OSCR

Neural encoding of pain is robust within but unstable between individuals.

Code ↔ Paper

12 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 12 matches
  1. [1] § Methods › Extracting EEG responses › Individual-level responses. ↔ statistical_analysis/plot_results_GAs.m, lines 47–65 · score 0.95 · 150–350 ms, 300–600 ms, 500–900 ms, 7–12 Hz, 14–30 Hz, 70–90 Hz
  2. [2] § Methods › Statistical analyses › Models of inter-individual variability. ↔ statistical_analysis/varPain_StatisticalAnalysis_INTER.R, lines 101–151 · score 0.70 · inter individual variability, Bayes factors, pain rating, regression, N2, N1
  3. [3] § Methods › Statistical analyses › Models of inter-individual variability. ↔ statistical_analysis/varPain_StatisticalAnalyses_INTRA_D2D.R, lines 91–140 · score 0.70 · inter individual variability, Bayes factors, pain rating, regression, N2, N1
  4. [4] § Methods › Extracting EEG responses › Individual-level responses. ↔ statistical_analysis/plot_results_GAs.m, lines 47–65 · score 0.68 · 14–30 Hz, 70–90 Hz, FieldTrip, 14 Hz, 70 Hz, beta
  5. [5] § Methods › Noxious stimulation ↔ paradigm/varPain_familiarization_laser.m, lines 120–156 · score 0.68 · spot diameter, pulse duration, stimulation intensities, laser, stimuli, Pain
  6. [6] § Methods › Noxious stimulation ↔ paradigm/varPain_Paradigm_IntensityRating.m, lines 298–349 · score 0.67 · spot diameter, stimulation intensities, pulse duration, laser, Pain
  7. [7] § Methods › Statistical analyses › Models of intra-individual variability. ↔ statistical_analysis/varPain_StatisticalAnalyses_INTRA_D2D.R, lines 91–140 · score 0.60 · bayesFactor, inter individual variability, pain rating, N2, N1, beta
  8. [8] § Methods › Statistical analyses › Models of intra-individual variability. ↔ statistical_analysis/varPain_StatisticalAnalyses_INTRA_M2M.R, lines 94–143 · score 0.60 · bayesFactor, inter individual variability, pain rating, N2, N1, beta
  9. [9] § Methods › Statistical analyses › Inference criteria. ↔ statistical_analysis/varPain_StatisticalAnalysis_INTER.R, lines 101–151 · score 0.57 · model fitting, inter individual variability, sex, age, posterior, BF
  10. [10] § Methods › Procedure and paradigm ↔ paradigm/varPain_Paradigm_IntensityRating.m, lines 434–546 · score 0.57 · pain paradigm, laser stimuli, prompted, sounds, intensities, EEG
  11. [11] § Methods › External replication data ↔ paradigm/varPain_familiarization_laser.m, lines 120–156 · score 0.56 · spot diameter, pulse duration, stimulation, laser, stimuli
  12. [12] § Methods › External replication data ↔ paradigm/varPain_Paradigm_IntensityRating.m, lines 298–349 · score 0.55 · spot diameter, pulse duration, stimulation, laser

