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Ventrolateral prefrontal cortex in macaques guides decisions in different learning conditions.

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  1. [1] § Methods › fMRI data analyses ↔ Fujimoto_NatComm_2026_gPPI.m, lines 1–25 · score 0.56 · gPPI, 4–8 seconds, seed, vlPFC, 4 seconds, window

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The authors' code

MATLAB · 588 lines · 48 KB · no license · 1 match

  1. %---------------------------------------------------------
  2. % Fujimoto et al (2026) Functional connectivity analysis
  3. % vlPFC seed to ACC or MD ROI FC
  4. %---------------------------------------------------------
  5. clear all; close all;
  7. % set directory
  8. maindir = char('C:\MATLAB_analy\LRN_analy\data\'); %my computer
  9. cd(maindir);
  10. TR_dur = 2.12*1000; %set TR
  12. %analysis window settings
  13. psth_window = [-4000,8000];
  14. w_bin = 1000; w_step = 200; % bin and step sizes - 1000, 200
  15. nstep = floor((psth_window(2)-psth_window(1)+1-w_bin)/w_step)+1;
  17. %subjects
  18. monkey_list = ["Mew","Eevee","Starmie","Genger"];
  19. sbj_num = length(monkey_list);
  21. % ROIs generated based on gPPI result
  22. seed_name_tmp = 'roi_clus6_vlpfc_R_2mm'; % seed - right vlPFC
  23. % roi_name_list = ["roi_ppi6_clus15_acc_R_2mm"]; % ACC
  24. roi_name_list = ["roi_ppi6_clus8_md_L_2mm"]; % MD
  25. an_table_all = {};
  26. %%
  27. for roi_cnt = 1:length(roi_name_list)
  29. %% Each ROI analysis
  30. roi_name_tmp = roi_name_list(roi_cnt);
  31. block_rval_nov_rstay = []; block_rval_nov_nrstay = []; block_rval_nov_rswitch = []; block_rval_nov_nrswitch = [];
  32. block_rval_fam_rstay = []; block_rval_fam_nrstay = []; block_rval_fam_rswitch = []; block_rval_fam_nrswitch = [];
  33. block_rval_nov_rew = []; block_rval_fam_rew = []; block_rval_nov_nrew = []; block_rval_fam_nrew = [];
  34. block_rval_nov_stay = []; block_rval_nov_switch = []; block_rval_fam_stay = []; block_rval_fam_switch = [];
  35. pfm_allmk_each_block_nov = []; pfm_allmk_each_block_fam = [];
  36. for sbj = 1:sbj_num
  38. %read list
  39. lname = strcat('LRN_data_',monkey_list{sbj},'.csv');
  40. list = readtable(lname); %specify .csv file!
  41. sesnum = size(list,1); %total number of sessions to analyze
  43. pfm_each_block_nov = []; pfm_each_block_fam = []; pfm_z_each_block_nov = []; pfm_z_each_block_fam = [];
  44. parfor ses = 1:sesnum
  46. %specify data to analyze from list
  47. day = char(string(list.day(ses))); %yyyymmdd
  48. monkey = string(list.monkey(ses));
  49. %block configuration
  50. listchar = num2str(list.block(ses));
  51. block_config = [];
  52. for ea_char=1:length(listchar)
  53. block_config = [block_config,str2num(listchar(ea_char))];
  54. end
  55. %analyze each data
  57. % if length(unique(block_config))>1
  58. for blc = 1:length(block_config)
  60. % load session file
  61. fname = strcat(day(1,3:end),'_',monkey,'_learning',monkey,'MRI_',num2str(blc),'.bhv2');
  62. A = [];
  63. A = mlread(fname);
  64. bsize = size([A.Trial],2); %block size
  66. % MRI trigger time
  67. trig_time = A(1).BehavioralCodes.CodeTimes(A(1).BehavioralCodes.CodeNumbers>=311 & A(1).BehavioralCodes.CodeNumbers<=313);
  69. % read ROI timeseries - already aligned to scan data collection initiation
  70. % to average over trials, make pseudo activity for milisecond resolution
