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Functional dissociation of language and theory of mind in the developing superior temporal lobe.

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  1. [1] § Methods › Statistics and Reproducibility ↔ Hiersche_2026_CommBio_rmANOVA_code.R, the whole file · a weak match · score 0.92 · rm ANOVA, anova_test, cohens_d, pairwise_t_test, fROIs, outliers
  2. [2] § Results › Connectivity fingerprints that underlie STL function › Connectivity fingerprints for language vs. ToM in the STL ↔ Hiersche_2026_CommBio_stats.m, lines 506–534 · score 0.67 · connectivity fingerprints, adult models, Lang LH, Lang RH, pbh, beta
  3. [3] § Methods › Defining maximally responsive regions and overlap ↔ Hiersche_2026_CommBio_stats.m, lines 243–267 · score 0.59 · fROI, 1–5 %, hotspot, thresholds, dice, Ns

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The authors' code

MATLAB · 632 lines · 30 KB · CC-BY-4.0 · 2 matches

  1. %% KJH 03/182026
  2. % code for reproducing all statistics in Hiersche, Osher, Saygin (2026)
  3. % Functional Dissociation of Language and Theory of Mind in the Developing
  4. % Superior Temporal Lobe. Communications Biology
  6. % code and data are all available on https://github.com/SayginLab/STL_languageToM
  8. % most analyses were performed in matlab (version 2024a)
  9. % some anlayses were performed in R Studio
  10. %% load all data
  11. % this is the selectivity & overlap data data
  12. load('df_LangToM_STL_github.mat')
  14. %% Do fROIs show selectivity to lang or ToM?
  15. % stats for table S1
  16. % output of interest is table_lang and table_ToM
  17. % column 1: t-stat (dof) for selectivity vs 0 one-tailed t-test
  18. % column 2: p value (*p<0.05, **p0.05 after bonferroni-holm correction
  19. % for 7 comparisons)
  20. % column 3: Cohen's D [95% CI]
  21. % the columns are the 7 networks of interest in the network variable
  23. group='kidsEnTx'; % or adultsEnTx or the EnNs versions for supplemental
  24. for i=1:length(networks)
  25. [~,plangv0(i,1),~,stats]=ttest(df.langsel1.(group)(:,i),0,'tail','right');
  26. [~,ptomv0(i,1),~,stats]=ttest(df.tomsel1.(group)(:,i),0,'tail','right');
  27. end
  29. for i=1:length(networks)
  30. ROI=i;
  31. [~,p,~,stats]=ttest(df.langsel1.(group)(:,i),0,'tail','right');
  32. results.Lang.(group)(i,1)=stats.tstat;
  33. results.Lang.(group)(i,2)=p;
  34. results.Lang.(group)(i,3)=stats.df;
  35. d=meanEffectSize(df.langsel1.(group)(:,i),'Effect','cohen');
  36. results.Lang.(group)(i,5)=table2array(d(1,1));
  37. ci.lang(i,:)=table2array(d(1,2));
  38. % ToM v O
  39. [~,p,~,stats]=ttest(df.tomsel1.(group)(:,i),0,'tail','right');
  40. results.ToM.(group)(i,1)=stats.tstat;
  41. results.ToM.(group)(i,2)=p;
  42. results.ToM.(group)(i,3)=stats.df;
  43. d=meanEffectSize(df.tomsel1.(group)(:,i),'Effect','cohen');
  44. results.ToM.(group)(i,5)=table2array(d(1,1));
  45. ci.ToM(i,:)=table2array(d(1,2));
  46. end
  48. % do multiple comparison correction
  49. %results.LANGvToM.(group)(:,4)=bonf_holm(results.LANGvToM.(group)(:,2));
  50. results.Lang.(group)(:,4)=bonf_holm(results.Lang.(group)(:,2));
  51. results.ToM.(group)(:,4)=bonf_holm(results.ToM.(group)(:,2));
  53. % prep the Lang table
  54. for i=1:length(networks)
  55. pval=results.Lang.(group)(i,2);
  56. pval_bf=results.Lang.(group)(i,4);
  57. dval=results.Lang.(group)(i,5);
  58. table_lang{i,1} = sprintf('%2.2f (%d)',results.Lang.(group)(i,1),results.Lang.(group)(i,3));
  59. if pval < 0.05 && pval_bf > 0.05
  60. table_lang{i,2} = sprintf('%0.5g*',pval);
  61. elseif pval <0.05 && pval_bf <0.05
  62. table_lang{i,2} = sprintf('%0.5g**',pval);
  63. else
  64. table_lang{i,2} = sprintf('%0.5g',pval);
  65. end
  66. table_lang{i,3} = sprintf('%2.2f, [%2.2f,%2.2f] ',dval,ci.lang(i,1),ci.lang(i,2));
  67. end
  69. % prep the ToM table for i=1:length(plotROIs)
  70. for i=1:length(networks)
  71. pval=results.ToM.(group)(i,2);
  72. pval_bf=results.ToM.(group)(i,4);
  73. dval=results.ToM.(group)(i,5);
  74. table_ToM{i,1} = sprintf('%2.2f (%d)',results.ToM.(group)(i,1),results.ToM.(group)(i,3));
  75. if pval < 0.05 && pval_bf > 0.05
  76. table_ToM{i,2} = sprintf('%0.5g*',pval);
  77. elseif pval <0.05 && pval_bf <0.05
  78. table_ToM{i,2} = sprintf('%0.5g**',pval);
  79. else
  80. table_ToM{i,2} = sprintf('%0.5g',pval);
  81. end
  82. table_ToM{i,3} = sprintf('%2.2f, [%2.2f,%2.2f] ',dval,ci.ToM(i,1),ci.ToM(i,2));
  83. end
  85. tablev0=[table_lang;table_ToM];
  88. %% Plot Figure 1A
  89. group='kidsEnTx'; % or 'kidsEnTx' 'adultsEnTx'
  91. meanselkid=[nanmean(df.langsel.(group)(:,[1,2]),'all'),nanmean(df.tomsel.(group)(:,[1,2]),'all');nanmean(df.langsel.(group)(:,[6,7]),'all'),nanmean(df.tomsel.(group)(:,[6,7]),'all');nanmean(df.langsel.(group)(:,[5]),'all'),nanmean(df.tomsel.(group)(:,[5]),'all');nanmean(df.langsel.(group)(:,[10]),'all'),nanmean(df.tomsel.(group)(:,[10]),'all')];