Paper

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The authors' code

MATLAB · 546 lines · 20 KB · no license · 3 matches

  1. % before starting of this function:
  2. % 1. execute this line to open the serial port:
  3. % [spHandle, errmsg] = IOPort('OpenSerialPort', 'COM2');
  5. % 2. set laser from "panel" to "serial" on the laser's touchpad
  6. % establish handshake with laser by executing this line:
  7. % IOPort('Write', spHandle, uint8([204,080,000,000,000,185]));
  9. % in case you need to repeat the procedure, close serial port first by executing this line:
  10. % IOPort('Close', spHandle)
  12. function ME = varPain_Paradigm_IntensityRating()
  13. %% adjust parameters before each session %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  14. subject_ID = 'vp00';
  15. intens_1 = 2.5;
  16. intens_2 = 3.0;
  17. intens_3 = 3.5
  18. intens_4 = 4.0
  20. number_trials = 80;
  21. withTraining = true;
  22. number_test_trials = 3;
  24. % number_trials_block1 = 20;
  25. % number_trials_block2 = 20;
  26. % number_trials_block3 = 20;
  27. % number_trials_block4 = 20;
  30. % LASER parameters (fixed for experiment)
  31. Energy_1 = 4*(intens_1) - 1; % energy of the laser pulse in J (range: 1 - 59); E = 4*(energy in J) - 1
  32. Energy_2 = 4*(intens_2) - 1;
  33. Energy_3 = 4*(intens_3) - 1;
  34. Energy_4 = 4*(intens_4) - 1;
  35. pulseDuration = 3; % = 4ms; duration of the laser pulse in ms (range: 0 - 19); pulseDuration = pulse duration in ms - 1)
  36. spotDiameter = 3; % = 7mm; spot size of the laser pulse in mm (range: 0 - 11); spotDiameter = spot size in mm - 4
  38. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  40. close all
  41. % clear all
  42. % delete(instrfindall);
  43. % Screen('CloseAll');
  45. % clear ports
  46. % delete(instrfindall); % clear objects in ports
  47. % IOPort('CloseAll')
  48. % lptwrite(888,192); % set port with active 6th and 7th bit (response box)
  51. %% set isi
  52. isi = [1.0,5.0]; % random ISI between these two values (in seconds), specify as 7 seconds less as desired ISI because of the additional WaitSecs(3). to the beep, and additional WaitSecs(4). before applying the laser
  53. isi = isi(1) + (isi(2)-isi(1)).*rand(1,number_trials);
  54. isi = round(isi*10)/10;
  56. %% generate, randomize, and save matrix with stimulation intensities
  57. % save matrix with stimulation intensities
  59. max_num_same_intens = 2; % Number of identical elements who are allowed to follow after one another in the stim-sequence
  61. % for four stimulation intensities
  62. % block 1
  63. flag = 1;
  64. count = 0;
  65. while flag == 1
  66. % Randomisation
  67. stim_sequence1 = [repmat(intens_1,1,(number_trials/4)/4),repmat(intens_2,1,(number_trials/4)/4),repmat(intens_3,1,(number_trials/4)/4),repmat(intens_4,1,(number_trials/4)/4)];
  68. stim_sequence1 = stim_sequence1(randperm(numel(stim_sequence1)));
  70. % Create Matrix with 0 and 1 for 1, 2, 3, and 4_intens
  71. log_intens_1 = stim_sequence1 == intens_1;
  72. log_intens_2 = stim_sequence1 == intens_2;
  73. log_intens_3 = stim_sequence1 == intens_3;
  74. log_intens_4 = stim_sequence1 == intens_4;
  76. % sum consecutive elements in the 0/1 vector for 1, 2, 3, and 4_intens
  77. for i = 1:length(stim_sequence1)-max_num_same_intens
  78. sum_1_quad = sum(log_intens_1(1,i:i+max_num_same_intens));
  79. sum_2_quad = sum(log_intens_2(1,i:i+max_num_same_intens));
  80. sum_3_quad = sum(log_intens_3(1,i:i+max_num_same_intens));
  81. sum_4_quad = sum(log_intens_4(1,i:i+max_num_same_intens));
  83. % randomize until no sum is larger than max_num_same_intens
  84. if sum_1_quad == max_num_same_intens+1 || sum_2_quad == max_num_same_intens+1 || sum_3_quad == max_num_same_intens+1 || sum_4_quad == max_num_same_intens+1
  85. flag = 1;
  86. break
  87. else flag = 0;
  88. end
  89. end
  90. count = count + 1;
  91. end
  93. % block 2
  94. flag = 1;
  95. count = 0;
  96. while flag == 1
  97. % Randomisation
  98. stim_sequence2 = [repmat(intens_1,1,(number_trials/4)/4),repmat(intens_2,1,(number_trials/4)/4),repmat(intens_3,1,(number_trials/4)/4),repmat(intens_4,1,(number_trials/4)/4)];
  99. stim_sequence2 = stim_sequence2(randperm(numel(stim_sequence2)));
  101. % Create Matrix with 0 and 1 for 1, 2, 3, and 4_intens