  71. seed_ts_fill = []; roi_ts_fill = [];
  72. seed_name = strcat("ts_",seed_name_tmp,'_',monkey,string(day),"r0",string(blc),".1D"); % seed time series
  73. seed_ts = importdata(seed_name)*(-1); % MION inverse signal
  74. for slice_cnt = 1:length(seed_ts)
  75. for i = 1:TR_dur
  76. seed_ts_fill(TR_dur*(slice_cnt-1)+i) = seed_ts(slice_cnt); %filling in for msec data
  77. end
  78. end
  79. roi_name = strcat("ts_",roi_name_tmp,'_',monkey,string(day),"r0",string(blc),".1D"); % ROI time series
  80. roi_ts = importdata(roi_name)*(-1); % MION inverse signal
  81. for slice_cnt = 1:length(roi_ts)
  82. for i = 1:TR_dur
  83. roi_ts_fill(TR_dur*(slice_cnt-1)+i) = roi_ts(slice_cnt); %filling in for msec data
  84. end
  85. end
  87. % extract event codes and time stamps for correct trials
  88. tcnt = 0; tdat = zeros(100,7); evtime = []; hvchoice = [];
  89. for i=1:bsize
  90. if A(i).TrialError==0
  91. tcnt = tcnt+1;
  92. % events
  93. tdat(tcnt,1) = tcnt; %trial number
  94. tdat(tcnt,2) = A(i).BehavioralCodes.CodeNumbers(ismember(A(i).BehavioralCodes.CodeNumbers,[501,502]))-500; %response L/R
  95. tdat(tcnt,3) = A(i).BehavioralCodes.CodeNumbers(ismember(A(i).BehavioralCodes.CodeNumbers,[311,312,313]))-310; %left option
  96. tdat(tcnt,4) = A(i).BehavioralCodes.CodeNumbers(ismember(A(i).BehavioralCodes.CodeNumbers,[321,322,323]))-320; %right option
  97. if A(i).RewardRecord.StartTimes>0
  98. tdat(tcnt,5) = 1; %reward
  99. else
  100. tdat(tcnt,5) = 0; %no reward
  101. end
  102. if tdat(tcnt,2)==1
  103. tdat(tcnt,6) = tdat(tcnt,3); %chosen option
  104. tdat(tcnt,7) = tdat(tcnt,4); %unchosen option
  105. else
  106. tdat(tcnt,6) = tdat(tcnt,4); %chosen option
  107. tdat(tcnt,7) = tdat(tcnt,3); %unchosen option
  108. end
  109. %hv choice
  110. if tdat(tcnt,6)<tdat(tcnt,7)
  111. hvchoice(tcnt) = 1;
  112. else
  113. hvchoice(tcnt) = 0;
  114. end
  115. % time stamps
  116. if i>1
  117. evtime(tcnt,1) = A(i).AbsoluteTrialStartTime+A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==100); %FP on
  118. evtime(tcnt,2) = A(i).AbsoluteTrialStartTime+A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==110); %Fixation start
  119. evtime(tcnt,3) = A(i).AbsoluteTrialStartTime+A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==120); %Target on
  120. evtime(tcnt,4) = A(i).AbsoluteTrialStartTime+A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==150); %Response
  121. evtime(tcnt,5) = A(i).AbsoluteTrialStartTime+A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==160); %Reward
  122. else
  123. evtime(tcnt,1) = A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==100); %FP on
  124. evtime(tcnt,2) = A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==110); %Fixation start
  125. evtime(tcnt,3) = A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==120); %Target on
  126. evtime(tcnt,4) = A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==150); %Response
  127. evtime(tcnt,5) = A(i).BehavioralCodes.CodeTimes(A(i).BehavioralCodes.CodeNumbers==160); %Reward
  128. end
  129. end
  130. end
  131. evtime = evtime-trig_time; %msec, aligned to scan data collection initiation
  132. tnum = tcnt;
  134. %hv choice probability
  135. hv_prob = sum(hvchoice(1:50))/50;
  137. %align ROI timeseries to trial event and divided trials into
  138. %conditions of interest
