OSCR

Dorsal Posterior Parietal Cortex Lesions Disrupt Spatial- but Not Motor-Based Inhibition.

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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. [1] § Materials and Methods › Preliminary Analysis › Stop Signal Task ↔ Archive of OSF Storage/analysis/ssanalysis.m, lines 170–220 · score 0.83 · 80–1000 ms, Failed go trials, successful go trials, 80 ms, 2.5 deg, Successful stop signal
  2. [2] § Materials and Methods › Preliminary Analysis › IOR Task ↔ Archive of OSF Storage/analysis/ssanalysis.m, lines 170–220 · score 0.74 · 80–1000 ms, target onset, 80 ms, anticipatory, delayed, amplitude
  3. [3] § Materials and Methods › Preliminary Analysis › IOR Task ↔ Archive of OSF Storage/analysis/IORanalysis.m, lines 112–132 · score 0.69 · left quadrant, right quadrant, invalid trials, right hemifield, cue, patients
  4. [4] § Materials and Methods › Preliminary Analysis › Stop Signal Task ↔ Archive of OSF Storage/analysis/ctltaskanalysis.m, lines 109–140 · score 0.60 · target position, saccade amplitudes, target location, angular
  5. [5] § Materials and Methods › Procedure › Stop Signal Task ↔ Archive of OSF Storage/analysis/ssanalysis.m, lines 280–284 · score 0.56 · stop signal onset, stop signal delay, SSD

