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Closed-loop readout of anterior insula high-gamma activity steers value-based decisions.

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2 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.

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  1. [1] § Methods › Statistical analyses ↔ a1_behav_archoices_bci.m, lines 398–461 · score 0.67 · regression coefficients, random slopes, fitglme, linear, intercepts, predictor
  2. [2] § Methods › Statistical analyses › Behavioral effects of BCI-triggered neural states ↔ a1_behav_archoices_bci.m, lines 332–396 · score 0.54 · inter trial interval, reject, mixed, behavior, variables, aIns

Paper

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The authors' code

MATLAB · 837 lines · 40 KB · CC-BY-4.0 · 2 matches

  1. %% a1_behav_archoices_bci
  2. % Runs behavioural analyses for the simple choices stage.
  3. %
  4. % Input:
  5. % - analysis level: 1 for individual analyses, 2 for group analyses.
  6. % Lvl 1 must be run before lvl 2 (lvl 2 uses output saved during lvl 1).
  7. %
  8. % Output:
  9. % - none. Runs function of corresponding analysis level, which saves
  10. % its own output struct and plots to corresponding 'out' folder.
  11. %
  12. % Clarissa Baratin, April 2021 / G. Becq 20260603
  13. % parts of script adapted from Teddy Landron
  15. %% options struct
  17. options.smooth_fit = {'yes'}; % fit curve smoothing? 'yes' or 'no'
  18. options.n_eltBin = 10; %
  19. options.nBin = 7;
  20. options.prctile = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100];
  21. options.mean_med = 'mean'; % mean or median 'med'RT
  22. options.plot_optional_figI = false;
  23. options.plot_optional_figII = false;
  25. %% individual vs. multiple subjects scripts
  27. % subjects = importdata('./subjects.mat');
  28. subjects = get_subjects();
  29. [subjects, stats_Lvl1] = f_behav_archoices_lvl1_bci(options, subjects);
  30. f_behav_archoices_lvl2_bci(options, subjects, stats_Lvl1);
  32. %%
  33. % transform subjects. mat to readable csv files
  34. % change to true if you want to generate csv file from subjects.mat file.
  35. if false
  36. subids = ["sub-01", "sub-02", "sub-03", "sub-04", "sub-05", "sub-06", "sub-07", "sub-08", "sub-09", "sub-10", "sub-11", "sub-12"];
  37. colnames1 = {'stimnumber', 'type1', 'type2', 'value', 'RT', '1stRT', 'Z'};
  38. colnames3 = {'Spatial_configuration', 'Pleasant_stimulus_number', 'Unpleasant_stimulus_number', ...
  39. 'Pleasant_stimulus_value', 'Unpleasant_stimulus_value', 'Value_difference', ...
  40. 'Value_sum', 'Accept?', 'Reaction_time', 'Threshold_reached', 'Nothing', ...
  41. 'Trial_onset', 'Onset_of_confirmation_screen', 'col14'};
  43. for i = 1 : 12
  44. x1 = subjects(i).trial_characteristics1;
  45. T1 = array2table(x1, 'VariableNames', colnames1);
  46. fn1 = subids(i) + "_trialchar1.csv";
  47. writetable(T1, fn1);
  48. end
  50. rois = {'vmPFC', 'aIns'};
  51. for i = 1 : 12
  52. for iroi = 1 : 2
  53. roi = rois{iroi};
  54. if isfield(subjects(i).trial_characteristics3, roi)
  55. T3 = array2table(subjects(i).trial_characteristics3.(roi), 'VariableNames', colnames3);
  56. fn3 = subids(i) + "_trialchar3_" + roi + ".csv";
  57. writetable(T3, fn3);
  58. end
  59. end
  60. end
  61. end
  63. %%
  64. function subjects = get_subjects()
  65. subids = ["sub-01", "sub-02", "sub-03", "sub-04", "sub-05", "sub-06", "sub-07", "sub-08", "sub-09", "sub-10", "sub-11", "sub-12"];
  66. rois = {'vmPFC', 'aIns'};
  67. subjects = [];
  68. for isub = 1 : 12
  69. fn1 = subids(isub) + "_trialchar1.csv";
  70. T1 = readtable(fn1);
  71. subjects(isub).name = subids(isub);
  72. subjects(isub).trial_characteristics1 = table2array(T1);
  73. for iroi = 1 : 2
  74. roi = rois{iroi};
  75. fn3 = subids(isub) + "_trialchar3_" + roi + ".csv";
  76. disp(exist(fn3))
  77. if exist(fn3)
  78. T3 = readtable(fn3);
  79. subjects(isub).trial_characteristics3.(roi) = table2array(T3);
  80. end
  81. end
  82. end
  83. end
  86. %%
  87. function [subjects, stats_Lvl1] = f_behav_archoices_lvl1_bci(options, subjects)
  88. % # Initialize structures
  90. nSub = size(subjects, 2);
  91. stats = struct();
  92. stats_Lvl1 = [];
  94. % # MEDIAN PERCENTAGE YES RESPONSES
  96. % figure
  97. %
  98. % T_percentAccept = [];
  99. %
  100. % for iSub = 1:nSub
  101. %
  102. % % get percentage of trials where participant accepts
  103. % stats.percentAccept(iSub) = sum(subjects(iSub).trial_characteristics3(:,8) == 1)/length(subjects(iSub).trial_characteristics3(:,8))*100;
  104. %
  105. % % plot nb of trials where participant accepts
  106. % hold on
  107. % scatter(iSub, stats.percentAccept(iSub))
  108. % xlabel('Subjects')
  109. % ylabel('Acceptance %')
  110. % xlim([0 nSub + 1])
  111. % ylim([0 100])
  112. %
  113. % end
  114. %
  115. % f_saveToMultipleFormats(fullfile(PLOTS_PATH, 'AcceptancePercent'))
  117. % # P(accept) dependent on condition
  119. count_vmPFC = 0;
  120. count_aIns = 0;
  122. for iSub = 1:nSub
  124. fields = fieldnames(subjects(iSub).trial_characteristics3);
  126. for iROI = 1:size(fields, 1)
  128. roi_name = fields{iROI};
  130. switch roi_name
  131. case 'vmPFC'
  132. count_vmPFC = count_vmPFC + 1;
  133. count = count_vmPFC;
  134. case 'aIns'
  135. count_aIns = count_aIns + 1;
  136. count = count_aIns;
  137. end
  139. stats.(roi_name).subject{count} = subjects(iSub).name;
  141. UP_subset = find(subjects(iSub).trial_characteristics3.(roi_name)(:,10) == 2);
  142. DOWN_subset = find(subjects(iSub).trial_characteristics3.(roi_name)(:,10) == 1);
  144. stats.(roi_name).UP.percentAccept(count) = sum(subjects(iSub).trial_characteristics3.(roi_name)(UP_subset,8) == 1)/length(subjects(iSub).trial_characteristics3.(roi_name)(UP_subset,8))*100;
  145. UP_percentaccept = stats.(roi_name).UP.percentAccept(count);
