OSCR

The role of fear learning in the development of psychosis: an EEG study utilizing a differential fear conditioning paradigm in people with psychotic vulnerability.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods and materials › EEG and FPS data acquisition and processing ↔ Codes/LPP/LPP_preprocessing_afterICA.m, lines 201–234 · score 0.69 · channel deviation, variance, rejection, EEGLAB, Preprocessing, amplitude
  2. [2] § Methods and materials › EEG and FPS data acquisition and processing ↔ Codes/LPP/LPP_preprocessing_untilICA.m, lines 363–406 · score 0.59 · segmented, rejected, Component, EEGLAB, Preprocessing, filtered
  3. [3] § Methods and materials › Statistical analyses ↔ Codes/Statistics/Statistics_LMER.R, lines 3610–3663 · score 0.52 · Pairwise comparisons, emmeans, linear, models, LPPs

Paper

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The authors' code

MATLAB · 451 lines · 19 KB · no license · 1 match

  1. %% ANALYSIS SCRIPT - ***EEG*** - PROOF - Classical Paradigm (Real Data) / Metin Ozyagcilar / PREPROCESSING AFTER ICA / NOV 2023
  3. %% TOOLBOXES / PLUG-INs
  5. % (1) EEGLAB (v2023.1)
  6. % Delorme A & Makeig S (2004) EEGLAB: an open-source toolbox for analysis of single-trial EEG dynamics,
  7. % Journal of Neuroscience Methods 134:9-21
  9. %%
  11. clear all; close all; clc;
  14. %% DEFINE FOLDERS
  16. % mainpath = 'D:\PROOF';
  17. %mainpath = 'C:\Users\metin\Desktop\PROOF_EEGSCRFPS_Analysis\AnalysisHome'; % if you work laptop
  18. % mainpath ='C:\Users\metin\Desktop\PROOF_EEGSCRFPS_Analysis\AnalysisHome'; % if youwork on Uni comp
  19. mainpath = 'D:\PROOF' % if you work on harddisk
  20. path_eeglab = [mainpath, '\eeglab2023.1']; % where eeglab is located
  21. %path_eeglab = [mainpath, '\eeglab2022.0']; % where eeglab is located
  22. path_rawdata = [mainpath, '\Real\Raw\']; % where raw data is located
  23. path_preprocessed = [mainpath, '\Real\Preprocessed\']; % where pre-processed data is saved
  24. path_condspecific = [mainpath, '\Real\Epoched\']; % where pre-processed data is saved
  27. %% EXTRACT SUBJECT IDs
  29. % Extract subject IDs (CHECKS ALL OF THE RAW DATA IN THE FOLDER):
  30. %cd (path_rawdata)
  31. %sub = dir('*.vhdr');
  32. %sub = {sub.name}; % subject IDs are stored here
  33. %for i = 1:length(sub)
  34. %sub{i} = sub{i}(1:7); % remove .vhdr extension and only keep the subject IDs
  35. %end
  36. %clear i;
  38. % LOAD SUBJECT IDs
  39. %load([mainpath, '\THESIS\Analysis\files.mat'], 'files');
  41. % DEFINE SUBJECT IDs MANUALLY
  42. sub = {'CF_189' 'CF_193'}
  43. %% TRIGGERS:
  45. % 20: CS- / 24: CS+ / 21: GS- / 23: GS+ / 22: GSU % keep writing here[1
  47. %% DEFINE PARAMETERS
  50. irr_1 = 'SCR'; % irrelevant channel 1
  51. irr_2 = 'Startle'; % irrelevant channel 2
  52. irr_3 = 'EKG'; % irrelevant channel 2 % +++ CHECK THAT AGAIN +++
  53. irr_4 = 'IO2' % irrelevant channel 3
  54. highpass = .01; % cut-off for the first high-pass filter
  55. highpass_ica = 1; % cut-off for the high-pass filter to be applied for ICA preparetaion
  56. lowpass = 30; % cut-off for the low-pass filter
  57. %notch_1 = 45; % first cut-off for the notch filter
  58. %notch_2 = 55; % second cut-off for the notch filter
  60. events_phase_wholeacq = {'S 2021' 'S 2022' 'S 2421' 'S 2422'};
  61. conds_phase_wholeacq = {'ACSMComb', 'ACSPComb'};
  63. events_phase_wholeext= {'S 2041' 'S 2042' 'S 2043' 'S 2441' 'S 2442' 'S 2443'};
  64. conds_phase_wholeext= {'ECSMComb', 'ECSPComb'};
  66. events_phase = {'S 201' 'S 241' 'S 2021' 'S 2421' 'S 2022' 'S 2422' 'S 203' 'S 213' 'S 223' 'S 233' 'S 243' 'S 2041' 'S 2441' 'S 2042' 'S 2442' ...
  67. 'S 2043' 'S 2443' 'S 205' 'S 245'}; % short version
  68. conds_phase = {'HCSM','HCSP','ACSMFirst', 'ACSPFirst', 'ACSMSecond', 'ACSPSecond', 'GCSM', 'GGSM', 'GGSU', 'GGSP', 'GCSP', ...
  69. 'ECSMFirst', 'ECSPFirst', 'ECSMSecond', 'ECSPSecond', 'ECSMThird', 'ECSPThird', 'ROFCSM', 'ROFCS+'}; % short version / G2 = RO
  71. %TEMP
  72. %events_phase = {'S 2021' 'S 2421' 'S 2022' 'S 2422' 'S 203' 'S 213' 'S 223' 'S 233' 'S 243' 'S 2041' 'S 2441' 'S 2042' 'S 2442' ...
  73. % 'S 2043' 'S 2443' 'S 205' 'S 245'}; % short version