  102. log_intens_1 = stim_sequence2 == intens_1;
  103. log_intens_2 = stim_sequence2 == intens_2;
  104. log_intens_3 = stim_sequence2 == intens_3;
  105. log_intens_4 = stim_sequence2 == intens_4;
  107. % sum consecutive elements in the 0/1 vector for 1, 2, 3, and 4_intens
  108. for i = 1:length(stim_sequence2)-max_num_same_intens
  109. sum_1_quad = sum(log_intens_1(1,i:i+max_num_same_intens));
  110. sum_2_quad = sum(log_intens_2(1,i:i+max_num_same_intens));
  111. sum_3_quad = sum(log_intens_3(1,i:i+max_num_same_intens));
  112. sum_4_quad = sum(log_intens_4(1,i:i+max_num_same_intens));
  114. % randomize until no sum is larger than max_num_same_intens
  115. if sum_1_quad == max_num_same_intens+1 || sum_2_quad == max_num_same_intens+1 || sum_3_quad == max_num_same_intens+1 || sum_4_quad == max_num_same_intens+1 || stim_sequence1(1,number_trials/4) == stim_sequence2(1,1)
  116. flag = 1;
  117. break
  118. else flag = 0;
  119. end
  120. end
  121. count = count + 1;
  122. end
  124. stim_sequence = [stim_sequence1, stim_sequence2];
  126. % block 3
  127. flag = 1;
  128. count = 0;
  129. while flag == 1
  130. % Randomisation
  131. stim_sequence3 = [repmat(intens_1,1,(number_trials/4)/4),repmat(intens_2,1,(number_trials/4)/4),repmat(intens_3,1,(number_trials/4)/4),repmat(intens_4,1,(number_trials/4)/4)];
  132. stim_sequence3 = stim_sequence3(randperm(numel(stim_sequence3)));
  134. % Create Matrix with 0 and 1 for 1, 2, 3, and 4_intens
  135. log_intens_1 = stim_sequence3 == intens_1;
  136. log_intens_2 = stim_sequence3 == intens_2;
  137. log_intens_3 = stim_sequence3 == intens_3;
  138. log_intens_4 = stim_sequence3 == intens_4;
  140. % sum consecutive elements in the 0/1 vector for 1, 2, 3, and 4_intens
  141. for i = 1:length(stim_sequence3)-max_num_same_intens
  142. sum_1_quad = sum(log_intens_1(1,i:i+max_num_same_intens));
  143. sum_2_quad = sum(log_intens_2(1,i:i+max_num_same_intens));
  144. sum_3_quad = sum(log_intens_3(1,i:i+max_num_same_intens));
  145. sum_4_quad = sum(log_intens_4(1,i:i+max_num_same_intens));
  147. % randomize until no sum is larger than max_num_same_intens
  148. if sum_1_quad == max_num_same_intens+1 || sum_2_quad == max_num_same_intens+1 || sum_3_quad == max_num_same_intens+1 || sum_4_quad == max_num_same_intens+1
  149. flag = 1;
  150. break
  151. else flag = 0;
  152. end
  153. end
  154. count = count + 1;
  155. end
  157. stim_sequence = [stim_sequence1, stim_sequence2, stim_sequence3];
  159. % block 4
  160. flag = 1;
  161. count = 0;
  162. while flag == 1
  163. % Randomisation
  164. stim_sequence4 = [repmat(intens_1,1,(number_trials/4)/4),repmat(intens_2,1,(number_trials/4)/4),repmat(intens_3,1,(number_trials/4)/4),repmat(intens_4,1,(number_trials/4)/4)];
  165. stim_sequence4 = stim_sequence4(randperm(numel(stim_sequence4)));
  167. % Create Matrix with 0 and 1 for 1, 2, 3, and 4_intens
  168. log_intens_1 = stim_sequence4 == intens_1;
  169. log_intens_2 = stim_sequence4 == intens_2;
  170. log_intens_3 = stim_sequence4 == intens_3;
  171. log_intens_4 = stim_sequence4 == intens_4;
  173. % sum consecutive elements in the 0/1 vector for 1, 2, 3, and 4_intens
  174. for i = 1:length(stim_sequence4)-max_num_same_intens
  175. sum_1_quad = sum(log_intens_1(1,i:i+max_num_same_intens));
  176. sum_2_quad = sum(log_intens_2(1,i:i+max_num_same_intens));
  177. sum_3_quad = sum(log_intens_3(1,i:i+max_num_same_intens));
  178. sum_4_quad = sum(log_intens_4(1,i:i+max_num_same_intens));
  180. % randomize until no sum is larger than max_num_same_intens
  181. if sum_1_quad == max_num_same_intens+1 || sum_2_quad == max_num_same_intens+1 || sum_3_quad == max_num_same_intens+1 || sum_4_quad == max_num_same_intens+1 || stim_sequence3(1,number_trials/4) == stim_sequence4(1,1)
  182. flag = 1;
  183. break
  184. else flag = 0;
  185. end
  186. end
  187. count = count + 1;
  188. end
  190. stim_sequence = [stim_sequence1, stim_sequence2, stim_sequence3, stim_sequence4];
  193. % mkdir(['C:\Users\PainLabPres\Documents\experiments\variability of pain responses\',subject_ID]);
  194. PATHOUT = ['C:\Users\PainLabPres\Desktop\VarPain\StimSequences\'];
  195. save([PATHOUT,subject_ID,'_stim_sequence.mat'],'stim_sequence');
  196. % load([PATHOUT,Subject_ID,'_stim_sequence.mat']);