  92. steselkid=[nanste(nanmean(df.langsel.(group)(:,[1,2]),2)),nanste(nanmean(df.tomsel.(group)(:,[1,2]),2));nanste(nanmean(df.langsel.(group)(:,[6,7]),2)),nanste(nanmean(df.tomsel.(group)(:,[6,7]),2));nanste(nanmean(df.langsel.(group)(:,[5]),2)),nanste(nanmean(df.tomsel.(group)(:,[5]),2));nanste(nanmean(df.langsel.(group)(:,[10]),2)),nanste(nanmean(df.tomsel.(group)(:,[10]),2))];
  94. errorbar_groups(meanselkid',steselkid')
  95. hold on
  96. scatter(ones([length(df.langsel.(group)(:,1)),1])*1.05,nanmean(df.langsel.(group)(:,[1,2]),2),'k')
  97. scatter(ones([length(df.langsel.(group)(:,1)),1])*3.05,nanmean(df.langsel.(group)(:,[6,7]),2),'k')
  98. scatter(ones([length(df.langsel.(group)(:,1)),1])*5.05,nanmean(df.langsel.(group)(:,5),2),'k')
  99. scatter(ones([length(df.langsel.(group)(:,1)),1])*7.05,df.langsel.(group)(:,10),'k')
  101. scatter(ones([length(df.tomsel.(group)(:,1)),1])*1.95,nanmean(df.tomsel.(group)(:,[1,2]),2),'k')
  102. scatter(ones([length(df.tomsel.(group)(:,1)),1])*3.95,nanmean(df.tomsel.(group)(:,[6,7]),2),'k')
  103. scatter(ones([length(df.tomsel.(group)(:,1)),1])*5.95,nanmean(df.tomsel.(group)(:,5),2),'k')
  104. scatter(ones([length(df.tomsel.(group)(:,1)),1])*7.95,nanmean(df.tomsel.(group)(:,10),2),'k')
  106. legend({'Lang','ToM'})
  107. xticklabels({'LH Lang','RH Lang','LH TPJ','RH TPJ'})
  108. ylabel({'Selectivity'})
  109. title(group)
  110. ylim([-1 2])
  112. %% Are language and ToM regions functionally distinct
  113. % this code if for reproducing Table 1
  114. % to make table S2, use motmatch groups
  115. % to make table S3, use the EnNs groups
  116. % prep the data for rmANOVA:
  117. clear sels task sub fwd aovtable
  118. group='adultsmotmatchEnTx'; subgroup='adultsmotmatch'; % sub group just needs to have the number of subjects
  119. sels=[df.langsel1.(group);df.tomsel1.(group)];
  120. task=[repmat({'lang'},[length(sels)/2,1]);repmat({'tom'},[length(sels)/2,1])]; %lang 0, ToM 1
  121. sub=[df.subs.(subgroup);df.subs.(subgroup)]; % subject
  122. if contains(group,'kids')
  123. fwd=[mean(df.motion.(subgroup).mean_fwd.lang,2);df.motion.(subgroup).mean_fwd.partlycloudy_rc_new(:,1)]; % fwd
  124. else
  125. % for adults
  126. fwd=[mean(df.motion.(subgroup).lang,2);df.motion.(subgroup).pc]; % fwd
  127. end
  128. aovtable=table(sub,task,sels,fwd);
  129. % see R studio script for calculating rmANOVAs
  131. %% table S4, differentiation with age
  132. % outcome of cross sectional correlation is in rlang and rtom
  133. % column 1: r value
  134. % column 2: p value
  135. % column 3: p value after bonferroni holm correction
  136. % rows are the 7 networks
  137. % longitudinal results are in ltp12: language changes across TPs; ttp12:
  138. % ToM changes across TPs
  139. group='kidsEnTx';con='EnTx'; %
  140. for i=1:length(networks)
  141. [rlang(i,1),rlang(i,2)]=partialcorr(df.langsel1.(group)(:,i),df.ages.kids,mean(df.motion.kids.mean_fwd.lang,2),'rows','complete');
  142. [rtom(i,1),rtom(i,2)]=partialcorr(df.tomsel1.(group)(:,i),df.ages.kids,df.motion.kids.mean_fwd.partlycloudy_rc_new(:,1),'rows','complete');
  144. [~,pl(i,1),~,stats]=ttest(df.langsel1.(['kidslongTP1' con])(:,i),df.langsel1.(['kidslongTP2' con])(:,i));
  145. ltp12(i,1)=strcat('t(' ,string(stats.df), ')=',string(round(stats.tstat,2)) );
  146. [~,pt(i,1),~,stats]=ttest(df.tomsel1.(['kidslongTP1' con])(:,i),df.tomsel1.(['kidslongTP2' con])(:,i));
  147. ttp12(i,1)=strcat('t(' ,string(stats.df), ')=',string(round(stats.tstat,2)) );
  148. end
  149. rlang(:,3)=bonf_holm(rlang(:,2));
  150. rtom(:,3)=bonf_holm(rtom(:,2));
  151. %scatter(df.xsec.ages,tomsel1.xsec(:,1))
  153. rtable=[rlang(:,1:2),rtom(:,1:2)];
  155. %% figure 1B overlap
  156. kidgroup='kidsEnTx';
  157. adultgroup='adultsEnTx';
  159. thresholds={'1%','2%','3%','4%','5%','10%','20%','30%'};
  160. for i=1:length(thresholds)
  161. sgtitle('LH STL')
  162. subplot(2,4,i)
  163. histogram(df.dice.(kidgroup).dice.LH_STL(:,i),'NumBins',8)
  164. hold on
  165. histogram(df.dice.(adultgroup).dice.LH_STL(:,i),'NumBins',8)
  166. title(thresholds{i})
  167. ylabel('subject count')
  168. xlabel('overlap')
  169. legend('off')
  170. ylim([0 50])
  171. xlim([0 .60])
  172. end
  174. figure
  175. thresholds={'1%','2%','3%','4%','5%','10%','20%','30%'};
  176. for i=1:length(thresholds)
  177. sgtitle('RH STL')
  178. subplot(2,4,i)
  179. histogram(df.dice.(kidgroup).dice.RH_STL(:,i),'NumBins',8)
  180. hold on
  181. histogram(df.dice.(adultgroup).dice.RH_STL(:,i),'NumBins',8)
  182. title(thresholds{i})
  183. ylabel('subject count')
  184. xlabel('overlap')
  185. legend('off')
  186. ylim([0 50])
  187. xlim([0 .60])
  188. end
  189. %% figure 1C: dice overlap
  190. group='kidsEnTx'; % change to adultsEnTx to plot adults
  191. prct={'1','2','3','4','5','10','20','30'};
  192. for i=1:length(prct)
