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When emotions hurt: negative interpretations of bodily signals and interoceptive difficulties in fibromyalgia.

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  1. [1] § Study 1 › Data analyses › Body coverage ↔ WP1 - Behavioural project/emBODY and interoception/PAID embody analysis.m, lines 275–354 · score 0.55 · pain today, PPP, coverage, painted, body, pixels

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The authors' code

MATLAB · 921 lines · 28 KB · no license · 1 match

  1. % Info:
  2. % Written by Aleksnadra Herman 2025, rev 2026
  3. %
  4. % The script has been tested on Windows computer with MATLAB R2024a
  5. % Ensure data structure is correct.
  6. %
  7. % Based on functions available here: https://version.aalto.fi/gitlab/eglerean/embody Reported in:
  8. % Nummenmaa L., Glerean E., Hari R., Hietanen, J.K. (2014)
  9. % Bodily maps of emotions, Proceedings of the National Academy of Sciences of United States of America doi:10.1073/pnas.1321664111
  10. % http://www.pnas.org/content/111/2/646.abstract
  11. %%%%
  13. %% 1. Data Loading and Setup
  15. clear all
  16. close all
  18. % the diretory wich contains all necessary functions (change):
  19. GTDIR = 'C:\Users\hermana\OneDrive - University of South Australia\TASKS backup\emBody\functions-Sven\functions';
  20. % the directory with all the project data (chage):
  21. main_dir = 'C:\Users\hermana\OneDrive - University of South Australia\TASKS backup\emBody\';
  22. addpath(genpath(main_dir));
  23. cd(GTDIR)
  25. if ~exist([main_dir 'outdata'], 'dir')
  26. mkdir([main_dir 'outdata'])
  27. end
  29. cfg.outdata = [main_dir 'outdata\'];
  30. cfg.datapath = [main_dir 'subjects\'];
  32. labels={'Fear'
  33. 'Sadness'
  34. 'Happiness'
  35. 'Anger'
  36. 'Disgust'
  37. 'Surprise'
  38. 'Anxiety'
  39. 'Stomach ache'
  40. 'Headache'
  41. 'Your pain today'
  42. 'Neutral state'
  43. 'Physical fatigue'
  44. 'Mental fatigue'
  45. };
  47. cfg.Nstimuli = length(labels);
  48. cfg.Nempty = 1;
  49. cfg.phenodata = 0;
  50. bspm=bodySPM_parseSubjects(cfg);
  52. out=[];
  53. ids=find(bspm.data_filter(:,3)==1);
  54. for i = 1:length(ids)
  55. out{i,1}=bspm.subjects(ids(i)).name;
  56. end
  58. fileID=fopen([cfg.outdata '/list.txt'],'w');
  59. for i=1:length(out)
  60. if(strcmp(out{i}(1),'.'))
  61. disp([out{i} ' has invalid ID for this study']);
  62. continue;
  63. end
  64. fprintf(fileID,'%s\n',[out{i}]);
  65. end
  66. fclose(fileID)
  68. %% 2. Preprocesssing
  69. cfg.list = [cfg.outdata 'list.txt']; %txt file with the list of subjects to process
  70. cfg.hasBaseline = 1;
  71. cfg.posneg=0;
  72. cfg.overwrite=1;
  74. base=uint8(imread('bodySPM_base2.png'));
  75. mask=uint8(imread('bodySPM_base3.png'));
  76. mask=mask*.85;
  77. base2=base(10:531,33:203,:);
  78. in_mask=find(mask>128);
  80. subjects=textread(cfg.list,'%s');
  81. Nsubj=size(subjects,1);
  82. allTimes=zeros(cfg.Nstimuli,2,Nsubj);
  83. tocheck=zeros(Nsubj,1);
  84. tocheck3=[];
  86. for ns=1:Nsubj
  87. subjID=subjects{ns,:};
  88. disp(['Processing subject ' subjID ' which is number ' num2str(ns) ' out of ' num2str(Nsubj)]);
  89. if(cfg.overwrite==0)
  90. matname=[cfg.outdata '/' subjID '.mat'];
  91. if(exist(matname)==2)
  92. a=load(matname);
  93. allTimes(:,:,ns)=a.times;
  94. disp('Already preprocessed, no overwrite')
  95. continue;
  96. end
  97. end
  99. try
  100. a=bodySPM_load([cfg.datapath '/' subjID '/'],2);
  101. S=length(a);
  102. if( S ~= cfg.Nstimuli)
  103. disp('Mismatch between expected number of trials and loaded trials')
  104. end
  105. resmat=zeros(522,171,cfg.Nstimuli);
  106. times=zeros(S,2);
  107. % go through each stimulus
  108. for n=1:S
  109. T=length(a(n).paint(:,2));
  110. over=zeros(size(base,1),size(base,2));
  111. for t=1:T
  112. y=ceil(a(n).paint(t,3)+1);
  113. x=ceil(a(n).paint(t,2)+1);
  114. if(x<=0) x=1; end
  115. if(y<=0) y=1; end
  116. if(x>=900) x=900; end
  117. if(y>=600) y=600; end
  118. over(y,x)=over(y,x)+1;
  119. end
  120. %Smoothing
  121. h=fspecial('gaussian',[15 15],5);
  122. over=imfilter(over,h);
  123. M1=1;
  124. M2=1;
  126. if(cfg.posneg==1)
  127. M2=0;
  128. end
  129. if(cfg.posneg==-1)
  130. M1=0;
  131. M2=-1;
  132. end
  133. over2=M1*over(10:531,33:203,:)-M2*over(10:531,696:866,:);
  134. resmat(:,:,n)=over2;
  136. % times vector, the first one is the amount of time in milliseconds, the second one is the total number of pixels painted