  198. try % used for debugging with PTB (error message can be found in the structure ME)
  200. % initialization of keyboard
  201. KbName('UnifyKeyNames');
  202. esc = KbName('escape');
  203. enter = KbName('return');
  204. reaction_key = KbName('space');
  205. pause_key = KbName('shift'); % key to pause experiment
  206. [keyisdown, secs, keycode] = KbCheck; % initializing KbCheck
  207. oldenablekeys = RestrictKeysForKbCheck([esc,enter,reaction_key,pause_key]); % just the escape, return, reaction key and pause key are responsive
  208. % pausekey = 'shift'; % key to pause experiment
  209. % pausekeycode = KbName(pausekey);
  211. % %% set up TCPIP connection with EEG laptop
  212. % tcpip_obj = tcpip('141.39.145.129', 6700, 'NetworkRole', 'client');
  213. % tcpip_obj.ByteOrder = 'littleEndian';
  214. % fopen(tcpip_obj);
  216. %% window specifications
  217. w_size = [];
  218. backgr_color = [192,192,192];
  219. instr_textsize = 40;
  220. text_color = 0;
  221. screenNumber = Screen('Screens');
  222. [w rect]=Screen('OpenWindow',screenNumber,backgr_color,w_size);
  223. Screen('TextSize', w, instr_textsize);
  224. Screen('TextStyle', w, 1);
  225. Screen('TextFont', w, 'Arial');
  226. [X,Y] = RectCenter(rect);
  227. Screen('FillRect',w,backgr_color);
  228. Screen('Flip',w);
  229. WaitSecs(1);
  230. HideCursor(screenNumber);
  232. % %% start Brain Vision Recorder
  233. % start_remote_recorder_EEG(tcpip_obj,Subject_ID,'_Mediation_Paradigm1_Perception')
  234. %
  235. %% instruction
  236. DrawFormattedText(w ,['Wir werden Ihnen gleich Schmerzreize ganz'...
  237. '\n unterschiedlicher Intensit?t auf dem linken Handr?cken verabreichen.'...
  238. ' \n\n Ihre Aufgabe ist es, die Intensit?t jedes Schmerzreizes zu bewerten,'...
  239. '\n auf einer Skala von: \n\n '...
  240. '0 = >kein Schmerz<'...
  241. '\n bis \n '...
  242. '100 = >maximal tolerierbarer Schmerz<. \n\n'...
  243. '\n Bitte warten Sie mit Ihrer Bewertung, \n bis Sie den Piepton h?ren.'], 'center', 'center', text_color,[],[],[],1.7);
  244. Screen('Flip',w);
  245. KbWait;
  246. [keyIsDown,~,keyCode] = KbCheck;
  247. if keyIsDown && keyCode(esc)
  248. Screen('CloseAll');
  249. return
  250. end
  251. Screen('FillRect',w,backgr_color);
  252. Screen('Flip',w);
  253. WaitSecs(1);
  255. DrawFormattedText(w,['Bitte lassen Sie Ihre Augen \n die gesamte Zeit geschlossen.'...
  256. ' \n\n Bitte versuchen Sie auch, w?hrend der \n gesamten Zeit m?glichst entspannt zu bleiben.'...
  257. ' \n\n Die Intensit?t der Schmerzreize wird die Intensit?t'...
  258. ' \n w?hrend der Gew?hnung nicht ?berschreiten.'...
  259. ' \n\n Wenn Sie unterbrechen m?chten, \n lassen Sie es uns bitte wissen.'...
  260. ' \n Dann beenden wir das Experiment.'], 'center', 'center', text_color,[],[],[],1.7);
  261. Screen('Flip',w);
  262. KbWait;
  263. [keyIsDown,~,keyCode] = KbCheck;
  264. if keyIsDown && keyCode(esc)
  265. Screen('CloseAll');
  266. return
  267. end
  268. Screen('FillRect',w,backgr_color);
  269. Screen('Flip',w);
  270. WaitSecs(1);
  272. DrawFormattedText(w,'Haben Sie noch Fragen?', 'center', 'center', text_color,[],[],[],1.7);
  273. Screen('Flip',w);
  274. KbWait;
  275. [keyIsDown,~,keyCode] = KbCheck;
  276. if keyIsDown && keyCode(esc)
  277. Screen('CloseAll');
  278. return
  279. end
  280. Screen('FillRect',w,backgr_color);
  281. Screen('Flip',w);
  282. WaitSecs(1);
  284. DrawFormattedText(w,['Dann starten wir nun mit drei Probedurchg?ngen!'...
  285. ' \n\n Bitte schlie?en Sie die Augen \n und halten Sie sich bereit.'], 'center', 'center', text_color,[],[],[],1.7);
  286. Screen('Flip',w);
  287. KbWait;
  288. [keyIsDown,~,keyCode] = KbCheck;
  289. if keyIsDown && keyCode(esc)
  290. Screen('CloseAll');
  291. return
  292. end
  293. Screen('FillRect',w,backgr_color);
  294. Screen('Flip',w);
  295. WaitSecs(1);
  298. %% prepare laser
  299. % open Serialport (alternative)
  300. [spHandle, errmsg] = IOPort('OpenSerialPort', 'COM2');
  301. WaitSecs(2);
  302. % set LASER ON
  303. IOPort('Write', spHandle, uint8([204,076,049,049,049,185])); % L111
  304. WaitSecs(2);
  305. % set OPERATE ON
  306. IOPort('Write', spHandle, uint8([204,079,049,049,049,185])); % O111
  307. WaitSecs(2);
  308. % calibrate parameters for all stimulation intensities