  193. meandice=squeeze(mean(df.diceperm.(group).real_dice));
  194. %sig_dice=1-(sum(df.diceperm.(group).pval_dice(:,:,i)<0.05)./size(df.spin.(group).pval_dice,1));
  195. subplot(2,4,i)
  196. imagesc([meandice(1:2,i),meandice(3:4,i)])
  197. hold on
  198. title(sprintf('%s',prct{i}))
  199. colorbar
  200. sgtitle(group,'FontSize',25)
  201. clim([0 1])
  202. xticks([1 2])
  203. %xticklabels({'LH Lang','RH Lang'})
  204. yticks([1 2])
  205. %yticklabels({'LH ToM','RH ToM'})
  206. set(gcf,'color','w'); % set graph parameters
  207. set(gca,'box','off','LineWidth',1.5,'FontSize',20);
  208. set(gca,'TickLabelInterpreter','none') % interpreter none is helpful for plotting names with '_'
  209. set(gcf,'renderer','painters'); % your plots are made slower but prettier
  211. end
  214. %% overlap stats in text
  216. % adult results listed in text
  217. % mean and std of overlap
  218. mean(df.dice.adultsEnTx.dice.LH_STL)
  219. std(df.dice.adultsEnTx.dice.LH_STL)
  220. % percent overlap similar to change
  221. sum(squeeze(df.diceperm.adultsEnTx.pval_dice(:,1,:))>=0.05)/length(df.subs.adults)
  223. mean(df.dice.adultsEnTx.dice.RH_STL)
  224. std(df.dice.adultsEnTx.dice.RH_STL)
  225. sum(squeeze(df.diceperm.adultsEnTx.pval_dice(:,4,:))>=0.05)/length(df.subs.adults)
  227. % LH to RH overlap
  228. LHlang_RHToM=[squeeze(mean(df.diceperm.adultsEnTx.real_dice(:,3,:))),squeeze(std(df.diceperm.adultsEnTx.real_dice(:,3,:)))]
  229. sum(squeeze(df.diceperm.adultsEnTx.pval_dice(:,3,:))>=0.05)/length(df.subs.adults)
  231. % kids results
  232. mean(df.dice.kidsEnTx.dice.LH_STL)
  233. std(df.dice.kidsEnTx.dice.LH_STL)
  234. sum(squeeze(df.diceperm.kidsEnTx.pval_dice(:,1,:))>=0.05)/length(df.subs.kids)
  236. mean(df.dice.kidsEnTx.dice.RH_STL)
  237. std(df.dice.kidsEnTx.dice.RH_STL)
  238. sum(squeeze(df.diceperm.kidsEnTx.pval_dice(:,4,:))>=0.05)/length(df.subs.kids)
  240. LHlang_RHToM=[squeeze(mean(df.diceperm.kidsEnTx.real_dice(:,3,:))),squeeze(std(df.diceperm.kidsEnTx.real_dice(:,3,:)))]
  241. sum(squeeze(df.diceperm.kidsEnTx.pval_dice(:,3,:))>=0.05)/length(df.subs.kids)
  243. %% overlap stats in table 2
  244. % output of interest is ov
  245. % column 1: mean (std) % similar change for LH overlap
  246. % column 2: mean (std) % similar change for RH overlap
  247. % column 3: mean (std) % similar change for LH lang-RH ToM overlap
  248. % the rows are the different threshold for making hotspot fROI: 1%-5%,
  249. % 10%,20%,30%
  251. % change to motmatch for Table S5
  252. % change the group to EnNs for Table S6
  253. group='adultsEnNs';group1='adults';
  254. for i=1:8
  255. meanov=nanmean(df.dice.(group).dice.LH_STL(:,i));
  256. stdov=nanstd(df.dice.(group).dice.LH_STL(:,i));
  257. perc_chance=(sum(squeeze(df.diceperm.(group).pval_dice(:,1,i))>=0.05)./length(df.subs.(group1)))*100;
  258. ov{i,1} = sprintf('%2.3f (%2.3f); %2.1f%%',meanov,stdov,perc_chance);
  259. meanov=nanmean(df.dice.(group).dice.RH_STL(:,i));
  260. stdov=nanstd(df.dice.(group).dice.RH_STL(:,i));
  261. perc_chance=(sum(squeeze(df.diceperm.(group).pval_dice(:,4,i))>=0.05)./length(df.subs.(group1)))*100;
  262. ov{i,2} = sprintf('%2.3f (%2.3f); %2.1f%%',meanov,stdov,perc_chance);
  263. meanov=nanmean(df.diceperm.(group).real_dice(:,3,i));
  264. stdov=nanstd(df.diceperm.(group).real_dice(:,3,i));
  265. perc_chance=(sum(squeeze(df.diceperm.(group).pval_dice(:,3,i))>=0.05)./length(df.subs.(group1)))*100;
  266. ov{i,3} = sprintf('%2.3f (%2.3f); %2.1f%%',meanov,stdov,perc_chance);
  267. end
  268. %% overlap and age changes
  269. % table S7
  270. % for table S8, use EnNs results
  272. % this is cross sectional results: results.ovcor
  273. % column 1: r value
  274. % column 2: pval
  275. % column 3: bonferrnoi-holm p val
  276. % rows: thresholds, 1-5%, 10%, 20%, 30%
  277. sec={'LH_STL','RH_STL'};
  278. group='kidsEnNs'; group1='kids';
  279. motion=nanmean([df.motion.(group1).mean_fwd.lang,df.motion.(group1).mean_fwd.partlycloudy_rc_new],2);
  280. for s=1:length(sec)
  281. for i=1:8
  283. [r,p]=partialcorr(df.dice.(group).dice.(sec{s})(:,i),df.ages.(group1),motion);
  284. results.ovcor.(group).(sec{s})(i,1)=r;
  285. results.ovcor.(group).(sec{s})(i,2)=p;
  287. [r,p]=partialcorr(squeeze(df.diceperm.(group).real_dice(:,3,i)),df.ages.(group1),motion);
  288. results.ovcor.(group).LHlangRHToM(i,1)=r;
  289. results.ovcor.(group).LHlangRHToM(i,2)=p;
  292. end
  293. end
  295. results.ovcor.(group).RH_STL(:,3)=bonf_holm(results.ovcor.(group).RH_STL(:,2));
  296. results.ovcor.(group).LH_STL(:,3)=bonf_holm(results.ovcor.(group).LH_STL(:,2));
  297. results.ovcor.(group).LHlangRHToM(:,3)=bonf_holm(results.ovcor.(group).LHlangRHToM(:,2));
  300. % this is the longitudinal results: results.ovttest.long
  301. % table results are ov_tp
  302. % column 1: t stat (dof)
  303. % column 2: p value
  304. % column 3: cohen's D
  305. % column 4: cohen's d 95% CI