  137. if(size(a(n).paint,1)>0 && size(a(n).mouse,1)>0)
  138. times(n,1)=a(n).mouse(end,1)-a(n).mouse(1,1);
  139. else
  140. tocheck(ns)=tocheck(ns)+1;
  141. times(n,1)=0;
  142. end
  143. times(n,2)=T;
  144. end
  146. matname=[cfg.outdata subjID '.mat'];
  148. save (matname, 'resmat','times');
  150. % store all times for diagnostic purposes
  151. allTimes(:,:,ns)=times;
  153. %% visualize subject's data
  154. M=max(abs(resmat(:))); % max range for colorbar
  155. NumCol=64;
  156. hotmap=hot(NumCol);
  157. coldmap=flipud([hotmap(:,3) hotmap(:,2) hotmap(:,1) ]);
  158. hotcoldmap=[
  159. coldmap
  160. hotmap
  161. ];
  163. % visualize all responses for each subject into a grid of numcolumns
  164. plotcols = 5; %set as desired
  165. plotrows = 3;%ceil((NC+1)/plotcols); % number of rows is equal to number of conditions+1 (for the colorbar)
  167. for n=1:S%NC
  168. figure(ns)
  169. set(gcf, 'Visible', 'on');
  170. subplot(plotrows,plotcols,n)
  171. imagesc(base2);
  172. axis('off');
  173. set(gcf,'Color',[1 1 1],'Position', [200, 200, 1000, 800]);
  174. hold on;
  175. over2=resmat(:,:,n);
  176. fh=imagesc(over2,[-M,M]);
  177. axis('off');
  178. axis equal
  179. colormap(hotcoldmap);
  180. set(fh,'AlphaData',mask)
  181. title(labels(n),'FontSize',12)
  182. sgtitle(subjID)
  183. if(n==S) %(n==NC)
  184. subplot(plotrows,plotcols,n+1)
  185. fh=imagesc(ones(size(base2)),[-M,M]);
  186. axis('off');
  187. colorbar;
  188. % save a screenshot, useful for quality control (commented)
  189. saveas(gcf,[cfg.outdata subjID '.png'])
  190. end
  191. end
  192. clf
  193. catch
  194. disp(['Sth is wrong with subject ' subjID ' which is number ' num2str(ns) ' out of ' num2str(Nsubj)])
  195. tocheck3=[tocheck3;ns];
  196. end
  197. end
  199. tocheck2 =[];
  200. for i=1:length(tocheck)
  201. if tocheck(i)~= 0
  202. tocheck2 =[tocheck2;i];
  203. end
  204. end
  205. bspm=cfg;
  206. bspm.allTimes=allTimes;
  207. bspm.tocheck=tocheck;
  208. bspm.tocheck2=tocheck2;
  209. bspm.tocheck3=tocheck3;
  211. save([cfg.outdata '/bspm.mat'], 'bspm')
  213. %% 3. Quality control and filtering
  214. % find the number of stimuli painted by each subjects
  216. B = squeeze(bspm.allTimes(:,2,:)); %squeezing pos and neg matrices
  217. B(B==0) = NaN; %I'm replacing all zero's with NaN --> cause zeros means that nothing has been painted
  218. npainted = sum(~isnan(B));
  220. figure
  221. subplot(2,1,1)
  222. stem(npainted);
  223. xticks(1:(length(subjects)))
  224. set(gca, 'XTickLabel', subjects','fontsize',10);
  225. xtickangle(30)
  226. title('N pained Body Maps', 'Fontsize', 20)
  227. subplot(2,1,2)
  228. [sortedB, sortedI] = sort(npainted);
  229. stem(sortedB);
  230. xticks(1:(length(subjects)))
  231. sortedL = subjects(sortedI)';
  232. set(gca, 'Xticklabel',sortedL,'fontsize',10);
  233. xtickangle(30)
  234. title('N pained Body Maps - Ascending', 'Fontsize', 20)
  236. M = mean(nonzeros(npainted));
  237. Std = std(nonzeros(npainted));
  238. thr_std = 0.5*length(labels);
  240. disp(['Mean N BSMs pained: ' num2str(M) ' +/- ' num2str(Std) '. Threshold to accept is ' num2str(thr_std)]);
  241. disp(['Min N BSMs pained is: ' num2str(min(nonzeros(npainted)))]);
  243. ids=find(npainted>=thr_std);
  244. exc=find(npainted<thr_std);
  245. out=[];
  246. for i = 1:length(ids)
  247. out{i,1}=subjects{ids(i)};
  248. end
  250. % Descriptive stats in kept subjects
  251. Descr(1,1) = mean(nonzeros(npainted(ids)));
  252. Descr(1,2) = std(nonzeros(npainted(ids)));
  253. Descr(1,3) = min(nonzeros(npainted(ids)));
  255. % saving the list of participants to inlude in the analysis
  256. fileID=fopen([cfg.outdata 'whitelist.txt'],'w');
  257. for i=1:length(out)
  258. fprintf(fileID,'%s\n',[out{i}]);
  259. end
  260. fclose(fileID);
  262. cfg.whitelist = [cfg.outdata 'whitelist.txt'];
  263. %% 4. Identyfy groups
  264. % comparing BSMs between groups
  265. numArray = str2double(out);
  267. subjects=textread(cfg.whitelist,'%s');
  268. numArray = str2double(subjects);
  270. g1 = find(numArray<200); %FM
  271. g2 = find(numArray>199); %CO
  272. cfg.g1=g1; %FM
  273. cfg.g2=g2; %Control
  275. %% 5. Total pixels painted analysis
  276. % Exploring the coverage of embodiment.
  278. TPP = bodySPM_global_rev(cfg);
  279. save([cfg.outdata '/TPP.mat'],'TPP')
  281. %excluding pain today
  282. labels2 = labels;
  283. labels2(10) = [];
  285. scr_siz = get(0,'ScreenSize') ;
  287. %bar plot
  288. figure
  289. set(gcf,'Color',[1 1 1]); %,'Position', [200, 200, 1400, 800]);