  74. %conds_phase = {'ACSMFirst', 'ACSPFirst', 'ACSMSecond', 'ACSPSecond', 'GCSM', 'GGSM', 'GGSU', 'GGSP', 'GCSP', ...
  75. % 'ECSMFirst', 'ECSPFirst', 'ECSMSecond', 'ECSPSecond', 'ECSMThird', 'ECSPThird', 'ROFCSM', 'ROFCS+'}; % short version / G2 = RO
  77. epoch_start = -0.4;
  78. epoch_end = 2.6;
  79. base_start = -200;
  80. nobad = 0;
  82. %badcompz ={[1,3,'CF_001'], [1,2,'CF_002'],[1,4,'CF_003'], [1,4,'CF_004'], [1,2,5 'CF_006'], [1,7,'CF_0047'], ...
  83. % [1,2,'CF_009'], [1,5,'CF_013'], [1,5,'CF_015'], [1,2,'CF_016'], [1,2, 3,'CF_020'], [1,3,'CF_021'], [1,4,'CF_022'], ...
  84. % [1,2,'CF_023'], [1,3,'CF_024'], [1,4,'CF_026'], [1,4,'CF_027_02'], [1,5,'CF_028'], [1,3,'CF_031'], [1,4,'CF_032'], ...
  85. % [1,3,'CF_033'], [1,4,'CF_034'], [1,8,'CF_035'], [1,5,'CF_036'], [1,3,'CF_038'], [1,2,'CF_039'], [1,6,'CF_41'], ...
  86. % [2,3,5,'CF_043'], [2,3,'CF_044'], [1,5,'CF_045'], [1,3,'CF_046'], [1,7,'CF_048'], [1,3,'CF_049'], [1,4,'CF_051'], ...
  87. %[1,2,'CF_052'], [1,3,'CF_054'], [1,3,'CF_055'], [1,3,'CF_056'], [1,2,'CF_057'], [1,3,'CF_058'], ...
  88. %[1,2,'CF_061'], [1,3,'CF_063'], [1,3 'CF_064'],}; % TEMP for automatic comp rejection in the loop with predefined comp indexes
  90. %% PREPROCESSING AFTER ICA
  92. a = 0; % create and index variable here to create seperate datasets on EEGLAB after each step
  94. for i = 1:length(sub); % loops through subjects
  95. %% OCULAR CORRECTION (ICA) - REJECT BAD COMPONENTS
  97. cd(path_eeglab);
  98. eeglab; % first, re-start the eeglab
  99. eeglab redraw
  101. EEG = pop_loadset('filename',[sub{i}, '_icaed', '.set'],'filepath', [path_preprocessed, '\', sub{i}]);
  103. % pop_topoplot(EEG,0,[1:size(EEG.icawinv,2)],EEG.setname,[9 9] ,0,'electrodes','on'); % plot the components
  104. pop_selectcomps(EEG, [1:size(EEG.icawinv,2)] ); % plot the components - this one is better because you can also click on them to see the properties
  105. pop_eegplot(EEG, 0, 1, 1); % ?
  106. EEG.badcomps = input('Enter bad component indices [] : '); % enter bad component indices (after visually inspecting them on the plot)
  107. badcomp = EEG.badcomps;
  108. EEG = pop_subcomp(EEG, badcomp, 0); % bye bye bad components
  109. %EEG = pop_subcomp(EEG, badcompz{i}, 0); % bye bye bad components % TEMP for automatic comp rejection in the loop with predefined comp indexes
  110. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_icapruned'],'savenew',[path_preprocessed,'\\', ...
  111. sub{i} '\\', sub{i}, '_icapruned', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  112. eeglab redraw;
  113. a = a+1; % increase a by 1 so that it creates a different dataset on the next ste
  116. %for i = 1:length(sub); % loops through subjects % TEMP
  117. %%
  118. for r = 1:2 % (first rerefav then rerefmast, with another inner loop starting here)
  120. cd(path_eeglab);
  121. eeglab; % first, re-start the eeglab
  122. eeglab redraw
  124. a = 0; % re-set the index variable a
  126. EEG = pop_loadset('filename',[sub{i}, '_icapruned', '.set'],'filepath', [path_preprocessed, '\', sub{i}]);
  128. % EEG = pop_loadset('filename',[sub{i}, '_50hzremoved', '.set'],'filepath', [path_preprocessed, '\', sub{i}]);
  129. % TEMP for comparing data with & withouth eyeblinks removed
  131. if r == 1;
  132. %% RE-REFERENE (AVERAGE)
  134. chan_IO_reref_av = find(strcmpi('IO1',{EEG.chanlocs.labels})); % find the channel index of IO1
  136. EEG = pop_reref( EEG, [],'exclude', chan_IO_reref_av);
  137. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefav'],'savenew',[path_preprocessed,'\\', ...
  138. sub{i} '\\', sub{i}, '_rerefav', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  139. eeglab redraw;
  140. a = a+1; % increase a by 1 so that it creates a different dataset on the next ste
  142. end
  144. if r == 2;
  145. %% RE-REFERENCE (MASTOIDS)
  147. chan_IO_reref_mast = find(strcmpi('IO1',{EEG.chanlocs.labels})); % find the channel index of IO1
  149. chan_mast_1 = find(strcmpi('T9',{EEG.chanlocs.labels})); % find the channel index of the mastoid elec 1
  150. chan_mast_2 = find(strcmpi('T10',{EEG.chanlocs.labels})); % find the channel index of the mastoid elec 2