  309. IOPort('Write', spHandle, uint8([204,067,pulseDuration,Energy_1,000,185])); % Cdef (f is empty)
  310. WaitSecs(8);
  312. IOPort('Write', spHandle, uint8([204,067,pulseDuration,Energy_2,000,185])); % Cdef (f is empty)
  313. WaitSecs(8);
  315. IOPort('Write', spHandle, uint8([204,067,pulseDuration,Energy_3,000,185])); % Cdef (f is empty)
  316. WaitSecs(8);
  318. IOPort('Write', spHandle, uint8([204,067,pulseDuration,Energy_4,000,185])); % Cdef (f is empty)
  319. WaitSecs(8);
  321. % set parameters for all stimulation intensities
  322. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_1,spotDiameter,185])); % Pdes
  323. WaitSecs(2);
  325. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_2,spotDiameter,185])); % Pdes
  326. WaitSecs(2);
  328. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_3,spotDiameter,185])); % Pdes
  329. WaitSecs(2);
  331. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_4,spotDiameter,185])); % Pdes
  332. WaitSecs(2);
  334. DrawFormattedText(w,['Footswitch pressed? \n Press >space< to start the test trials.'...
  335. ' \n\n >space<'], 'center', 'center', text_color,[],[],[],1.7);
  336. Screen('Flip',w);
  337. KbWait;
  338. [keyIsDown,~,keyCode] = KbCheck;
  339. if keyIsDown && keyCode(esc)
  340. Screen('CloseAll');
  341. return
  342. end
  343. Screen('FillRect',w,backgr_color);
  344. Screen('Flip',w);
  345. WaitSecs(1);
  347. % create beep or later use
  348. beep = MakeBeep(1000,0.2);
  349. Snd('Open');
  350. %% loop for test trials
  352. for i = 1:number_test_trials
  354. [keyisdown, secs, keycode] = KbCheck; % initializing KbCheck
  355. if keyisdown && keycode(esc)
  356. Screen('CloseAll');
  357. return
  358. end
  360. if keyisdown && keycode(pause_key)
  361. DrawFormattedText(w,['Pause requested.'...
  362. ' \n\n Press space to continue.'], 'center', 'center', text_color);
  363. Screen('Flip',w);
  364. KbPressWait;
  365. end
  367. % display trial number on screen
  368. DrawFormattedText(w,num2str(i), 'center', 'center', text_color);
  369. Screen('Flip',w);
  371. % LASER: set laser intensity
  372. LasEnerg = stim_sequence(i);
  373. LasEnerg_str = num2str(LasEnerg);
  374. if LasEnerg == intens_1
  375. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_1,spotDiameter,185])); % Pdes
  376. lptwrite(888,1); WaitSecs(0.004); lptwrite(888,0); % EEG marker laserpulse
  377. elseif LasEnerg == intens_2
  378. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_2,spotDiameter,185])); % Pdes
  379. lptwrite(888,2); WaitSecs(0.004); lptwrite(888,0); % EEG marker laserpulse
  380. elseif LasEnerg == intens_3
  381. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_3,spotDiameter,185])); % Pdes
  382. lptwrite(888,3); WaitSecs(0.004); lptwrite(888,0); % EEG marker laserpulse
  383. elseif LasEnerg == intens_4
  384. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_4,spotDiameter,185])); % Pdes
  385. lptwrite(888,4); WaitSecs(0.004); lptwrite(888,0); % EEG marker laserpulse
  386. end
  388. WaitSecs(4);
  389. % LASER: apply laser stimulus
  390. IOPort('Write', spHandle, uint8([204,071,049,049,049,185])); % G11
  392. % Wait for specified time (3 sec until beep)
  393. WaitSecs(3);
  395. % play sound which prompts rating
  396. lptwrite(888,7); WaitSecs(0.004); lptwrite(888,0); % EEG marker beep
  397. Snd('Play',beep);
  399. % Wait for specified time (ISI)
  400. WaitSecs(isi(i));
  402. end
  404. %% Instructions before start of main experiment
  406. DrawFormattedText(w,['Alles klar? Dann geht es jetzt los!'], 'center', 'center', text_color,[],[],[],1.7);
  407. Screen('Flip',w);
  408. KbWait;
  409. [keyIsDown,~,keyCode] = KbCheck;
  410. if keyIsDown && keyCode(esc)
  411. Screen('CloseAll');
  412. return
  413. end
  414. Screen('FillRect',w,backgr_color);
  415. Screen('Flip',w);
  416. WaitSecs(1);
  418. DrawFormattedText(w,['Footswitch pressed? EEG saved? \n Press >space< to start the experiment.'...
  419. ' \n\n >space<'], 'center', 'center', text_color,[],[],[],1.7);
  420. Screen('Flip',w);
  421. KbWait;
  422. [keyIsDown,~,keyCode] = KbCheck;
  423. if keyIsDown && keyCode(esc)
  424. Screen('CloseAll');
  425. return
  426. end
  427. Screen('FillRect',w,backgr_color);
  428. Screen('Flip',w);