  290. data = [mean((TPP.tpp(:,g1)/50291),2), mean((TPP.tpp(:,g2)/50291),2)];
  291. data(10,:) = []; %excluding pain today from vizualization
  292. k = bar(data);
  293. k(1).FaceColor = [0.4961 0.7461 0.4805]; %green FM
  294. k(2).FaceColor = [0.6836 0.5508 0.7617]; %purple CO
  295. %ylabel(cases(c),'fontsize',14)
  296. ylabel('Proportion of body coloured','fontsize',14)
  297. ylim([0 1])
  298. % errhigh = [std(TPP.tpp(:,g1)'/50291)', std(TPP.tpp(:,g2)'/50291)']; %std
  299. errhigh = [std(TPP.tpp(:,g1)'/50291)'/sqrt(length(TPP.tpp(:,g1)')),...
  300. std(TPP.tpp(:,g2)'/50291)'/sqrt(length(TPP.tpp(:,g2)'))]; %SE
  301. errhigh(10,:) = [];
  302. hold on
  303. [ngroups,nbars] = size(data);
  304. x = nan(nbars, ngroups);
  305. for i = 1:nbars
  306. x(i,:) = k(i).XEndPoints;
  307. end
  308. hold on
  309. errorbar(x',data,errhigh,'k','linestyle','none');
  310. legend([k(1),k(2)],'FM','CO')
  311. set(gca, 'Xticklabel',labels2,'fontsize',12)
  312. xtickangle(30)
  313. title('Body image coverage','fontsize',14)
  314. hold off
  315. saveas(gcf,[cfg.outdata '/PPP_SE.png'])
  317. % 2-way Interaction Type x Group plot
  318. Act = mean(TPP.tppPOS(:,:)/50291,1)';
  319. Dea = mean(TPP.tppNEG(:,:)/50291,1)';
  321. figure
  322. set(gcf,'Color',[1 1 1]); %,'Position', [200, 200, 1400, 800]);
  323. data = [mean(Act(g1),'all','omitnan'), mean(Act(g2),'all','omitnan');...
  324. mean(Dea(g1),'all','omitnan'), mean(Dea(g2),'all','omitnan')];
  325. k = bar(data);
  326. k(1).FaceColor = [0.4961 0.7461 0.4805]; %green FM
  327. k(2).FaceColor = [0.6836 0.5508 0.7617]; %purple CO
  328. ylabel('PPP','fontsize',14)
  329. ylim([0 0.6])
  330. set(gca, 'Xticklabel',{'Activations', 'Deactivations'},'fontsize',14)
  331. errhigh = [std(Act(g1)) std(Act(g2));std(Dea(g1)) std(Dea(g2))];
  333. hold on
  334. [ngroups,nbars] = size(data);
  335. x = nan(nbars, ngroups);
  336. for i = 1:nbars
  337. x(i,:) = k(i).XEndPoints;
  338. end
  339. allData = {Act(g1),Act(g2),...
  340. Dea(g1),Dea(g2)};
  341. spread = 0.1; % 0=no spread; 0.5=random spread within box bounds (can be any value)
  342. for i = 1:numel(allData)
  343. p = plot(rand(size(allData{i}))*spread -(spread/2) + x(i), allData{i}, 'o','MarkerEdgeColor',[0.5 0.5 0.5]);
  344. end
  345. hold on
  346. e = errorbar(x',data,errhigh,'k','linestyle','none');
  347. hold off
  348. legend([k,p,e],'FM','CNT')
  349. saveas(gcf,[cfg.outdata '/PPP_2way_interaction.png'])
  351. % save coverage information
  352. grouping = [repmat(1,length(g1),1);repmat(2,length(g2),1)];
  353. Table = array2table([numArray,(TPP.tpp'),grouping],'VariableNames',['subjectNo',labels','group']);
  354. writetable(Table,[cfg.outdata 'PPP.csv'])
  356. %% 6. Load data
  357. Nsubj=size(subjects,1);
  359. for ns=1:Nsubj
  360. disp(['Processing subject ' subjects{ns} ' which is number ' num2str(ns) ' out of ' num2str(Nsubj)]);
  361. %matname=[cfg.outdata subjects{ns} '.mat'];
  362. matname = fullfile(cfg.outdata, [num2str(subjects{ns}) '.mat']);
  364. load (matname) % 'resmat','times'
  365. tempdata=reshape(resmat,[],size(resmat,3));
  366. if(ns==1)
  367. alldata=zeros(length(in_mask),Nsubj,size(tempdata,2));
  368. end
  369. alldata(:,ns,:)=tempdata(in_mask,:);
  370. end
  372. outfile = fullfile(cfg.outdata, 'alldata.mat');
  373. save(outfile, 'alldata');
  374. %% 7. Calculate mean intensity
  375. meanIntensity = zeros(size(alldata,2),size(alldata,3)); %no subjects x no emotions
  376. for ns=1:Nsubj
  377. for e = 1:cfg.Nstimuli
  378. map = abs(alldata(:,ns,e));
  379. %find non-zero pixels
  380. validPixels = map(map ~= 0);
  381. %sum of intensity
  382. meanIntensity(ns,e) = sum(validPixels)/numel(validPixels);
  383. end
  384. end
  386. Table = array2table([numArray,(meanIntensity),grouping],'VariableNames',['subjectNo',labels','group']);
  387. writetable(Table,[cfg.outdata 'Intensity.csv'])
  389. % bar plot - intensity
  390. figure
  391. set(gcf,'Color',[1 1 1]); %,'Position', [200, 200, 1400, 800]);
  392. data = [nanmean(meanIntensity(g1,:),1)', nanmean(meanIntensity(g2,:),1)'];
  393. k = bar(data);
  394. k(1).FaceColor = [0.4961 0.7461 0.4805]; %green FM
  395. k(2).FaceColor = [0.6836 0.5508 0.7617]; %purple CO
  396. %ylabel(cases(c),'fontsize',14)
  397. ylabel('Colouring intensity','fontsize',14)
  398. %ylim([0 1])
  399. errhigh = [nanstd(meanIntensity(g1,:))', nanstd(meanIntensity(g2,:))'];
  400. hold on
  401. [ngroups,nbars] = size(data);
  402. x = nan(nbars, ngroups);
  403. for i = 1:nbars