  152. EEG = pop_reref( EEG, [chan_mast_1 chan_mast_2], 'exclude', chan_IO_reref_mast);
  153. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefmast'],'savenew',[path_preprocessed,'\\', ...
  154. sub{i} '\\', sub{i}, '_rerefmast', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  155. eeglab redraw;
  156. a = a+1; % increase a by 1 so that it creates a different dataset on the next ste
  158. end
  160. end
  162. %for i = 1:length(sub); % loops through subjects % TEMP
  163. %a = 0; %TEMP
  165. %% CREATE EPOCHS
  167. for z = 1:2 % load the datasets (load first rerefav then rerefmast and do the rest, with another loop starting here)
  169. eeglab; % first, re-start the eeglab
  170. eeglab redraw
  172. a = 0; % re-set the index variable a
  174. if z == 1; % for rerefav dataset
  175. EEG = pop_loadset('filename',[sub{i}, '_rerefav', '.set'],'filepath', [path_preprocessed, '\', sub{i}]); % this may not work check it
  176. end
  178. if z == 2; % for rerefmast dataset
  179. EEG = pop_loadset('filename',[sub{i}, '_rerefmast', '.set'],'filepath', [path_preprocessed, '\', sub{i}]); % this may not work check it
  180. end
  182. EEG = pop_epoch(EEG, events_phase, [epoch_start epoch_end], 'newname', [sub{i}, '_epoched'], 'epochinfo', 'yes'); % create epochs
  183. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'gui','off');
  185. if z == 1;
  186. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefav', '_epoched'],'savenew',[path_preprocessed,'\\', ...
  187. sub{i}, '\\', sub{i}, '_rerefav', '_epoched', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  188. eeglab redraw;
  189. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  190. end
  192. if z == 2;
  193. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefmast', '_epoched'],'savenew',[path_preprocessed,'\\', ...
  194. sub{i}, '\\', sub{i}, '_rerefmast', '_epoched', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  195. eeglab redraw;
  196. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  197. end
  199. EEG = eeg_checkset( EEG );
  200. eeglab redraw;
  201. %% ARTEFACT REJECTION
  203. % (Criteria: amplitude, variance and channel deviation larger than 3-z scores)
  205. % +++ maybe add again a temporary loop here to change stuff post processesing?
  207. if nobad == 0;
  209. datachan = [1:size(EEG.data,1)];
  210. list_props = epoch_properties(EEG,datachan); % determine contaminated epochs
  211. marked_trials = find(min_z(list_props,prep_rej_opt(list_props,3))); % store indices of contaminated epochs
  212. bad_trials = zeros(1,EEG.trials);
  213. bad_trials(marked_trials) = 1; % index to bad trials in the data
  214. EEG = pop_rejepoch(EEG,bad_trials,0); % bad trials are removed
  215. eeglab redraw;
  217. if z == 1;
  218. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefav', '_badtrialsrejected'],'savenew',[path_preprocessed,'\\', ...
  219. sub{i}, '\\', sub{i}, '_rerefav', '_badtrialsrejected', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  220. eeglab redraw;
  221. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  222. end
  224. if z == 2;
  225. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefmast', '_badtrialsrejected'],'savenew',[path_preprocessed,'\\', ...
  226. sub{i}, '\\', sub{i}, '_rerefmast', '_badtrialsrejected', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  227. eeglab redraw;
  228. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  229. end
  231. elseif nobad == 1;
  233. end
  234. eeglab redraw;
  236. %% ARTEFACT REJECTION Option 2 EPOCH INT
  238. % Option A
  240. % EEG = pop_eegmaxmin(EEG, [],[], 75, [], 1, 0);
  242. % EEG = pop_TBT(EEG, EEG.reject.rejmaxminE , 10, 0.15, 1); % can I put here the last stuff from below [] EEG.chanlocs? ASK?
  244. % ~~ %
  246. % Option B (To add back all channels from the input EEG data-set) IS IT BETTER THAN A? WITH A YOU GET MISSING CHANNELS...:
  248. % EEG = pop_eegmaxmin(EEG); % Is this then takes the default stuff? Is it the same as above?
  250. % EEG = pop_eegmaxmin(EEG, [],[], 150, [], 1, 0); %START FROM HERE
  252. % my_bads = EEG.reject.rejmaxminE;
  254. % EEG = pop_TBT(EEG,my_bads,10,0.3,[],EEG.chanlocs); % or any other chanloc