  146. stats.(roi_name).DOWN.percentAccept(count) = sum(subjects(iSub).trial_characteristics3.(roi_name)(DOWN_subset,8) == 1)/length(subjects(iSub).trial_characteristics3.(roi_name)(DOWN_subset,8))*100;
  147. DOWN_percentaccept = stats.(roi_name).DOWN.percentAccept(count);
  149. %RT data
  150. stats.(roi_name).UP.RT(count) = mean(subjects(iSub).trial_characteristics3.(roi_name)(UP_subset,9));
  151. stats.(roi_name).DOWN.RT(count) = mean(subjects(iSub).trial_characteristics3.(roi_name)(DOWN_subset,9));
  153. if options.plot_optional_figI
  154. figure('Position',[100 100 150 150])
  155. X = categorical({'Up','Down'});
  156. b = bar(X, [UP_percentaccept, DOWN_percentaccept],0.5, 'EdgeColor',[0 0.4470 0.7410]);
  157. b.FaceColor = 'flat';
  158. b.CData(1,:) = [0.8500 0.3250 0.0980];
  159. ylim([0 100])
  160. title(sprintf('Subject %d', iSub))
  161. ylabel('Acceptance %')
  162. set(gca, 'box', 'off')
  163. % f_saveToMultipleFormats(fullfile(strcat(subjects(iSub).name,'_AcceptancePercent_UP_DOWN', roi_name)))
  164. end
  166. %%%%%%% Compare distributions of value differences between conditions
  168. value_diff_UP = subjects(iSub).trial_characteristics3.(roi_name)(UP_subset, 6);
  169. value_diff_DOWN = subjects(iSub).trial_characteristics3.(roi_name)(DOWN_subset, 6);
  170. if options.plot_optional_figI
  171. figure('Position',[100 100 250 170])
  172. histogram(value_diff_UP, 15, 'FaceColor',[0 0.4470 0.7410])
  173. hold on
  174. histogram(value_diff_DOWN, 15, 'FaceColor', [0.8500 0.3250 0.0980])
  175. xlabel('P-UP values')
  176. set(gca, 'box', 'off')
  177. % saveas(gcf, fullfile(strcat(subjects(iSub).name,'_ValueDistr_UP_DOWN', roi_name)),'epsc');
  178. end
  179. end
  180. end
  182. % # ANALYSES, per bin method, subject
  184. count_vmPFC = 0;
  185. count_aIns = 0;
  187. % supblot formatting
  188. binMethods = {'nBin', 'nElt', 'prctile'};
  190. % figure('Position',[100 100 11000 300]);
  192. for iSub = 1:nSub %GLMs per subject loop: layer 1
  194. fields = fieldnames(subjects(iSub).trial_characteristics3);
  196. for iROI = 1:size(fields, 1)
  198. roi_name = fields{iROI};
  200. switch roi_name
  201. case 'vmPFC'
  202. count_vmPFC = count_vmPFC + 1;
  203. count = count_vmPFC;
  204. case 'aIns'
  205. count_aIns = count_aIns + 1;
  206. count = count_aIns;
  207. end
  209. behav_data = subjects(iSub).trial_characteristics3.(roi_name);
  211. P_value = behav_data(:, 4);
  212. UP_value = behav_data(:, 5);
  213. P_UP_diff = behav_data(:, 6);
  214. RT = behav_data(:, 9);
  215. accept = behav_data(:, 8);
  217. P_value_zscored = normalize(behav_data(:, 4));
  218. UP_value_zscored = normalize(behav_data(:, 5));
  220. UP_subset = find(behav_data(:,10) == 2);
  221. DOWN_subset = find(behav_data(:,10) == 1);
  223. % DOWN trials
  224. % logistic regression: choice as a function of P-UP
  225. [DOWN_betas_P_UP_diff, ~, DOWN_stats_P_UP_diff] = glmfit(P_UP_diff(DOWN_subset), accept(DOWN_subset), 'binomial', 'logit');
  226. DOWN_P_UP_diff_fit = glmval(DOWN_betas_P_UP_diff, P_UP_diff(DOWN_subset), 'logit');
  227. DOWN_P_UP_diff_fit_toplot = sortrows([P_UP_diff(DOWN_subset), DOWN_P_UP_diff_fit]); % only to make plotting fit as line easier
  229. % UP trials
  230. % logistic regression: choice as a function of P-UP
  231. [UP_betas_P_UP_diff, ~, UP_stats_P_UP_diff] = glmfit(P_UP_diff(UP_subset), accept(UP_subset), 'binomial', 'logit');
  232. UP_P_UP_diff_fit = glmval(UP_betas_P_UP_diff, P_UP_diff(UP_subset), 'logit');
  233. UP_P_UP_diff_fit_toplot = sortrows([P_UP_diff(UP_subset), UP_P_UP_diff_fit]); % only to make plotting fit as line easier
  235. %%%% PSE calculation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  236. % calculate point of subjective equality
  237. %http://rstudio-pubs-static.s3.amazonaws.com/446272_5aa2c51c9e2d4e71b2cf8229e205ce64.html
  239. UP_PSE_P_UP_diff(iSub, 1) = -(UP_betas_P_UP_diff(1)/UP_betas_P_UP_diff(2)); % if you let a + BX =0, then p = .50
  240. DOWN_PSE_P_UP_diff(iSub, 1) = -(DOWN_betas_P_UP_diff(1)/DOWN_betas_P_UP_diff(2)); % if you let a + BX =0, then p = .50
  242. % plot glms
  243. if options.plot_optional_figI
  244. figure('Position',[100 100 150 150]);
  245. cmap = [[0.4660 0.6740 0.1880]; [0.6350 0.0780 0.1840]]; %red, green
  246. g1 = gramm('x', UP_P_UP_diff_fit_toplot(:,1), 'y', UP_P_UP_diff_fit_toplot(:,2));
  247. g1.geom_line();
  248. g1.set_line_options('base_size', 2);
  249. g1.set_title(sprintf('Subject %d', iSub));
  250. g1.set_names('x', 'P- UP value', 'y', 'Choice');
  251. g1.axe_property('YLim', [0 1], 'XGrid', 'on', 'YGrid', 'on');
  252. g1.set_color_options('map', [0.6350 0.0780 0.1840; 0 0 0]);
  253. g1.update('x', DOWN_P_UP_diff_fit_toplot(:,1), 'y', DOWN_P_UP_diff_fit_toplot(:,2));
  254. g1.geom_line();
  255. g1.set_color_options('map', [0.4660 0.6740 0.1880; 0 0 0]);
  256. g1.draw();
  257. end
  258. % f_saveToMultipleFormats(fullfile(PLOTS_PATH, strcat(subjects(iSub).name,'P_UP_diff_regression_', roi_name)));
  260. %%%%%%% MODEL FOR PAPER %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  261. % logistic regression: predict choice with weights for pleasant and unpleasant values, for up and down trials separately
  263. xP_UP = [P_value_zscored,UP_value_zscored]; % column 1: Pleasant rating values; 2: Unpleasant rating values
  265. % up trials
  266. [UP_betas_P_UP,~,UP_stats_P_UP] = glmfit(xP_UP(UP_subset, :), accept(UP_subset),'binomial','link','logit');
  268. UP_fit_pleasant = glmval(UP_betas_P_UP(1:2), xP_UP(UP_subset, 1), 'logit');
  269. UP_fit_toplot_pleasant = sortrows([xP_UP(UP_subset, 1), UP_fit_pleasant]); % only to make plotting fit as line easier
  271. UP_fit_unpleasant = glmval(UP_betas_P_UP([1, 3]), xP_UP(UP_subset, 2), 'logit');
  272. UP_fit_toplot_unpleasant = sortrows([xP_UP(UP_subset, 2), UP_fit_unpleasant]); % only to make plotting fit as line easier