  404. x(i,:) = k(i).XEndPoints;
  405. end
  406. hold on
  407. errorbar(x',data,errhigh,'k','linestyle','none');
  408. legend([k(1),k(2)],'FM','CN','Location','northwest')
  409. set(gca, 'Xticklabel',labels,'fontsize',12)
  410. xtickangle(30)
  411. %title('Painting ','fontsize',14)
  412. hold off
  414. saveas(gcf,[cfg.outdata '/Painting_Intensity.png'])
  416. %% 8. Nonparametric body-maps analysis
  417. %Analysis for small sample sizes (N < 40 per group).
  418. %The analysis modeled after:
  419. %Torregrossa LJ, Snodgress MA, Hong SJ, Nichols HS, Glerean E, Nummenmaa L,
  420. %Park S. Anomalous Bodily Maps of Emotions in Schizophrenia. Schizophr
  421. %Bull. 2019 Sep 11;45(5):1060-1067. doi: 10.1093/schbul/sby179. PMID:
  422. %30551180; PMCID: PMC6737484.
  424. groups = {'FM', 'Control'};
  425. num_groups = length(groups);
  426. num_subjects = size(alldata, 2);
  427. num_emotions = size(alldata, 3);
  428. body_map_size = size(alldata,1);
  430. % Initialize matrices to store group maps
  431. activation_maps = zeros([body_map_size, num_emotions, num_groups]);
  432. deactivation_maps = zeros([body_map_size, num_emotions, num_groups]);
  434. for group_id = 1:num_groups
  435. if strcmp(groups{group_id}, 'FM')
  436. g = g1;
  437. else
  438. g=g2;
  439. end
  441. activation_sum = zeros(body_map_size,num_emotions);
  442. deactivation_sum = zeros(body_map_size,num_emotions);
  444. for subject_id = 1:length(g)
  445. map = alldata(:,g(subject_id),:);
  446. map = squeeze(map);
  448. % Count activation and deactivation
  449. activation_sum = activation_sum + (map > 0);
  450. deactivation_sum = deactivation_sum + (map < 0);
  451. end
  453. % Compute proportions
  454. activation_maps(:, :, group_id) = activation_sum / length(g);
  455. deactivation_maps(:, :, group_id) = deactivation_sum / length(g);
  456. end
  458. % Combine activation and deactivation maps for each group
  459. product_maps = (activation_maps - deactivation_maps) .* (activation_maps + deactivation_maps);
  460. D = zeros([body_map_size, num_emotions, num_groups]);
  461. D(:,:,1) = activation_maps(:, :, 2)- activation_maps(:, :, 1); % Activations CO - FM (FM green)
  462. D(:,:,2) = deactivation_maps(:, :, 2)- deactivation_maps(:, :, 1); % Deactivations Co - FM
  464. disp(['Max endorsement FM = ' num2str(max(abs(product_maps(:,:,1)),[],'all')) ' Max endorsement Control = ' num2str(max(abs(product_maps(:,:,2)),[],'all'))])
  466. % Visualization
  467. base=uint8(imread('bodySPM_base2.png'));
  468. mask=uint8(imread('bodySPM_base3.png'));
  469. mask=mask*.85;
  470. in_mask=find(mask>128);
  471. base2=base(10:531,33:203,:);
  473. %Product map
  474. NumCol=21;
  475. non_sig=1;
  476. hotmap=hot(NumCol-non_sig);
  477. coldmap=flipud([hotmap(:,3) hotmap(:,2) hotmap(:,1) ]);
  478. hotcoldmap=[
  479. coldmap
  480. zeros(2*non_sig,3)+0.3; %grey background
  481. hotmap
  482. ];
  483. M=max(abs(product_maps(:)));
  485. figure;
  486. set(gcf,'Color',[1 1 1],'Position', [100, 100, 1200, 380]);
  487. i=0;
  488. for group_id = 1:num_groups
  489. for emotion_id = 1:num_emotions
  490. i=i+1;
  491. temp=zeros(size(mask));
  492. temp(in_mask)=product_maps(:,emotion_id,group_id);
  493. subplot(2,num_emotions,i)
  494. %imagesc(temp);
  495. h=imagesc(temp,[-M M]);
  496. set(h,'AlphaData',mask)
  497. colormap(hotcoldmap)
  498. axis('off');
  499. axis equal
  500. if i==1
  501. text(-100, 250, 'FM', 'Fontsize', 12, 'FontWeight', 'bold', 'HorizontalAlignment','center','Rotation', 90);
  502. elseif i==num_emotions+1
  503. text(-100, 220, 'Control', 'Fontsize', 12, 'FontWeight', 'bold', 'HorizontalAlignment','center','Rotation', 90);
  504. end
  505. box off
  506. lbl = labels{emotion_id};
  507. lbl = strrep(lbl, ' ', newline);
  509. if group_id == 1
  510. title(lbl, 'FontSize', 12);
  511. end
  512. % if group_id == 1
  513. % title(labels(emotion_id),'fontsize',12);
  514. % end
  515. end
  516. end
  517. h = colorbar;
  518. h.Position = [0.93 0.1 0.02 0.8];
  520. saveas(gcf,[cfg.outdata 'Products_maps_by_group.png'])
  522. % Difference maps
  523. map=cbrewer('div','PiYG',21);
  524. map=flipud(map);
  525. map(11,:)=[.3 .3 .3];
  526. M=max(abs(D(:)));
  528. figure
  529. set(gcf,'Color',[1 1 1],'Position', [100, 100, 1200, 380]);
  530. i=0;
  531. for group_id = 1:num_groups
  532. for emotion_id = 1:num_emotions
  533. i=i+1;
  534. temp=zeros(size(mask));
  535. temp(in_mask)=D(:,emotion_id,group_id);
  536. subplot(2,num_emotions,i)
  537. h=imagesc(temp,[-M M]);
