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A cortico-subthalamic circuit rapidly engages and releases inhibition of specific movements depending on the environmental context.

Code ↔ Paper

5 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 5 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 › Connectivity analyses ↔ Step2BetaCorrelations.m, lines 25–117 · score 0.71 · beta power, post SSRT, pre SSRT, correlated, Hilbert, zero
  2. [2] § Materials and methods › Connectivity analyses ↔ Step2BetaCorrelations.m, lines 216–342 · score 0.69 · beta power, SSRT locked, lagged, ROI, locations, correlated
  3. [3] § Materials and methods › Motoric independent component selection ↔ helper_functions/get_motor_ic.m, the whole file · a weak match · score 0.56 · pre response period, backprojected, beta burst, components, motor, stimulus
  4. [4] § Materials and methods › Time–frequency analyses ↔ helper_functions/convert_to_bursts.m, lines 15–92 · score 0.53 · imregional, median, burst rate, convolved, wavelet, cycles
  5. [5] § Materials and methods › Time–frequency analyses ↔ Step1STNLFP.m, lines 24–101 · score 0.53 · Full spectrum, 125 Hz, ERSP, Hilbert, transforming, event

Paper

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The authors' code

MATLAB · 547 lines · 26 KB · no license · 2 matches

  1. %% initialize (EEGLAB has to be on path)
  2. eeglab; clear; clc; close all;
  4. % files and folders;
  5. eeginfolder = '/data/backed_up/PERCEPT_SST/jan/MergedFiles'; % use CSD transformed EEG data
  6. eegfiles = dir(fullfile(eeginfolder,'*.set')); eegfiles = {eegfiles.name};
  8. % load essential info
  9. load('/data/backed_up/PERCEPT_SST/chanlocs.mat'); % chanlocs for this study workspace
  10. load('/data/backed_up/PERCEPT_SST/jan/FINAL/helper_functions/AGF_cmap.mat'); % colormap that has whites for zero
  11. load('/data/backed_up/PERCEPT_SST/SSssrt.mat'); % behavior for selective stopping
  12. load('/data/backed_up/PERCEPT_SST/GSssrt.mat'); % behavior for non-selective stopping
  14. % global settings
  15. epochlength = [-.5 1.5]; % in secs
  16. baseline = [-100 0]; % in ms
  17. srate = 500; % in hz
  18. frequencies = 13:30; % in hz
  20. % settings
  21. preSSRTwindow = 25; % in sample points before SSRT
  22. postSSRTwindow = 50; % in sample points after SSRT
  23. presteps = fliplr(0:25:100); % in samples before SSRT
  24. poststeps = 0:25:250; % in samples after SSRT
  25. %% Which scalp locations correlate with preSSRT beta / postSSRT beta
  27. % preassign: global stop
  28. LPREwind = nan(length(eegfiles),length(chanlocs),length(presteps)-1);
  29. RPREwind = nan(length(eegfiles),length(chanlocs),length(presteps)-1);
  30. LPOSTwind = nan(length(eegfiles),length(chanlocs),length(poststeps)-1);
  31. RPOSTwind = nan(length(eegfiles),length(chanlocs),length(poststeps)-1);
  33. % go through files
  34. for is = 1:length(eegfiles)
  36. % load
  37. EEG = pop_loadset(fullfile(eeginfolder,eegfiles{is}));
  39. % find STN channels indices
  40. leftstn = find(~cellfun(@isempty,strfind({chanlocs.labels},'LEFT')) & cellfun(@isempty,strfind({chanlocs.labels},'EMG')));
  41. rightstn = find(~cellfun(@isempty,strfind({chanlocs.labels},'RIGHT')) & cellfun(@isempty,strfind({chanlocs.labels},'EMG')));
  43. % Beta power conversion
  44. for ic = 1:EEG.nbchan
  45. EEG.data(ic,:) = abs(hilbert(eegfilt(double(EEG.data(ic,:)),srate,frequencies(1),frequencies(end),0,[],0,'fir1')'))'.^2;
  46. end
  48. % events
  49. events = EEG.event; % get all events
  51. % get events
  52. ssrt_signal = events(strcmpi({events.type},'STOP_Global_SSRT')); % find ssrt locked stop trials
  53. ssrt_signal = ssrt_signal(strcmpi({ssrt_signal.ACC},'SS')); % successful only
  54. stgEEG = pop_epoch(EEG,'STOP_Global_SSRT',epochlength); % epoch
  55. [~,keep,keep2] = intersect(([stgEEG.event.nr]),[ssrt_signal.nr]); % retain trials that weren't removed based on EEG
  56. stgEEG = pop_select(stgEEG,'trial',[stgEEG.event(keep).epoch]); % keep only those trials
  57. ssrt_signal = ssrt_signal(keep2); % keep only those trials
  59. % get events
  60. ignore_signal = events(strcmpi({events.type},'IGNORE_Global_SSRT')); % find ssrt matched ignore trials
  61. ignore_signal = ignore_signal(strcmpi({ignore_signal.ACC},'CORRECT')); % correct only
  62. ignEEG = pop_epoch(EEG,'IGNORE_Global_SSRT',epochlength); % epoch
  63. [~,keep,keep2] = intersect(([ignEEG.event.nr]),[ignore_signal.nr]); % retain trials that weren't removed based on EEG
  64. ignEEG = pop_select(ignEEG,'trial',[ignEEG.event(keep).epoch]); % keep only those trials
  65. ignore_signal = ignore_signal(keep2); % keep only those trials
  67. % get events
  68. select_signal = events(strcmpi({events.type},'STOP_Select_SSRT')); % find ssrt matched select stop trials
  69. select_signal = select_signal(strcmpi({select_signal.ACC},'SS')); % successful only