  306. % rows are the 8 thresholds (1%-5%,10%,20%,30%)
  307. groupTP1='kidslongTP1EnNs';
  308. groupTP2='kidslongTP2EnNs';
  309. sec={'LH_STL','RH_STL'};
  311. for s=1:length(sec)
  312. for i=1:8
  313. [~,p,~,stats]=ttest(df.dice.(groupTP1).dice.(sec{s})(:,i),df.dice.(groupTP2).dice.(sec{s})(:,i));
  314. cd=meanEffectSize(df.dice.(groupTP1).dice.(sec{s})(:,i),df.dice.(groupTP2).dice.(sec{s})(:,i),'Effect','cohen');
  316. results.ovttest.long.(sec{s})(i,1)=stats.tstat;
  317. results.ovttest.long.(sec{s})(i,2)=stats.df;
  318. results.ovttest.long.(sec{s})(i,3)=p;
  319. ci=string(round(table2array(cd(1,2)),2));
  320. ov_tp.(sec{s}){i,1} = sprintf('%2.2f (%d)',stats.tstat,stats.df);
  321. ov_tp.(sec{s}){i,2} = sprintf('%2.2f',p);
  322. ov_tp.(sec{s}){i,3} = sprintf('%2.2f,',table2array(cd(1,1)));
  323. ov_tp.(sec{s}){i,4} = sprintf('[%s,%s]',ci(1),ci(2));
  324. end
  325. end
  326. results.ovttest.long.LH_STL(:,4)=bonf_holm( results.ovttest.long.LH_STL(:,3));
  327. results.ovttest.long.RH_STL(:,4)=bonf_holm( results.ovttest.long.RH_STL(:,3));
  329. for i=1:8
  330. [~,p,~,stats]=ttest(df.diceperm.(groupTP1).real_dice(:,i),squeeze(df.diceperm.(groupTP2).real_dice(:,3,i)));
  331. cd=meanEffectSize(df.diceperm.(groupTP1).real_dice(:,i),squeeze(df.diceperm.(groupTP2).real_dice(:,3,i)),'Effect','cohen');
  333. results.ovttest.long.LHlang_RHToM(i,1)=stats.tstat;
  334. results.ovttest.long.LHlang_RHToM(i,2)=stats.df;
  335. results.ovttest.long.LHlang_RHToM(i,3)=p;
  336. ci=string(round(table2array(cd(1,2)),2));
  337. ov_tp.LHlang_RHToM{i,1} = sprintf('%2.2f (%d)',stats.tstat,stats.df);
  338. ov_tp.LHlang_RHToM{i,2} = sprintf('%2.2f',p);
  339. ov_tp.LHlang_RHToM{i,3} = sprintf('%2.2f,',table2array(cd(1,1)));
  340. ov_tp.LHlang_RHToM{i,4} = sprintf('[%s,%s]',ci(1),ci(2));
  341. end
  343. %% predicting adult STL activation
  344. group='kids'; % 'kids'
  345. % set the group to either kids or adults depending on stats you are
  346. % trying to reproduce
  347. % language activation prediction
  348. % get mean and std of self prediction (using a subject's own connectivity
  349. mean(cfmodelresults.(group).selfprediction.lh.R2_EnTx_lang)
  350. std(cfmodelresults.(group).selfprediction.lh.R2_EnTx_lang)
  351. mean(cfmodelresults.(group).selfprediction.rh.R2_EnTx_lang)
  352. std(cfmodelresults.(group).selfprediction.rh.R2_EnTx_lang)
  354. % get mean and std of other prediction (using each other subs connectivty)
  355. mean(cfmodelresults.(group).otherpred.lh.R2_EnTx_lang,'all')
  356. std(mean(cfmodelresults.(group).otherpred.lh.R2_EnTx_lang,2))
  357. mean(cfmodelresults.(group).otherpred.rh.R2_EnTx_lang,'all')
  358. std(mean(cfmodelresults.(group).otherpred.rh.R2_EnTx_lang,2))
  359. % self v other t-test: do fischer z transform
  360. [~,p,~,stats]=ttest(atanh(cfmodelresults.(group).selfprediction.lh.R2_EnTx_lang),mean(atanh(cfmodelresults.(group).otherpred.lh.R2_EnTx_lang),2))
  361. meanEffectSize(atanh(cfmodelresults.(group).selfprediction.lh.R2_EnTx_lang),mean(atanh(cfmodelresults.(group).otherpred.lh.R2_EnTx_lang),2),Effect="cohen")
  362. [~,p,~,stats]=ttest(atanh(cfmodelresults.(group).selfprediction.rh.R2_EnTx_lang),mean(atanh(cfmodelresults.(group).otherpred.rh.R2_EnTx_lang),2))
  363. meanEffectSize(atanh(cfmodelresults.(group).selfprediction.rh.R2_EnTx_lang),mean(atanh(cfmodelresults.(group).otherpred.rh.R2_EnTx_lang),2),Effect="cohen")
  365. % ToM activation prediction
  366. % get mean and std of self prediction (using a subject's own connectivity
  367. mean(cfmodelresults.(group).selfprediction.lh.R2_MntlPain_ToM)
  368. std(cfmodelresults.(group).selfprediction.lh.R2_MntlPain_ToM)
  369. mean(cfmodelresults.(group).selfprediction.rh.R2_MntlPain_ToM)
  370. std(cfmodelresults.(group).selfprediction.rh.R2_MntlPain_ToM)
  372. % get mean and std of other prediction (using each other subs connectivty)
  373. mean(cfmodelresults.(group).otherpred.lh.R2_MntlPain_ToM,'all')
  374. std(mean(cfmodelresults.(group).otherpred.lh.R2_MntlPain_ToM,2))
  375. mean(cfmodelresults.(group).otherpred.rh.R2_MntlPain_ToM,'all')
  376. std(mean(cfmodelresults.(group).otherpred.rh.R2_MntlPain_ToM,2))
  377. % self v other t-test: do fischer z transform
  378. [~,p,~,stats]=ttest(atanh(cfmodelresults.(group).selfprediction.lh.R2_MntlPain_ToM),mean(atanh(cfmodelresults.(group).otherpred.lh.R2_MntlPain_ToM),2))
  379. meanEffectSize(atanh(cfmodelresults.(group).selfprediction.lh.R2_MntlPain_ToM),mean(atanh(cfmodelresults.(group).otherpred.lh.R2_MntlPain_ToM),2),Effect="cohen")
  380. [~,p,~,stats]=ttest(atanh(cfmodelresults.(group).selfprediction.rh.R2_MntlPain_ToM),mean(atanh(cfmodelresults.(group).otherpred.rh.R2_MntlPain_ToM),2))
  381. meanEffectSize(atanh(cfmodelresults.(group).selfprediction.rh.R2_MntlPain_ToM),mean(atanh(cfmodelresults.(group).otherpred.rh.R2_MntlPain_ToM),2),Effect="cohen")
  383. %% model fit and age correlations