  538. set(h,'AlphaData',mask)
  539. colormap(map)
  540. axis('off');
  541. axis equal
  542. if i==1
  543. text(-100, 200, 'Activations', 'Fontsize', 12, 'FontWeight', 'bold', 'HorizontalAlignment','center','Rotation', 90);
  544. elseif i==num_emotions+1
  545. text(-100, 200, 'Deactivations', 'Fontsize', 12, 'FontWeight', 'bold', 'HorizontalAlignment','center','Rotation', 90);
  546. end
  547. box off
  548. title(labels(emotion_id),'fontsize',12);
  549. end
  550. end
  551. h = colorbar;
  552. h.Position = [0.93 0.1 0.02 0.8];
  554. saveas(gcf,[cfg.outdata 'Difference_maps.png'])
  555. %Legend: Group difference maps for bodily sensation of emotions. Magenta
  556. %represents regions where CO consistently reported more activation (top),
  557. %or deactivation (bottom) than FM. Green represents regions where FM
  558. %consistently reported more activation (top), or deactivation (bottom) than
  559. %CO. The color bar represents the difference between FM and CO in pixel
  560. %values.
  562. %% 9. Cosine simmilarity
  563. % To what extent are group-level BSMs similar to each other?
  564. % Based on Herman et al 2024: 10.1111/adb.13364
  566. CS_FM = zeros(cfg.Nstimuli-1); % cosine similarity for FM
  567. CS_CO = zeros(cfg.Nstimuli-1); % cosine similarity for CO
  568. CS = zeros(cfg.Nstimuli-1); % cosine similarity of both
  569. %taking out pain today as it is not relevant for CO
  570. product_maps2 = product_maps;
  571. product_maps2(:,10,:) = [];
  572. labels2 = labels;
  573. labels2(10) = [];
  575. for e = 1:cfg.Nstimuli-1
  576. for f = 1:cfg.Nstimuli-1
  577. CS_FM(e,f) = getCosineSimilarity(product_maps2(:,e,1),product_maps2(:,f,1));
  578. CS_CO(e,f) = getCosineSimilarity(product_maps2(:,e,2),product_maps2(:,f,2));
  579. CS(e,f) = getCosineSimilarity(product_maps2(:,e,1),product_maps2(:,f,2));
  580. end
  581. end
  582. save([cfg.outdata '/CSs.mat'],'CS_FM','CS_CO','CS');
  584. %Cosine figure
  585. figure
  586. set(gcf,'Color',[1 1 1],'Position', [200, 200, 900, 600]);
  587. subplot(2,2,1)
  588. imagesc(CS_FM); % Display correlation matrix as an image
  589. set(gca, 'XTick', 1:cfg.Nstimuli-1); % center x-axis ticks on bins
  590. set(gca, 'YTick', 1:cfg.Nstimuli-1); % center y-axis ticks on bins
  591. set(gca, 'XTickLabel', labels2, 'FontSize', 11); % set x-axis labels
  592. xtickangle(45)
  593. set(gca, 'YTickLabel', labels2, 'FontSize', 11); % set y-axis labels
  594. title('FM', 'FontSize', 14); % set title
  595. colormap('jet'); % Choose jet or any other color scheme
  596. colorbar;
  597. caxis([-1 1])
  599. subplot(2,2,2)
  600. imagesc(CS_CO); % Display correlation matrix as an image
  601. set(gca, 'XTick', 1:cfg.Nstimuli-1); % center x-axis ticks on bins
  602. set(gca, 'YTick', 1:cfg.Nstimuli-1); % center y-axis ticks on bins
  603. set(gca, 'XTickLabel', labels2, 'FontSize', 11); % set x-axis labels
  604. xtickangle(45)
  605. set(gca, 'YTickLabel', labels2, 'FontSize', 11); % set y-axis labels
  606. title('CO', 'FontSize', 14); % set title
  607. colormap('jet'); % Choose jet or any other color scheme
  608. caxis([-1 1])
  609. colorbar;
  611. subplot(2,2,3)
  612. imagesc(CS); % Display correlation matrix as an image
  613. set(gca, 'XTick', 1:cfg.Nstimuli-1); % center x-axis ticks on bins
  614. set(gca, 'YTick', 1:cfg.Nstimuli-1); % center y-axis ticks on bins
  615. set(gca, 'XTickLabel', labels2, 'FontSize', 10); % set x-axis labels
  616. xtickangle(45)
  617. set(gca, 'YTickLabel', labels2, 'FontSize', 10); % set y-axis labels
  618. xlabel('Control')
  619. ylabel('FM')
  620. title('FM:CO', 'FontSize', 14); % set title
  621. colormap('jet'); % Choose jet or any other color scheme
  622. caxis([-1 1])
  623. colorbar;
  625. subplot(2,2,4)
  626. DCS=CS_FM-CS_CO;
  627. imagesc(DCS); % Display correlation matrix as an image
  628. set(gca, 'XTick', 1:cfg.Nstimuli-1); % center x-axis ticks on bins
  629. set(gca, 'YTick', 1:cfg.Nstimuli-1); % center y-axis ticks on bins
  630. set(gca, 'XTickLabel', labels2, 'FontSize', 11); % set x-axis labels
  631. xtickangle(45)
  632. set(gca, 'YTickLabel', labels2, 'FontSize', 11); % set y-axis labels
  633. title('Difference: FM > CO', 'FontSize', 14); % set title
  634. map=cbrewer('div','PiYG',101);
  635. map=flip(map); % flip map so that green detones greater FM
  636. map=flipud(map);
  637. colormap(gca,map);
  638. m = round(max(abs(DCS),[],'all'),1);
  639. caxis([-m m]);
  641. hcb3=colorbar;
  643. saveas(gcf,[cfg.outdata '/Cosine_simmilarity.png'])