  139. trial_seed_ts_rstay = []; trial_seed_ts_nrstay = []; trial_seed_ts_rswitch = []; trial_seed_ts_nrswitch = []; trial_seed_ts_stay = []; trial_seed_ts_switch = [];
  140. trial_roi_ts_rstay = []; trial_roi_ts_nrstay = []; trial_roi_ts_rswitch = []; trial_roi_ts_nrswitch = []; trial_roi_ts_stay = []; trial_roi_ts_switch = [];
  141. trial_seed_ts_all = []; trial_roi_ts_all = []; trial_roi_ts_rew = []; trial_roi_ts_nrew = []; trial_seed_ts_rew = []; trial_seed_ts_nrew = [];
  142. tr_rstay_cnt = 0; tr_nrstay_cnt = 0; tr_rswitch_cnt = 0; tr_nrswitch_cnt = 0; tr_rew_cnt = 0; tr_nrew_cnt = 0;
  143. tr_stay_cnt = 0; tr_switch_cnt = 0;
  144. for tcnt2 = 1:tnum
  145. %reward vs no reward (reward timing)
  146. if tdat(tcnt2,5)==1 && round(evtime(tcnt2,5))+psth_window(1)>0 && round(evtime(tcnt2,5))+psth_window(2)<length(roi_ts_fill) %rewarded trials
  147. tr_rew_cnt = tr_rew_cnt+1;
  148. trial_roi_ts_rew(tr_rew_cnt,1:(psth_window(2)-psth_window(1)+1)) = roi_ts_fill(round(evtime(tcnt2,5))+psth_window(1):round(evtime(tcnt2,5))+psth_window(2));
  149. trial_seed_ts_rew(tr_rew_cnt,1:(psth_window(2)-psth_window(1)+1)) = seed_ts_fill(round(evtime(tcnt2,5))+psth_window(1):round(evtime(tcnt2,5))+psth_window(2));
  150. elseif tdat(tcnt2,5)==0 && round(evtime(tcnt2,5))+psth_window(1)>0 && round(evtime(tcnt2,5))+psth_window(2)<length(roi_ts_fill) %no reward trials
  151. tr_nrew_cnt = tr_nrew_cnt+1;
  152. trial_roi_ts_nrew(tr_nrew_cnt,1:(psth_window(2)-psth_window(1)+1)) = roi_ts_fill(round(evtime(tcnt2,5))+psth_window(1):round(evtime(tcnt2,5))+psth_window(2));
  153. trial_seed_ts_nrew(tr_nrew_cnt,1:(psth_window(2)-psth_window(1)+1)) = seed_ts_fill(round(evtime(tcnt2,5))+psth_window(1):round(evtime(tcnt2,5))+psth_window(2));
  154. end
  155. %switch vs stay (timing of previous reward)
  156. if tcnt2>1 && ismember(tdat(tcnt2-1,6),[tdat(tcnt2,3),tdat(tcnt2,4)]) %chosen opt available as current option
  157. if tdat(tcnt2-1,6)==tdat(tcnt2,6) && round(evtime(tcnt2-1,5))+psth_window(1)>0 && round(evtime(tcnt2-1,5))+psth_window(2)<length(roi_ts_fill)
  158. tr_stay_cnt = tr_stay_cnt+1;
  159. trial_seed_ts_stay(tr_stay_cnt,1:(psth_window(2)-psth_window(1)+1)) = seed_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  160. trial_roi_ts_stay(tr_stay_cnt,1:(psth_window(2)-psth_window(1)+1)) = roi_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  161. elseif tdat(tcnt2-1,6)~=tdat(tcnt2,6) && round(evtime(tcnt2-1,5))+psth_window(1)>0 && round(evtime(tcnt2-1,5))+psth_window(2)<length(roi_ts_fill)
  162. tr_switch_cnt = tr_switch_cnt+1;
  163. trial_seed_ts_switch(tr_switch_cnt,1:(psth_window(2)-psth_window(1)+1)) = seed_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  164. trial_roi_ts_switch(tr_switch_cnt,1:(psth_window(2)-psth_window(1)+1)) = roi_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  165. end
  166. if tdat(tcnt2-1,5)==1 && tdat(tcnt2-1,6)==tdat(tcnt2,6) && round(evtime(tcnt2-1,5))+psth_window(1)>0 && round(evtime(tcnt2-1,5))+psth_window(2)<length(roi_ts_fill)
  167. tr_rstay_cnt = tr_rstay_cnt+1;
  168. trial_seed_ts_rstay(tr_rstay_cnt,1:(psth_window(2)-psth_window(1)+1)) = seed_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  169. trial_roi_ts_rstay(tr_rstay_cnt,1:(psth_window(2)-psth_window(1)+1)) = roi_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  170. elseif tdat(tcnt2-1,5)==0 && tdat(tcnt2-1,6)==tdat(tcnt2,6) && round(evtime(tcnt2-1,5))+psth_window(1)>0 && round(evtime(tcnt2-1,5))+psth_window(2)<length(roi_ts_fill)