  70. stsEEG = pop_epoch(EEG,'STOP_Select_SSRT',epochlength); % epoch
  71. [~,keep,keep2] = intersect(([stsEEG.event.nr]),[select_signal.nr]); % retain trials that weren't removed based on EEG
  72. stsEEG = pop_select(stsEEG,'trial',[stsEEG.event(keep).epoch]); % keep only those trials
  73. select_signal = select_signal(keep2); % keep only those trials
  75. % merge
  76. allEEG = pop_mergeset(stsEEG,ignEEG); % merge select stop and ignore
  77. allEEG = pop_mergeset(allEEG,stgEEG); % merge global stop with that
  79. % get pre and post SSRT STN signal
  80. preSSRTleft = nan(allEEG.trials,1); % preassign
  81. postSSRTleft = nan(allEEG.trials,1);
  82. preSSRTright = preSSRTleft;
  83. postSSRTright = postSSRTleft;
  84. % go through trials of merged dataset
  85. for ie = 1:allEEG.trials
  87. % find zero sample in epoch
  88. evlat = find(allEEG.times==0);
  89. % mean STN data right before SSRT
  90. preSSRTleft(ie,1) = mean(allEEG.data(leftstn,evlat-preSSRTwindow:evlat,ie));
  91. preSSRTright(ie,1) = mean(allEEG.data(rightstn,evlat-preSSRTwindow:evlat,ie));
  92. % mean STN data right after SSRT
  93. postSSRTleft(ie,1) = mean(allEEG.data(leftstn,evlat:evlat+postSSRTwindow,ie));
  94. postSSRTright(ie,1) = mean(allEEG.data(rightstn,evlat:evlat+postSSRTwindow,ie));
  96. end
  98. % go through EEG channels and correlate with that STN signal
  99. for ic = 1:allEEG.nbchan-2
  100. % get channel data
  101. cdata = squeeze(allEEG.data(ic,:,:))';
  102. % go through timewindows before SSRT, get mean channel data in those windows, zscore, and correlate with STN
  103. for iw = 1:length(presteps)-1
  104. LPREwind(is,ic,iw) = regress(zscore(mean(cdata(:,evlat-presteps(iw):evlat-presteps(iw+1)),2)),zscore(preSSRTleft));
  105. RPREwind(is,ic,iw) = regress(zscore(mean(cdata(:,evlat-presteps(iw):evlat-presteps(iw+1)),2)),zscore(preSSRTright));
  106. end
  107. % go through timewindows after SSRT, get mean channel data in those windows, zscore, and correlate with STN
  108. for iw = 1:length(poststeps)-1
  109. LPOSTwind(is,ic,iw) = regress(zscore(mean(cdata(:,evlat+poststeps(iw):evlat+poststeps(iw+1)),2)),zscore(postSSRTleft));
  110. RPOSTwind(is,ic,iw) = regress(zscore(mean(cdata(:,evlat+poststeps(iw):evlat+poststeps(iw+1)),2)),zscore(postSSRTright));
  111. end
  112. end
  114. end
  116. % save
  117. save(fullfile('/data/backed_up/PERCEPT_SST/WindowedBetaCorrelations.mat'),'LPREwind','RPREwind','LPOSTwind','RPOSTwind');
  119. %% Plot whole-scalp windowed beta band correlation
  120. clc; close all;
  122. % load
  123. load(fullfile('/data/backed_up/PERCEPT_SST/WindowedBetaCorrelations.mat'));
  125. % alpha level correct for number of windows
  126. preSSRT_alpha = .05 / (size(RPREwind,3) + size(LPREwind,3));
  127. postSSRT_alpha = .05 / (size(LPOSTwind,3) + size(RPOSTwind,3));
  129. % fisher's z transform
  130. LPREwind = atanh(LPREwind);
  131. RPREwind = atanh(RPREwind);
  132. LPOSTwind = atanh(LPOSTwind);
  133. RPOSTwind = atanh(RPOSTwind);
  135. % stats
  136. [~,p] = ttest(LPREwind); pLpre = squeeze(p);
  137. [~,p] = ttest(RPREwind); pRpre = squeeze(p);
  138. [~,p] = ttest(LPOSTwind); pLpost = squeeze(p);
  139. [~,p] = ttest(RPOSTwind); pRpost = squeeze(p);
  141. % open figure: preSSRT
  142. h = figure('Position',[1 1 1400 860],'color','w'); CLIMS = [0 0];
  144. % go through time windows
  145. for ip = 1:size(pLpre,2)
  147. % make subplot: left STN
  148. subplot(2,size(pLpre,2),ip);
  149. data = squeeze(mean(LPREwind(:,:,ip),1)); % get window data
  150. data(pLpre(:,ip)>preSSRT_alpha) = 0; % mask with significance
  151. topoplot(data,chanlocs,'electrodes','off','style','map','shading','interp'); % make topooplot
  152. % topoplot(data,chanlocs,'electrodes','off'); % make topooplot
  153. %topoplot_jjf(data, chanlocs,'style','map','electrodes','on','nosedir','+X','emarker2',{find(pLpre(:,ip)<preSSRT_alpha),'o','k',7,1}); % 'electrodes','ptslabels', 'plotrad',.7
  154. clims = get(gca,'clim'); if max(abs(clims)) > max(abs(CLIMS)); CLIMS = clims; end % scale clims to max on the fly
  155. title(['L STN:' num2str(presteps(ip)*1000/srate) '-' num2str(presteps(ip+1)*1000/srate) 'ms']); % plot title
  157. % make subplot: right STN
  158. subplot(2,size(pRpre,2),size(pRpre,2)+ip);
  159. data = squeeze(mean(RPREwind(:,:,ip),1)); % get window data
  160. data(pRpre(:,ip)>preSSRT_alpha) = 0; % mask with significance
  161. topoplot(data,chanlocs,'electrodes','off','style','map','shading','interp'); % make topooplot
  162. % topoplot(data,chanlocs,'electrodes','off'); % make topooplot
  163. %topoplot_jjf(data, chanlocs,'style','map','electrodes','on','nosedir','+X','emarker2',{find(pRpre(:,ip)<preSSRT_alpha),'o','k',7,1}); % 'electrodes','ptslabels', 'plotrad',.7