Paper

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The authors' code

MATLAB · 1,090 lines · 45 KB · no license · 3 matches

  1. % -------------------------------------------------------------------------
  2. % MATLAB analysis scripts for: Dorsal posterior parietal cortex lesions disrupt spatial- but not motor-based inhibition
  3. %
  4. % Copyright (c) 2026 Julie Ouerfelli-Ethier, Aarlenne Z. Khan and Laure
  5. % Pisella
  6. %
  7. % This code is licensed under the MIT License.
  8. % You may use, modify, and distribute this code in accordance with the license.
  9. % See the LICENCE file in the /licences folder for full terms:
  10. % https://spdx.org/licenses/MIT.html
  11. % ---------------------------------------------------------------------
  13. %% Analysis for stop signal task
  14. % The analysis has the following workflow:
  15. % 1) Data preprocessing
  16. % 2) SSRT estimation using the integration method
  17. % 3) Statistical analysis with modified t-tests
  18. % 4) Data visualization (plots for each patient and their controls)
  20. %% Data structure description
  21. % Each row of the param corresponds to one trial.
  23. % Columns are:
  24. % ---- Subject information ----
  25. % 1 = subject ID
  27. % ---- Trial information -----
  28. % 2 = block ID
  29. % 3 = ratio of stop signal during the block: 20 = 20%, 40 = 40%
  30. % 4 = trial ID
  31. % 5 = trial validity based on visual inspection: invalid (0) and valid (1)
  32. % 6 = trial condition: 0 = stop signal; 1 = go trials
  33. % 7 = fixation position x (pixels coordinates, origin at top-left corner)
  34. % 8 = fixation position y (pixels coordinates, origin at top-left corner)
  35. % 9 = trial start times (ms)
  36. % 10 = target onset times (ms)
  37. % 11 = stop signal onset times (ms)
  38. % 12 = ITI onset times (ms)
  39. % 13 = trial end times (ms)
  40. % 14 = target durations (duration between target onset and stop signal
  41. % onset) or stop signal delay (SSD)
  42. % 15 = responses during stop trials: NaN = go trial, 0 = incorrect (failed stop), 1 = correct (successful stop)
  43. % 16 = target location in hemifield: 1 = right; 2 = left
  45. % ---- Information about first saccade (sac 1 below) detected ----
  46. % If no saccade is detected columns 17-25 contain NaNs
  47. % All timings are relative to trial start.
  48. %
  49. % 17 = sac 1 onset (ms)
  50. % 18 = sac 1 peak time (ms)
  51. % 19 = sac 1 offset (ms)
  52. % 20 = sac 1 x start (deg of visual angle)
  53. % 21 = sac 1 y start (deg of visual angle)
  54. % 22 = sac 1 x end (deg of visual angle)
  55. % 23 = sac 1 y end (deg of visual angle)
  56. % 24 = sac 1 peak velocity (deg of visual angle/second)
  57. % 25 = sac 1 duration (ms)
  59. % ---- Information about second saccade (sac 2 below) detected ----
  60. % If no saccade is detected columns 26-34 contain NaNs
  61. % All timings are relative to trial start.
  62. %
  63. % 26 = sac 2 onset (ms)
  64. % 27 = sac 2 peak time (ms)
  65. % 28 = sac 2 offset (ms)
  66. % 29 = sac 2 x start (deg of visual angle)
  67. % 30 = sac 2 y start (deg of visual angle)
  68. % 31 = sac 2 x end (deg of visual angle)
  69. % 32 = sac 2 y end (deg of visual angle)
  70. % 33 = sac 2 peak velocity (deg of visual angle/second)
  71. % 34 = sac 2 duration (ms)
  73. % ---- Variables computed in the present script ----
  74. % 35 = saccade reaction times (ms, saccade onset relative to target onset)
  75. % 36 = saccade amplitudes (deg of visual angle)
  76. % 37 = saccade direction (polar coordinates, 0 to 360°)
  77. % 38 = target location in angle degree (polar coordinates, 0 = right, 1 = left)
  78. % 39 = absolute direction error (deg of visual angle, difference between
  79. % saccade landing position and target position)
  80. % 40 = trial types (see comments): 1 = successful go, 2 = failed go (no
  81. % response), 3 = successful stop, 4 = failed stop, 5= failed go
  82. % (anticipatory or delayed response), 6-7: reserved/error codes (should not occur; indicate unexpected data).
  83. % 42 = Participant groups: 1 = cf; 2 = cf's controls, 3 = ig, 4 = ig's controls
  84. % 43 = new SSDs calculated (accounting for screen delays during experiment)
  86. %%
  87. clear all;
  88. close all;
  90. %% ================= USER PARAMETERS =================
  91. % Instructions: Choose parameter here for analysis of I.G. groups (bilateral)
  92. % and C.F. groups for left or right hemifields. Hemifields here correspond
  93. % to where targets were presented.
  95. analysis_mode = "IG_groups";
  96. % "CF_groups_L"
  97. % "CF_groups_R"
  99. %% load param
  100. load paramstopsignal.txt;
  101. param = paramstopsignal;
  103. %% Subjects IDs and Task parameters
  104. sub = unique(param(:,1)); % subject ID
  105. ratio = [20 40]; % stop signal ratios
  107. %% ---- Data preprocessing ---- %%
  108. %% Remove trials marked as invalid after visual inspection
  110. % During visual inspection, trials marked as "invalid" are assigned 0 in
  111. % column 5.
  112. ind = find(param(:,5)==0);
  113. param1 = param(ind,:);
  115. %% Calculate saccade reaction times (SRTs) relative to target onset
  116. % SRTs are calculated as follows: saccade onset time - target onset time
  118. param1(:,35) = param1(:,17) - param1(:,10);
  120. %% Calculate saccade amplitude and saccade direction
  121. % Saccade amplitude is in degree of visual angle
  122. % Saccade direction is in radians, then converted into polar coordinates
  124. [theta(:,1), param1(:,36)] = cart2pol(param1(:,22)-param1(:,20), param1(:,23)-param1(:,21));
  126. % Saccade direction (radians) is converted in polar coordinates (degrees)
  127. theta(:,2) = degrees(theta(:,1));
  129. % Safeguard to ensure, polar coordinates span across 360 degrees (no
  130. % negatives)
  131. for i=1:length(theta(:,1)) % convert to 360 degrees
  132. if theta(i,2)<-25, param1(i,37) = theta(i,2)+360; theta(i,3) = theta(i,2)+360;
  133. else
  134. param1(i,37) = theta(i,2); theta(i,3) = theta(i,2);
  135. end
  136. end
  138. % Recompute target position in the hemifield in polar coordinates (angular
  139. % degrees).
  140. % 0° = target is on the right side of the screen
  141. % 180° = target is on the left side of the screen
  142. for i = 1:length(param1(:,1))
  143. if param1(i,16) == 1, param1(i,38) = 0; % right
  144. elseif param1(i,16) == 2, param1(i,38) = 180; % left
  145. end
  146. end
  148. % Calculate directional error of the saccade relative to target positions
  149. % This is calculated as follows: target location in polar coordinates -
  150. % saccade direction in polar coordinates
  151. param1(:,39) = abs(param1(:,38) - param1(:,37));
  153. %% % Filtering trials based on saccade amplitude and saccade direction
  154. % This section removes all trials with big amplitudes and big directional errors
  155. % - Amplitudes over 15° are removed
  156. % - Directional errors over 45° for normal saccades between 2.5° and 15° (exclusive)
  158. % Inititalize column 40
  159. param1(:,40)=1;
  161. for i = 1:length(param1(:,1))
  162. if param1(i,36) > 15, param1(i,40)=0; end % Removing any saccades that have big amplitudes
  163. if param1(i,39) > 45 && param1(i,36) > 2.5 && param1(i,36) < 15, param1(i,40)=0; end % Removing regular saccades to the targets wrong direction
  164. end
  166. % Build new param without filtered out trials
  167. ind = find(param1(:,40) == 1);
  168. param2 = param1(ind,:);
  170. %% Trial sorting
  171. % each trial is assigned a number according to trial type (go vs. stop) and
  172. % whether specific conditions are met to define it as successful or failed,
  173. % as follows:
  174. % 1 = successful go trials = go trial and SRTs above 80 ms and above mean+2SD of all go trials and amplitude more than 2.5 degrees and less than 15 degrees and abs error less than 45 deg