  171. tr_nrstay_cnt = tr_nrstay_cnt+1;
  172. trial_seed_ts_nrstay(tr_nrstay_cnt,1:(psth_window(2)-psth_window(1)+1)) = seed_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  173. trial_roi_ts_nrstay(tr_nrstay_cnt,1:(psth_window(2)-psth_window(1)+1)) = roi_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  174. elseif tdat(tcnt2-1,5)==1 && tdat(tcnt2-1,6)~=tdat(tcnt2,6) && round(evtime(tcnt2-1,5))+psth_window(1)>0 && round(evtime(tcnt2-1,5))+psth_window(2)<length(roi_ts_fill)
  175. tr_rswitch_cnt = tr_rswitch_cnt+1;
  176. trial_seed_ts_rswitch(tr_rswitch_cnt,1:(psth_window(2)-psth_window(1)+1)) = seed_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  177. trial_roi_ts_rswitch(tr_rswitch_cnt,1:(psth_window(2)-psth_window(1)+1)) = roi_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  178. elseif tdat(tcnt2-1,5)==0 && tdat(tcnt2-1,6)~=tdat(tcnt2,6) && round(evtime(tcnt2-1,5))+psth_window(1)>0 && round(evtime(tcnt2-1,5))+psth_window(2)<length(roi_ts_fill)
  179. tr_nrswitch_cnt = tr_nrswitch_cnt+1;
  180. trial_seed_ts_nrswitch(tr_nrswitch_cnt,1:(psth_window(2)-psth_window(1)+1)) = seed_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  181. trial_roi_ts_nrswitch(tr_nrswitch_cnt,1:(psth_window(2)-psth_window(1)+1)) = roi_ts_fill(round(evtime(tcnt2-1,5))+psth_window(1):round(evtime(tcnt2-1,5))+psth_window(2));
  182. end
  183. end
  184. end
  185. % Compute correlation between seed and roi time series (each block)
  186. rval_rstay = []; rval_nrstay = []; rval_rswitch = []; rval_nrswitch = []; rval_wsls = []; rval_anti = []; rval_rew = []; rval_nrew = []; rval_stay = []; rval_switch = [];
  187. for bn = 1:nstep
  188. [R,~] = corrcoef(trial_seed_ts_rstay(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin),trial_roi_ts_rstay(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin));
  189. rval_rstay(bn) = R(1,2);
  190. [R,~] = corrcoef(trial_seed_ts_nrstay(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin),trial_roi_ts_nrstay(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin));
  191. rval_nrstay(bn) = R(1,2);
  192. [R,~] = corrcoef(trial_seed_ts_rswitch(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin),trial_roi_ts_rswitch(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin));
  193. rval_rswitch(bn) = R(1,2);
  194. [R,~] = corrcoef(trial_seed_ts_nrswitch(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin),trial_roi_ts_nrswitch(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin));
  195. rval_nrswitch(bn) = R(1,2);
  196. [R,~] = corrcoef(trial_seed_ts_rew(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin),trial_roi_ts_rew(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin));
  197. rval_rew(bn) = R(1,2);
  198. [R,~] = corrcoef(trial_seed_ts_nrew(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin),trial_roi_ts_nrew(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin));
  199. rval_nrew(bn) = R(1,2);
  200. [R,~] = corrcoef(trial_seed_ts_stay(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin),trial_roi_ts_stay(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin));
  201. rval_stay(bn) = R(1,2);
  202. [R,~] = corrcoef(trial_seed_ts_switch(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin),trial_roi_ts_switch(:,w_step*(bn-1)+1:w_step*(bn-1)+w_bin));
  203. rval_switch(bn) = R(1,2);
  204. end
  206. % Classify data by block type