  164. clims = get(gca,'clim'); if max(abs(clims)) > max(abs(CLIMS)); CLIMS = clims; end % scale clims to max on the fly
  165. title(['R STN:' num2str(presteps(ip)*1000/srate) '-' num2str(presteps(ip+1)*1000/srate) 'ms']); % plot title
  167. end
  168. % format
  169. for ip = 1:size(pRpre,2)
  170. colormap(AGF_cmap); % colormap
  171. set(gca,'clim',[-max(CLIMS) max(CLIMS)]); % color limits
  172. end
  173. % overlay a new axis to make overall figure title
  174. a = axes; t1 = title('Correlation STN and Cortex relative to SSRT');
  175. a.Visible = 'off'; t1.Visible = 'on'; set(gcf,'color','w')
  177. % open figure: postSSRT
  178. h = figure('Position',[1 1 1900 860],'color','w'); CLIMS = [0 0];
  180. % go through time windows
  181. for ip = 1:size(pLpost,2)
  183. % make subplot: left STN
  184. subplot(2,size(pLpost,2),ip);
  185. data = squeeze(mean(LPOSTwind(:,:,ip),1)); % get window data
  186. data(pLpost(:,ip)>postSSRT_alpha) = 0; % mask with significance
  187. topoplot(data,chanlocs,'electrodes','off','style','map'); % make topooplot
  188. % topoplot(data,chanlocs,'electrodes','off'); % make topooplot
  189. clims = get(gca,'clim'); if max(abs(clims)) > max(abs(CLIMS)); CLIMS = clims; end % scale clims to max on the fly
  190. title(['L STN:' num2str(poststeps(ip)*1000/srate) '-' num2str(poststeps(ip+1)*1000/srate) 'ms']);
  192. % make subplot: right STN
  193. subplot(2,size(pRpost,2),size(pRpost,2)+ip);
  194. data = squeeze(mean(RPOSTwind(:,:,ip),1)); % get window data
  195. data(pRpost(:,ip)>postSSRT_alpha) = 0; % mask with significance
  196. topoplot(data,chanlocs,'electrodes','off','style','map','shading','interp'); % make topooplot
  197. % topoplot(data,chanlocs,'electrodes','off'); % make topooplot
  198. clims = get(gca,'clim'); if max(abs(clims)) > max(abs(CLIMS)); CLIMS = clims; end % scale clims to max on the fly
  199. title(['R STN:' num2str(poststeps(ip)*1000/srate) '-' num2str(poststeps(ip+1)*1000/srate) 'ms']);
  201. end
  202. % format
  203. for ip = 1:size(pRpre,2)
  204. colormap(AGF_cmap);
  205. set(gca,'clim',[-max(CLIMS) max(CLIMS)]);
  206. end
  207. % overlay a new axis to make overall figure title
  208. a = axes; t1 = title('Correlation STN and Cortex relative to SSRT');
  209. a.Visible = 'off'; t1.Visible = 'on'; set(gcf,'color','w');
  211. % exportgraphics(figure(1), fullfile('/data/backed_up/PERCEPT_SST/CS_perceptSST/Figs/', ...
  212. % 'Fig5_topo1.eps'), 'ContentType', 'vector');
  213. % exportgraphics(figure(2), fullfile('/data/backed_up/PERCEPT_SST/CS_perceptSST/Figs/', ...
  214. % 'Fig5_topo2.eps'), 'ContentType', 'vector');
  216. %% ROI based time-lagged correlations
  217. CCroi = {'FC1','FC2','FCz','C1','C2','Cz','CP1','CP2','Cpz'};
  218. FCroi = {'F1','F2','Fz','Fp1','Fp2','Fpz','AF3','AF4'};
  219. PCroi = {'P1','P2','PO3','PO4','POz','O1','O2','Oz'};
  220. CLroi = {'FT7','FC5','FC3','TP7','CP5','CP3','T7','C5','C3'};
  221. CRroi = {'FT8','FC6','FC4','TP8','CP6','CP4','T8','C6','C4'};
  222. FLroi = {'AF7','F7','F3','F5'};
  223. FRroi = {'AF8','F8','F4','F6'};
  224. PLroi = {'P5','P7','PO7'};
  225. PRroi = {'P6','P8','PO8'};
  227. % preassign
  228. LCC = nan(length(eegfiles),diff(epochlength)*srate,diff(epochlength)*srate);
  229. LFC = LCC; LPC = LCC; LCL = LCC; LCR = LCC; LFL = LCC; LFR = LCC; LPL = LCC; LPR = LCC;
  230. RCC = nan(length(eegfiles),diff(epochlength)*srate,diff(epochlength)*srate);
  231. RFC = LCC; RPC = LCC; RCL = LCC; RCR = LCC; RFL = LCC; RFR = LCC; RPL = LCC; RPR = LCC;
  233. % go through files
  234. for is = 1:length(eegfiles)
  236. % load
  237. EEG = pop_loadset(fullfile(eeginfolder,eegfiles{is}));
  238. oEEG = EEG; % make a copy so you can replace channels with ROIs
  240. % find STN channels indices
  241. leftstn = find(~cellfun(@isempty,strfind({chanlocs.labels},'LEFT')) & cellfun(@isempty,strfind({chanlocs.labels},'EMG')));
  242. rightstn = find(~cellfun(@isempty,strfind({chanlocs.labels},'RIGHT')) & cellfun(@isempty,strfind({chanlocs.labels},'EMG')));
  243. % get labels
  244. leftstn = chanlocs(leftstn).labels;
  245. rightstn = chanlocs(rightstn).labels;
  247. % Make new datasets with ROI channels only. Then get average of activity and channel locations, and replace the original EEG data
  248. CC = pop_select(oEEG,'channel',CCroi); EEG.data(1,:) = mean(CC.data,1); chanlocs(1).labels = 'CC';
  249. FC = pop_select(oEEG,'channel',FCroi); EEG.data(2,:) = mean(FC.data,1); chanlocs(2).labels = 'FC';
  250. PC = pop_select(oEEG,'channel',PCroi); EEG.data(3,:) = mean(PC.data,1); chanlocs(3).labels = 'PC';
  251. CL = pop_select(oEEG,'channel',CLroi); EEG.data(4,:) = mean(CL.data,1); chanlocs(4).labels = 'CL';
  252. CR = pop_select(oEEG,'channel',CRroi); EEG.data(5,:) = mean(CR.data,1); chanlocs(5).labels = 'CR';
  253. FL = pop_select(oEEG,'channel',FLroi); EEG.data(6,:) = mean(FL.data,1); chanlocs(6).labels = 'FL';