  274. stats.(roi_name).UP.glm_P_UP_betas(count, :) = UP_betas_P_UP;
  276. % down trials
  277. [DOWN_betas_P_UP,~,DOWN_stats_P_UP] = glmfit(xP_UP(DOWN_subset, :),accept(DOWN_subset),'binomial','link','logit');
  279. DOWN_fit_pleasant = glmval(DOWN_betas_P_UP(1:2), xP_UP(DOWN_subset, 1), 'logit');
  280. DOWN_fit_toplot_pleasant = sortrows([xP_UP(DOWN_subset, 1), DOWN_fit_pleasant]); % only to make plotting fit as line easier
  282. DOWN_fit_unpleasant = glmval(DOWN_betas_P_UP([1, 3]), xP_UP(DOWN_subset, 2), 'logit');
  283. DOWN_fit_toplot_unpleasant = sortrows([xP_UP(DOWN_subset, 2), DOWN_fit_unpleasant]); % only to make plotting fit as line easier
  285. stats.(roi_name).DOWN.glm_P_UP_betas(count, :) = DOWN_betas_P_UP;
  287. % plot glms: pleasant trials in green, unpleasant in red
  288. if options.plot_optional_figI
  289. figure('Position',[100 100 600 150]);
  291. subplot(1, 4, 1);
  292. plot(UP_fit_toplot_pleasant(:, 1), UP_fit_toplot_pleasant(:, 2), 'LineWidth', 2);
  293. title('Pleasant - up');
  294. ylim([0 1]);
  295. xlabel('Value');
  296. ylabel('Choice');
  298. subplot(1, 4, 2);
  299. plot(UP_fit_toplot_unpleasant(:, 1), UP_fit_toplot_unpleasant(:, 2), 'LineWidth', 2);
  300. title('Unpleasant - up');
  301. ylim([0 1]);
  302. xlabel('Value');
  303. ylabel('Choice');
  305. subplot(1, 4, 3);
  306. plot(DOWN_fit_toplot_pleasant(:, 1), DOWN_fit_toplot_pleasant(:, 2), 'LineWidth', 2);
  307. title('Pleasant - down');
  308. ylim([0 1]);
  309. xlabel('Value');
  310. ylabel('Choice');
  312. subplot(1, 4, 4);
  313. plot(DOWN_fit_toplot_unpleasant(:, 1), DOWN_fit_toplot_unpleasant(:, 2), 'LineWidth', 2);
  314. title('Unpleasant - down');
  315. ylim([0 1]);
  316. xlabel('Value');
  317. ylabel('Choice');
  318. end
  319. % f_saveToMultipleFormats(fullfile(PLOTS_PATH, strcat(subjects(iSub).name,'glm_w_weights_', roi_name)));
  320. end
  321. end
  323. % stats.options = options;
  324. stats_Lvl1 = stats;
  326. % save(fullfile(OUT_PATH, 'subjects.mat'), 'subjects')
  327. % save(fullfile(OUT_PATH, 'stats_Lvl1.mat'), 'stats_Lvl1')
  328. end
  331. %%
  332. function f_behav_archoices_lvl2_bci(options, subjects, indiv_stats)
  334. % f_behav_rating_lvl2 Function used to analyze behavioural data from all
  335. % participants together.
  336. %
  337. %
  338. %
  339. % Clarissa Baratin, April 2021
  340. % parts of script adapted from Teddy Landron
  342. nSub = size(subjects, 2);
  345. % # create a table for the mixed models
  347. roidata_allsubs = [];
  349. for iSub = 1:nSub
  351. fields = fieldnames(subjects(iSub).trial_characteristics3);
  353. for iROI = 1:length(fields)
  355. roi = fields{iROI};
  357. if isequal(roi,'aIns'); roi_nb = 1; else roi_nb = 2; end
  359. %if ~exist(fullfile(PLOTS_PATH, roi), 'dir') % Si le dossier d'analyses n'existe pas on le créé
  360. % mkdir(fullfile(PLOTS_PATH, roi));
  361. %end
  363. subdata_forT = [];
  364. sesdata = subjects(iSub).trial_characteristics3.(roi);
  366. % get info
  367. ntrials = length(sesdata);
  369. subdata_forT(:, 1) = repelem(iSub, ntrials);
  370. subdata_forT(:, 2) = repelem(roi_nb, ntrials);
  371. subdata_forT(:, 3) = normalize(sesdata(:, 6)); %P-UP values
  372. subdata_forT(:, 4) = sesdata(:, 8); % accept = 1, reject = 0
  373. subdata_forT(:, 5) = sesdata(:, 10); % 1 = down, 2 = up
  374. subdata_forT(:, 6) = sesdata(:, 9); %RT
  375. subdata_forT(:, 7) = normalize(sesdata(:, 9)); %RT normalized
  377. %inter-trial interval
  378. subdata_forT(:, 8) = [NaN; subjects(iSub).trial_characteristics3.(roi)(2:ntrials, 12) - subjects(iSub).trial_characteristics3.(roi)(1:ntrials-1, 13)];
  380. subdata_forT(:, 9) = abs(sesdata(:, 6)); %P-UP values, not normalized
  382. % % add pleasant and unpleasant stimulus values, z-scored
  383. % subdata_forT(:, 9) = normalize(sesdata(:, 4)); %P value
  384. % subdata_forT(:, 10) = normalize(sesdata(:, 5)); %UP value
  386. for itrial = 1:ntrials
  387. if subdata_forT(itrial, 8) > 60 || subdata_forT(itrial, 8) == 0; subdata_forT(itrial, 8) = NaN; end %avoid counting re-launched trials or between different sessions
  388. end
  390. roidata_allsubs = vertcat(roidata_allsubs, subdata_forT);
  392. T_data = array2table(roidata_allsubs, 'VariableNames',{'Sub', 'ROI', 'Value_diff', 'Accept', 'Up_down', 'RT', 'RT-zscored', 'ItI_s', 'Value_diff_nonorm_abs'});
  393. end
  395. RT_meanpersub(iSub, 1) = mean(subdata_forT(:, 6));
  396. end
  398. %% ADDED 26/10/2025: PREDICT RT FROM UNSIGNED VALUE DIFF
  400. % #Mixed linear regression
  401. % predict RT with a random slope and intercept per subject
  402. glme2 = [];
  403. glme2 = fitglme(T_data,'RT~Value_diff_nonorm_abs+(Value_diff_nonorm_abs|Sub)', 'Distribution', 'Normal', 'Link', 'identity')
  404. [fixed_betas,fixedbetanames] = fixedEffects(glme2);
  405. fixed_rows_valuediff = strcmp(fixedbetanames.Name, 'Value_diff_nonorm_abs');
  407. [random_betas,beta_names] = randomEffects(glme2);
  408. fixed_effects_predictions_valuediff = glmval(fixed_betas(fixed_rows_valuediff, :), T_data.Value_diff_nonorm_abs, 'identity');
  409. fixed_effects_predictions_toplot_valuediff = sortrows([T_data.Value_diff_nonorm_abs, fixed_effects_predictions_valuediff]); %sorts the rows of a matrix in ascending order based on the elements in the first column
  411. if options.plot_optional_figII
  412. figure('Position',[100 100 130 130]);
  413. for iSub = 1:nSub
  414. rows = strcmp(beta_names.Level, num2str(iSub)) & (strcmp(beta_names.Name, 'Value_diff_nonorm_abs') | strcmp(beta_names.Name, '(Intercept)'));
  415. random_betas_persub = fixed_betas(fixed_rows_valuediff, :) + random_betas(rows);
  416. random_effects_predictions_persub = glmval(random_betas_persub, T_data.Value_diff_nonorm_abs, 'identity');
  417. random_effects_predictions_toplot_persub = sortrows([T_data.Value_diff_nonorm_abs, random_effects_predictions_persub]);
  418. hold on