  207. if block_config(blc)==1 %novel block
  208. block_rval_nov_rstay = [block_rval_nov_rstay;rval_rstay];
  209. block_rval_nov_nrstay = [block_rval_nov_nrstay;rval_nrstay];
  210. block_rval_nov_rswitch = [block_rval_nov_rswitch;rval_rswitch];
  211. block_rval_nov_nrswitch = [block_rval_nov_nrswitch;rval_nrswitch];
  212. block_rval_nov_rew = [block_rval_nov_rew;rval_rew];
  213. block_rval_nov_nrew = [block_rval_nov_nrew;rval_nrew];
  214. pfm_each_block_nov = [pfm_each_block_nov;hv_prob];
  215. block_rval_nov_stay = [block_rval_nov_stay;rval_stay];
  216. block_rval_nov_switch = [block_rval_nov_switch;rval_switch];
  217. else % familiar block
  218. block_rval_fam_rstay = [block_rval_fam_rstay;rval_rstay];
  219. block_rval_fam_nrstay = [block_rval_fam_nrstay;rval_nrstay];
  220. block_rval_fam_rswitch = [block_rval_fam_rswitch;rval_rswitch];
  221. block_rval_fam_nrswitch = [block_rval_fam_nrswitch;rval_nrswitch];
  222. block_rval_fam_rew = [block_rval_fam_rew;rval_rew];
  223. block_rval_fam_nrew = [block_rval_fam_nrew;rval_nrew];
  224. pfm_each_block_fam = [pfm_each_block_fam;hv_prob];
  225. block_rval_fam_stay = [block_rval_fam_stay;rval_stay];
  226. block_rval_fam_switch = [block_rval_fam_switch;rval_switch];
  227. end
  228. end % each block
  229. end % each session
  231. % normalize
  232. pfm_z_each_block_nov = (pfm_each_block_nov-mean(pfm_each_block_nov))./std(pfm_each_block_nov);
  233. pfm_z_each_block_fam = (pfm_each_block_fam-mean(pfm_each_block_fam))./std(pfm_each_block_fam);
  234. pfm_allmk_each_block_nov = [pfm_allmk_each_block_nov;pfm_z_each_block_nov];
  235. pfm_allmk_each_block_fam = [pfm_allmk_each_block_fam;pfm_z_each_block_fam];
  237. end % each monkey
  239. %% Multiple regression
  241. % number of blocks
  242. bnum_nov = size(block_rval_nov_rstay,1);
  243. bnum_fam = size(block_rval_fam_rstay,1);
  245. %multiple regression for each block data
  246. block_rval_ts_nov = [block_rval_nov_rstay;block_rval_nov_nrstay;block_rval_nov_rswitch;block_rval_nov_nrswitch];
  247. block_rval_rew_nov = [ones(size(block_rval_nov_rstay,1),1);zeros(size(block_rval_nov_nrstay,1),1);ones(size(block_rval_nov_rswitch,1),1);zeros(size(block_rval_nov_nrswitch,1),1)];
  248. block_rval_str_nov = [ones(size(block_rval_nov_rstay,1),1);ones(size(block_rval_nov_nrstay,1),1);zeros(size(block_rval_nov_rswitch,1),1);zeros(size(block_rval_nov_nrswitch,1),1)];
  249. block_rval_ts_fam = [block_rval_fam_rstay;block_rval_fam_nrstay;block_rval_fam_rswitch;block_rval_fam_nrswitch];
  250. block_rval_rew_fam = [ones(size(block_rval_fam_rstay,1),1);zeros(size(block_rval_fam_nrstay,1),1);ones(size(block_rval_fam_rswitch,1),1);zeros(size(block_rval_fam_nrswitch,1),1)];
  251. block_rval_str_fam = [ones(size(block_rval_fam_rstay,1),1);ones(size(block_rval_fam_nrstay,1),1);zeros(size(block_rval_fam_rswitch,1),1);zeros(size(block_rval_fam_nrswitch,1),1)];
  252. ef_rval_nov = []; ef_rval_fam = [];
  253. parfor bn = 1:nstep
  254. bn_x(bn) = psth_window(1)+w_bin/2+w_step*(bn-1); % for x axis label
  255. tbl_nov = table(block_rval_rew_nov,block_rval_str_nov,block_rval_ts_nov(:,bn),'VariableNames',{'Reward','Decision','TS'});
  256. lm_nov = fitlm(tbl_nov,'interactions');
  257. ef_rval_nov(:,bn) = lm_nov.Coefficients.Estimate(2:4);
  258. tbl_fam = table(block_rval_rew_fam,block_rval_str_fam,block_rval_ts_fam(:,bn),'VariableNames',{'Reward','Decision','TS'});