  254. FR = pop_select(oEEG,'channel',FRroi); EEG.data(7,:) = mean(FR.data,1); chanlocs(7).labels = 'FR';
  255. PL = pop_select(oEEG,'channel',PLroi); EEG.data(8,:) = mean(PL.data,1); chanlocs(8).labels = 'PL';
  256. PR = pop_select(oEEG,'channel',PRroi); EEG.data(9,:) = mean(PR.data,1); chanlocs(9).labels = 'PR';
  257. % remove all but ROI data and LFP channels
  258. EEG = pop_select(EEG,'channel',{'CC','FC','PC','CL','CR','FL','FR','PL','PR',leftstn,rightstn});
  260. % check eeg integrity
  261. EEG = eeg_checkset(EEG);
  262. % delete superfluous
  263. clear oEEG
  265. % Beta power conversion
  266. for ic = 1:EEG.nbchan
  267. EEG.data(ic,:) = abs(hilbert(eegfilt(double(EEG.data(ic,:)),srate,frequencies(1),frequencies(end),0,[],0,'fir1')'))'.^2;
  268. end
  270. % find stn channel indices in new file
  271. leftstn = find(~cellfun(@isempty,strfind({chanlocs.labels},'LEFT')) & cellfun(@isempty,strfind({chanlocs.labels},'EMG')));
  272. rightstn = find(~cellfun(@isempty,strfind({chanlocs.labels},'RIGHT')) & cellfun(@isempty,strfind({chanlocs.labels},'EMG')));
  273. events = EEG.event;
  275. % get events
  276. ssrt_signal = events(strcmpi({events.type},'STOP_Global_SSRT')); % find ssrt locked stop trials
  277. ssrt_signal = ssrt_signal(strcmpi({ssrt_signal.ACC},'SS')); % successful only
  278. stgEEG = pop_epoch(EEG,'STOP_Global_SSRT',epochlength); % epoch
  279. [~,keep,keep2] = intersect(([stgEEG.event.nr]),[ssrt_signal.nr]); % retain trials that weren't removed based on EEG
  280. stgEEG = pop_select(stgEEG,'trial',[stgEEG.event(keep).epoch]); % keep only those trials
  281. ssrt_signal = ssrt_signal(keep2); % keep only those trials
  283. % get events
  284. ignore_signal = events(strcmpi({events.type},'IGNORE_Global_SSRT')); % find ssrt matched ignore trials
  285. ignore_signal = ignore_signal(strcmpi({ignore_signal.ACC},'CORRECT')); % correct only
  286. ignEEG = pop_epoch(EEG,'IGNORE_Global_SSRT',epochlength); % epoch
  287. [~,keep,keep2] = intersect(([ignEEG.event.nr]),[ignore_signal.nr]); % retain trials that weren't removed based on EEG
  288. ignEEG = pop_select(ignEEG,'trial',[ignEEG.event(keep).epoch]); % keep only those trials
  289. ignore_signal = ignore_signal(keep2); % keep only those trials
  291. % get events
  292. select_signal = events(strcmpi({events.type},'STOP_Select_SSRT')); % find ssrt matched select stop trials
  293. select_signal = select_signal(strcmpi({select_signal.ACC},'SS')); % successful only
  294. stsEEG = pop_epoch(EEG,'STOP_Select_SSRT',epochlength); % epoch
  295. [~,keep,keep2] = intersect(([stsEEG.event.nr]),[select_signal.nr]); % retain trials that weren't removed based on EEG
  296. stsEEG = pop_select(stsEEG,'trial',[stsEEG.event(keep).epoch]); % keep only those trials
  297. select_signal = select_signal(keep2); % keep only those trials
  299. % merge
  300. allEEG = pop_mergeset(stsEEG,ignEEG); % merge select stop and ignore
  301. allEEG = pop_mergeset(allEEG,stgEEG); % merge global stop with that
  303. % go through lag samples and correlate
  304. for it1 = 1:allEEG.pnts
  305. for it2 = 1:allEEG.pnts
  306. % get ROI EEG data and zscore
  307. dataCC = zscore(squeeze(allEEG.data(1,it1,:)));
  308. dataFC = zscore(squeeze(allEEG.data(2,it1,:)));
  309. dataPC = zscore(squeeze(allEEG.data(3,it1,:)));
  310. dataCL = zscore(squeeze(allEEG.data(4,it1,:)));
  311. dataCR = zscore(squeeze(allEEG.data(5,it1,:)));
  312. dataFL = zscore(squeeze(allEEG.data(6,it1,:)));
  313. dataFR = zscore(squeeze(allEEG.data(7,it1,:)));
  314. dataPL = zscore(squeeze(allEEG.data(8,it1,:)));
  315. dataPR = zscore(squeeze(allEEG.data(9,it1,:)));
  316. % get LFP data and zscore
  317. dataR = zscore(squeeze(allEEG.data(rightstn,it2,:)));
  318. dataL = zscore(squeeze(allEEG.data(leftstn,it2,:)));
  319. % store. EEG data along first dim, LFP data along second dim
  320. LCC(is,it1,it2) = corr(dataCC,dataL);
  321. LFC(is,it1,it2) = corr(dataFC,dataL);
  322. LPC(is,it1,it2) = corr(dataPC,dataL);
  323. LCL(is,it1,it2) = corr(dataCL,dataL);
  324. LCR(is,it1,it2) = corr(dataCR,dataL);
  325. LFL(is,it1,it2) = corr(dataFL,dataL);
  326. LFR(is,it1,it2) = corr(dataFR,dataL);
  327. LPL(is,it1,it2) = corr(dataPL,dataL);
  328. LPR(is,it1,it2) = corr(dataPR,dataL);
  329. RCC(is,it1,it2) = corr(dataCC,dataR);
  330. RFC(is,it1,it2) = corr(dataFC,dataR);
  331. RPC(is,it1,it2) = corr(dataPC,dataR);
  332. RCL(is,it1,it2) = corr(dataCL,dataR);
  333. RCR(is,it1,it2) = corr(dataCR,dataR);
  334. RFL(is,it1,it2) = corr(dataFL,dataR);
  335. RFR(is,it1,it2) = corr(dataFR,dataR);
  336. RPL(is,it1,it2) = corr(dataPL,dataR);
  337. RPR(is,it1,it2) = corr(dataPR,dataR);
  338. end
  339. end
  341. end
  342. save('/data/backed_up/PERCEPT_SST/ROIcorrelations.mat','L*','R*');
  343. %% Cluster-corrected statistics
  344. clear p;