  419. plot(random_effects_predictions_toplot_persub(:, 1), random_effects_predictions_toplot_persub(:, 2), 'Color', [0.8 0.8 0.8])
  420. end
  421. plot(fixed_effects_predictions_toplot_valuediff(:, 1), fixed_effects_predictions_toplot_valuediff(:, 2), 'LineWidth', 3, 'Color', 'black')
  422. % title(blk)
  423. xlabel('|Pleasant-unpleasant rating|')
  424. xticks([min(fixed_effects_predictions_toplot_valuediff(:, 1)) max(fixed_effects_predictions_toplot_valuediff(:, 1))])
  425. %xticklabels({'0', '100'})
  426. ylabel('RT')
  427. xlim([min(fixed_effects_predictions_toplot_valuediff(:, 1)) max(fixed_effects_predictions_toplot_valuediff(:, 1))])
  428. % saveas(gcf, fullfile('Mixed models - RT by abs(pleas-unpleas).png'));
  429. end
  431. % #GLM WEIGHTS for pleasant and unpleasant/up and down/vmPFC and aIns
  433. % Bar plots with two bars: up-down pleasant trials and up-down unpleasant trials %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  435. P_UP_col_vmPFC = [repelem({'Pleasant'}, length(indiv_stats.vmPFC.subject)), repelem({'Unpleasant'}, length(indiv_stats.vmPFC.subject))]';
  436. %UP_DOWN_col_vmPFC = [repelem({'Up'}, length(indiv_stats.vmPFC.subject)), repelem({'Up'}, length(indiv_stats.vmPFC.subject)), repelem({'Down'}, length(indiv_stats.vmPFC.subject)), repelem({'Down'}, length(indiv_stats.vmPFC.subject))]';
  437. UP_minus_DOWN_betas_col_vmPFC = [indiv_stats.vmPFC.UP.glm_P_UP_betas(:, 2)-indiv_stats.vmPFC.DOWN.glm_P_UP_betas(:, 2); indiv_stats.vmPFC.UP.glm_P_UP_betas(:, 3)-indiv_stats.vmPFC.DOWN.glm_P_UP_betas(:, 3)]; % col 2 = pleasant betas, col 3 = unpleasant betas
  438. T_glm_vmPFC = table(P_UP_col_vmPFC, UP_minus_DOWN_betas_col_vmPFC);
  441. if options.plot_optional_figII
  442. % Bar plot of pleasant vs unpleasant regression coefficients in the vmPFC for UP minus DOWN trials
  443. figure('Position',[100 100 300 300]);
  444. figfilename = 'barplot_betas_UPminusDOWN_vmPFC';
  445. g = gramm('x', T_glm_vmPFC.P_UP_col_vmPFC, 'y', T_glm_vmPFC.UP_minus_DOWN_betas_col_vmPFC);
  446. g.set_title('vmPFC');
  447. g.stat_summary('geom', {'bar', 'black_errorbar'}, 'width', 0.6);
  448. %g.set_color_options('map', [0.8 0.8 0.8]); %
  449. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  450. %g.axe_property('XLim', [0.5 2.5], 'XTick', [0.75 2.25], 'XTickLabels', {'Pleas','Unpleas'}); %
  451. g.set_names('y', 'Regression coefficients', 'x', ' ');
  452. g.geom_hline('yintercept', 0);
  453. %g.no_legend();
  454. g.update('x', T_glm_vmPFC.P_UP_col_vmPFC, 'y', T_glm_vmPFC.UP_minus_DOWN_betas_col_vmPFC);
  455. g.geom_jitter('dodge', 0.5);
  456. g.set_point_options('base_size', 4);
  457. %g.set_color_options('map', [0.1 0.1 0.1]); %
  458. g.no_legend();
  459. g.draw();
  460. % saveas(gcf, fullfile(PLOTS_PATH,[figfilename '.png']));
  461. end
  463. % ttests against zero
  464. % Pleasant betas - vmPFC
  465. fprintf('T-test pleasant regression coefficients vmPFC \n')
  466. [h,p,ci,stats] = ttest(indiv_stats.vmPFC.UP.glm_P_UP_betas(:, 2)-indiv_stats.vmPFC.DOWN.glm_P_UP_betas(:, 2))
  468. % Unpleasant betas - vmPFC
  469. fprintf('T-test unpleasant regression coefficients vmPFC \n')
  470. %[h,p,ci,stats] = ttest(indiv_stats.vmPFC.UP.glm_P_UP_betas(:, 3)indiv_stats.vmPFC.DOWN.glm_P_UP_betas(:, 3))
  472. % Bar plot of pleasant vs unpleasant regression coefficients in the aIns for UP minus DOWN trials
  473. P_UP_col_aIns = [repelem({'Pleasant'}, length(indiv_stats.aIns.subject)), repelem({'Unpleasant'}, length(indiv_stats.aIns.subject))]';
  474. %UP_DOWN_col_vmPFC = [repelem({'Up'}, length(indiv_stats.vmPFC.subject)), repelem({'Up'}, length(indiv_stats.vmPFC.subject)), repelem({'Down'}, length(indiv_stats.vmPFC.subject)), repelem({'Down'}, length(indiv_stats.vmPFC.subject))]';
  475. UP_minus_DOWN_betas_col_aIns = [indiv_stats.aIns.UP.glm_P_UP_betas(:, 2)-indiv_stats.aIns.DOWN.glm_P_UP_betas(:, 2); indiv_stats.aIns.UP.glm_P_UP_betas(:, 3)-indiv_stats.aIns.DOWN.glm_P_UP_betas(:, 3)]; % col 2 = pleasant betas, col 3 = unpleasant betas
  476. T_glm_aIns = table(P_UP_col_aIns, UP_minus_DOWN_betas_col_aIns);
  478. if options.plot_optional_figII
  479. % Bar plot of pleasant vs unpleasant regression coefficients in the vmPFC for UP and DOWN trials
  480. figure('Position',[100 100 300 300]);
  481. figfilename = 'barplot_betas_UPminusDOWN_aIns';
  482. g = gramm('x', T_glm_aIns.P_UP_col_aIns, 'y', T_glm_aIns.UP_minus_DOWN_betas_col_aIns);
  483. g.set_title('aIns');
  484. g.stat_summary('geom', {'bar', 'black_errorbar'}, 'width', 0.6);
  485. %g.set_color_options('map', [0.8 0.8 0.8]); %
  486. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  487. %g.axe_property('XLim', [0.5 2.5], 'XTick', [0.75 2.25], 'XTickLabels', {'Pleas','Unpleas'}); %
  488. g.set_names('y', 'Regression coefficients', 'x', ' ');
  489. g.geom_hline('yintercept', 0);
  490. %g.no_legend();
  491. g.update('x', T_glm_aIns.P_UP_col_aIns, 'y', T_glm_aIns.UP_minus_DOWN_betas_col_aIns);
  492. g.geom_jitter('dodge', 0.5);
  493. g.set_point_options('base_size', 4);
  494. %g.set_color_options('map', [0.1 0.1 0.1]); %
  495. g.no_legend();
  496. g.draw();
  497. % saveas(gcf, fullfile([figfilename '.png']));
  498. end
  500. % ttests against zero
  501. % Pleasant betas - aIns
  502. fprintf('T-test pleasant regression coefficients aIns \n')
  503. [h,p,ci,stats] = ttest(indiv_stats.aIns.UP.glm_P_UP_betas(:, 2)-indiv_stats.aIns.DOWN.glm_P_UP_betas(:, 2))
  505. % Unpleasant betas - aIns
  506. fprintf('T-test unpleasant regression coefficients aIns \n')
  507. [h,p,ci,stats] = ttest(indiv_stats.aIns.UP.glm_P_UP_betas(:, 3)-indiv_stats.aIns.DOWN.glm_P_UP_betas(:, 3))
  509. % Bar plots with four bars: up & down pleasant trials and up & down unpleasant trials %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  510. % VMPFC
