;
  268. % cfg.t_ftimwin = ones(1, length(cfg.foi))*.4;
  269. cfg.channel = [1 2 3 4];
  270. cfg.keeptrials = 'yes';
  272. cfg.trials = F1; % select F1 mot trials
  273. F1_fft{fi} = ft_freqanalysis(cfg, data)
  274. F1_fft{fi}.powspctrm = log10(F1_fft{fi}.powspctrm);
  275. F1_fft{fi} = ft_freqdescriptives([], F1_fft{fi});
  277. cfg.trials = F2; % select F1 aud trials
  278. F2_fft{fi} = ft_freqanalysis(cfg, data)
  279. F2_fft{fi}.powspctrm = log10(F2_fft{fi}.powspctrm);
  280. F2_fft{fi} = ft_freqdescriptives([], F2_fft{fi});
  282. end
  284. % GA
  285. GAF1 = ft_freqgrandaverage([], F1_fft{:})
  286. GAF2 = ft_freqgrandaverage([], F2_fft{:})
  288. %% plt
  290. figure;
  291. plot(GAF1.freq, GAF1.powspctrm(4,:))
  292. hold on; plot(GAF2.freq, GAF2.powspctrm(4,:))
  294. %% stat
  295. % this way of making neighbours might not be working
  296. neighbours = [];
  297. label = GAF1.label;
  299. % neighbours(1).label = label{1};
  300. % neighbours(1).neighblabel = {[label{2}, '; ' label{3}, '; ' label{4}]};
  301. % neighbours(2).label = label{2};
  302. % neighbours(2).neighblabel = {[label{1}, '; ' label{3}, '; ' label{4}]};
  303. % neighbours(3).label = label{3};
  304. % neighbours(3).neighblabel = {[label{1}, '; ' label{2}, '; ' label{4}]};
  305. % neighbours(4).label = label{4};
  306. % neighbours(4).neighblabel = {[label{1}, '; ' label{2}, '; ' label{3}]};
  308. neighbours(1).label = label{1};
  309. neighbours(1).neighblabel = label(3);
  310. neighbours(2).label = label{2};
  311. neighbours(2).neighblabel = label(4);
  312. neighbours(3).label = label{3};
  313. neighbours(3).neighblabel = label(1);
  314. neighbours(4).label = label{4};
  315. neighbours(4).neighblabel = label(2);
  316. %
  317. cfg = [];
  318. cfg.method = 'montecarlo';
  319. cfg.statistic = 'depsamplesT';
  321. cfg.correctm = 'cluster';
  322. cfg.clusteralpha = 0.05;
  324. cfg.frequency = [13 35];
  325. cfg.latency = [0 2];
  327. cfg.clusterstatistic = 'maxsum';
  328. cfg.tail = 0; % -1, 1 or 0 (default = 0); one-sided or two-sided test
  329. cfg.clustertail = 0;
  330. cfg.alpha = 0.05; % alpha level of the permutation test
  331. cfg.numrandomization = 10000;
  333. % design
  334. ll = numel(F1_fft);
  335. design = [];
  336. design = repmat(1:ll, 1, 2);
  337. design(2,:) = [repmat(1, 1, ll) repmat(2, 1, ll)];
  338. cfg.design = design;
  340. cfg.ivar = 2;
  341. cfg.uvar = 1;
  342. cfg.neighbours = neighbours;
  343. %cfg.channel = 5;
  345. stat = ft_freqstatistics(cfg, F1_fft{:}, F2_fft{:})
  347. %% plot stat
  348. poscluster = stat.posclusterslabelmat==1;
  349. negcluster = stat.negclusterslabelmat==1;
  351. figure; imagesc(stat.time, stat.freq, squeeze(poscluster(2,:,:))); axis xy
  352. figure; imagesc(stat.time, stat.freq, squeeze(negcluster(4,:,:))); axis xy
  354. %% decoding
  356. clear
  357. addpath /project/3035003.01/MVPA-Light-master/startup
  358. startup_MVPA_Light
  360. load('/project/3015079.02/categorization EEG/elie/inst_freq_sens/instfreq_decdelay.mat')
  362. for fi = 1:numel(decdelay_instfreq)
  365. % load dataset from 1 subj to try
  366. trl = []; trl = decdelay_instfreq{fi}.trialinfo;
  368. F1_trls = find(trl(:,3)==1 & trl(:,4)==1); % correct short
  369. F2_trls = find(trl(:,3)==2 & trl(:,4)==1); % correct long
  371. cfg = [];
  372. cfg.latency = [-.4 1.9];
  373. cfg.trials = F1_trls;
  374. data_F1 = ft_selectdata(cfg, decdelay_instfreq{fi})
  375. cfg.trials = F2_trls;
  376. data_F2 = ft_selectdata(cfg, decdelay_instfreq{fi})
  378. cfg = [];
  379. cfg.method = 'mvpa';
  380. cfg.features = 'chan';
  381. %cfg.features = [];
  382. cfg.mvpa.classifier = 'lda'; % or lda
  383. cfg.mvpa.metric = 'auc';
  384. cfg.mvpa.k = 8;
  385. cfg.mvpa.repeat = 2;
  386. %cfg.neighbours = neighbours;
  388. cfg.design = [ones(numel(F1_trls),1); 2*ones(numel(F2_trls),1)];
  389. cfg.mvpa.preprocess = 'zscore';
  391. statx{fi} = ft_timelockstatistics(cfg, data_F1, data_F2)
  393. fi
  394. end
  396. %% average the stat?
  398. % below chance ! -> do on sens level (it works there)
  400. for fi = 1:24
  402. auc(fi, :) = statx{fi}.auc;
  404. end
  406. figure; plot(statx{1}.time, smooth(mean(auc), 7))
  408. %% do we see freqshift on the fft spectra?
  409. clear
  410. folder = '/project/3015079.02/categorization EEG/elie/svs_data_dec_2.5/';
  411. files = dir(fullfile(folder, '*mat'))
  413. for fi = 1:numel(files)
  415. load([folder files(fi).name])
  417. trl = []; trl = svs_data.trialinfo;
  419. F1_trls = find(trl(:,3)==1 & trl(:,4)==1); % correct short
  420. F2_trls = find(trl(:,3)==2 & trl(:,4)==1);
  422. min_trls = min(numel(F1_trls), numel(F2_trls));
  424. F1 = randsample(F1_trls, min_trls);
  425. F2 = randsample(F2_trls, min_trls);
  427. cfg = [];
  428. cfg.derivative = 'yes';
  429. svs_data = ft_preprocessing(cfg, svs_data)
  431. cfg = [];
  432. cfg.method = 'mtmfft';
  433. cfg.taper = 'hanning';
  434. % cfg.output = 'fooof_peaks';
  435. % cfg.taper = 'dpss';
  436. % cfg.tapsmofrq = 2;
  437. cfg.pad = 4;
  438. cfg.foilim = [8 35];
  440. cfg.trials = F1;
  441. fft_short{fi} = ft_freqanalysis(cfg, svs_data)
  443. cfg.trials = F2;
  444. fft_long{fi} = ft_freqanalysis(cfg, svs_data)
  446. end
  448. %% GA
  450. GA_short = ft_freqgrandaverage([], fft_short{:})
  451. GA_long = ft_freqgrandaverage([], fft_long{:})
  453. %% plot
  455. figure; plot(GA_short.freq, GA_short.powspctrm(2,:))
  456. hold on; plot(GA_long.freq, GA_long.powspctrm(2,:))
  457. xlim([8 35])