  345. load('/data/backed_up/PERCEPT_SST/ROIcorrelations.mat');
  347. % settings
  348. pixel_p = .05/18; % pixel wise alpha, corrected for number of ROIs
  349. cluster_p = .05/18; % cluster wise alpha, corrected for number of ROIs
  350. nPerm = 5000; % number of permutations
  352. % fisher's z
  353. LCC = atanh(LCC); LFC = atanh(LFC); LPC = atanh(LPC);
  354. LCL = atanh(LCL); LFL = atanh(LFL); LPL = atanh(LPL);
  355. LCR = atanh(LCR); LFR = atanh(LFR); LPR = atanh(LPR);
  356. RCC = atanh(RCC); RFC = atanh(RFC); RPC = atanh(RPC);
  357. RCL = atanh(RCL); RFL = atanh(RFL); RPL = atanh(RPL);
  358. RCR = atanh(RCR); RFR = atanh(RFR); RPR = atanh(RPR);
  360. % run
  361. p.LCC = cPERM2(LCC, zeros(size(LCC)), nPerm, pixel_p, cluster_p);
  362. p.LFC = cPERM2(LFC, zeros(size(LFC)), nPerm, pixel_p, cluster_p);
  363. p.LPC = cPERM2(LPC, zeros(size(LPC)), nPerm, pixel_p, cluster_p);
  364. p.LCL = cPERM2(LCL, zeros(size(LCL)), nPerm, pixel_p, cluster_p);
  365. p.LCR = cPERM2(LCR, zeros(size(LCR)), nPerm, pixel_p, cluster_p);
  366. p.LFL = cPERM2(LFL, zeros(size(LFL)), nPerm, pixel_p, cluster_p);
  367. p.LFR = cPERM2(LFR, zeros(size(LFR)), nPerm, pixel_p, cluster_p);
  368. p.LPL = cPERM2(LPL, zeros(size(LPL)), nPerm, pixel_p, cluster_p);
  369. p.LPR = cPERM2(LPR, zeros(size(LPR)), nPerm, pixel_p, cluster_p);
  370. p.RCC = cPERM2(RCC, zeros(size(RCC)), nPerm, pixel_p, cluster_p);
  371. p.RFC = cPERM2(RFC, zeros(size(RFC)), nPerm, pixel_p, cluster_p);
  372. p.RPC = cPERM2(RPC, zeros(size(RPC)), nPerm, pixel_p, cluster_p);
  373. p.RCL = cPERM2(RCL, zeros(size(RCL)), nPerm, pixel_p, cluster_p);
  374. p.RCR = cPERM2(RCR, zeros(size(RCR)), nPerm, pixel_p, cluster_p);
  375. p.RFL = cPERM2(RFL, zeros(size(RFL)), nPerm, pixel_p, cluster_p);
  376. p.RFR = cPERM2(RFR, zeros(size(RFR)), nPerm, pixel_p, cluster_p);
  377. p.RPL = cPERM2(RPL, zeros(size(RPL)), nPerm, pixel_p, cluster_p);
  378. p.RPR = cPERM2(RPR, zeros(size(RPR)), nPerm, pixel_p, cluster_p);
  380. save('/data/backed_up/PERCEPT_SST/ROIcorrelationsP.mat','p');
  382. %% figure of ROI corrs
  383. close all;
  385. % settings
  386. ssrt = mean([GSssrt.SSRT_integ]); % get sample mean SSRT
  387. lw = 1.5; % line width for significance plot
  388. % load
  389. load(fullfile('/data/backed_up/PERCEPT_SST/ROIcorrelations.mat'));
  390. load(fullfile('/data/backed_up/PERCEPT_SST/ROIcorrelationsP.mat'));
  392. % fisher's z
  393. LCC = atanh(LCC); LFC = atanh(LFC); LPC = atanh(LPC);
  394. LCL = atanh(LCL); LFL = atanh(LFL); LPL = atanh(LPL);
  395. LCR = atanh(LCR); LFR = atanh(LFR); LPR = atanh(LPR);
  396. RCC = atanh(RCC); RFC = atanh(RFC); RPC = atanh(RPC);
  397. RCL = atanh(RCL); RFL = atanh(RFL); RPL = atanh(RPL);
  398. RCR = atanh(RCR); RFR = atanh(RFR); RPR = atanh(RPR);
  400. % Left figure first
  401. ROIs = {'LFL','LFC','LFR','LCL','LCC','LCR','LPL','LPC','LPR'}; % define ROIs
  402. h = figure('Position',[1 1 1000 1000],'Color','w'); % open figure
  403. clims = nan(length(ROIs),2); % preassing clim matrix
  404. % go through ROIs and plot
  405. for ir = 1:length(ROIs)
  406. % open subplot
  407. subplot(3,3,ir);
  408. % get data
  409. data = eval(['squeeze(mean(' ROIs{ir} ',1));']); % data
  410. eval(['px = p.' ROIs{ir} ';']); % pvalue
  411. % plot data
  412. contourf(1:1000,1:1000,data,60,'linecolor','none'); hold; % plot data
  413. % pcolor(data); hold; % plot data
  414. contour(px,[-.5 .5],'k','LineWidth',lw); % plot significance contour
  415. clims(ir,:) = get(gca,'clim'); % store clims
  416. title(ROIs{ir}); % put ROI in title
  417. %format
  418. % shading interp; % shading
  419. colormap(AGF_cmap); % colormap
  420. xlabel('STN: time (ms)'); % x label
  421. ylabel('EEG: time (ms)'); % y label
  422. % ticks
  423. set(gca,'XTick',[1 abs(epochlength(1))*srate+ssrt*srate/1000 diff(epochlength)*srate],'Xticklabel',{'S','SSRT',num2str(epochlength(2)*1000)});
  424. set(gca,'YTick',[1 abs(epochlength(1))*srate+ssrt*srate/1000 diff(epochlength)*srate],'Yticklabel',{'S','SSRT',num2str(epochlength(2)*1000)});
  425. % lines
  426. line([abs(epochlength(1))*srate+ssrt*srate/1000 abs(epochlength(1))*srate+ssrt*srate/1000],[1 diff(epochlength)*srate],'Color','k','LineWidth',.5,'LineStyle',':');
  427. line([1 diff(epochlength)*srate],[abs(epochlength(1))*srate+ssrt*srate/1000 abs(epochlength(1))*srate+ssrt*srate/1000],'Color','k','LineWidth',.5,'LineStyle',':');
  428. line([1 diff(epochlength)*srate],[1 diff(epochlength)*srate],'Color','k','LineWidth',.5,'LineStyle','-');
  429. % limits
  430. set(gca,'Xlim',[abs(epochlength(1))*srate diff(epochlength)*srate]);
  431. set(gca,'Ylim',[abs(epochlength(1))*srate diff(epochlength)*srate]);
  432. end
  433. % go through again and set climits to common max
  434. for ir = 1:length(ROIs)
  435. subplot(3,3,ir);
  436. set(gca,'Clim',[-max(max(abs(clims))) max(max(abs(clims)))]);