  255. % struct, this is like option 1
  257. %EEG = pop_TBT(EEG,my_bads,[],0.3,[],EEG.chanlocs); % or any other chanloc struct --> I've put [] instead of 10, now it is doing option 4 (in the notes), I hope??
  260. %if z == 1;
  261. %[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefav', '_badtrialsrejected'],'savenew',[path_preprocessed,'\\', ...
  262. %sub{i}, '\\', sub{i}, '_rerefav', '_badtrialsrejected', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  263. %eeglab redraw;
  264. %a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  265. %end
  267. %if z == 2;
  268. %[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefmast', '_badtrialsrejected'],'savenew',[path_preprocessed,'\\', ...
  269. %sub{i}, '\\', sub{i}, '_rerefmast', '_badtrialsrejected', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  270. %eeglab redraw;
  271. %a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  272. %end
  274. %eeglab redraw;
  276. % Option A explaination
  277. % Epoched data were subjected to an automated bad-channel and artifact detection using EEGPLAB's TBT plugin (Ben-Shachar, 2018): within each
  278. % epoch, channels that exceeded a differential average amplitude of 75μV were marked for rejection. Channels that were marked as bad
  279. % on more then 15/% of all epochs were excluded. Epochs having more than 10 bad channels were excluded. Epochs with less
  280. % than 10 bad channels were included, while replacing the bad-channel data with spherical interpolation of the neighboring channel values.
  282. %% ARTEFACT REJECTION Option 3 like BVA
  284. % ?
  286. %% BASELINE CORRECTION
  288. EEG = eeg_checkset( EEG );
  289. EEG = pop_rmbase(EEG, [base_start 0], []); % baseline correction
  291. if z == 1;
  292. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefav', '_baselineremoved'],'savenew',[path_preprocessed,'\\', ...
  293. sub{i}, '\\', sub{i}, '_rerefav', '_baselineremoved', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  294. eeglab redraw;
  295. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  296. end
  298. if z == 2;
  299. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefmast', '_baselineremoved'],'savenew',[path_preprocessed,'\\', ...
  300. sub{i}, '\\', sub{i}, '_rerefmast', '_baselineremoved', '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  301. eeglab redraw;
  302. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  303. end
  305. eeglab redraw;
  307. %% CREATE SEPERATE DATASETS
  309. idx = length(ALLEEG);
  310. e = 1;
  311. while e <= size(events_phase) % loop through triggers
  313. for c = 1:size(conds_phase,2) % loop through condition names
  315. EEG = pop_selectevent(ALLEEG(idx), 'latency','-2<=2','type',{events_phase{e}},...
  316. 'deleteevents','off','deleteepochs','on','invertepochs','off'); % create a condition specific dataset
  319. if z == 1;
  320. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefav', '_', conds_phase{c}],'savenew',[path_condspecific,'\\', ...
  321. sub{i}, '\\', sub{i}, '_rerefav', '_', conds_phase{c}, '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  322. eeglab redraw;
  323. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  324. end
  326. if z == 2;
  327. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefmast', '_', conds_phase{c}],'savenew',[path_condspecific,'\\', ...
  328. sub{i}, '\\', sub{i}, '_rerefmast', '_', conds_phase{c}, '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  329. eeglab redraw;
  330. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  331. end
  332. EEG = eeg_checkset(EEG);
  333. [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG);
  334. e = e+1;
  335. end
  336. end
  338. end
  339. end
  341. %% CREATE SEPERATE DATASETS FOR ACQ AS A WHOLE
  345. for i = 1:length(sub); % loop through all subjects
  346. for z = 1:2 % load the datasets (load first rerefav then rerefmast and do the rest, with another loop starting here)
  347. e= 1;
  348. a = 1;
  349. cd(path_eeglab);
  350. eeglab; % first, re-start the eeglab
  351. eeglab redraw
  354. if z == 1;