  385. [r,p]=partialcorr(atanh(cfmodelresults.kids.selfprediction.lh.R2_EnTx_lang),df.ages.kidscf,df.motion.kidscf.restmot.fwd)
  386. [r,p]=partialcorr(atanh(cfmodelresults.kids.selfprediction.rh.R2_EnTx_lang),df.ages.kidscf,df.motion.kidscf.restmot.fwd)
  388. [r,p]=partialcorr(atanh(cfmodelresults.kids.selfprediction.lh.R2_MntlPain_ToM),df.ages.kidscf,df.motion.kidscf.restmot.fwd)
  389. [r,p]=partialcorr(atanh(cfmodelresults.kids.selfprediction.rh.R2_MntlPain_ToM),df.ages.kidscf,df.motion.kidscf.restmot.fwd)
  391. %% model fit across TPs
  392. TP1self.lh.lang=diag(cfmodelresults.kidsTP1.selfprediction.lh.R2_EnTx_lang);
  393. TP1self.rh.lang=diag(cfmodelresults.kidsTP1.selfprediction.rh.R2_EnTx_lang);
  394. TP1self.lh.ToM=diag(cfmodelresults.kidsTP1.selfprediction.lh.R2_MntlPain_ToM);
  395. TP1self.rh.ToM=diag(cfmodelresults.kidsTP1.selfprediction.rh.R2_MntlPain_ToM);
  397. mean(TP1self.lh.lang)
  398. std(TP1self.lh.lang)
  399. mean(TP1self.rh.lang)
  400. std(TP1self.rh.lang)
  402. mean(TP1self.lh.ToM)
  403. std(TP1self.lh.ToM)
  404. mean(TP1self.rh.ToM)
  405. std(TP1self.rh.ToM)
  407. [~,tp2_idx]=intersect(df.subs.kidscf,df.subs.kidscfTP2);
  408. TP2self.lh.lang=cfmodelresults.kids.selfprediction.lh.R2_EnTx_lang(tp2_idx);
  409. TP2self.rh.lang=cfmodelresults.kids.selfprediction.rh.R2_EnTx_lang(tp2_idx);
  410. TP2self.lh.ToM=cfmodelresults.kids.selfprediction.lh.R2_MntlPain_ToM(tp2_idx);
  411. TP2self.rh.ToM=cfmodelresults.kids.selfprediction.rh.R2_MntlPain_ToM(tp2_idx);
  413. [~,p,~,stats]=ttest(atanh(TP1self.lh.lang),atanh(TP2self.lh.lang))
  414. meanEffectSize(atanh(TP1self.lh.lang),atanh(TP2self.lh.lang),Effect='cohen')
  415. [~,p,~,stats]=ttest(atanh(TP1self.rh.lang),atanh(TP2self.rh.lang))
  416. meanEffectSize(atanh(TP1self.rh.lang),atanh(TP2self.rh.lang),Effect='cohen')
  418. [~,p,~,stats]=ttest(atanh(TP1self.lh.ToM),atanh(TP2self.lh.ToM))
  419. meanEffectSize(atanh(TP1self.lh.ToM),atanh(TP2self.lh.ToM),Effect='cohen')
  420. [~,p,~,stats]=ttest(atanh(TP1self.rh.ToM),atanh(TP2self.rh.ToM))
  421. meanEffectSize(atanh(TP1self.rh.ToM),atanh(TP2self.rh.ToM),Effect='cohen')
  424. %% Plot Figure 2A
  425. group='adults'; subs=length(df.subs.adultscf);
  426. % change to kids and df.subs.kidscf when plotting kids
  427. clear predcorr predcorr_b predcorr_other
  428. predcorr(:,1)=cfmodelresults.(group).selfprediction.lh.R2_EnTx_lang;
  429. predcorr(:,2)=cfmodelresults.(group).selfprediction.rh.R2_EnTx_lang;
  430. predcorr(:,3)=cfmodelresults.(group).selfprediction.lh.R2_MntlPain_ToM;
  431. predcorr(:,4)=cfmodelresults.(group).selfprediction.rh.R2_MntlPain_ToM;
  433. predcorr_other(:,1)=mean(cfmodelresults.(group).otherpred.lh.R2_EnTx_lang,2);
  434. predcorr_other(:,2)=mean(cfmodelresults.(group).otherpred.rh.R2_EnTx_lang,2);
  435. predcorr_other(:,3)=mean(cfmodelresults.(group).otherpred.lh.R2_MntlPain_ToM,2);
  436. predcorr_other(:,4)=mean(cfmodelresults.(group).otherpred.rh.R2_MntlPain_ToM,2);
  438. predcorr_b(:,1)=cfmodelresults.(group).wrongBpred.lh.R2_EnTx_lang;
  439. predcorr_b(:,2)=cfmodelresults.(group).wrongBpred.rh.R2_EnTx_lang;
  440. predcorr_b(:,3)=cfmodelresults.(group).wrongBpred.lh.R2_MntlPain_ToM;
  441. predcorr_b(:,4)=cfmodelresults.(group).wrongBpred.rh.R2_MntlPain_ToM;
  442. xvals=errorbar_groups([mean(predcorr);mean(predcorr_other);mean(predcorr_b)],[ste(predcorr);ste(predcorr_other);ste(predcorr_b)]);
  443. hold on
  444. for i=1:length(xvals)
  445. xval=[ones([subs,1]).*(xvals(i)-0.9),ones([subs,1]).*xvals(i),ones([subs,1]).*(xvals(i)+0.9)];
  446. yval=[predcorr(:,i),predcorr_other(:,i),predcorr_b(:,i)];
  447. scatter(xval,yval,'k')
  448. for s=1:subs
  449. plot(xval(s,:),yval(s,:),'r')
  451. end
  452. end
  453. title([ group 'Model Performance'])
  454. legend({'Self','Other','Opposite Task Betas'})
  455. ylabel('Correlation Real and Predicted')
  456. xticklabels({'Lang LH','Lang RH','ToM LH','ToM RH'})
  457. set(gcf,'color','w');
  458. set(gca,'box','off','LineWidth',2.5,'FontSize',15,'Layer', 'Top');
  460. %% Figure 2B: TP1 vs TP2
  461. subs=length(df.subs.kidscfTP1);
  462. xvals=errorbar_groups([mean(TP1self.lh.lang),mean(TP1self.rh.lang),mean(TP1self.lh.ToM),mean(TP1self.rh.ToM);mean(TP2self.lh.lang),mean(TP2self.rh.lang),mean(TP2self.lh.ToM),mean(TP2self.rh.ToM)],[ste(TP1self.lh.lang),ste(TP1self.rh.lang),ste(TP1self.lh.ToM),ste(TP1self.rh.ToM);ste(TP2self.lh.lang),ste(TP2self.rh.lang),ste(TP2self.lh.ToM),ste(TP2self.rh.ToM)]);
  463. hold on
  464. title('Kids Longitudinal Model Fit')
  465. xticklabels({'LH Lang','RH Lang','LH ToM','RH ToM'})
  466. %xticklabels({'LH Lang','RH Lang'}); %;,'LH ToM','RH ToM'})
  468. ylabel('Predicted-Actual Correlation')
  470. scatter(ones([subs,1])*1.95,TP2self.lh.lang,'k')
  471. scatter(ones([subs,1])*1.05,TP1self.lh.lang,'k')
  473. scatter(ones([subs,1])*3.95,TP2self.rh.lang,'k')
  474. scatter(ones([subs,1])*3.05,TP1self.rh.lang,'k')