  175. % 2 = failed go trials = go trial and SRTS above mean+2SD (go omission, to take on the MAX good go trials srts)
  176. % 2 = failed go trials = go trial and no saccade was made (go omission, to take on the MAX good go trials srts)
  177. % 2 = failed go trials = go trial and saccades with amplitude less than 2.5 deg (go omission, to take on MAX good go trial srts)
  178. % 5 = failed go trials = go trial and saccades made before 80ms (to remove from analysis)
  180. % 3 = successful stop trials = stop trial and no saccade was made
  181. % 3 = successful stop trials = stop trial and SRT above mean+2SD of all go trials
  182. % 3 = successful stop trials = stop trial and amplitude less than 2.5 deg
  183. % 4 = failed stop trials = stop trial and saccades made before 80ms (to remove from analysis)
  185. % 6-7 = unexpected value. Used for diagnostics.
  187. % Trial sorted per subject, stop signal ratio and block
  188. param2(:,40)=NaN; % Initialize the column
  189. for j=1:length(sub) % subject ID
  190. for r= 1:2 % stop signal ratio
  191. for b = 1:11 % block ID
  192. s = sub(j);
  193. indgo = find(param2(:,1)==s & param2(:,6)==1 & param2(:,3) == ratio(r) & param2(:,2) == b);
  194. meansrt = nanmean(param1(indgo,35));
  195. sdsrt = nanstd(param1(indgo,35));
  196. % go trials
  197. for i = 1:length(indgo)
  198. if isnan(param2(indgo(i),35)), param2(indgo(i),40) = 2; % failed go trial (go omission, no saccade was made)
  199. elseif param2(indgo(i),36)<=2.5, param2(indgo(i),40) = 2; % failed go trial (go ommision, no saccade was made)
  200. elseif param2(indgo(i),35) > 79 && param2(indgo(i),35) < 1000 && param2(indgo(i),36)>2.5, param2(indgo(i),40) = 1; % successful go trial (saccades made between 80 ms and 1000 ms from target onset)
  201. elseif param2(indgo(i),35) >= 1000 && param2(indgo(i),36)>2.5, param2(indgo(i),40) = 5; % failed go trial (delayed saccade)
  202. elseif param2(indgo(i),35)< 80 && param2(indgo(i),36)>2.5, param2(indgo(i),40) = 5; % failed go trial (anticipatory saccade)
  203. else
  204. param2(indgo(i),40) = 6; % Unexpected value
  205. end
  206. end
  207. % stop trials
  208. indstop = find(param2(:,1)==s & param2(:,6)==0 & param2(:,3) == ratio(r) & param2(:,2) == b);
  209. for i=1:length(indstop)
  210. if isnan(param2(indstop(i),35)), param2(indstop(i),40) = 3; % successful stop (no saccade)
  211. elseif param2(indstop(i),35) < 1000 && param2(indstop(i),36) > 2.5, param2(indstop(i),40) = 4; % failed stop trial (a saccade was made)
  212. elseif param2(indstop(i),35) < 1000 && param2(indstop(i),36) <= 2.5, param2(indstop(i),40) = 3; % successful stop (barely a saccade)
  213. elseif param2(indstop(i),35) >= 1000, param2(indstop(i),40) = 3; % successful stop
  214. else
  215. param2(indstop(i),40) = 7; % Unexpected value
  216. end
  217. end
  218. end
  219. end
  220. end
  222. %% Sort go omissions
  223. % Go omissions reaction times are changed to maxSRT (for each participant and ratio separately)
  224. % Note: This method is only used to replace trials without a saccade,
  225. % anticipatory and delayed responses are kept as such (based on Verbruggen
  226. % et al., 2019)
  227. for j = 1:length(sub) % subject ID
  228. for r = 1:2 % stop signal ratio
  229. for b = 1:11 % block ID
  230. s = sub(j);
  231. % identify maximum RT for successful go trials for each ratio and participant
  232. indmaxgo = find(param2(:,1) == s & param2(:,40) == 1 & param2(:,3) == ratio(r) & param2(:,2) == b);
  233. maximum = max(param2(indmaxgo,35));
  234. ind = find(param2(:,1) == s & param2(:,40) == 2 & param2(:,3)==ratio(r) & param2(:,2) == b);
  235. % replace go omissions by the maximum
  236. for i=1:length(ind)
  237. param2(ind(i),35) = maximum;
  238. end
  239. end
  240. end
  241. end
  243. %% Optional data check: number of trials per participant/trial type
  244. % Thi section was used to verify the number of trials per stop signal ratio
  245. % and trial type for each participant.
  246. numtrials = [];
  247. for i = 1:length(sub) % subject ID
  248. for r = 1:2 % stop signal ratio
  249. for type = 1:4 % trial type (successful go, failed go, successful stop, failed stop)
  250. s = sub(i);
  251. ind = find(param2(:,1)==s & param2(:,40)==type & param2(:,3)==ratio(r));
  252. % numtrials has this structure (each row is subject ID x stop signal ratio x trial type).
  253. % Columns are:
  254. % 1 = subject ID
  255. % 2 = stop signal ratio
  256. % 3 = trial type
  257. % 4 = number of trials
  258. numtrials = [numtrials; s ratio(r) type length(ind)];
  259. end
  260. end
  261. end
  264. %% Sort participants into groups
  265. % 1 = cf
  266. % 2 = cf's controls
  267. % 3 = ig
  268. % 4 = ig's controls
  270. for i = 1:length(param2(:,1))
  271. if param2(i,1) == 1, param2(i,42) = 1; % cf
  272. elseif param2(i,1) > 1 && param2(i,1) < 13, param2(i,42) = 2; % cf's controls: subject ID 2 to 12
  273. elseif param2(i,1) == 13, param2(i,42) = 3; % ig
  274. elseif param2(i,1) > 13 && param2(i,1) < 24, param2(i,42) = 4; % ig's 's controls: subject ID 14 to 23
  275. else
  276. disp('Subject ID not assigned to a group!') % safeguard
  277. end
  278. end
  280. %% Adjust stop signal delays (col 14) to account for delays related to screen and computer
  281. % New SSDs are calculated as follows: signal onset time - target onset time
  283. param2(:,43)=param2(:,11)-param2(:,10);
  284. param3=param2;
  286. %% Calculate proportions of trials per participant per block
  288. meanpropblock = [];
  289. for i = 1:length(sub) % subject ID
  290. for t = 1:4 % trial type (successful go, failed go, successful stop, failed stop)
  291. for r = 1:2 % stop signal ratio
  292. for b = 1:11 % block ID
  293. s = sub(i);
  294. ind = find(param3(:,1) == s & param3(:,40) == t & param3(:,3) == ratio(r) & param3(:,2) == b); % total per group
  295. if ~isempty(ind)
  296. indgo = find(param3(:,1) == s & param3(:,40) < 3 & param3(:,3) == ratio(r) & param3(:,2) == b); % total go trials (failed + successful)
  297. indstop = find(param3(:,1) == s & param3(:,40) > 2 & param3(:,40) < 5 & param3(:,3) == ratio(r) & param3(:,2) == b); % total stop trials (failed + successful)
  298. % meanpropblock has this structure (each row is subject
  299. % ID x trial type)
  300. % columns are:
  301. % 1 = subject ID
  302. % 2 = trial type
  303. % 3 = stop signal ratio
  304. % 4 = block ID
  305. % 5 = mean SRT
  306. % 6 = number of trials per trial type
  307. % 7 = number of go trials
  308. % 8 = number of stop trials
  309. % 9 = proportion of go trials
  310. % 10 = proportion of stop trials,
  311. meanpropblock = [meanpropblock; s t ratio(r) b ...
  312. nanmean(param3(ind,35)) length(ind) length(indgo) length(indstop)...
  313. length(ind)/length(indgo) length(ind)/length(indstop)];
  314. end
  315. end
  316. end
  317. end
  318. end
  320. %% Accoridng to analysis_mode, proceed with measures of the stop signal task
  322. switch analysis_mode
  323. case "IG_groups"
  324. % No hemifield filtering for I.G.
  325. param4 = param3;
  326. case "CF_groups_L"
  327. % left hemifield for C.F. groups
  328. analysis_ind = param3(:, 16) == 2;
  329. param4 = param3(analysis_ind,:);
  330. case "CF_groups_R"
  331. % right hemifield for C.F. groups
  332. analysis_ind = param3(:,16) == 1;
  333. param4 = param3(analysis_ind,:);
  334. otherwise
  335. error('Invalid analysis_mode. Please check the spelling!')
  336. end
  338. %% ---- Estimating SSRT ----- %%
  340. %% SSRT is estimated using the integration method (Verbruggen et al., 2019).
  341. % This method is based on the independence assumption of the race model (Logan et al., 1984),
  342. % which states that the finishing time of the go process is independent of