  645. pos = length(find(tril(DCS,-1)>0));
  646. total = (length(labels2)*length(labels2)-length(labels2))/2;
  648. disp(['FM had greater cosine similarity for ' num2str(pos) ' out of ' num2str(total) ' BSMs pairs than CO.']);
  650. figure
  651. set(gcf,'Color',[1 1 1]);
  652. b=diag(CS,0);
  653. [sortedB, sortedI] = sort(b,'descend');
  654. bar(sortedB,'k');
  655. sortedL = labels2(sortedI);
  656. set(gca, 'Xticklabel',sortedL,'fontsize',14);
  657. % get(gca,'OuterPosition')
  658. % set(gca,'OuterPosition',[0.3781 0.05 0.5822 1.0000])
  659. xlim([0 length(labels2)+1])
  660. set(gca,'xtick',1:length(labels2),'fontsize',12)
  661. xtickangle(30)
  662. ylabel('Cosine similarity','fontsize',14)
  663. box off
  665. saveas(gcf,[cfg.outdata '/Cosine_simmilarity_diag.png'])
  667. disp(['Average cosine simmilarity ' num2str(mean(b))]);
  669. %% 10. LDA - revised
  670. % Are BSMs of emotions unique in each group? Can the algoryth correctly
  671. % classify them?
  673. for ns=1:length(subjects)
  674. subjID=subjects{ns,:};
  675. disp(['Processing subject ' subjID ' which is number ' num2str(ns) ' out of ' num2str(length(subjects))]);
  676. matname=[cfg.outdata '/' subjID '.mat'];
  677. load (matname) % 'resmat','times'
  678. all_data(:,:,:,ns) = resmat;
  679. end
  680. all_data = all_data(:,:,(1:cfg.Nstimuli),:);
  681. all_data = all_data(:,:,[1:9,11:end],:);
  684. % for FM only
  685. [FM_confusion, FM_accuracy, FM_ids, FM_subject_acc] = calc_model_rev(all_data(:,:,:,g1), labels2);
  686. save([cfg.outdata '/LDA_FM_rev_k5.mat'],'FM_confusion', 'FM_accuracy', 'FM_ids', 'FM_subject_acc');
  688. % Control only
  689. [CO_confusion, CO_accuracy, CO_ids, CO_subject_acc] = calc_model_rev(all_data(:,:,:,g2), labels2);
  690. save([cfg.outdata '/LDA_CO_rev_k5.mat'],'CO_confusion', 'CO_accuracy', 'CO_ids', 'CO_subject_acc');
  692. Chance_Level = round(100/length(labels2));
  693. mean_acc_FM = mean(FM_accuracy)*100;
  695. disp(['For FM Chance Level = ' num2str(Chance_Level) '%. Mean classification accuracy is ' num2str(mean_acc_FM)]);
  697. % One-sample t-tests
  698. FM_subj_mean = mean(FM_subject_acc, 2);
  699. CO_subj_mean = mean(CO_subject_acc, 2);
  701. chance = round(100/length(labels2));
  703. [h_FM, p_FM, ci_FM, stats_FM] = ttest(FM_subj_mean, chance);
  705. disp(['FM t-test vs chance: p = ' num2str(p_FM)]);
  706. disp(stats_FM);
  708. [h_CO, p_CO, ci_CO, stats_CO] = ttest(CO_subj_mean, chance);
  710. disp(['CO t-test vs chance: p = ' num2str(p_CO)]);
  711. disp(stats_CO);
  713. % PERMUTATION TESTING
  714. % FM group
  715. [p_FM, null_FM, acc_FM, thr95_FM, thr99_FM] = ...
  716. permutation_test_LDA(all_data(:,:,:,g1), labels2, 1000);
  718. % CO group
  719. [p_CO, null_CO, acc_CO, thr95_CO, thr99_CO] = ...
  720. permutation_test_LDA(all_data(:,:,:,g2), labels2, 1000);
  722. % Plot it
  723. figure;
  724. tiledlayout(1,2)
  725. nexttile %FM
  726. histogram(null_FM, 30);
  727. hold on;
  728. xline(acc_FM, 'r', 'LineWidth', 2);
  729. xline(thr95_FM, '--k');
  730. title('Permutation test - FM');
  731. xlabel('Accuracy');
  732. ylabel('Frequency');
  734. nexttile % CO
  735. histogram(null_CO, 30);
  736. hold on;
  737. xline(acc_CO, 'r', 'LineWidth', 2);
  738. xline(thr95_CO, '--k');
  739. title('Permutation test - CO');
  740. xlabel('Accuracy');
  741. ylabel('Frequency');
  743. % check for differences:
  744. all_acc = [FM_subject_acc; CO_subject_acc];
  745. nFM = size(FM_subject_acc,1);
  746. labels = [ones(nFM,1); zeros(size(CO_subject_acc,1),1)];
  747. obs_diff = mean(FM_subject_acc(:)) - mean(CO_subject_acc(:));
  748. n_perm = 1000;
  749. perm_diff = zeros(n_perm,1);
  751. for i = 1:n_perm
  753. perm_labels = labels(randperm(length(labels2)));
  754. FM_perm = all_acc(perm_labels == 1,:);
  755. CO_perm = all_acc(perm_labels == 0,:);
  756. perm_diff(i) = mean(FM_perm(:)) - mean(CO_perm(:));
  758. end
  760. p_group = (sum(abs(perm_diff) >= abs(obs_diff)) + 1) / (n_perm + 1);
  762. %get the results:
  763. chance = 1 / length(labels2); % e.g., 1/12
  765. fprintf('\n--- FM ---\n');
  766. fprintf('Accuracy: %.3f (%.1f%%)\n', acc_FM, acc_FM*100);
  767. fprintf('Chance: %.3f (%.1f%%)\n', chance, chance*100);
  768. fprintf('p-value: %.4f\n', p_FM);
  769. fprintf('95%% threshold: %.3f\n', thr95_FM);
  770. fprintf('99%% threshold: %.3f\n', thr99_FM);
  772. fprintf('\n--- CO ---\n');
  773. fprintf('Accuracy: %.3f (%.1f%%)\n', acc_CO, acc_CO*100);
  774. fprintf('Chance: %.3f (%.1f%%)\n', chance, chance*100);