  476. scatter(ones([subs,1])*5.95,TP2self.lh.ToM,'k')
  477. scatter(ones([subs,1])*5.05,TP1self.lh.ToM,'k')
  479. scatter(ones([subs,1])*7.95,TP2self.rh.ToM,'k')
  480. scatter(ones([subs,1])*7.05,TP1self.rh.ToM,'k')
  482. for s=1:subs
  483. plot([1.05,1.95],[TP1self.lh.lang(s,1),TP2self.lh.lang(s,1)],'r')
  484. plot([3.05,3.95],[TP1self.rh.lang(s,1),TP2self.rh.lang(s,1)],'r')
  485. plot([5.05,5.95],[TP1self.lh.ToM(s,1),TP2self.lh.ToM(s,1)],'r')
  486. plot([7.05,7.95],[TP1self.rh.ToM(s,1),TP2self.rh.ToM(s,1)],'r')
  487. end
  488. legend({'TP1','TP2'})
  489. set(gcf,'color','w');
  490. set(gca,'box','off','LineWidth',2.5,'FontSize',15,'Layer', 'Top');
  492. %% comparing across models for predicting lang and ToM
  494. % adults lang v ToM
  495. [~,p,~,stats]=ttest(atanh(cfmodelresults.adults.selfprediction.lh.R2_EnTx_lang),atanh(cfmodelresults.adults.selfprediction.lh.R2_MntlPain_ToM))
  496. meanEffectSize(atanh(cfmodelresults.adults.selfprediction.lh.R2_EnTx_lang),atanh(cfmodelresults.adults.selfprediction.lh.R2_MntlPain_ToM),Effect="cohen")
  497. [~,p,~,stats]=ttest(atanh(cfmodelresults.adults.selfprediction.rh.R2_EnTx_lang),atanh(cfmodelresults.adults.selfprediction.rh.R2_MntlPain_ToM))
  498. meanEffectSize(atanh(cfmodelresults.adults.selfprediction.rh.R2_EnTx_lang),atanh(cfmodelresults.adults.selfprediction.rh.R2_MntlPain_ToM),Effect="cohen")
  500. % children lang v ToM
  501. [~,p,~,stats]=ttest(atanh(cfmodelresults.kids.selfprediction.lh.R2_EnTx_lang),atanh(cfmodelresults.kids.selfprediction.lh.R2_MntlPain_ToM))
  502. meanEffectSize(atanh(cfmodelresults.kids.selfprediction.lh.R2_EnTx_lang),atanh(cfmodelresults.kids.selfprediction.lh.R2_MntlPain_ToM),Effect="cohen")
  503. [~,p,~,stats]=ttest(atanh(cfmodelresults.kids.selfprediction.rh.R2_EnTx_lang),atanh(cfmodelresults.kids.selfprediction.rh.R2_MntlPain_ToM))
  504. meanEffectSize(atanh(cfmodelresults.kids.selfprediction.rh.R2_EnTx_lang),atanh(cfmodelresults.kids.selfprediction.rh.R2_MntlPain_ToM),Effect="cohen")
  506. %% Connectivity fingerprints for Language vs. ToM in the STL
  507. [r_beta,p_beta]=corr([meanbetas.kids.R2_EnTx_lang.lh,meanbetas.kids.R2_EnTx_lang.rh,meanbetas.kids.R2_MntlPain_ToM.lh,meanbetas.kids.R2_MntlPain_ToM.rh,meanbetas.adults.R2_EnTx_lang.lh,meanbetas.adults.R2_EnTx_lang.rh,meanbetas.adults.R2_MntlPain_ToM.lh,meanbetas.adults.R2_MntlPain_ToM.rh],'Type','Pearson','rows','pairwise');
  508. p_beta(p_beta==1)=0;
  509. ptri=tril(p_beta);
  510. pval=squareform(p_beta)';
  511. pbf=bonf_holm(pval);
  512. pbf_square=squareform(pbf);
  513. % r beta & p_beta (rows and columns) is in the order of
  514. % kids lang lh
  515. % kids lang rh
  516. % kids ToM lh
  517. % kids ToM rh
  518. % adults lang lh
  519. % adults lang rh
  520. % adults ToM lh
  521. % adults ToM rh
  523. % look at adult to adult models
  524. adults_r=r_beta(5:8,5:8);
  525. adults_pbh=pbf_square(5:8,5:8);
  526. adults_sig=r_beta(5:8,5:8).*(pbf_square(5:8,5:8)<0.05);
  528. kids_r=r_beta(1:4,1:4);
  529. kids_pbh=pbf_square(1:4,1:4);
  530. kids_sig=r_beta(1:4,1:4).*(pbf_square(1:4,1:4)<0.05);
  532. ak_r=r_beta(5:8,1:4);
  533. ak_pbh=pbf_square(5:8,1:4);
  534. ak_sig=r_beta(5:8,1:4).*(pbf_square(5:8,1:4)<0.05);
  536. %% Figure 2C
  537. imagesc(r_beta)
  538. hold on
  539. colorbar
  540. clim([0 1])
  541. colormap(turbo)
  542. title('Correlation of Betas Across Models')
  543. xticklabels({'Kids LH Lang','Kids RH Lang','Kids LH ToM','Kids RH ToM', ...
  544. 'Adults LH Lang','Adults RH Lang','Adults LH ToM','Adults RH ToM'})
  545. yticklabels({'Kids LH Lang','Kids RH Lang','Kids LH ToM','Kids RH ToM', ...
  546. 'Adults LH Lang','Adults RH Lang','Adults LH ToM','Adults RH ToM'})
  548. %% What contributes to these differences across domains?
  549. lang='R2_EnTx_lang';
  550. ToM='R2_MntlPain_ToM';
  551. for i=1:145
  552. % kids LH
  553. [~,p,~,stats]=ttest(betas.kids.(lang).lh(i,:),betas.kids.(ToM).lh(i,:));
  554. betacomp.kids_lh(i,2)=p;
  555. betacomp.kids_lh(i,1)=stats.tstat;
  556. % kids RH
  557. [~,p,~,stats]=ttest(betas.kids.(lang).rh(i,:),betas.kids.(ToM).rh(i,:));
  558. betacomp.kids_rh(i,2)=p;
  559. betacomp.kids_rh(i,1)=stats.tstat;
  561. % adults LH
  562. [~,p,~,stats]=ttest(betas.adults.(lang).lh(i,:),betas.adults.(ToM).lh(i,:));
  563. betacomp.adults_lh(i,2)=p;
  564. betacomp.adults_lh(i,1)=stats.tstat;
  565. %adults RH
  566. [~,p,~,stats]=ttest(betas.adults.(lang).rh(i,:),betas.adults.(ToM).rh(i,:));
  567. betacomp.adults_rh(i,2)=p;
  568. betacomp.adults_rh(i,1)=stats.tstat;
  570. end
  572. betacomp.adults_rh(:,3)=bonf_holm(betacomp.adults_rh(:,2));
  573. betacomp.adults_lh(:,3)=bonf_holm(betacomp.adults_lh(:,2));
  574. betacomp.kids_rh(:,3)=bonf_holm(betacomp.kids_rh(:,2));
  575. betacomp.kids_lh(:,3)=bonf_holm(betacomp.kids_lh(:,2));
  577. % get the % of predictors that significantly differ across models