  343. % the stopping process. Under this assumption the distribution of RTs on
  344. % stop trials is the same as the RT distribution on go trials.
  345. %
  346. % SSRT is calculated as the finishing time of the stopping process minus the
  347. % starting time of the sopping process. This estimation is achieved through
  348. % the three steps outlined below.
  349. %
  350. % FIRST STEP: The starting time of the stopping process is estimated as the mean
  351. % stop-signal delay (SSD) for each participant.
  352. %
  353. % SECOND STEP: The finishing time of the stopping process is estimated using the
  354. % integration procedure. First, we compute the proportion of failed stop
  355. % trials (p(respond|signal)). Then, we sort the RTs from the correct go
  356. % trials and compute their cumulative distribution. We next identify the RT
  357. % corresponding to the percentile of the go RT distribution equal to
  358. % p(respond|signal). This RT represents the estimated finishing time of the
  359. % go process at which stopping fails.
  360. %
  361. % THIRD STEP: Finally, SSRTs are calculated per participant and per signal
  362. % stop ratio as follows: SSRT = RT percentile - mean SSD
  364. %% FIRST STEP: Calculate the mean SSD for each participant for each ratio. This is the start time of the stopping process
  366. meanssd = [];
  367. for r = 1:2 % stop signal ratio
  368. for i = 1:length(sub) % subject ID
  369. s = sub(i);
  370. ind = find(param4(:,1) == s & param4(:,6) == 0 & param4(:,3) == ratio(r)); % stop signal trials
  371. % meanssd has the following structure (each row is subject ID x stop signal ratio:
  372. % columns are:
  373. % 1 = subject ID
  374. % 2 = stop signal ratio
  375. % 3 = mean SSD
  376. % 4 = sem SSD
  377. % 5 = number of trials
  378. meanssd = [meanssd; s ratio(r) nanmean(param4(ind,43)) nansem(param4(ind,43)) length(ind)];
  379. end
  380. end
  382. %% SECOND STEP: calculate the finishing time of the stopping process.
  383. % 2.1. Calculate the cumulative distribution for each subject for each stop signal ratio
  385. % 2.1.1. Calculate the number of trials for each SRT bin for go and stop trials
  387. % Go SRT distributions (trial types 1-2, & 5)
  388. bins = 80:5:1500;
  389. meansrtbin = [];
  390. for r = 1:2 % ratio condition
  391. for i = 1:length(sub)
  392. for b = 1:length(bins)-1
  393. s = sub(i);
  394. ind = find(param4(:,1) == s & (param4(:,40) < 3 | param4(:,40) == 5) & param4(:,3) == ratio(r) & param4(:,35)>=bins(b) & param4(:,35)<bins(b+1));
  395. meansrtbin = [meansrtbin; s ratio(r) (bins(b) + bins(b+1))/2 length(ind)];
  396. end
  397. end
  398. end
  400. % Failed stop SRT distributions (trial type 4)
  401. meanfailsrtbin = [];
  402. for r = 1:2 % ratio condition
  403. for i = 1:length(sub)
  404. for b = 1:length(bins)-1
  405. s = sub(i);
  406. ind = find(param4(:,1) == s & param4(:,40) == 4 & param4(:,3) == ratio(r) & param4(:,35)>=bins(b) & param4(:,35)<bins(b+1));
  407. meanfailsrtbin = [meanfailsrtbin; s ratio(r) (bins(b) + bins(b+1))/2 length(ind)];
  408. end
  409. end
  410. end
  412. % 2.1.2. Based on SRT distributions calculated above, calculate the cumulative proportion of trials
  414. % go SRT distribution
  415. for r = 1:2 % stop signal ratio
  416. for i = 1:length(sub) % signal ID
  417. s = sub(i);
  418. ind = find(meansrtbin(:,1) == s & meansrtbin(:,2) == ratio(r));
  419. indsum = sum(meansrtbin(ind,4)); % sum of trials per sub
  420. meansrtbin(ind,6) = cumsum(meansrtbin(ind,4)/indsum); % cumulative proportion
  421. end
  422. end
  424. % Failed stop distribution
  425. for r = 1:2 % stop signal ratio
  426. for i = 1:length(sub) % subject ID
  427. s = sub(i);
  428. ind = find(meanfailsrtbin(:,1) == s & meanfailsrtbin(:,2) == ratio(r));
  429. indsum = sum(meanfailsrtbin(ind,4)); % sum of trials per sub
  430. meanfailsrtbin(ind,6) = cumsum(meanfailsrtbin(ind,4)/indsum); % cumulative proportion
  431. end
  432. end
  434. %% 2.2. Calculate proportions of trials overall
  435. % This section computes the proportion of go and stop trials for each
  436. % participant, trial type, and stop signal tation. These proportions are
  437. % lated used to estimate SSRT by identifying the SRT bin where the
  438. % proporition of go trials matches the proportion of failed stop trials.
  440. meanprop = []; % initialize matrix
  441. for r = 1:2 % stop signal ratio
  442. for i = 1:length(sub) % subject ID
  443. for t = 1:4 % trial type
  444. s = sub(i);
  445. ind = find(param4(:,1) == s & param4(:,40) == t & param4(:,3) == ratio(r)); % total per group
  446. indgo = find(param4(:,1) == s & (param4(:,40) <3 | param4(:,40) == 5) & param4(:,3) == ratio(r)); % total go trials (failed + successful), cases 1, 2 and 5
  447. indstop = find(param4(:,1) == s & param4(:,40) >2 & param4(:,40) < 5 & param4(:,3) == ratio(r)); % total stop trials (failed + successful), cases 3 and 4
  448. % meanprop has the following structure (one row per subject X trial type x ratio):
  449. % The columns are:
  450. % 1 = subject ID
  451. % 2 = trial type
  452. % 3 = stop signal ratio
  453. % 4 = participant group
  454. % 5 = mean SRT
  455. % 6 = total number of trials
  456. % 7 = number of go trials (trial types 1, 2 and 5)
  457. % 8 = number of stop trials (trial types 3 and 4)
  458. % 9 = proportion of trials for each trial type over total number of go trials
  459. % 10 = proportion of trials for each trial type over total
  460. % number of stop trials.
  461. meanprop = [meanprop; s t ratio(r) ...
  462. nanmean(param4(ind,42)) nanmean(param4(ind,35)) length(ind) length(indgo) length(indstop)...
  463. length(ind)/length(indgo) length(ind)/length(indstop)];
  464. end
  465. end
  466. end
  467. %
  468. % Here we add the proportion of failed stop trials to meansrtbin.
  469. % This value is constant for each participant and stop signal ratio and
  470. % will later be compared to the cumulative proportion of go trials in each
  471. % SRT bin.
  472. for r = 1:2 % stop signal ratio
  473. for i = 1:length(sub) % subject ID
  474. s = sub(i);
  475. ind = find(meanprop(:,1) == s & meanprop(:,2) == 4 & meanprop(:,3) == ratio(r)); % proportion of failed stop trials for a given ratio
  476. ind2 = find(meansrtbin(:,1) == s & meansrtbin(:,2) == ratio(r));
  477. meansrtbin(ind2,7) = meanprop(ind,10);
  478. end
  479. end
  481. % Compute the absolute difference between:
  482. % 1) proportion of failed stop, and;
  483. % 2) proportion of go trials in each SRT bin
  484. % The SRT bin with the smallest difference approximates the point where the
  485. % probabilities are equal.
  486. meansrtbin(:,8) = abs(meansrtbin(:,7) - meansrtbin(:,6));
  488. %% THIRD STEP: Estimate SSRT using the integration method:
  490. % 1) we identify the SRT bin where the proportion of go trials is closest to the
  491. % proportion of failed stop signals.
  492. % 2) we use the SRT value in this bin.
  493. % 3) we subtract the participant's mean SSD to obtain SSRT.
  494. ssrt = [];
  495. for r = 1:2 % ratio condition
  496. for i = 1:length(sub)
  497. s = sub(i);
  498. ind = find(meansrtbin(:,1) == s & meansrtbin(:,2) == ratio(r));
  499. ind2 = find(meansrtbin(ind,8) == nanmin(meansrtbin(ind,8)));
  500. ind3 = find(meanssd(:,1)== s & meanssd(:,2)==ratio(r));
  501. if ~isempty(ind2)
  502. % SSRT structure (each row is a subject x stop signal ratio):
  503. % Columns are:
  504. % 1 = Subject ID
  505. % 2 = Stop signal ratio
  506. % 3 = SRT bin
  507. % 4 = SSRT (srt bin - mean ssd)
  508. ssrt = [ssrt; s ratio(r) meansrtbin(ind(ind2(1)), 3) - meanssd(ind3, 3)];
  509. else
  510. ssrt = [ssrt; s ratio(r) NaN];
  511. end
  512. end
  513. end