  775. fprintf('p-value: %.4f\n', p_CO);
  776. fprintf('95%% threshold: %.3f\n', thr95_CO);
  777. fprintf('99%% threshold: %.3f\n', thr99_CO);
  778. fprintf('group differences p-val: %.3f\n', p_group);
  780. % ANCOVA with permutations (correcting for education and socioeconomic
  781. % status)
  782. all_acc = [FM_subject_acc; CO_subject_acc];
  783. nFM = size(FM_subject_acc,1);
  784. group = [ones(nFM,1); zeros(size(CO_subject_acc,1),1)];
  785. [nSubj, nLabels] = size(all_acc);
  786. z_edu = zscore(edu);
  787. z_ses = zscore(ses);
  789. % Remove covariance effects for group effect
  790. subj_mean = mean(all_acc,2);
  791. Xcov_group = [ones(nSubj,1), z_edu, z_ses];
  792. b_group = Xcov_group \ subj_mean;
  794. % residualised subject-level accuracy
  795. subj_mean_adj = subj_mean - Xcov_group(:,2:end) * b_group(2:end);
  797. % Main effect of GROUP
  798. obs_group = mean(subj_mean_adj(group==1)) - ...
  799. mean(subj_mean_adj(group==0));
  801. % permutation test
  802. n_perm = 1000;
  803. perm_group_stat = zeros(n_perm,1);
  805. for p = 1:n_perm
  806. perm_group = group(randperm(nSubj));
  807. perm_group_stat(p) = ...
  808. mean(subj_mean_adj(perm_group==1)) - ...
  809. mean(subj_mean_adj(perm_group==0));
  810. end
  812. p_group = ...
  813. (sum(abs(perm_group_stat) >= abs(obs_group)) + 1) ...
  814. / (n_perm + 1);
  815. disp(['Main effect of GROUP p = ' num2str(p_group)]);
  817. % Main effect of BODYMAP
  818. Xcov = [ones(nSubj,1), z_edu, z_ses];
  819. Y_adj = zeros(size(all_acc));
  821. for lab = 1:nLabels
  822. y = all_acc(:,lab);
  823. b = Xcov \ y;
  825. % residualise covariates
  826. Y_adj(:,lab) = y - Xcov(:,2:end) * b(2:end);
  828. end
  830. label_means = mean(Y_adj,1);
  831. obs_label = sum((label_means - mean(label_means)).^2);
  832. perm_label_stat = zeros(n_perm,1);
  834. for p = 1:n_perm
  836. Y_perm = zeros(size(Y_adj));
  837. % permute labels WITHIN subjects
  838. for s = 1:nSubj
  839. Y_perm(s,:) = Y_adj(s, randperm(nLabels));
  840. end
  841. perm_label_means = mean(Y_perm,1);
  842. perm_label_stat(p) = ...
  843. sum((perm_label_means - mean(perm_label_means)).^2);
  844. end
  846. p_label = ...
  847. (sum(perm_label_stat >= obs_label) + 1) ...
  848. / (n_perm + 1);
  850. disp(['Main effect of BODYMAP p = ' num2str(p_label)]);
  852. % INTERACTION
  853. FM_mean = mean(Y_adj(group==1,:),1);
  854. CO_mean = mean(Y_adj(group==0,:),1);
  856. obs_interaction = FM_mean - CO_mean;
  857. obs_interaction_stat = sum(obs_interaction.^2);
  858. perm_interaction_stat = zeros(n_perm,1);
  860. for p = 1:n_perm
  862. perm_group = group(randperm(nSubj));
  863. FM_perm = mean(Y_adj(perm_group==1,:),1);
  864. CO_perm = mean(Y_adj(perm_group==0,:),1);
  865. perm_diff = FM_perm - CO_perm;
  866. perm_interaction_stat(p) = sum(perm_diff.^2);
  868. end
  870. p_interaction = ...
  871. (sum(perm_interaction_stat >= obs_interaction_stat) + 1) ...
  872. / (n_perm + 1);
  874. disp(['INTERACTION p = ' ...
  875. num2str(p_interaction)]);
  877. %Variance explainced:
  878. subj_mean = mean(all_acc,2);
  879. mdl = fitlm([edu ses], subj_mean);
  880. disp(mdl.Rsquared)
  882. % Calculate PCA variance CV
  883. [mean_explained_FM, cumvar_FM] = ...
  884. calc_pca_variance(all_data(:,:,:,g1), labels2);
  886. [mean_explained_CO, cumvar_CO] = ...
  887. calc_pca_variance(all_data(:,:,:,g2), labels2);
  889. cumvar_5FM = cumvar_FM(5);
  890. cumvar_5CO = cumvar_CO(5);
  892. fprintf('FM: Variance explained by 5 PCs = %.2f%%\n', cumvar_5FM);
  893. fprintf('CO: Variance explained by 5 PCs = %.2f%%\n', cumvar_5CO);
  895. fprintf('FM: Variance explained by 30 PCs = %.2f%%\n', cumvar_FM(30));
  896. fprintf('CO: Variance explained by 30 PCs = %.2f%%\n', cumvar_CO(30));
  898. % Plot confusion matrix
  900. cm = CO_confusion{:,:};
  901. row_sums = sum(cm, 2);
  902. rev_ct = cm .* 100 ./ row_sums;
  903. global_max = max(rev_ct(:));
  904. fprintf('CO global max is %.2f%%\n', global_max);
  906. limits2 = [0 round(global_max)];
  908. figure;
  909. fig = gcf;
  910. fig.Position = [100 100 1500 600]; % wider figure, adjust as needed
  911. tiledlayout(1,2, 'Padding', 'compact', 'TileSpacing', 'compact');
  913. ax1 = nexttile;
  914. confusion_matrix_perc_v2(FM_confusion, labels2, limits2, ax1);
  915. title(ax1, 'A. Fibromyalgia Group');
  917. ax2 = nexttile;
  918. confusion_matrix_perc_v2(CO_confusion, labels2, limits2, ax2);
  919. title(ax2, 'B. Control Group');
  921. saveas(gcf,[cfg.outdata '/Confusion_matrix_rev.png'])