exp1_freqshift.m, no license · at the source

Overview

Authors: Elie Rassi1,2, Julio Rodriguez-Larios3, Camille Gret1, Hugo Merchant4, Alma Elshafei1, Saskia Haegens1,5,6
ORCID iDs: Elie Rassi
  1. Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
  2. Department of Psychology and Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
  3. Brunel University of London, London, UK
  4. Instituto de Neurobiología, UNAM, Campus Juriquilla, Queretaro, Mexico
  5. Department of Psychiatry, Columbia University, New York, NY, USA
  6. Division of Systems Neuroscience, New York State Psychiatric Institute, New York, NY, USA
Institutions: Radboud University Nijmegen (Netherlands); University of Salzburg (Austria); Donders Institute for Brain, Cognition and Behaviour (Netherlands); Brunel University of London (United Kingdom); Universidad Nacional Autónoma de México (Mexico); New York State Psychiatric Institute; Columbia University (United States)
Journal: n/a, volume 28, issue 11, article 113806
Dates: received 11 April 2025; accepted 15 October 2025; published online 17 October 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1016/j.isci.2025.113806 · PMCID PMC12629917 · OpenAlex W4415328298
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Physiology & signal measures
Keywords: Natural sciences, Biological sciences, Neuroscience, Clinical neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Austrian Science; Erwin Schrödinger Fellowship (J4580); Consejo Nacional de Humanidades (R01 MH123679); Ciencia y Tecnología (CBF-2025-G-89); UNAM-DGAPA PAPIIT (IG200424); Nederlandse Organisatie voor Wetenschappelijk Onderzoek; NIH; NSF (CRCNS 2424100)
Citations: cited by 2 papers (Europe PMC); 57 references in the paper

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OSF a5m7q

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Found in: “Data and code availability”
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Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 8 funders, 57 references.

Cite

This paper

Rassi, E., Rodriguez-Larios, J., Gret, C., Merchant, H., Elshafei, A., & Haegens, S. (2025). Beta-band frequency shifts signal decisions in human prefrontal cortex. iScience, 28(11), 113806. https://doi.org/10.1016/j.isci.2025.113806

BibTeX

@article{rassi2025beta,
author = {Rassi, Elie and Rodriguez-Larios, Julio and Gret, Camille and Merchant, Hugo and Elshafei, Alma and Haegens, Saskia},
title = {{Beta-band frequency shifts signal decisions in human prefrontal cortex}},
journal = {iScience},
year = {2025},
volume = {28},
number = {11},
pages = {113806},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2025.113806},
url = {https://doi.org/10.1016/j.isci.2025.113806},
pmcid = {PMC12629917}
}

RIS

TY - JOUR
AU - Rassi, Elie
AU - Rodriguez-Larios, Julio
AU - Gret, Camille
AU - Merchant, Hugo
AU - Elshafei, Alma
AU - Haegens, Saskia
TI - Beta-band frequency shifts signal decisions in human prefrontal cortex
T2 - iScience
J2 - iScience
PY - 2025
DA - 2025
VL - 28
IS - 11
SP - 113806
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2025.113806
UR - https://doi.org/10.1016/j.isci.2025.113806
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2025.113806",
"type": "article-journal",
"title": "Beta-band frequency shifts signal decisions in human prefrontal cortex",
"container-title": "iScience",
"author": [
{
"family": "Rassi",
"given": "Elie"
},
{
"family": "Rodriguez-Larios",
"given": "Julio"
},
{
"family": "Gret",
"given": "Camille"
},
{
"family": "Merchant",
"given": "Hugo"
},
{
"family": "Elshafei",
"given": "Alma"
},
{
"family": "Haegens",
"given": "Saskia"
}
],
"container-title-short": "iScience",
"volume": "28",
"issue": "11",
"page": "113806",
"DOI": "10.1016/j.isci.2025.113806",
"PMCID": "PMC12629917",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2025.113806",
"language": "en",
"issued": {
"date-parts": [
[
2025
]
]
}
}

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