  437. end
  439. % Right figure second
  440. ROIs = {'RFL','RFC','RFR','RCL','RCC','RCR','RPL','RPC','RPR'}; % define ROIs
  441. h = figure('Position',[1 1 1000 1000],'Color','w'); % open figure
  442. clims = nan(length(ROIs),2); % preassing clim matrix
  446. % go through ROIs and plot
  447. for ir = 1:length(ROIs)
  448. % open subplot
  449. subplot(3,3,ir);
  450. % get data
  451. data = eval(['squeeze(mean(' ROIs{ir} ',1));']); % data
  452. eval(['px = p.' ROIs{ir} ';']); % pvalue
  453. % plot data
  454. contourf(1:1000,1:1000,data,60,'linecolor','none'); hold; % plot data
  455. % pcolor(data); hold; % plot data
  456. contour(px,[-.5 .5],'k','LineWidth',lw); % plot significance contour
  457. clims(ir,:) = get(gca,'clim'); % store clims
  458. title(ROIs{ir}); % put ROI in title
  459. %format
  460. % shading interp; % shading
  461. colormap(AGF_cmap); % colormap
  462. xlabel('STN: time (ms)'); % x label
  463. ylabel('EEG: time (ms)'); % y label
  464. % ticks
  465. set(gca,'XTick',[1 abs(epochlength(1))*srate+ssrt*srate/1000 diff(epochlength)*srate],'Xticklabel',{'S','SSRT',num2str(epochlength(2)*1000)});
  466. set(gca,'YTick',[1 abs(epochlength(1))*srate+ssrt*srate/1000 diff(epochlength)*srate],'Yticklabel',{'S','SSRT',num2str(epochlength(2)*1000)});
  467. % lines
  468. line([abs(epochlength(1))*srate+ssrt*srate/1000 abs(epochlength(1))*srate+ssrt*srate/1000],[1 diff(epochlength)*srate],'Color','k','LineWidth',.5,'LineStyle',':');
  469. line([1 diff(epochlength)*srate],[abs(epochlength(1))*srate+ssrt*srate/1000 abs(epochlength(1))*srate+ssrt*srate/1000],'Color','k','LineWidth',.5,'LineStyle',':');
  470. line([1 diff(epochlength)*srate],[1 diff(epochlength)*srate],'Color','k','LineWidth',.5,'LineStyle','-');
  471. % limits
  472. set(gca,'Xlim',[abs(epochlength(1))*srate diff(epochlength)*srate]);
  473. set(gca,'Ylim',[abs(epochlength(1))*srate diff(epochlength)*srate]);
  474. end
  475. % go through again and set climits to common max
  476. for ir = 1:length(ROIs)
  477. subplot(3,3,ir);
  478. set(gca,'Clim',[-max(max(abs(clims))) max(max(abs(clims)))]);
  479. end
  482. % exportgraphics(figure(1), fullfile('/data/backed_up/PERCEPT_SST/CS_perceptSST/Figs/', ...
  483. % 'Fig5_PLV1.eps'), 'ContentType', 'vector');
  484. %
  485. % exportgraphics(figure(2), fullfile('/data/backed_up/PERCEPT_SST/CS_perceptSST/Figs/', ...
  486. % 'Fig5_PLV2.eps'), 'ContentType', 'vector');
  489. %% TOPO plot with channels no data (CS added)
  490. % EEG = pop_loadset(fullfile(eeginfolder,eegfiles{1}));
  492. CCroi = {'FC1','FC2','FCz','C1','C2','Cz','CP1','CP2','Cpz'};
  493. FCroi = {'F1','F2','Fz','Fp1','Fp2','Fpz','AF3','AF4'};
  494. PCroi = {'P1','P2','PO3','PO4','POz','O1','O2','Oz'};
  495. CLroi = {'FT7','FC5','FC3','TP7','CP5','CP3','T7','C5','C3'};
  496. CRroi = {'FT8','FC6','FC4','TP8','CP6','CP4','T8','C6','C4'};
  497. FLroi = {'AF7','F7','F3','F5'};
  498. FRroi = {'AF8','F8','F4','F6'};
  499. PLroi = {'P5','P7','PO7'};
  500. PRroi = {'P6','P8','PO8'};
  502. dsize = 100;
  504. figure('Position',[1 1 1000 800]);
  505. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  506. 'plotchans',find(matches({EEG.chanlocs.labels},FCroi)),...
  507. 'emarker',{'.',[1 0 0],dsize,1}); axis square; hold on;
  509. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  510. 'plotchans',find(matches({EEG.chanlocs.labels},FLroi)),...
  511. 'emarker',{'.',[1 .5 0],dsize,1}); axis square;
  513. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  514. 'plotchans',find(matches({EEG.chanlocs.labels},FRroi)),...
  515. 'emarker',{'.',[1 0 .5 ],dsize,1}); axis square;
  517. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  518. 'plotchans',find(matches({EEG.chanlocs.labels},CCroi)),...
  519. 'emarker',{'.',[0 1 0],dsize,1}); axis square;
  521. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  522. 'plotchans',find(matches({EEG.chanlocs.labels},CLroi)),...
  523. 'emarker',{'.',[1 1 0],dsize,1}); axis square;
  525. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  526. 'plotchans',find(matches({EEG.chanlocs.labels},CRroi)),...
  527. 'emarker',{'.',[0 1 1],dsize,1}); axis square;
  530. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  531. 'plotchans',find(matches({EEG.chanlocs.labels},PCroi)),...
  532. 'emarker',{'.',[0 0 1],dsize,1});
  534. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  535. 'plotchans',find(matches({EEG.chanlocs.labels},PLroi)),...
  536. 'emarker',{'.',[0 .5 1],dsize,1});
  537. topoplot(zeros(EEG.nbchan,1), EEG.chanlocs,'electrodes','on', ...
  538. 'plotchans',find(matches({EEG.chanlocs.labels},PRroi)),...
  539. 'emarker',{'.',[.5 0 1],dsize,1});
  544. print(fullfile('/data/backed_up/PERCEPT_SST/CS_perceptSST/Figs/','Fig5_emptyTOPO.eps'), ...
  545. '-depsc2','-r300','-painters');