  511. P_UP_col_vmPFC = [repelem({'Pleasant'}, length(indiv_stats.vmPFC.subject)), repelem({'Unpleasant'}, length(indiv_stats.vmPFC.subject)), repelem({'Pleasant'}, length(indiv_stats.vmPFC.subject)), repelem({'Unpleasant'}, length(indiv_stats.vmPFC.subject))]';
  512. UP_DOWN_col_vmPFC = [repelem({'Up'}, length(indiv_stats.vmPFC.subject)), repelem({'Up'}, length(indiv_stats.vmPFC.subject)), repelem({'Down'}, length(indiv_stats.vmPFC.subject)), repelem({'Down'}, length(indiv_stats.vmPFC.subject))]';
  513. UP_DOWN_betas_col_vmPFC = [indiv_stats.vmPFC.UP.glm_P_UP_betas(:, 2); indiv_stats.vmPFC.UP.glm_P_UP_betas(:, 3); indiv_stats.vmPFC.DOWN.glm_P_UP_betas(:, 2); indiv_stats.vmPFC.DOWN.glm_P_UP_betas(:, 3)]; % col 2 = pleasant betas, col 3 = unpleasant betas
  514. T_glm_vmPFC2 = table(P_UP_col_vmPFC, UP_DOWN_betas_col_vmPFC, UP_DOWN_col_vmPFC);
  516. if options.plot_optional_figII
  517. % Bar plot of pleasant vs unpleasant regression coefficients in the vmPFC for UP and DOWN trials
  518. figure('Position',[100 100 300 300]);
  519. figfilename = 'barplot_betas_UPandDOWN_vmPFC';
  520. g = gramm('x', T_glm_vmPFC2.P_UP_col_vmPFC, 'y', T_glm_vmPFC2.UP_DOWN_betas_col_vmPFC, 'color', T_glm_vmPFC2.UP_DOWN_col_vmPFC);
  521. g.set_title('vmPFC');
  522. g.stat_summary('geom', {'bar', 'black_errorbar'}, 'width', 0.6);
  523. %g.set_color_options('map', [0.8 0.8 0.8]); %
  524. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  525. g.geom_hline('yintercept', 0)
  526. %g.axe_property('XLim', [0.5 2.5], 'XTick', [0.75 2.25], 'XTickLabels', {'Pleas','Unpleas'}); %
  527. g.set_names('y', 'Regression coefficients', 'x', ' ');
  528. %g.no_legend();
  529. g.update('x', T_glm_vmPFC2.P_UP_col_vmPFC, 'y', T_glm_vmPFC2.UP_DOWN_betas_col_vmPFC, 'color', T_glm_vmPFC2.UP_DOWN_col_vmPFC);
  530. g.geom_jitter('dodge', 0.5, 'alpha', 0.5);
  531. g.set_point_options('base_size', 4);
  532. %g.set_color_options('map', [0.1 0.1 0.1]); %
  533. g.no_legend();
  534. g.draw();
  535. % saveas(gcf, fullfile([figfilename '.png']));
  536. end
  538. % aINS
  540. P_UP_col_aINS = [repelem({'Pleasant'}, length(indiv_stats.aIns.subject)), repelem({'Unpleasant'}, length(indiv_stats.aIns.subject)), repelem({'Pleasant'}, length(indiv_stats.aIns.subject)), repelem({'Unpleasant'}, length(indiv_stats.aIns.subject))]';
  541. UP_DOWN_col_aINS = [repelem({'Up'}, length(indiv_stats.aIns.subject)), repelem({'Up'}, length(indiv_stats.aIns.subject)), repelem({'Down'}, length(indiv_stats.aIns.subject)), repelem({'Down'}, length(indiv_stats.aIns.subject))]';
  542. UP_DOWN_betas_col_aINS = [indiv_stats.aIns.UP.glm_P_UP_betas(:, 2); indiv_stats.aIns.UP.glm_P_UP_betas(:, 3); indiv_stats.aIns.DOWN.glm_P_UP_betas(:, 2); indiv_stats.aIns.DOWN.glm_P_UP_betas(:, 3)]; % col 2 = pleasant betas, col 3 = unpleasant betas
  543. T_glm_aINS2 = table(P_UP_col_aINS, UP_DOWN_betas_col_aINS, UP_DOWN_col_aINS);
  545. if options.plot_optional_figII
  546. % Bar plot of pleasant vs unpleasant regression coefficients in the vmPFC for UP and DOWN trials
  547. figure('Position',[100 100 300 300]);
  548. figfilename = 'barplot_betas_UPandDOWN_aIns';
  549. g = gramm('x', T_glm_aINS2.P_UP_col_aINS, 'y', T_glm_aINS2.UP_DOWN_betas_col_aINS, 'color', T_glm_aINS2.UP_DOWN_col_aINS);
  550. g.set_title('aIns');
  551. g.stat_summary('geom', {'bar', 'black_errorbar'}, 'width', 0.6);
  552. %g.set_color_options('map', [0.8 0.8 0.8]); %
  553. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  554. g.geom_hline('yintercept', 0);
  555. %g.axe_property('XLim', [0.5 2.5], 'XTick', [0.75 2.25], 'XTickLabels', {'Pleas','Unpleas'}); %
  556. g.set_names('y', 'Regression coefficients', 'x', ' ');
  557. %g.no_legend();
  558. g.update('x', T_glm_aINS2.P_UP_col_aINS, 'y', T_glm_aINS2.UP_DOWN_betas_col_aINS, 'color', T_glm_aINS2.UP_DOWN_col_aINS);
  559. g.geom_jitter('dodge', 0.5, 'alpha', 0.5);
  560. g.set_point_options('base_size', 4);
  561. %g.set_color_options('map', [0.1 0.1 0.1]); %
  562. g.no_legend();
  563. g.draw();
  565. % saveas(gcf, fullfile([figfilename '.png']));
  566. end
  568. % #MIXED MODELS
  570. % Mixed logistic regression: overall subject performance
  571. % predict P(accept based on the Value diff (P-UP) with a random slope and intercept per subject
  572. glme1 = [];
  573. glme1 = fitglme(T_data,'Accept~Value_diff+(Value_diff|Sub)', 'Distribution', 'Binomial', 'Link', 'logit')
  574. [fixed_betas,fixedbetanames] = fixedEffects(glme1);
  575. fixed_rows_valuediff = strcmp(fixedbetanames.Name, 'Value_diff') | strcmp(fixedbetanames.Name, '(Intercept)');
  577. [random_betas,beta_names] = randomEffects(glme1);
  578. fixed_effects_predictions = glmval(fixed_betas, T_data.Value_diff, 'logit');
  579. fixed_effects_predictions_toplot = sortrows([T_data.Value_diff, fixed_effects_predictions]);
  581. figfilename = 'P_accept_overall';
  583. if true %Fig.1.d
  584. figure('Position',[100 100 130 130]);
  586. for iSub = 1:nSub
  587. rows = strcmp(beta_names.Level, num2str(iSub)) & (strcmp(beta_names.Name, 'Value_diff') | strcmp(beta_names.Name, '(Intercept)'));
  588. random_betas_persub = fixed_betas(fixed_rows_valuediff, :) + random_betas(rows);
  589. random_effects_predictions_persub = glmval(random_betas_persub, T_data.Value_diff, 'logit');
  590. random_effects_predictions_toplot_persub = sortrows([T_data.Value_diff, random_effects_predictions_persub]);
  591. hold on
  592. plot(random_effects_predictions_toplot_persub(:, 1), random_effects_predictions_toplot_persub(:, 2), 'Color', [0.8 0.8 0.8], 'LineWidth', 1)
  593. end
  595. plot(fixed_effects_predictions_toplot(:, 1), fixed_effects_predictions_toplot(:, 2), 'LineWidth', 2, 'Color', [0 0 0]) % , 'Color', options.aesthetics.choice_colors(iblk, :)
  596. % title(blk)
  597. xlabel({'Pleasant-unpleasant';'rating'})
  598. ylabel('P(accept)')
  599. xticks([min(fixed_effects_predictions_toplot(:, 1)) max(fixed_effects_predictions_toplot(:, 1))])
  600. xlim([min(fixed_effects_predictions_toplot(:, 1)) max(fixed_effects_predictions_toplot(:, 1))])