PAID embody analysis.m, no license · at the source

Overview

Authors: Aleksandra M Herman1,2,3, Julia Szczawińska1, Carolyn Berryman2,4,5,6, Tasha R Stanton2,3
ORCID iDs: Aleksandra M Herman, Carolyn Berryman, Tasha R Stanton
  1. Laboratory of Brain Imaging, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Pasteur 3 St, Warsaw, Poland
  2. IIMPACT in Health, Adelaide University, Adelaide, South Australia Australia
  3. Persistent Pain Research Group, Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia Australia
  4. Brain Stimulation, Imaging and Cognition Research Group, College of Health, Adelaide University, Adelaide, SA Australia
  5. Hopwood Centre for Neurobiology, South Australian Health and Medical Research Institute, Adelaide, SA Australia
  6. Women’s and Children’s Hospital, Adelaide, SA Australia
Institutions: Instytut Biologii Doświadczalnej im. Marcelego Nenckiego (Poland); South Australian Health and Medical Research Institute (Australia); Adelaide University (Australia); The University of Adelaide (Australia); Polish Academy of Sciences (Poland); Women's and Children's Hospital (Australia)
Journal: Psychological research, volume 90, issue 4, article 125
Dates: received 4 February 2026; accepted 25 June 2026; published online 2 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s00426-026-02341-2 · PMID 42390617 · PMCID PMC13328158 · OpenAlex W7167021962
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), pain (population), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Physiology & signal measures
MeSH: Affective Symptoms*, Awareness*, Emotions*, Fibromyalgia*, Interoception*, Pain*, Adult, Cross-Sectional Studies, Female, Humans, Male, Middle Aged (* major topic)
Topic: Fibromyalgia and Chronic Fatigue Syndrome Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: European Commission (101059716)
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

People with fibromyalgia often demonstrate hypersensitivity to benign bodily signals and misattribute non-noxious physical signals as pain, suggesting alterations in body-brain communication and interoception. However, little is known about whether altered body-brain communication may extend to misattribution of emotion as pain. Here, using a cross-sectional design, we aimed to investigate how individuals with fibromyalgia perceive, identify, and interpret emotions and other bodily sensations, including pain, compared to healthy controls. Across two independent studies, individuals with fibromyalgia and age- and gender-matched pain-free controls completed the emBODY task, which assesses the topography of bodily sensations experienced for different states (emotional states, neutral state, pain- and physiology-related states, fatigue). Additionally, participants completed assessments of alexithymia, bodily sensation interpretation, and interoception. In both studies, linear discriminant analysis, performed to test whether different emotions and states are associated with statistically distinct bodily patterns, indicated comparable classification accuracy of body sensation maps in the fibromyalgia group and controls. However, compared with controls, the fibromyalgia group showed higher levels of alexithymia, higher awareness of bodily signals, and more negative interpretation of ambiguous bodily sensations in daily life, together with greater self-reported interoceptive difficulties. Our findings support an amplified perception of bodily signals coupled with interoceptive difficulties in people with fibromyalgia. This raises the possibility that interventions that target perceptual retraining, and re-interpretation of bodily sensations might decrease the impact of pain and promote engagement in everyday activities for people with fibromyalgia.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s00426-026-02341-2.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

OSF 4du3f

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (6), R (2)
Size: 28 files, 8 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Psychtoolbox (4 files), emmeans (2 files), ggplot2 (2 files), ggpubr (2 files), lme4 (2 files), tidyverse (2 files), Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
8 files
At the source: osf.io/4du3f/overview?view_only=9f346447658e465f896b02275ba712f8

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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

Data availability

Data and analyses scripts are available via Open Science Framework (OSF): < https://osf.io/4du3f/overview?view_only=9f346447658e465f896b02275ba712f8>.

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 → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 12 MeSH terms, 1 funder, 61 references.

Cite

This paper

Herman, A. M., Szczawińska, J., Berryman, C., & Stanton, T. R. (2026). When emotions hurt: negative interpretations of bodily signals and interoceptive difficulties in fibromyalgia. Psychological research, 90(4), 125. https://doi.org/10.1007/s00426-026-02341-2

BibTeX

@article{herman2026when,
author = {Herman, Aleksandra M and Szczawińska, Julia and Berryman, Carolyn and Stanton, Tasha R},
title = {{When emotions hurt: negative interpretations of bodily signals and interoceptive difficulties in fibromyalgia}},
journal = {Psychological research},
year = {2026},
month = jul,
volume = {90},
number = {4},
pages = {125},
publisher = {Springer Science+Business Media},
issn = {0340-0727},
doi = {10.1007/s00426-026-02341-2},
url = {https://doi.org/10.1007/s00426-026-02341-2},
pmid = {42390617},
pmcid = {PMC13328158}
}

RIS

TY - JOUR
AU - Herman, Aleksandra M
AU - Szczawińska, Julia
AU - Berryman, Carolyn
AU - Stanton, Tasha R
TI - When emotions hurt: negative interpretations of bodily signals and interoceptive difficulties in fibromyalgia
T2 - Psychological research
J2 - Psychol Res
PY - 2026
DA - 2026/07/02
VL - 90
IS - 4
SP - 125
SN - 0340-0727
PB - Springer Science+Business Media
DO - 10.1007/s00426-026-02341-2
UR - https://doi.org/10.1007/s00426-026-02341-2
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s00426-026-02341-2",
"type": "article-journal",
"title": "When emotions hurt: negative interpretations of bodily signals and interoceptive difficulties in fibromyalgia",
"container-title": "Psychological research",
"author": [
{
"family": "Herman",
"given": "Aleksandra M"
},
{
"family": "Szczawińska",
"given": "Julia"
},
{
"family": "Berryman",
"given": "Carolyn"
},
{
"family": "Stanton",
"given": "Tasha R"
}
],
"container-title-short": "Psychol Res",
"volume": "90",
"issue": "4",
"page": "125",
"DOI": "10.1007/s00426-026-02341-2",
"PMID": "42390617",
"PMCID": "PMC13328158",
"ISSN": "0340-0727",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s00426-026-02341-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
2
]
]
}
}

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

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