  430. % fprintf(tcpip_obj, '%s','S');
  431. WaitSecs(10);
  432. lptwrite(888,8); WaitSecs(0.004); lptwrite(888,0); % start the experiment / start block 1
  434. %% loop for main experiment
  436. for i = 1:number_trials
  438. [keyisdown, secs, keycode] = KbCheck; % initializing KbCheck
  439. if keyisdown && keycode(esc)
  440. Screen('CloseAll');
  441. return
  442. end
  444. if keyisdown && keycode(pause_key)
  445. DrawFormattedText(w,['Pause requested.'...
  446. ' \n \n Press space to continue.'], 'center', 'center', text_color);
  447. Screen('Flip',w);
  448. KbPressWait;
  449. end
  451. % display trial number on screen
  452. DrawFormattedText(w,num2str(i), 'center', 'center', text_color);
  453. Screen('Flip',w);
  455. % send marker end / start blocks and pause after 40 trials
  456. if i == 20
  457. lptwrite(888,9); WaitSecs(0.004); lptwrite(888,0); % end block 1
  458. elseif i == 21
  459. lptwrite(888,10); WaitSecs(0.004); lptwrite(888,0); % start block 2
  460. elseif i == 40
  461. lptwrite(888,11); WaitSecs(0.004); lptwrite(888,0); % end block 2
  462. WaitSecs(0.5);
  464. DrawFormattedText(w,'Kurze Pause. \n \n Alles klar? Dann geht es jetzt weiter! \n \n >space<', 'center', 'center', text_color);
  465. Screen('Flip',w);
  466. KbWait;
  468. [keyIsDown,~,keyCode] = KbCheck;
  469. if keyIsDown && keyCode(esc)
  470. Screen('CloseAll');
  471. return
  472. end
  473. Screen('FillRect',w,backgr_color);
  474. Screen('Flip',w);
  475. % fprintf(tcpip_obj, '%s','S');
  476. WaitSecs(3);
  477. end
  479. % LASER: set laser intensity
  480. LasEnerg = stim_sequence(i);
  481. LasEnerg_str = num2str(LasEnerg);
  482. if LasEnerg == intens_1
  483. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_1,spotDiameter,185])); % Pdes
  484. lptwrite(888,1); WaitSecs(0.004); lptwrite(888,0); % EEG marker laserpulse
  485. elseif LasEnerg == intens_2
  486. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_2,spotDiameter,185])); % Pdes
  487. lptwrite(888,2); WaitSecs(0.004); lptwrite(888,0); % EEG marker laserpulse
  488. elseif LasEnerg == intens_3
  489. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_3,spotDiameter,185])); % Pdes
  490. lptwrite(888,3); WaitSecs(0.004); lptwrite(888,0); % EEG marker laserpulse
  491. elseif LasEnerg == intens_4
  492. IOPort('Write', spHandle, uint8([204,080,pulseDuration,Energy_4,spotDiameter,185])); % Pdes
  493. lptwrite(888,4); WaitSecs(0.004); lptwrite(888,0); % EEG marker laserpulse
  494. end
  496. WaitSecs(4);
  497. % LASER: apply laser stimulus
  498. IOPort('Write', spHandle, uint8([204,071,049,049,049,185])); % G111
  500. % Wait for specified time (3 sec until beep)
  501. WaitSecs(3);
  503. % play sound which prompts rating
  504. lptwrite(888,7); WaitSecs(0.004); lptwrite(888,0); % EEG marker beep
  505. beep = MakeBeep(1000,0.2);
  506. Snd('Open');
  507. Snd('Play',beep);
  508. Snd('Quiet');
  510. % Wait for specified time (ISI)
  511. WaitSecs(isi(i));
  513. end
  515. catch ME
  516. Screen('CloseAll');
  517. rethrow(ME);
  518. end
  520. WaitSecs(5);
  521. lptwrite(888,15); WaitSecs(0.004); lptwrite(888,0); % end of the experiment / end block 4
  522. WaitSecs(1);
  524. % fprintf(tcpip_obj, '%s','Q'); % stop the recording
  525. % fprintf(tcpip_obj, '%s','X'); % quit the recorder
  526. % fclose(tcpip_obj); delete(tcpip_obj); clear tcpip_obj; % clear and delete the object
  528. % fclose(s); delete(s); clear s; % clear and delete the object
  530. DrawFormattedText(w,['The experiment is completed.'], 'center', 'center', text_color);
  531. Screen('Flip',w);
  532. WaitSecs(5);
  534. Screen('CloseAll');
  536. %close object used or beep
  537. Snd('Quiet');
  539. % %% function starting EEG recording
  540. % function start_remote_recorder_EEG(tcpip_obj,Subject_ID,file_extension)
  541. % fprintf(tcpip_obj, '%s','1f:\WorkFiles\Pain64ch_1kHz_mediation.rwksp'); % workspace (numbers 1-4 necessary for remote recorder)
  542. % WaitSecs(1); fprintf(tcpip_obj, '%s',['2',file_extension]); % experiment name
  543. % WaitSecs(1); fprintf(tcpip_obj, '%s',['3',Subject_ID]); % vp number
  544. % WaitSecs(1); fprintf(tcpip_obj, '%s','4'); % start recorder
  545. % WaitSecs(1); fprintf(tcpip_obj, '%s','M'); % Monitor Mode
  546. % WaitSecs(1);