  355. EEG = pop_loadset('filename',[sub{i}, '_rerefav', '_baselineremoved', '.set'],'filepath', [path_preprocessed, '\', sub{i}]);
  356. EEG = eeg_checkset(EEG);
  357. [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG);
  358. idx = length(ALLEEG);
  359. end
  360. if z == 2;
  361. EEG = pop_loadset('filename',[sub{i}, '_rerefmast', '_baselineremoved', '.set'],'filepath', [path_preprocessed, '\', sub{i}]);
  362. EEG = eeg_checkset(EEG);
  363. [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG);
  364. idx = length(ALLEEG);
  365. end
  368. while e <= size(events_phase_wholeacq) % loop through triggers
  370. for c = 1:size(conds_phase_wholeacq,2) % loop through condition names
  372. EEG = pop_selectevent(ALLEEG(idx), 'latency','-2<=2','type',{events_phase_wholeacq{e}, events_phase_wholeacq{e+1}},...
  373. 'deleteevents','off','deleteepochs','on','invertepochs','off'); % create a condition specific dataset
  375. if z == 1;
  377. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefav', '_', conds_phase_wholeacq{c}],'savenew',[path_condspecific,'\\', ...
  378. sub{i}, '\\', sub{i}, '_rerefav', '_', conds_phase_wholeacq{c}, '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  379. eeglab redraw;
  380. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  382. end
  384. if z == 2;
  385. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefmast', '_', conds_phase_wholeacq{c}],'savenew',[path_condspecific,'\\', ...
  386. sub{i}, '\\', sub{i}, '_rerefmast', '_', conds_phase_wholeacq{c}, '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  387. eeglab redraw;
  388. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  390. end
  391. EEG = eeg_checkset(EEG);
  392. [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG);
  393. e = e+2;
  394. end
  395. end
  396. end
  397. end
  398. eeglab redraw;
  400. %% CREATE SEPERATE DATASETS FOR EXT AS A WHOLE
  402. for i = 1:length(sub); % loop through all subjects
  403. for z = 1:2 % load the datasets (load first rerefav then rerefmast and do the rest, with another loop starting here)
  404. e= 1;
  405. a = 1;
  406. cd(path_eeglab);
  407. eeglab; % first, re-start the eeglab
  408. eeglab redraw
  410. if z == 1;
  411. EEG = pop_loadset('filename',[sub{i}, '_rerefav', '_baselineremoved', '.set'],'filepath', [path_preprocessed, '\', sub{i}]);
  412. EEG = eeg_checkset(EEG);
  413. [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG);
  414. idx = length(ALLEEG);
  415. end
  416. if z == 2;
  417. EEG = pop_loadset('filename',[sub{i}, '_rerefmast', '_baselineremoved', '.set'],'filepath', [path_preprocessed, '\', sub{i}]);
  418. EEG = eeg_checkset(EEG);
  419. [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG);
  420. idx = length(ALLEEG);
  421. end
  423. while e <= size(events_phase_wholeext) % loop through triggers
  425. for c = 1:size(conds_phase_wholeext,2) % loop through condition names
  427. EEG = pop_selectevent(ALLEEG(idx), 'latency','-2<=2','type',{events_phase_wholeext{e}, events_phase_wholeext{e+1}, events_phase_wholeext{e+2}},...
  428. 'deleteevents','off','deleteepochs','on','invertepochs','off'); % create a condition specific dataset
  431. if z == 1;
  432. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefav', '_', conds_phase_wholeext{c}],'savenew',[path_condspecific,'\\', ...
  433. sub{i}, '\\', sub{i}, '_rerefav', '_', conds_phase_wholeext{c}, '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  434. eeglab redraw;
  435. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  436. end
  438. if z == 2;
  439. [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, a,'setname',[sub{i}, '_rerefmast', '_', conds_phase_wholeext{c}],'savenew',[path_condspecific,'\\', ...
  440. sub{i}, '\\', sub{i}, '_rerefmast', '_', conds_phase_wholeext{c}, '.set'],'gui','off'); % create a dataset on EEGLAB and assign a name to the data
  441. eeglab redraw;
  442. a = a+1; % increase a by 1 so that it creates a different dataset on the next step
  443. end
  444. EEG = eeg_checkset(EEG);
  445. [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG);
  446. e = e+3;
  447. end
  448. end
  449. end
  450. end
  451. eeglab redraw;