  515. %% Build a matrix with all stop signal task measures for analysis
  517. meansall = []; % initialize meansall
  518. for r = 1:2 % stop signal ratio
  519. for i = 1:length(sub)
  520. s = sub(i);
  521. ind = find(meanprop(:,1) == s & meanprop(:,3) == ratio(r) & meanprop(:,2) == 1); % successful go trials
  522. ind2 = find(meanprop(:,1) == s & meanprop(:,3) == ratio(r) & meanprop(:,2) == 3); % successful stop trials
  523. ind3 = find(meanprop(:,1) == s & meanprop(:,3) == ratio(r) & meanprop(:,2) == 4); % failed stop trials
  524. ind4 = find(ssrt(:,1)== s & ssrt(:,2)==ratio(r)); % mean ssrt for each participant and ratio
  525. % Meansall structure (each row is a subject ID x stop signal ration)
  526. % Columns are:
  527. % 1 = subject ID
  528. % 2 = stop signal ratio
  529. % 3 = successful go proportion
  530. % 4 = successful go SRT (in ms)
  531. % 5 = successful stop proportion
  532. % 6 = failed stop SRT (in ms)
  533. % 7 = SSRT
  534. % 8 = participant group
  535. meansall = [meansall; s ratio(r) meanprop(ind,9) meanprop(ind,5)...
  536. meanprop(ind2,10) meanprop(ind3,5) ssrt(ind4,3)...
  537. meanprop(ind3,4)]; %
  538. end
  539. end
  541. %% ---- Data analysis: Case study statistics ---- %%
  543. % Stats are performed according to analysis_mode defined at L. 80
  544. % Specify constants
  545. alpha = 0.05;
  546. n_IG = numel(unique(param4(param4(:,42) == 4, 1))); % number of controls for IG
  547. n_CF = numel(unique(param4(param4(:,42) == 2, 1))); % number of controls for CF
  550. if analysis_mode == "IG_groups"
  551. % Specify constants
  552. patient_group = 3;
  553. ctl_group = 4;
  554. ss_ratio = 20;
  555. data = meansall(meansall(:,2) == ss_ratio, :);
  556. controls = data(data(:,8) == ctl_group, :);
  557. patient = data(data(:,8) == patient_group, :);
  559. disp('---------------------------------------')
  560. fprintf('\nAnalysis mode: %s\n', analysis_mode)
  561. disp('---------------------------------------')
  562. % -- 20 % stop ratio -- %
  563. disp('Analysis for 20% stop signal ratio...');
  564. % successful go proportion
  565. Xm_IG = nanmean(controls(:,3));
  566. Xs_IG = nanstd(controls(:,3));
  567. Xc_IG = patient(:,3);
  568. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  569. fprintf('Successful go proportion\n')
  570. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  572. % successful go SRTs (ms)
  573. Xm_IG = nanmean(controls(:,4));
  574. Xs_IG = nanstd(controls(:,4));
  575. Xc_IG = patient(:,4);
  576. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  577. fprintf('Successful go SRTs (ms)\n')
  578. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  580. % successful stop proportion
  581. Xm_IG = nanmean(controls(:,5));
  582. Xs_IG = nanstd(controls(:,5));
  583. Xc_IG = patient(:,5);
  584. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  585. fprintf('Successful stop proportion\n')
  586. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  588. % failed stop SRTs (ms)
  589. Xm_IG = nanmean(controls(:,6));
  590. Xs_IG = nanstd(controls(:,6));
  591. Xc_IG = patient(:,6);
  592. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  593. fprintf('Successful go proportion\n')
  594. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  596. % SSRTs (ms)
  597. Xm_IG = nanmean(controls(:,7));
  598. Xs_IG = nanstd(controls(:,7));
  599. Xc_IG = patient(:,7);
  600. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  601. fprintf('SSRTs (ms)\n')
  602. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  604. % -- 40 % stop ratio -- %
  605. % Specify constants
  606. ss_ratio = 40;
  607. data = meansall(meansall(:,2) == ss_ratio, :);
  608. controls = data(data(:,8) == ctl_group, :);
  609. patient = data(data(:,8) == patient_group, :);
  611. disp('---------------------------------------')
  612. disp('Analysis for 40% stop signal ratio...');
  613. % successful go proportion
  614. Xm_IG = nanmean(controls(:,3));
  615. Xs_IG = nanstd(controls(:,3));
  616. Xc_IG = patient(:,3);
  617. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  618. fprintf('Successful go proportion\n')
  619. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  621. % successful go SRTs (ms)
  622. Xm_IG = nanmean(controls(:,4));
  623. Xs_IG = nanstd(controls(:,4));
  624. Xc_IG = patient(:,4);
  625. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  626. fprintf('Successful go SRTs (ms)\n')
  627. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  629. % successful stop proportion
  630. Xm_IG = nanmean(controls(:,5));
  631. Xs_IG = nanstd(controls(:,5));
  632. Xc_IG = patient(:,5);
  633. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  634. fprintf('Successful stop proportion\n')
  635. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  637. % failed stop SRTs (ms)
  638. Xm_IG = nanmean(controls(:,6));
  639. Xs_IG = nanstd(controls(:,6));
  640. Xc_IG = patient(:,7);
  641. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  642. fprintf('Successful go proportion\n')
  643. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  645. % SSRTs (ms)
  646. Xm_IG = nanmean(controls(:,7));
  647. Xs_IG = nanstd(controls(:,7));
  648. Xc_IG = patient(:,8);
  649. [h p t df] = ttestch(Xm_IG, Xs_IG, Xc_IG, n_IG, alpha);
  650. fprintf('SSRTs (ms)\n')
  651. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  653. % -- Difference in ssrts for 20% vs. 40% -- %
  654. % Specify constants
  655. ss20ratio = 20;
  656. data20 = meansall(meansall(:,2) == ss20ratio, :);
  657. controls20 = data20(data20(:,8) == ctl_group, :);
  658. patient20 = data20(data20(:,8) == patient_group, :);
  659. ss40ratio = 40;
  660. data40 = meansall(meansall(:,2) == ss40ratio, :);
  661. controls40 = data40(data40(:,8) == ctl_group, :);
  662. patient40 = data40(data40(:,8) == patient_group, :);
  664. Xm = nanmean(controls20(:,7));
  665. Xs = nanstd(controls20(:,7));
  666. Xc = patient20(:,7);
  667. Ym = nanmean(controls40(:,7));
  668. Ys = nanstd(controls40(:,7));
  669. Yc = patient40(:,7);
  670. r = corrcoef(controls20(:,7), controls40(:,7));
  671. [h p t df] = rsdt(Xm, Xs, Xc, Ym, Ys, Yc, n_IG, r(2), alpha);
  672. fprintf('SSRTs (ms) - Difference between 20%% and 40%%\n')
  673. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  674. elseif analysis_mode == "CF_groups_L"
  675. % Specify constants
  676. patient_group = 1;
  677. ctl_group = 2;
  678. ss_ratio = 20;
  679. data = meansall(meansall(:,2) == ss_ratio, :);
  680. controls = data(data(:,8) == ctl_group, :);
  681. patient = data(data(:,8) == patient_group, :);
  683. disp('---------------------------------------')
  684. fprintf('\nAnalysis mode: %s\n', analysis_mode)
  685. disp('---------------------------------------')
  686. % -- 20 % stop ratio -- %
  687. disp('Analysis for 20% stop signal ratio...');
  688. % successful go proportion
  689. Xm_CF = nanmean(controls(:,3));
  690. Xs_CF = nanstd(controls(:,3));
  691. Xc_CF = patient(:,3);
  692. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  693. fprintf('Successful go proportion\n')
  694. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  696. % successful go SRTs (ms)
  697. Xm_CF = nanmean(controls(:,4));
  698. Xs_CF = nanstd(controls(:,4));
  699. Xc_CF = patient(:,4);
  700. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  701. fprintf('Successful go SRTs (ms)\n')
  702. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  704. % successful stop proportion
  705. Xm_CF = nanmean(controls(:,5));
  706. Xs_CF = nanstd(controls(:,5));
  707. Xc_CF = patient(:,5);
  708. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  709. fprintf('Successful stop proportion\n')
  710. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  712. % failed stop SRTs (ms)
  713. Xm_CF = nanmean(controls(:,6));