varPain_Paradigm_IntensityRating.m, no license · at the source

Overview

Authors: Laura Tiemann1, Felix S Bott1, Elisabeth S May1, Moritz M Nickel1, Vanessa D Hohn1, Cristina Gil Ávila1, Nicolò Bruna1, Paul Theo Zebhauser1, Markus Ploner1
ORCID iDs: Felix S Bott, Moritz M Nickel, Markus Ploner
  1. Center for Interdisciplinary Pain Medicine, Department of Neurology and TUM-Neuroimaging Center, TUM School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
Institutions: TUM Klinikum (Germany); Technical University of Munich (Germany)
Journal: PLoS biology, volume 24, issue 8, article e3003948
Dates: received 28 April 2026; accepted 29 July 2026; published online 10 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003948 · PMID 42574470 · PMCID PMC13475984 · OpenAlex W7202151556
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), pain (population), cognitive (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Statistics, Preprocessing, Evoked potentials, Connectivity
MeSH: Brain*, Pain*, Pain Perception*, Adult, Bayes Theorem, Electroencephalography, Female, Humans, Male, Pain Measurement, Young Adult (* major topic)
Topic: Pain Mechanisms and Treatments (Physiology, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (PL321/14-1, PL321/16-1, SFB 1158); Technische Universität München (TUM Innovation Network Neurotechnology in Mental Health)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