Step2BetaCorrelations.m, no license · at the source

Overview

Authors: Cheol Soh1,2,3, Mario Hervault4, Nathan H. Chalkley1,2, Kien Huynh1,3, Qiang Zhang2, Ergun Y. Uc2,5, Jeremy D. W. Greenlee6, Jan R. Wessel1,2,3
ORCID iDs: Jan R. Wessel
  1. Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa, United States of America
  2. Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States of America
  3. Cognitive Control Collaborative, University of Iowa, Iowa City, Iowa, United States of America
  4. University of Grenoble, Grenoble, France
  5. Neurology Service, Iowa City VA Medical Center, Iowa City, Iowa, United States of America
  6. Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States of America
Institutions: University of Iowa (United States); University of Iowa Hospitals and Clinics (United States); Pôle de recherche et d'enseignement supérieur Université de Grenoble (France); Iowa City VA Medical Center (United States)
Journal: PLoS biology, volume 24, issue 4, article e3003635
Dates: received 7 January 2026; accepted 8 April 2026; published online 24 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003635 · PMID 42030369 · PMCID PMC13132434 · OpenAlex W7155541812
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging, Physiology & signal measures
MeSH: Cerebral Cortex*, Subthalamic Nucleus*, Electroencephalography, Female, Humans, Male, Movement, Neural Pathways (* major topic)
Journal subjects: Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Biology and Life Sciences, Physiology, Electrophysiology, Neurophysiology, Neuroscience, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Muscle Electrophysiology, Electromyography, Cell Biology, Signal Transduction, Cell Signaling, Signal Inhibition, Anatomy, Head, Scalp, Brain, Motor Cortex, Engineering and Technology, Signal Processing, Signal Filtering, Medical Conditions, Neurodegenerative Diseases, Movement Disorders, Parkinson Disease, Neurology, Cognitive Science, Cognitive Neuroscience, Reaction Time
Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex
Funding: HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (2R01NS117753); Clement T. and Sylvia H. Hanson Family (Endowment); Todd and Val Meyerhoff through the Kevin Dill Golf Tournament for Dementia, Parkinson’s and Veterans (Donation)
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