  578. % in betacomp, the rows are the 145 regions
  579. % column 1 is t stat
  580. % column 2 is pval
  581. % column 3 is bonferroni-holm pval
  582. (sum(betacomp.adults_lh(:,3)<0.05)/145)*100
  583. (sum(betacomp.adults_rh(:,3)<0.05)/145)*100
  584. (sum(betacomp.kids_lh(:,3)<0.05)/145)*100
  585. (sum(betacomp.kids_rh(:,3)<0.05)/145)*100
  587. %% compare true models with using the wrong betas
  588. % adults
  589. mean(cfmodelresults.adults.selfprediction.lh.R2_EnTx_lang-cfmodelresults.adults.wrongBpred.lh.R2_EnTx_lang)
  590. std(cfmodelresults.adults.selfprediction.lh.R2_EnTx_lang-cfmodelresults.adults.wrongBpred.lh.R2_EnTx_lang)
  591. mean(cfmodelresults.adults.selfprediction.rh.R2_EnTx_lang-cfmodelresults.adults.wrongBpred.rh.R2_EnTx_lang)
  592. std(cfmodelresults.adults.selfprediction.rh.R2_EnTx_lang-cfmodelresults.adults.wrongBpred.rh.R2_EnTx_lang)
  593. mean(cfmodelresults.adults.selfprediction.lh.R2_MntlPain_ToM-cfmodelresults.adults.wrongBpred.lh.R2_MntlPain_ToM)
  594. std(cfmodelresults.adults.selfprediction.lh.R2_MntlPain_ToM-cfmodelresults.adults.wrongBpred.lh.R2_MntlPain_ToM)
  595. mean(cfmodelresults.adults.selfprediction.rh.R2_MntlPain_ToM-cfmodelresults.adults.wrongBpred.rh.R2_MntlPain_ToM)
  596. std(cfmodelresults.adults.selfprediction.rh.R2_MntlPain_ToM-cfmodelresults.adults.wrongBpred.rh.R2_MntlPain_ToM)
  598. [~,p,~,stats]=ttest(atanh(cfmodelresults.adults.selfprediction.lh.R2_EnTx_lang),atanh(cfmodelresults.adults.wrongBpred.lh.R2_EnTx_lang))
  599. meanEffectSize(atanh(cfmodelresults.adults.selfprediction.lh.R2_EnTx_lang),atanh(cfmodelresults.adults.wrongBpred.lh.R2_EnTx_lang),Effect="cohen")
  600. [~,p,~,stats]=ttest(atanh(cfmodelresults.adults.selfprediction.rh.R2_EnTx_lang),atanh(cfmodelresults.adults.wrongBpred.rh.R2_EnTx_lang))
  601. meanEffectSize(atanh(cfmodelresults.adults.selfprediction.rh.R2_EnTx_lang),atanh(cfmodelresults.adults.wrongBpred.rh.R2_EnTx_lang),Effect="cohen")
  603. [~,p,~,stats]=ttest(atanh(cfmodelresults.adults.selfprediction.lh.R2_MntlPain_ToM),atanh(cfmodelresults.adults.wrongBpred.lh.R2_MntlPain_ToM))
  604. meanEffectSize(atanh(cfmodelresults.adults.selfprediction.lh.R2_MntlPain_ToM),atanh(cfmodelresults.adults.wrongBpred.lh.R2_MntlPain_ToM),Effect="cohen")
  605. [~,p,~,stats]=ttest(atanh(cfmodelresults.adults.selfprediction.rh.R2_MntlPain_ToM),atanh(cfmodelresults.adults.wrongBpred.rh.R2_MntlPain_ToM))
  606. meanEffectSize(atanh(cfmodelresults.adults.selfprediction.rh.R2_MntlPain_ToM),atanh(cfmodelresults.adults.wrongBpred.rh.R2_MntlPain_ToM),Effect="cohen")
  609. % kids
  610. mean(cfmodelresults.kids.selfprediction.lh.R2_EnTx_lang-cfmodelresults.kids.wrongBpred.lh.R2_EnTx_lang)
  611. std(cfmodelresults.kids.selfprediction.lh.R2_EnTx_lang-cfmodelresults.kids.wrongBpred.lh.R2_EnTx_lang)
  612. mean(cfmodelresults.kids.selfprediction.rh.R2_EnTx_lang-cfmodelresults.kids.wrongBpred.rh.R2_EnTx_lang)
  613. std(cfmodelresults.kids.selfprediction.rh.R2_EnTx_lang-cfmodelresults.kids.wrongBpred.rh.R2_EnTx_lang)
  614. mean(cfmodelresults.kids.selfprediction.lh.R2_MntlPain_ToM-cfmodelresults.kids.wrongBpred.lh.R2_MntlPain_ToM)
  615. std(cfmodelresults.kids.selfprediction.lh.R2_MntlPain_ToM-cfmodelresults.kids.wrongBpred.lh.R2_MntlPain_ToM)
  616. mean(cfmodelresults.kids.selfprediction.rh.R2_MntlPain_ToM-cfmodelresults.kids.wrongBpred.rh.R2_MntlPain_ToM)
  617. std(cfmodelresults.kids.selfprediction.rh.R2_MntlPain_ToM-cfmodelresults.kids.wrongBpred.rh.R2_MntlPain_ToM)
  619. [~,p,~,stats]=ttest(atanh(cfmodelresults.kids.selfprediction.lh.R2_EnTx_lang),atanh(cfmodelresults.kids.wrongBpred.lh.R2_EnTx_lang))
  620. meanEffectSize(atanh(cfmodelresults.kids.selfprediction.lh.R2_EnTx_lang),atanh(cfmodelresults.kids.wrongBpred.lh.R2_EnTx_lang),Effect="cohen")
  621. [~,p,~,stats]=ttest(atanh(cfmodelresults.kids.selfprediction.rh.R2_EnTx_lang),atanh(cfmodelresults.kids.wrongBpred.rh.R2_EnTx_lang))
  622. meanEffectSize(atanh(cfmodelresults.kids.selfprediction.rh.R2_EnTx_lang),atanh(cfmodelresults.kids.wrongBpred.rh.R2_EnTx_lang),Effect="cohen")
  624. [~,p,~,stats]=ttest(atanh(cfmodelresults.kids.selfprediction.lh.R2_MntlPain_ToM),atanh(cfmodelresults.kids.wrongBpred.lh.R2_MntlPain_ToM))
  625. meanEffectSize(atanh(cfmodelresults.kids.selfprediction.lh.R2_MntlPain_ToM),atanh(cfmodelresults.kids.wrongBpred.lh.R2_MntlPain_ToM),Effect="cohen")
  626. [~,p,~,stats]=ttest(atanh(cfmodelresults.kids.selfprediction.rh.R2_MntlPain_ToM),atanh(cfmodelresults.kids.wrongBpred.rh.R2_MntlPain_ToM))
  627. meanEffectSize(atanh(cfmodelresults.kids.selfprediction.rh.R2_MntlPain_ToM),atanh(cfmodelresults.kids.wrongBpred.rh.R2_MntlPain_ToM),Effect="cohen")
  629. %% For figure 3 and Figure S4
  630. % plot the top 10 most positive and top 10 most negative t stats in
  631. % betacomp for each hemisphere of kids & adults separately
  632. % for brain plotting code, please contanct [email hidden]