LPP_preprocessing_afterICA.m, no license · at the source

Overview

Authors: Metin Özyagcilar1, Nilay Esin Ahrens-Demirdal1, Anja Riesel2, Tina B Lonsdorf3,4, Tania M Lincoln1
ORCID iDs: Metin Özyagcilar, Nilay Esin Ahrens-Demirdal, Tania M Lincoln
  1. Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Human Movement Science, Universität Hamburg, Hamburg, Germany
  2. Clinical Psychology and Neuroscience, Institute of Psychology, Faculty of Psychology and Human Movement Science, Universität Hamburg, Hamburg, Germany
  3. Biological Psychology and Cognitive Neuroscience, Department of Psychology, Universität Bielefeld, Bielefeld, Germany
  4. Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Institutions: Universität Hamburg (Germany); Bielefeld University (Germany); University Medical Center Hamburg-Eppendorf (Germany)
Journal: Schizophrenia (Heidelberg, Germany), volume 12, issue 1, article 45
Dates: received 12 February 2026; accepted 23 April 2026; published online 15 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41537-026-00761-y · PMID 42140955 · PMCID PMC13179330 · OpenAlex W7161252776
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), schizophrenia / psychosis (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Physiology & signal measures
Keywords: Human behaviour, Biomarkers
Topic: Schizophrenia research and treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (German Research Foundation) (449640848)
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