  714. Xs_CF = nanstd(controls(:,6));
  715. Xc_CF = patient(:,6);
  716. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  717. fprintf('Successful go proportion\n')
  718. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  720. % SSRTs (ms)
  721. Xm_CF = nanmean(controls(:,7));
  722. Xs_CF = nanstd(controls(:,7));
  723. Xc_CF = patient(:,7);
  724. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  725. fprintf('SSRTs (ms)\n')
  726. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  728. % -- 40 % stop ratio -- %
  729. % Specify constants
  730. ss_ratio = 40;
  731. data = meansall(meansall(:,2) == ss_ratio, :);
  732. controls = data(data(:,8) == ctl_group, :);
  733. patient = data(data(:,8) == patient_group, :);
  735. disp('---------------------------------------')
  736. disp('Analysis for 40% stop signal ratio...');
  737. % successful go proportion
  738. Xm_CF = nanmean(controls(:,3));
  739. Xs_CF = nanstd(controls(:,3));
  740. Xc_CF = patient(:,3);
  741. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  742. fprintf('Successful go proportion\n')
  743. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  745. % successful go SRTs (ms)
  746. Xm_CF = nanmean(controls(:,4));
  747. Xs_CF = nanstd(controls(:,4));
  748. Xc_CF = patient(:,4);
  749. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  750. fprintf('Successful go SRTs (ms)\n')
  751. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  753. % successful stop proportion
  754. Xm_CF = nanmean(controls(:,5));
  755. Xs_CF = nanstd(controls(:,5));
  756. Xc_CF = patient(:,5);
  757. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  758. fprintf('Successful stop proportion\n')
  759. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  761. % failed stop SRTs (ms)
  762. Xm_CF = nanmean(controls(:,6));
  763. Xs_CF = nanstd(controls(:,6));
  764. Xc_CF = patient(:,6);
  765. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  766. fprintf('Successful go proportion\n')
  767. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  769. % SSRTs (ms)
  770. Xm_CF = nanmean(controls(:,7));
  771. Xs_CF = nanstd(controls(:,7));
  772. Xc_CF = patient(:,7);
  773. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  774. fprintf('SSRTs (ms)\n')
  775. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  777. % -- Difference in ssrts for 20% vs. 40% -- %
  778. ss20ratio = 20;
  779. data20 = meansall(meansall(:,2) == ss20ratio, :);
  780. controls20 = data20(data20(:,8) == ctl_group, :);
  781. patient20 = data20(data20(:,8) == patient_group, :);
  782. ss40ratio = 40;
  783. data40 = meansall(meansall(:,2) == ss40ratio, :);
  784. controls40 = data40(data40(:,8) == ctl_group, :);
  785. patient40 = data40(data40(:,8) == patient_group, :);
  787. Xm = nanmean(controls20(:,7));
  788. Xs = nanstd(controls20(:,7));
  789. Xc = patient20(:,7);
  790. Ym = nanmean(controls40(:,7));
  791. Ys = nanstd(controls40(:,7));
  792. Yc = patient40(:,7);
  793. r = corrcoef(controls20(:,7), controls40(:,7));
  794. [h p t df] = rsdt(Xm, Xs, Xc, Ym, Ys, Yc, n_CF, r(2), alpha);
  795. fprintf('SSRTs (ms) - Difference between 20%% and 40%%\n')
  796. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  797. elseif analysis_mode == "CF_groups_R"
  798. % Specify constants
  799. patient_group = 1;
  800. ctl_group = 2;
  801. ss_ratio = 20;
  802. data = meansall(meansall(:,2) == ss_ratio, :);
  803. controls = data(data(:,8) == ctl_group, :);
  804. patient = data(data(:,8) == patient_group, :);
  806. disp('---------------------------------------')
  807. fprintf('\nAnalysis mode: %s\n', analysis_mode)
  808. disp('---------------------------------------')
  809. % -- 20 % stop ratio -- %
  810. disp('Analysis for 20% stop signal ratio...');
  811. % successful go proportion
  812. Xm_CF = nanmean(controls(:,3));
  813. Xs_CF = nanstd(controls(:,3));
  814. Xc_CF = patient(:,3);
  815. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  816. fprintf('Successful go proportion\n')
  817. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  819. % successful go SRTs (ms)
  820. Xm_CF = nanmean(controls(:,4));
  821. Xs_CF = nanstd(controls(:,4));
  822. Xc_CF = patient(:,4);
  823. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  824. fprintf('Successful go SRTs (ms)\n')
  825. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  827. % successful stop proportion
  828. Xm_CF = nanmean(controls(:,5));
  829. Xs_CF = nanstd(controls(:,5));
  830. Xc_CF = patient(:,5);
  831. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  832. fprintf('Successful stop proportion\n')
  833. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  835. % failed stop SRTs (ms)
  836. Xm_CF = nanmean(controls(:,6));
  837. Xs_CF = nanstd(controls(:,6));
  838. Xc_CF = patient(:,6);
  839. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  840. fprintf('Successful go proportion\n')
  841. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  843. % SSRTs (ms)
  844. Xm_CF = nanmean(controls(:,7));
  845. Xs_CF = nanstd(controls(:,7));
  846. Xc_CF = patient(:,7);
  847. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  848. fprintf('SSRTs (ms)\n')
  849. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  851. % -- 40 % stop ratio -- %
  852. % specify constants
  853. ss_ratio = 40;
  854. data = meansall(meansall(:,2) == ss_ratio, :);
  855. controls = data(data(:,8) == ctl_group, :);
  856. patient = data(data(:,8) == patient_group, :);
  858. disp('---------------------------------------')
  859. disp('Analysis for 40% stop signal ratio...');
  860. % successful go proportion
  861. Xm_CF = nanmean(controls(:,3));
  862. Xs_CF = nanstd(controls(:,3));
  863. Xc_CF = patient(:,3);
  864. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  865. fprintf('Successful go proportion\n')
  866. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  868. % successful go SRTs (ms)
  869. Xm_CF = nanmean(controls(:,4));
  870. Xs_CF = nanstd(controls(:,4));
  871. Xc_CF = patient(:,4);
  872. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  873. fprintf('Successful go SRTs (ms)\n')
  874. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  876. % successful stop proportion
  877. Xm_CF = nanmean(controls(:,5));
  878. Xs_CF = nanstd(controls(:,5));
  879. Xc_CF = patient(:,5);
  880. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  881. fprintf('Successful stop proportion\n')
  882. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  884. % failed stop SRTs (ms)
  885. Xm_CF = nanmean(controls(:,6));
  886. Xs_CF = nanstd(controls(:,6));
  887. Xc_CF = patient(:,6);
  888. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  889. fprintf('Successful go proportion\n')
  890. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  892. % SSRTs (ms)
  893. Xm_CF = nanmean(controls(:,7));
  894. Xs_CF = nanstd(controls(:,7));
  895. Xc_CF = patient(:,7);
  896. [h p t df] = ttestch(Xm_CF, Xs_CF, Xc_CF, n_CF, alpha);
  897. fprintf('SSRTs (ms)\n')
  898. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  900. % -- Difference in ssrts for 20% vs. 40% -- %
  901. %Specify constants
  902. ss20ratio = 20;
  903. data20 = meansall(meansall(:,2) == ss20ratio, :);
  904. controls20 = data20(data20(:,8) == ctl_group, :);
  905. patient20 = data20(data20(:,8) == patient_group, :);
  906. ss40ratio = 40;
  907. data40 = meansall(meansall(:,2) == ss40ratio, :);
  908. controls40 = data40(data40(:,8) == ctl_group, :);
  909. patient40 = data40(data40(:,8) == patient_group, :);
  911. Xm = nanmean(controls20(:,7));
  912. Xs = nanstd(controls20(:,7));
  913. Xc = patient20(:,7);
  914. Ym = nanmean(controls40(:,7));
  915. Ys = nanstd(controls40(:,7));
  916. Yc = patient40(:,7);
  917. r = corrcoef(controls20(:,7), controls40(:,7));