Response inhibition is an important cognitive control mechanism that enables flexible behavior by stopping inappropriate actions. Intracranial recordings across species have identified a neural circuit that implements response inhibition via the subthalamic nucleus of the basal ganglia. However, this work has been limited to simple tasks, in which unequivocal, salient “stop”-signals require the inhibition of all ongoing responses. Notably, response inhibition in the real world is substantially different. Real-world response inhibition is selective: it occurs only after specific salient signals (‘stimulus-selectivity’) and stops only specific movements while others continue (‘response-selectivity’). If and how the fronto-subthalamic system implements selective inhibition is largely unknown. Here, we recorded subthalamic local field potentials and scalp-EEG in humans performing a novel, selective inhibition task. Salient signals either required stopping all initiated responses (global inhibition), stopping only some responses (response-selective inhibition), or continuing all responses—i.e., ignoring the signal (which ensures stimulus-selectivity). All three signals initially triggered a common fronto-subthalamic inhibitory process, signified by a rapid increase in β-burst activity. During global inhibition, subthalamic β-bursting subsequently increased above baseline, persisting for over a second. During response-selective inhibition, this activity was delayed, which enabled a second bout of disinhibition and allowed appropriate responses to continue. Throughout this period, frontal cortical and subthalamic β-band activity were tightly coupled. This shows that selective inhibition is accompanied by rapid, context-dependent engagement and release of fronto-subthalamic inhibition. Moreover, subthalamic activity lasted substantially longer than assumed by classic behavioral-computational models. This supports recent theoretical models that assume protracted response inhibition during action-stopping.

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 5 matches between paragraphs and lines of code.

OSF bjd9n

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (14)
Size: 47 files, 14 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (8 files), Statistics and Machine Learning Toolbox (8 files), Signal Processing Toolbox (3 files), Image Processing Toolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
14 files
At the source: osf.io/bjd9n

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;
  • 14 scripts, each with its path and the digest of its content;
  • 5 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

All data and analysis code files are available from the OSF at https://osf.io/bjd9n.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 8 MeSH terms, 3 funders, 82 references.

Cite

This paper

Soh, C., Hervault, M., Chalkley, N. H., Huynh, K., Zhang, Q., Uc, E. Y., Greenlee, J. D. W., & Wessel, J. R. (2026). A cortico-subthalamic circuit rapidly engages and releases inhibition of specific movements depending on the environmental context. PLoS biology, 24(4), e3003635. https://doi.org/10.1371/journal.pbio.3003635

BibTeX

@article{soh2026cortico,
author = {Soh, Cheol and Hervault, Mario and Chalkley, Nathan H. and Huynh, Kien and Zhang, Qiang and Uc, Ergun Y. and Greenlee, Jeremy D. W. and Wessel, Jan R.},
title = {{A cortico-subthalamic circuit rapidly engages and releases inhibition of specific movements depending on the environmental context}},
journal = {PLoS biology},
year = {2026},
month = apr,
volume = {24},
number = {4},
pages = {e3003635},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003635},
url = {https://doi.org/10.1371/journal.pbio.3003635},
pmid = {42030369},
pmcid = {PMC13132434}
}

RIS

TY - JOUR
AU - Soh, Cheol
AU - Hervault, Mario
AU - Chalkley, Nathan H.
AU - Huynh, Kien
AU - Zhang, Qiang
AU - Uc, Ergun Y.
AU - Greenlee, Jeremy D. W.
AU - Wessel, Jan R.
TI - A cortico-subthalamic circuit rapidly engages and releases inhibition of specific movements depending on the environmental context
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/04/24
VL - 24
IS - 4
SP - e3003635
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003635
UR - https://doi.org/10.1371/journal.pbio.3003635
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pbio.3003635",
"type": "article-journal",
"title": "A cortico-subthalamic circuit rapidly engages and releases inhibition of specific movements depending on the environmental context",
"container-title": "PLoS biology",
"author": [
{
"family": "Soh",
"given": "Cheol"
},
{
"family": "Hervault",
"given": "Mario"
},
{
"family": "Chalkley",
"given": "Nathan H."
},
{
"family": "Huynh",
"given": "Kien"
},
{
"family": "Zhang",
"given": "Qiang"
},
{
"family": "Uc",
"given": "Ergun Y."
},
{
"family": "Greenlee",
"given": "Jeremy D. W."
},
{
"family": "Wessel",
"given": "Jan R."
}
],
"container-title-short": "PLoS Biol",
"volume": "24",
"issue": "4",
"page": "e3003635",
"DOI": "10.1371/journal.pbio.3003635",
"PMID": "42030369",
"PMCID": "PMC13132434",
"ISSN": "1544-9173",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pbio.3003635",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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