  601. xticklabels({'-100', '100'})
  602. ax = gca;
  603. ax.FontSize = 8;
  604. ax.FontName = 'arial';
  606. % saveas(gcf, fullfile([figfilename '.png']));
  607. % saveas(gcf, fullfile(figfilename), 'epsc');
  608. end
  610. if options.plot_optional_figII
  611. % Mixed logistic regressions: per roi and up/down block
  612. rois = {'aIns', 'vmPFC'};
  614. for iROI = 1:length(rois) % is aIns, 2 is vmPFC
  616. roi = rois{iROI};
  618. if isequal(roi,'aIns'); roi_nb = 1; else roi_nb = 2; end
  620. figure('Position',[100 100 150 150]);
  621. hold on
  623. condis = {'up', 'down'};
  625. for iCondi = 1:length(condis) % up or down
  627. condi = condis{iCondi};
  629. if isequal(condi,'up'); condi_nb = 2; else condi_nb = 1; end
  631. T = T_data;
  633. T([find(~(T_data.ROI == roi_nb) & ~(T_data.Up_down == condi_nb))], :) = []; % delete all rows which are not the correct roi and up/down
  635. glme = [];
  636. glme = fitglme(T,'Accept~Value_diff+(Value_diff|Sub)', 'Distribution', 'Binomial', 'Link', 'logit')
  637. fixed_betas = fixedEffects(glme);
  638. [random_betas1,beta_names] = randomEffects(glme);
  639. fixed_effects_predictions = glmval(fixed_betas, T.Value_diff, 'logit');
  640. fixed_effects_predictions_toplot = sortrows([T.Value_diff, fixed_effects_predictions]);
  642. plot(fixed_effects_predictions_toplot(:, 1), fixed_effects_predictions_toplot(:, 2), 'LineWidth', 2) % , 'Color', options.aesthetics.choice_colors(iblk, :)
  643. % title(blk)
  644. xlabel('Pleasant-unpleasant rating')
  645. ylabel('P(accept)')
  646. xticks([min(fixed_effects_predictions_toplot(:, 1)) max(fixed_effects_predictions_toplot(:, 1))])
  647. xlim([min(fixed_effects_predictions_toplot(:, 1)) max(fixed_effects_predictions_toplot(:, 1))])
  648. xticklabels({'-100', '100'})
  649. ax = gca;
  650. ax.FontSize = 8;
  651. ax.FontName = 'arial';
  653. end
  655. hold off
  656. end
  657. end
  659. % Mixed logistic regression: overall subject performance
  660. % predict P(accept based on the Value diff (P-UP) with a random slope and intercept per subject
  661. glme2 = [];
  662. glme2 = fitglme(T_data,'Accept~Value_diff+ItI_s+Up_down+ROI+ROI*Up_down+(Value_diff|Sub)', 'Distribution', 'Binomial', 'Link', 'logit')
  664. %% Bar plots: ALL BARS ON SAME PLOT %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  666. percent_accept_allcondis = nan(4, max(length(indiv_stats.vmPFC.UP.percentAccept), length(indiv_stats.aIns.UP.percentAccept)));
  667. percent_accept_allcondis(1, 1:length(indiv_stats.vmPFC.UP.percentAccept)) = indiv_stats.vmPFC.UP.percentAccept;
  668. percent_accept_allcondis(2, 1:length(indiv_stats.vmPFC.DOWN.percentAccept)) = indiv_stats.vmPFC.DOWN.percentAccept;
  669. percent_accept_allcondis(3, 1:length(indiv_stats.aIns.UP.percentAccept)) = indiv_stats.aIns.UP.percentAccept;
  670. percent_accept_allcondis(4, 1:length(indiv_stats.aIns.DOWN.percentAccept)) = indiv_stats.aIns.DOWN.percentAccept;
  672. if options.plot_optional_figII
  673. figure('Position',[100 100 150 150]);
  675. cat_vect = [repmat(0.5, length(percent_accept_allcondis), 1); repmat(1, length(percent_accept_allcondis), 1); repmat(2, length(percent_accept_allcondis), 1); repmat(2.5, length(percent_accept_allcondis), 1)];
  676. g = gramm('x', cat_vect, 'y', [percent_accept_allcondis(1, :)'; percent_accept_allcondis(2, :)'; percent_accept_allcondis(3, :)'; percent_accept_allcondis(4, :)'], 'color', cat_vect);
  677. g.stat_summary('geom', {'bar', 'black_errorbar'},'width', 3);
  678. %g.set_color_options('map', options.aesthetics.choice_colors); %
  679. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  680. g.axe_property('XLim', [0 3], 'XTick', [0.75 2.25], 'XTickLabels', {'vmPFC','aIns'}); %
  681. g.set_names('y', 'Acceptance (%)', 'x', ' ');
  682. g.no_legend();
  683. g.update('x', cat_vect, 'y', [percent_accept_allcondis(1, :)'; percent_accept_allcondis(2, :)'; percent_accept_allcondis(3, :)'; percent_accept_allcondis(4, :)'], 'color', cat_vect);
  684. g.geom_jitter('alpha', 0.5);
  685. g.set_point_options('base_size', 3);
  686. %g.set_color_options('map', options.aesthetics.choice_colors - [0.15 0.15 0.15; 0 0.15 0.15; 0.15 0.15 0.15; 0.15 0.15 0.15]); %
  687. g.no_legend();
  688. g.draw();
  689. end
  691. [h,p_vmPFC,ci,statsvmPFC] = ttest(indiv_stats.vmPFC.UP.percentAccept,indiv_stats.vmPFC.DOWN.percentAccept)
  692. [h,p_aIns,ci,stats2] = ttest(indiv_stats.aIns.UP.percentAccept,indiv_stats.aIns.DOWN.percentAccept)
  693. [h,p_vmPFC_aIns,ci,stats2] = ttest2(indiv_stats.vmPFC.UP.percentAccept-indiv_stats.vmPFC.DOWN.percentAccept, indiv_stats.aIns.UP.percentAccept-indiv_stats.aIns.DOWN.percentAccept)
  695. % test without AFTl, NAIn and VINm
  696. %[h,p_vmPFC,ci,statsvmPFC] = ttest(indiv_stats.vmPFC.UP.percentAccept(1, [1,2,5,6]),indiv_stats.vmPFC.DOWN.percentAccept(1, [1,2,5,6]));
  699. % RT bar plot
  700. figfilename = strcat('RT_', roi);
  702. RT_allcondis = nan(4, max(length(indiv_stats.vmPFC.UP.RT), length(indiv_stats.aIns.UP.RT)));
  703. RT_allcondis(1, 1:length(indiv_stats.vmPFC.UP.RT)) = indiv_stats.vmPFC.UP.RT;
  704. RT_allcondis(2, 1:length(indiv_stats.vmPFC.DOWN.RT)) = indiv_stats.vmPFC.DOWN.RT;
  705. RT_allcondis(3, 1:length(indiv_stats.aIns.UP.RT)) = indiv_stats.aIns.UP.RT;
  706. RT_allcondis(4, 1:length(indiv_stats.aIns.DOWN.RT)) = indiv_stats.aIns.DOWN.RT;
  708. if options.plot_optional_figII
  709. figure('Position',[100 100 150 150]);
  711. cat_vect = [repmat(0.5, length(RT_allcondis), 1); repmat(1, length(RT_allcondis), 1); repmat(2, length(RT_allcondis), 1); repmat(2.5, length(RT_allcondis), 1)];
  712. g = gramm('x', cat_vect, 'y', [RT_allcondis(1, :)'; RT_allcondis(2, :)'; RT_allcondis(3, :)'; RT_allcondis(4, :)'], 'color', cat_vect);
  713. g.stat_summary('geom', {'bar', 'black_errorbar'},'width', 3);