  918. [h p t df] = rsdt(Xm, Xs, Xc, Ym, Ys, Yc, n_CF, r(2), alpha);
  919. fprintf('SSRTs (ms) - Difference between 20%% and 40%%\n')
  920. fprintf('t(%d) = %.3f, p = %.4f\n', df, t, p)
  921. end
  925. %% ---- Data visualization ---- %%
  926. % Standard deviation (SD) is hidden in the plots below to match the manuscripts
  927. % figures, which display mean values with 95% confidence intervals only.
  929. if analysis_mode == "IG_groups"
  930. color_patient = [0 0.6 0];
  931. color_ctl = [0.4 0.4 0.4];
  932. patient_label = 'IG';
  933. ctl_label = 'ctls';
  935. datapatient20 = find(meansall(:, 2) == 20 & meansall(:, 8) == 3);
  936. datactl20 = find(meansall(:, 2) == 20 & meansall(:, 8) == 4);
  937. datapatient40 = find(meansall(:, 2) == 40 & meansall(:, 8) == 3);
  938. datactl40 = find(meansall(:, 2) == 40 & meansall(:, 8) == 4);
  939. elseif analysis_mode == "CF_groups_L"
  940. color_patient = 'r';
  941. color_ctl = [1 1 1];
  942. patient_label = 'CF left';
  943. ctl_label = 'ctls';
  945. datapatient20 = find(meansall(:, 2) == 20 & meansall(:, 8) == 1);
  946. datactl20 = find(meansall(:, 2) == 20 & meansall(:, 8) == 2);
  947. datapatient40 = find(meansall(:, 2) == 40 & meansall(:, 8) == 1);
  948. datactl40 = find(meansall(:, 2) == 40 & meansall(:, 8) == 2);
  949. elseif analysis_mode == "CF_groups_R"
  950. color_patient = 'r';
  951. color_ctl = [1 1 1];
  952. patient_label = 'CF right';
  953. ctl_label = 'ctls';
  956. datapatient20 = find(meansall(:, 2) == 20 & meansall(:, 8) == 1);
  957. datactl20 = find(meansall(:, 2) == 20 & meansall(:, 8) == 2);
  958. datapatient40 = find(meansall(:, 2) == 40 & meansall(:, 8) == 1);
  959. datactl40 = find(meansall(:, 2) == 40 & meansall(:, 8) == 2);
  960. end
  962. % Go proportition plot
  963. figure()
  964. hold on;
  965. datapoint = plot(1, meansall(datapatient20, 3), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  966. h = notBoxPlot(meansall(datactl20 , 3), 2);
  967. set([h.sdPtch], 'visible', 'off');
  968. set(h.semPtch, 'FaceColor', [0.8 0.8 0.8])
  969. set(h.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  970. set(h.data, 'MarkerFaceColor', color_ctl)
  971. set(h.mu, 'Color', 'k')
  972. hold on;
  973. plot(3, meansall(datapatient40, 3), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  974. h1 = notBoxPlot(meansall(datactl40, 3), 4);
  975. set([h1.sdPtch], 'visible', 'off');
  976. set(h1.semPtch, 'FaceColor', [0.8 0.8 0.8])
  977. set(h1.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  978. set(h1.data, 'MarkerFaceColor', color_ctl)
  979. set(h1.mu, 'Color', 'k')
  980. set(gca, 'XTick', [1.5 3.5])
  981. set(gca, 'XTickLabel', {'20%', '40%'})
  982. xlabel('Stop signal ratio');
  983. ylabel('Proportion');
  984. axis([0 5 0.90 1.1]);
  985. legend([h.mu h.semPtch datapoint h.data], {'Mean', '95% CI', patient_label, ctl_label})
  986. title('Go proportion');
  988. % Go SRTs (ms) plot
  989. figure()
  990. hold on;
  991. datapoint = plot(1, meansall(datapatient20, 4), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  992. h = notBoxPlot(meansall(datactl20 , 4), 2);
  993. set([h.sdPtch], 'visible', 'off');
  994. set(h.semPtch, 'FaceColor', [0.8 0.8 0.8])
  995. set(h.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  996. set(h.data, 'MarkerFaceColor', color_ctl)
  997. set(h.mu, 'Color', 'k')
  998. hold on;
  999. plot(3, meansall(datapatient40, 4), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  1000. h1 = notBoxPlot(meansall(datactl40, 4), 4);
  1001. set([h1.sdPtch], 'visible', 'off');
  1002. set(h1.semPtch, 'FaceColor', [0.8 0.8 0.8])
  1003. set(h1.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  1004. set(h1.data, 'MarkerFaceColor', color_ctl)
  1005. set(h1.mu, 'Color', 'k')
  1006. set(gca, 'XTick', [1.5 3.5])
  1007. set(gca, 'XTickLabel', {'20%', '40%'})
  1008. xlabel('Stop signal ratio');
  1009. ylabel('Go SRTs (ms)');
  1010. axis([0 5 0 500]);
  1011. legend([h.mu h.semPtch datapoint h.data], {'Mean', '95% CI', patient_label, ctl_label}, 'Location', 'northwest')
  1012. title('Go SRT(ms) across stop signal ratios');
  1014. % Stop proportion plot
  1015. figure()
  1016. hold on;
  1017. datapoint = plot(1, meansall(datapatient20, 5), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  1018. h = notBoxPlot(meansall(datactl20, 5), 2);
  1019. set([h.sdPtch], 'visible', 'off');
  1020. set(h.semPtch, 'FaceColor', [0.8 0.8 0.8])
  1021. set(h.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  1022. set(h.data, 'MarkerFaceColor', color_ctl)
  1023. set(h.mu, 'Color', 'k')
  1024. hold on;
  1025. plot(3, meansall(datapatient40, 5), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  1026. h1 = notBoxPlot(meansall(datactl40, 5), 4);
  1027. set([h1.sdPtch], 'visible', 'off');
  1028. set(h1.semPtch, 'FaceColor', [0.8 0.8 0.8])
  1029. set(h1.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  1030. set(h1.data, 'MarkerFaceColor', color_ctl)
  1031. set(h1.mu, 'Color', 'k')
  1032. set(gca, 'XTick', [1.5 3.5])
  1033. set(gca, 'XTickLabel', {'20%', '40%'})
  1034. xlabel('Stop signal ratio');
  1035. ylabel('Proportion');
  1036. axis([0 5 0.10 0.90]);
  1037. legend([h.mu h.semPtch datapoint h.data], {'Mean', '95% CI', patient_label, ctl_label})
  1038. title('Successful stop proportion');
  1040. % Stop SRTs (ms) plot
  1041. figure()
  1042. hold on;
  1043. datapoint = plot(1, meansall(datapatient20, 6), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  1044. h = notBoxPlot(meansall(datactl20 , 6), 2);
  1045. set([h.sdPtch], 'visible', 'off');
  1046. set(h.semPtch, 'FaceColor', [0.8 0.8 0.8])
  1047. set(h.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  1048. set(h.data, 'MarkerFaceColor', color_ctl)
  1049. set(h.mu, 'Color', 'k')
  1050. hold on;
  1051. plot(3, meansall(datapatient40, 6), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  1052. h1 = notBoxPlot(meansall(datactl40, 6), 4);
  1053. set([h1.sdPtch], 'visible', 'off');
  1054. set(h1.semPtch, 'FaceColor', [0.8 0.8 0.8])
  1055. set(h1.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  1056. set(h1.data, 'MarkerFaceColor', color_ctl)
  1057. set(h1.mu, 'Color', 'k')
  1058. set(gca, 'XTick', [1.5 3.5])
  1059. set(gca, 'XTickLabel', {'20%', '40%'})
  1060. xlabel('Stop signal ratio');
  1061. ylabel('Failed stop SRTs (ms)');
  1062. axis([0 5 0 500]);
  1063. legend([h.mu h.semPtch datapoint h.data], {'Mean', '95% CI', patient_label, ctl_label})
  1064. title('Failed stop SRT (ms) across stop signal ratios');
  1066. % SSRT (ms) plot
  1067. figure()
  1068. hold on;
  1069. datapoint = plot(1, meansall(datapatient20, 7), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  1070. h = notBoxPlot(meansall(datactl20, 7), 2);
  1071. set([h.sdPtch], 'visible', 'off');
  1072. set(h.semPtch, 'FaceColor', [0.8 0.8 0.8])
  1073. set(h.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  1074. set(h.data, 'MarkerFaceColor', color_ctl)
  1075. set(h.mu, 'Color', 'k')
  1076. hold on;
  1077. plot(3, meansall(datapatient40, 7), 'Marker', 'o', 'MarkerEdgeColor', [0.2 0.2 0.2], 'MarkerFaceColor', color_patient, 'LineStyle', 'none');
  1078. h1 = notBoxPlot(meansall(datactl40, 7), 4);
  1079. set([h1.sdPtch], 'visible', 'off');
  1080. set(h1.semPtch, 'FaceColor', [0.8 0.8 0.8])
  1081. set(h1.semPtch, 'EdgeColor', [0.2 0.2 0.2])
  1082. set(h1.data, 'MarkerFaceColor', color_ctl)
  1083. set(h1.mu, 'Color', 'k')
  1084. set(gca, 'XTick', [1.5 3.5])
  1085. set(gca, 'XTickLabel', {'20%', '40%'})
  1086. xlabel('Stop signal ratio');
  1087. ylabel('SSRT (ms)');
  1088. axis([0 5 0 400]);
  1089. legend([h.mu h.semPtch datapoint h.data], {'Mean', '95% CI', patient_label, ctl_label})
  1090. title('SSRTs across stop signal ratios');