The perception of pain varies both within and between individuals, even when sensory input remains constant. Understanding how the brain encodes these intra- and inter-individual variations is central to elucidating the neural mechanisms of pain and to developing reliable brain-based markers for clinical use. Yet, previous findings have been inconsistent, and their robustness across time and populations remains unclear. Here, we used electroencephalography (EEG) in 161 healthy participants to re-investigate the neural patterns explaining intra- and inter-individual variations in the perception of brief painful stimuli independent of stimulus intensity. Using Bayesian multivariate multi-model regression, we related pain ratings to canonical EEG responses. To directly assess robustness, the experiment was repeated after 4 weeks in the same participants and replicated in an independent cohort (n = 111). Neural patterns associated with inter-individual differences in pain perception were repeatable over time but not replicable across cohorts. In contrast, neural patterns underlying intra-individual fluctuations were robust both over time and across cohorts. These findings indicate that within-person and between-person variability in pain perception is encoded by distinct neural patterns that differ fundamentally in their robustness. Furthermore, they show that brain-based markers are particularly suited for tracking intra-individual fluctuations of pain, while being less sensitive to inter-individual differences. More broadly, they highlight the importance of within-person approaches for advancing both mechanistic models of pain and the development of clinically useful biomarkers.

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 12 matches between paragraphs and lines of code.

OSF bs3yj

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (5), R (4)
Size: 60 files, 9 scripts
Software Heritage: not checked
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: BayesFactor (3 files), data.table (3 files), easystats (3 files), ggplot2 (3 files), lme4 (3 files), lmerTest (3 files), tidyverse (3 files), Psychtoolbox (2 files), FieldTrip (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source: doi.org/10.17605/OSF.IO/BS3YJ

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;
  • 9 scripts, each with its path and the digest of its content;
  • 12 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

Datasets cited

Data Availability

All data in standardized EEG-BIDS format [44] are available at https://osf.io/z2h86. Code used for the current manuscript and data underlying the figures are publicly available at https://doi.org/10.17605/OSF.IO/BS3YJ.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 11 MeSH terms, 2 funders, 57 references.

Cite

This paper

Tiemann, L., Bott, F. S., May, E. S., Nickel, M. M., Hohn, V. D., Gil Ávila, C., Bruna, N., Zebhauser, P. T., & Ploner, M. (2026). Neural encoding of pain is robust within but unstable between individuals. PLoS biology, 24(8), e3003948. https://doi.org/10.1371/journal.pbio.3003948

BibTeX

@article{tiemann2026neural,
author = {Tiemann, Laura and Bott, Felix S and May, Elisabeth S and Nickel, Moritz M and Hohn, Vanessa D and Gil Ávila, Cristina and Bruna, Nicolò and Zebhauser, Paul Theo and Ploner, Markus},
title = {{Neural encoding of pain is robust within but unstable between individuals}},
journal = {PLoS biology},
year = {2026},
month = aug,
volume = {24},
number = {8},
pages = {e3003948},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003948},
url = {https://doi.org/10.1371/journal.pbio.3003948},
pmid = {42574470},
pmcid = {PMC13475984}
}

RIS

TY - JOUR
AU - Tiemann, Laura
AU - Bott, Felix S
AU - May, Elisabeth S
AU - Nickel, Moritz M
AU - Hohn, Vanessa D
AU - Gil Ávila, Cristina
AU - Bruna, Nicolò
AU - Zebhauser, Paul Theo
AU - Ploner, Markus
TI - Neural encoding of pain is robust within but unstable between individuals
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/08/10
VL - 24
IS - 8
SP - e3003948
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003948
UR - https://doi.org/10.1371/journal.pbio.3003948
LA - en
ER -

CSL-JSON

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"container-title": "PLoS biology",
"author": [
{
"family": "Tiemann",
"given": "Laura"
},
{
"family": "Bott",
"given": "Felix S"
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{
"family": "May",
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"date-parts": [
[
2026,
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10
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]
}
}

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