Hiersche_2026_CommBio_stats.m, under CC-BY-4.0 · at the source

Overview

Authors: Kelly J. Hiersche1,2, David E. Osher1,2, Zeynep M. Saygin1,2
ORCID iDs: Zeynep M. Saygin
  1. Department of Psychology, The Ohio State University,Columbus, OH USA
  2. Center for Cognitive and Behavioral Brain Imaging, The Ohio State University,Columbus, OH USA
Institutions: The Ohio State University (United States)
Journal: Communications biology, volume 9, issue 1, article 558
Dates: received 3 October 2025; accepted 30 March 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10040-2 · PMID 42026127 · PMCID PMC13106660 · OpenAlex W7155356255
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: Neuroscience, Cognitive neuroscience
MeSH: Language*, Temporal Lobe*, Theory of Mind*, Adult, Brain Mapping, Child, Child, Preschool, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: NICHD R01HD110401
Citations: cited by 3 papers (Europe PMC); 73 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

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Zenodo 19206150

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (1), MATLAB (1)
Size: 4 files, 2 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), Statistics and Machine Learning Toolbox (1 file), rstatix (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
2 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 19206150

Read it in the paper: doi.org/10.1038/s42003-026-10040-2.

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;
  • 2 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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

Data

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

Code and data availability statement

The paper has a code and data 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 19206150

Read it in the paper: doi.org/10.1038/s42003-026-10040-2.

Versions

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

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 2 keywords, 11 MeSH terms, 1 funder, 70 references.

Cite

This paper

Hiersche, K. J., Osher, D. E., & Saygin, Z. M. (2026). Functional dissociation of language and theory of mind in the developing superior temporal lobe. Communications biology, 9(1), 558. https://doi.org/10.1038/s42003-026-10040-2

BibTeX

@article{hiersche2026functional,
author = {Hiersche, Kelly J. and Osher, David E. and Saygin, Zeynep M.},
title = {{Functional dissociation of language and theory of mind in the developing superior temporal lobe}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {558},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10040-2},
url = {https://doi.org/10.1038/s42003-026-10040-2},
pmid = {42026127},
pmcid = {PMC13106660}
}

RIS

TY - JOUR
AU - Hiersche, Kelly J.
AU - Osher, David E.
AU - Saygin, Zeynep M.
TI - Functional dissociation of language and theory of mind in the developing superior temporal lobe
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/23
VL - 9
IS - 1
SP - 558
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10040-2
UR - https://doi.org/10.1038/s42003-026-10040-2
LA - en
ER -

CSL-JSON

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"id": "10.1038/s42003-026-10040-2",
"type": "article-journal",
"title": "Functional dissociation of language and theory of mind in the developing superior temporal lobe",
"container-title": "Communications biology",
"author": [
{
"family": "Hiersche",
"given": "Kelly J."
},
{
"family": "Osher",
"given": "David E."
},
{
"family": "Saygin",
"given": "Zeynep M."
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "558",
"DOI": "10.1038/s42003-026-10040-2",
"PMID": "42026127",
"PMCID": "PMC13106660",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10040-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
23
]
]
}
}

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

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