ssanalysis.m, no license · at the source

Overview

Authors: Julie Ouerfelli‐Ethier1,2, Tristan Jurkiewicz1, Isabella Comtois‐Bona2, Thomas Carrier2, Aarlenne Z. Khan2, Laure Pisella1
ORCID iDs: Julie Ouerfelli‐Ethier, Laure Pisella
  1. Université Claude Bernard Lyon 1, Centre de Recherche en Neurosciences de Lyon CRNL, INSERM U1028, CNRS UMR5292, Trajectoires, Centre Hospitalier Le Vinatier, Bâtiment 336 Bron France
  2. École d'Optométrie Université de Montréal Montréal Québec Canada
Institutions: Lyon 1 Université (France); Centre National de la Recherche Scientifique (France); Inserm (France); Centre de Recherche en Neurosciences de Lyon (France); Centre Hospitalier Le Vinatier (France); Université de Montréal (Canada)
Journal: The European journal of neuroscience, volume 63, issue 9, article e70528
Dates: received 12 November 2025; accepted 17 April 2026; published online 6 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/ejn.70528 · PMID 42087821 · PMCID PMC13147234 · OpenAlex W7160373273
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), human (organism), stroke (population), cognitive (subfield)
Methods: Connectivity, Physiology & signal measures
Keywords: countermanding, inhibition of return, motor intention, saccade planning, spatial attention
MeSH: Inhibition, Psychological*, Parietal Lobe*, Psychomotor Performance*, Space Perception*, Stroke*, Aged, Attention, Female, Humans, Male, Middle Aged, Reaction Time, Saccades (* major topic)
Topic: Spatial Neglect and Hemispheric Dysfunction (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Natural Sciences and Engineering Research Council of Canada (BESD‐546502‐2020); Études Supérieures et Post‐doctorales and École d’optométrie merit scholarship; Canada Research Chairs; Université de Lyon Idex Mobility fund; Réseau de la santé de la vision du Québec; Centre national de la recherche scientifique
Citations: cited by 1 paper (Europe PMC); 121 references in the paper

Abstract

Spatial and response inhibition are two different types of inhibition processes. Spatial inhibition refers to the suppression of a specific location, whereas response inhibition involves cancelling a planned movement and is motor based. Here we examined the effects of lesions on the dorsal posterior parietal cortex on performance during two saccade tasks that separately assessed spatial (inhibition of return task) and response inhibition (stop signal task). We tested two stroke patients, one with unilateral and one with bilateral lesions to the dorsal posterior parietal cortex, as well as 21 age‐matched controls. In our spatial inhibition task, control participants showed the typical inhibition of return effect, whereas patients exhibited no inhibition of return in their ataxic hemifields. In contrast, patients and their matched controls performed similarly on the stop signal task. These results reveal a simple dissociation in our patients, where motor‐based inhibition is preserved following damage to the dorsal posterior parietal cortex, whereas spatial inhibition is impaired. This highlights the specific role of the dorsal posterior parietal cortex in spatial inhibition, notably related to spatial attentional mechanisms.

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

Repository

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OSF bnh9e

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (15)
Size: 21 files, 15 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
15 files
At the source: doi.org/10.17605/OSF.IO/BNH9E

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 15 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);
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Data

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

Data Availability Statement

The data and analysis scripts required to reproduce the results and figures are available on the Open Science Framework repository at the following link: https://doi.org/10.17605/OSF.IO/BNH9E.

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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 13 MeSH terms, 6 funders, 115 references.

Cite

This paper

Ouerfelli‐Ethier, J., Jurkiewicz, T., Comtois‐Bona, I., Carrier, T., Khan, A. Z., & Pisella, L. (2026). Dorsal Posterior Parietal Cortex Lesions Disrupt Spatial- but Not Motor-Based Inhibition. The European journal of neuroscience, 63(9), e70528. https://doi.org/10.1111/ejn.70528

BibTeX

@article{ouerfelliethier2026dorsal,
author = {Ouerfelli‐Ethier, Julie and Jurkiewicz, Tristan and Comtois‐Bona, Isabella and Carrier, Thomas and Khan, Aarlenne Z. and Pisella, Laure},
title = {{Dorsal Posterior Parietal Cortex Lesions Disrupt Spatial- but Not Motor-Based Inhibition}},
journal = {The European journal of neuroscience},
year = {2026},
month = may,
volume = {63},
number = {9},
pages = {e70528},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70528},
url = {https://doi.org/10.1111/ejn.70528},
pmid = {42087821},
pmcid = {PMC13147234}
}

RIS

TY - JOUR
AU - Ouerfelli‐Ethier, Julie
AU - Jurkiewicz, Tristan
AU - Comtois‐Bona, Isabella
AU - Carrier, Thomas
AU - Khan, Aarlenne Z.
AU - Pisella, Laure
TI - Dorsal Posterior Parietal Cortex Lesions Disrupt Spatial- but Not Motor-Based Inhibition
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/05/01
VL - 63
IS - 9
SP - e70528
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70528
UR - https://doi.org/10.1111/ejn.70528
LA - en
ER -

CSL-JSON

{
"id": "10.1111/ejn.70528",
"type": "article-journal",
"title": "Dorsal Posterior Parietal Cortex Lesions Disrupt Spatial- but Not Motor-Based Inhibition",
"container-title": "The European journal of neuroscience",
"author": [
{
"family": "Ouerfelli‐Ethier",
"given": "Julie"
},
{
"family": "Jurkiewicz",
"given": "Tristan"
},
{
"family": "Comtois‐Bona",
"given": "Isabella"
},
{
"family": "Carrier",
"given": "Thomas"
},
{
"family": "Khan",
"given": "Aarlenne Z."
},
{
"family": "Pisella",
"given": "Laure"
}
],
"container-title-short": "Eur J Neurosci",
"volume": "63",
"issue": "9",
"page": "e70528",
"DOI": "10.1111/ejn.70528",
"PMID": "42087821",
"PMCID": "PMC13147234",
"ISSN": "0953-816X",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/ejn.70528",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
1
]
]
}
}

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