By studying how individuals in an “at-risk” state of psychosis learn about threat and safety cues – specifically, how they develop and unlearn fear responses to neutral cues – we might better understand the mechanisms leading to heightened arousal and fear that are characteristic of acute psychotic episodes. At-risk individuals (N = 88; of which 28 fulfilled ultra-high-risk criteria on the Comprehensive Assessment of At-Risk Mental States interview and 60 scored above a predefined threshold on the Community Assessment of Psychic Experiences) and healthy controls (N = 44) underwent a standardized and validated differential fear conditioning paradigm including an acquisition, generalization, and extinction phase. The main outcomes of interest were the late positive potential, fear-potentiated startle, and self-reported ratings of valence, arousal, fear, and expectancy elicited by the conditioned stimuli (CS). The at-risk group exhibited diminished fear learning, evident in significantly reduced differentiation between the CS+ vs. CS- in the valence ratings compared to controls. Additionally, they demonstrated impaired fear extinction, evident in valence and arousal ratings, in which their CS differentiation showed a slower reduction than the controls. There were no group differences in late positive potential responses. At risk mental states appear to be associated with problems in distinguishing dangerous from safe stimuli and a diminished ability to adjust affective responses to conditioned stimuli based on new information, while the late-positive potential and fear-potentiated startle are unaltered. Early interventions could focus on recalibrating subjective emotional evaluations of fear-associated events.

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 3 matches between paragraphs and lines of code.

OSF fj9cg

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (4), R (1)
Size: 35 files, 5 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (4 files), broom (1 file), data.table (1 file), emmeans (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), nlme (1 file), psych (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
5 files
At the source: osf.io/fj9cg/

Code availability

Analysis code(s) used in the processing of datasets are available in the OSF repository (https://osf.io/fj9cg/). The software(s) as well as their versions used are reported in the following sections.

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

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;
  • 5 scripts, each with its path and the digest of its content;
  • 3 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

The datasets generated during and/or analyzed during the current study are available in the OSF repository (https://osf.io/fj9cg/).

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 1 funder, 56 references.

Cite

This paper

Özyagcilar, M., Ahrens-Demirdal, N. E., Riesel, A., Lonsdorf, T. B., & Lincoln, T. M. (2026). The role of fear learning in the development of psychosis: an EEG study utilizing a differential fear conditioning paradigm in people with psychotic vulnerability. Schizophrenia (Heidelberg, Germany), 12(1), 45. https://doi.org/10.1038/s41537-026-00761-y

BibTeX

@article{ozyagcilar2026role,
author = {Özyagcilar, Metin and Ahrens-Demirdal, Nilay Esin and Riesel, Anja and Lonsdorf, Tina B and Lincoln, Tania M},
title = {{The role of fear learning in the development of psychosis: an EEG study utilizing a differential fear conditioning paradigm in people with psychotic vulnerability}},
journal = {Schizophrenia (Heidelberg, Germany)},
year = {2026},
month = may,
volume = {12},
number = {1},
pages = {45},
publisher = {Nature Publishing Group},
issn = {2754-6993},
doi = {10.1038/s41537-026-00761-y},
url = {https://doi.org/10.1038/s41537-026-00761-y},
pmid = {42140955},
pmcid = {PMC13179330}
}

RIS

TY - JOUR
AU - Özyagcilar, Metin
AU - Ahrens-Demirdal, Nilay Esin
AU - Riesel, Anja
AU - Lonsdorf, Tina B
AU - Lincoln, Tania M
TI - The role of fear learning in the development of psychosis: an EEG study utilizing a differential fear conditioning paradigm in people with psychotic vulnerability
T2 - Schizophrenia (Heidelberg, Germany)
J2 - Schizophrenia (Heidelb)
PY - 2026
DA - 2026/05/15
VL - 12
IS - 1
SP - 45
SN - 2754-6993
PB - Nature Publishing Group
DO - 10.1038/s41537-026-00761-y
UR - https://doi.org/10.1038/s41537-026-00761-y
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41537-026-00761-y",
"type": "article-journal",
"title": "The role of fear learning in the development of psychosis: an EEG study utilizing a differential fear conditioning paradigm in people with psychotic vulnerability",
"container-title": "Schizophrenia (Heidelberg, Germany)",
"author": [
{
"family": "Özyagcilar",
"given": "Metin"
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{
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"given": "Nilay Esin"
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{
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"given": "Anja"
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{
"family": "Lonsdorf",
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{
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}
],
"container-title-short": "Schizophrenia (Heidelb)",
"volume": "12",
"issue": "1",
"page": "45",
"DOI": "10.1038/s41537-026-00761-y",
"PMID": "42140955",
"PMCID": "PMC13179330",
"ISSN": "2754-6993",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41537-026-00761-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
15
]
]
}
}

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

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