  714. %g.set_color_options('map', options.aesthetics.choice_colors); %
  715. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  716. g.axe_property('XLim', [0 3], 'XTick', [0.75 2.25], 'XTickLabels', {'vmPFC','aIns'}); %
  717. g.set_names('y', 'Acceptance (%)', 'x', ' ');
  718. g.no_legend();
  719. g.update('x', cat_vect, 'y', [RT_allcondis(1, :)'; RT_allcondis(2, :)'; RT_allcondis(3, :)'; RT_allcondis(4, :)'], 'color', cat_vect);
  720. g.geom_jitter('alpha', 0.5);
  721. g.set_point_options('base_size', 3);
  722. %g.set_color_options('map', options.aesthetics.choice_colors - [0.15 0.15 0.15; 0 0.15 0.15; 0.15 0.15 0.15; 0.15 0.15 0.15]); %
  723. g.no_legend();
  724. g.draw();
  726. % saveas(gcf, fullfile(PLOTS_PATH, roi,[figfilename '.png']));
  727. % saveas(gcf, fullfile(PLOTS_PATH, roi, figfilename), 'epsc');
  728. end
  730. % #BAR PLOT PER ROI %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  732. rois = {'aIns', 'vmPFC'};
  734. for iROI = 1:length(rois) % is aIns, 2 is vmPFC
  736. roi = rois{iROI};
  738. percent_accept_UP = indiv_stats.(roi).UP.percentAccept;
  739. percent_accept_DOWN = indiv_stats.(roi).DOWN.percentAccept;
  741. switch roi
  742. case 'vmPFC'
  743. cmap = [hex2rgb('#7dcb9c'); hex2rgb('#7dcb9c')];
  744. cmap2 = repmat(hex2rgb('#1d8352'), nSub, 1);
  745. T_data2 = T_data(T_data.ROI==2, :);
  746. case 'aIns'
  747. cmap = [hex2rgb('#f795a2'); hex2rgb('#f795a2')];
  748. cmap2 = repmat(hex2rgb('#e44369'), nSub, 1);
  749. T_data2 = T_data(T_data.ROI==1, :);
  750. end
  752. figfilename = strcat('UpvsDown_Acceptance_', roi);
  754. if true % Fig2.b 2.f.
  756. % acceptance bar plot
  757. figure('Position',[200 200 120 120]);
  758. cat_vect = [repmat(1, length(percent_accept_UP), 1); repmat(2, length(percent_accept_DOWN), 1)];
  759. cat_vect2 = repmat([1:length(percent_accept_UP)], 1, 2);
  760. g = gramm('x', cat_vect, 'y', [percent_accept_UP'; percent_accept_DOWN']); % , 'color', cat_vect
  761. g.stat_summary('geom', {'bar', 'black_errorbar'},'width', 0.5);
  762. %g.set_color_options('map', options.aesthetics.choice_colors); %
  763. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  764. g.axe_property('XLim', [0.5 2.5], 'XTick', [1 2], 'XTickLabels', {'Up','Down'}); %
  765. g.set_names('y', 'Acceptance (%)', 'x', ' ');
  766. g.no_legend();
  767. g.set_color_options('map', cmap);
  768. g.update('x', cat_vect, 'y', [percent_accept_UP'; percent_accept_DOWN']); %, 'color', cat_vect
  769. g.geom_point('alpha', 0.5);
  770. g.set_point_options('base_size', 5);
  771. g.set_color_options('map', cmap2);
  772. g.draw();
  773. g.update('x', cat_vect, 'y', [percent_accept_UP'; percent_accept_DOWN'], 'color', cat_vect2);
  774. g.set_color_options('map', cmap2);
  775. g.geom_line()
  776. %g.set_color_options('map', options.aesthetics.choice_colors - [0.15 0.15 0.15; 0 0.15 0.15; 0.15 0.15 0.15; 0.15 0.15 0.15]); %
  777. g.no_legend();
  778. g.draw()
  779. % saveas(gcf, fullfile(roi,[figfilename '.png']));
  780. % saveas(gcf, fullfile(roi, figfilename), 'epsc');
  781. end
  782. end
  784. % #BAR PLOT OF vmPFC vs aINS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  785. percent_accept_upminusdown = nan(2, max(length(indiv_stats.vmPFC.UP.percentAccept), length(indiv_stats.aIns.UP.percentAccept)));
  786. percent_accept_upminusdown(1, 1:length(indiv_stats.vmPFC.UP.percentAccept)) = indiv_stats.vmPFC.UP.percentAccept-indiv_stats.vmPFC.DOWN.percentAccept;
  787. percent_accept_upminusdown(2, 1:length(indiv_stats.aIns.UP.percentAccept)) = indiv_stats.aIns.UP.percentAccept-indiv_stats.aIns.DOWN.percentAccept;
  789. if options.plot_optional_figII
  790. figure('Position',[200 200 160 170]);
  791. clear g
  792. figfilename = 'Up_minus_Down_Acceptance';
  794. cat_vect = [repmat(1, length(percent_accept_upminusdown), 1); repmat(2, length(percent_accept_upminusdown), 1)];
  795. g = gramm('x', cat_vect, 'y', [percent_accept_upminusdown(1, :)'; percent_accept_upminusdown(2, :)'], 'color', cat_vect);
  796. g.stat_boxplot('width', 1); %'geom', {'bar', 'black_errorbar'},'width', 1
  797. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  798. g.axe_property('XLim', [0.5 2.5], 'XTick', [1 2], 'YLim', [min(percent_accept_upminusdown(:))-7 max(percent_accept_upminusdown(:))+7], 'XTickLabels', {'vmPFC','aIns'}); %
  799. g.set_names('y', 'Up-down acceptance (%)', 'x', ' ');
  800. cmap = [hex2rgb('#1d8352'); hex2rgb('#e44369')];
  801. g.set_color_options('map', cmap); %
  802. g.no_legend();
  803. g.update('x', cat_vect, 'y', [percent_accept_upminusdown(1, :)'; percent_accept_upminusdown(2, :)'], 'color', cat_vect);
  804. g.geom_jitter('alpha', 0.5);
  805. g.set_point_options('base_size', 5);
  806. %g.set_color_options('map', options.aesthetics.choice_colors - [0.15 0.15 0.15; 0 0.15 0.15; 0.15 0.15 0.15; 0.15 0.15 0.15]); %
  807. g.no_legend();
  808. g.geom_abline('intercept', 0, 'slope', 0);
  809. g.draw();
  811. % saveas(gcf, fullfile([figfilename '.png']));
  812. % g.export('file_name', figfilename, 'file_type', 'eps')
  813. end
  815. if options.plot_optional_figII
  816. % # RTs bar plot
  817. figure('Position',[100 100 70 110]);
  818. g = gramm('x', repelem(1, nSub), 'y', [RT_meanpersub]);
  819. g.stat_summary('geom', {'bar', 'black_errorbar'}, 'width', 1);
  820. g.set_color_options('map', [0.8 0.8 0.8]); %
  821. g.axe_property('FontName', 'Arial', 'FontSize', 8);
  822. g.axe_property('XLim', [0 2], 'XTick',[]); %
  823. g.set_names('y', 'Mean RT (s)', 'x', ' ');
  824. g.no_legend();
  825. g.update('x', repelem(1, nSub), 'y', [RT_meanpersub]);
  826. g.geom_jitter('alpha', 0.5);
  827. g.set_point_options('base_size', 4);
  828. g.set_color_options('map', [0.1 0.1 0.1]); %
  829. g.no_legend();
  830. g.draw();
  831. end
  832. mean_RT = mean(RT_meanpersub)
  833. S_RT = std(RT_meanpersub);
  834. SEM_RT = S_RT /sqrt(length(RT_meanpersub))
  837. end

a1_behav_archoices_bci.m, under CC-BY-4.0 · at the source

Overview

Authors: Clarissa Baratin1, Mathias Pessiglione2, Philippe Kahane1,3, Alexis Robin1,3, Lorella Minotti1,3, Guillaume Jean-Paul Claude Becq4, Julien Bastin1
ORCID iDs: Mathias Pessiglione, Philippe Kahane, Alexis Robin, Guillaume Jean-Paul Claude Becq, Julien Bastin
  1. Univ. Grenoble Alpes, Inserm U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, GIN, Grenoble, France
  2. Paris Brain Institute (ICM), Sorbonne Université, Inserm UMR1127, CNRS UMR 7225, Paris, France
  3. Neurology Department, CHU Grenoble Alpes, Grenoble, France
  4. Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, Grenoble, France
Institutions: Inserm (France); Grenoble Institute of Neurosciences (France); Centre National de la Recherche Scientifique (France); Sorbonne Université (France); Institut du Cerveau (France); Centre Hospitalier Universitaire de Grenoble (France); Institut polytechnique de Grenoble (France); GIPSA-Lab (France); Université Grenoble Alpes (France)
Journal: Nature communications, volume 17, issue 1, article 8325
Dates: received 15 December 2025; accepted 24 June 2026; published online 4 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75265-5 · PMID 42401540 · PMCID PMC13470483 · OpenAlex W7167357835
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Cognitive neuroscience, Human behaviour
MeSH: Choice Behavior*, Decision Making*, Gamma Rhythm*, Insular Cortex*, Animals, Brain-Computer Interfaces, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Radiation Effects in Electronics (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: Agence Nationale de la Recherche (ANR-22-CE17-0057, ANR-15-IDEX-0002); Fondation pour la Recherche Médicale
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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Zenodo 20545301

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
5 files
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: Zenodo 20545301

Read it in the paper: doi.org/10.1038/s41467-026-75265-5.

Tracing map

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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;
  • 4 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41467-026-75265-5.

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

  • Funding: added Agence Nationale de la Recherche: ANR-22-CE17-0057, ANR-15-IDEX-0002; Fondation pour la Recherche Médicale

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 9 MeSH terms, 52 references.

Cite

This paper

Baratin, C., Pessiglione, M., Kahane, P., Robin, A., Minotti, L., Becq, G. J.-P. C., & Bastin, J. (2026). Closed-loop readout of anterior insula high-gamma activity steers value-based decisions. Nature communications, 17(1), 8325. https://doi.org/10.1038/s41467-026-75265-5

BibTeX

@article{baratin2026closed,
author = {Baratin, Clarissa and Pessiglione, Mathias and Kahane, Philippe and Robin, Alexis and Minotti, Lorella and Becq, Guillaume Jean-Paul Claude and Bastin, Julien},
title = {{Closed-loop readout of anterior insula high-gamma activity steers value-based decisions}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8325},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75265-5},
url = {https://doi.org/10.1038/s41467-026-75265-5},
pmid = {42401540},
pmcid = {PMC13470483}
}

RIS

TY - JOUR
AU - Baratin, Clarissa
AU - Pessiglione, Mathias
AU - Kahane, Philippe
AU - Robin, Alexis
AU - Minotti, Lorella
AU - Becq, Guillaume Jean-Paul Claude
AU - Bastin, Julien
TI - Closed-loop readout of anterior insula high-gamma activity steers value-based decisions
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/04
VL - 17
IS - 1
SP - 8325
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75265-5
UR - https://doi.org/10.1038/s41467-026-75265-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-75265-5",
"type": "article-journal",
"title": "Closed-loop readout of anterior insula high-gamma activity steers value-based decisions",
"container-title": "Nature communications",
"author": [
{
"family": "Baratin",
"given": "Clarissa"
},
{
"family": "Pessiglione",
"given": "Mathias"
},
{
"family": "Kahane",
"given": "Philippe"
},
{
"family": "Robin",
"given": "Alexis"
},
{
"family": "Minotti",
"given": "Lorella"
},
{
"family": "Becq",
"given": "Guillaume Jean-Paul Claude"
},
{
"family": "Bastin",
"given": "Julien"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8325",
"DOI": "10.1038/s41467-026-75265-5",
"PMID": "42401540",
"PMCID": "PMC13470483",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75265-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
4
]
]
}
}

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