  259. lm_fam = fitlm(tbl_fam,'interactions');
  260. ef_rval_fam(:,bn) = lm_fam.Coefficients.Estimate(2:4);
  261. end
  262. ef_block_nov_reward = ef_rval_nov(1,:);
  263. ef_block_nov_choice = ef_rval_nov(2,:);
  264. ef_block_nov_wsls = ef_rval_nov(3,:);
  265. ef_block_fam_reward = ef_rval_fam(1,:);
  266. ef_block_fam_choice = ef_rval_fam(2,:);
  267. ef_block_fam_wsls = ef_rval_fam(3,:);
  269. %shuffled data
  270. itr_max = 1000; % iteration
  271. block_sff_ts_nov = []; block_sff_ts_fam = []; ef_sff_nov_reward = []; ef_sff_nov_choice = []; ef_sff_nov_wsls = []; ef_sff_fam_reward = []; ef_sff_fam_choice = []; ef_sff_fam_wsls = [];
  272. parfor itr = 1:itr_max
  273. tmp_rand_nov = randperm(size(block_rval_ts_nov,1));
  274. block_sff_ts_nov = block_rval_ts_nov(tmp_rand_nov,:);
  275. tmp_rand_fam = randperm(size(block_rval_ts_fam,1));
  276. block_sff_ts_fam = block_rval_ts_fam(tmp_rand_fam,:);
  277. for bn = 1:nstep
  278. tbl_nov = table(block_rval_rew_nov,block_rval_str_nov,block_sff_ts_nov(:,bn),'VariableNames',{'Reward','Decision','TS'});
  279. lm_nov = fitlm(tbl_nov,'interactions');
  280. ef_sff_nov_reward(itr,bn) = lm_nov.Coefficients.Estimate(2);
  281. ef_sff_nov_choice(itr,bn) = lm_nov.Coefficients.Estimate(3);
  282. ef_sff_nov_wsls(itr,bn) = lm_nov.Coefficients.Estimate(4);
  283. tbl_fam = table(block_rval_rew_fam,block_rval_str_fam,block_sff_ts_fam(:,bn),'VariableNames',{'Reward','Decision','TS'});
  284. lm_fam = fitlm(tbl_fam,'interactions');
  285. ef_sff_fam_reward(itr,bn) = lm_fam.Coefficients.Estimate(2);
  286. ef_sff_fam_choice(itr,bn) = lm_fam.Coefficients.Estimate(3);
  287. ef_sff_fam_wsls(itr,bn) = lm_fam.Coefficients.Estimate(4);
  288. end
  289. end
  290. % confidence interval
  291. alpha_perm = 0.05; % 0.05 - 95% ci
  292. for bn = 1:nstep
  293. [muHat,sigmaHat,muCI,sigmaCI] = normfit(ef_sff_nov_reward(:,bn),alpha_perm);
  294. ci_sff_nov_reward(bn,1:2) = sigmaCI;
  295. [muHat,sigmaHat,muCI,sigmaCI] = normfit(ef_sff_nov_choice(:,bn),alpha_perm);
  296. ci_sff_nov_choice(bn,1:2) = sigmaCI;
  297. [muHat,sigmaHat,muCI,sigmaCI] = normfit(ef_sff_nov_wsls(:,bn),alpha_perm);
  298. ci_sff_nov_wsls(bn,1:2) = sigmaCI;
  299. [muHat,sigmaHat,muCI,sigmaCI] = normfit(ef_sff_fam_reward(:,bn),alpha_perm);
  300. ci_sff_fam_reward(bn,1:2) = sigmaCI;
  301. [muHat,sigmaHat,muCI,sigmaCI] = normfit(ef_sff_fam_choice(:,bn),alpha_perm);
  302. ci_sff_fam_choice(bn,1:2) = sigmaCI;
  303. [muHat,sigmaHat,muCI,sigmaCI] = normfit(ef_sff_fam_wsls(:,bn),alpha_perm);
  304. ci_sff_fam_wsls(bn,1:2) = sigmaCI;
  305. end
  306. %% PPI data plot
  307. sm_param = 20;
  308. pthr = 0.01;
  309. ts_win = [-2000 2000];
  310. % ts_win = [0 4000];
  311. yrange_psth = [-0.3 0.3];
  312. figure(1);
  313. FIGSIZE = 80;
  314. set(gcf,'Position',[50,50,15*FIGSIZE,8.5*FIGSIZE]);
  315. % time course of PPI (correlation coeff)
  316. subplot(2,4,1); hold on;
  317. boundedline(bn_x,smooth(nanmean(block_rval_nov_rstay,1),sm_param),smooth(nanstd(block_rval_nov_rstay)/(bnum_nov^0.5),sm_param),'transparency',0.1,'r');
  318. boundedline(bn_x,smooth(nanmean(block_rval_nov_rswitch,1),sm_param),smooth(nanstd(block_rval_nov_rswitch)/(bnum_nov^0.5),sm_param),'transparency',0.1,'m');
  319. for i = 1:nstep-3
  320. if ranksum(block_rval_nov_rstay(:,i),zeros(size(block_rval_nov_rstay,1),1))<pthr
  321. if ranksum(block_rval_nov_rstay(:,i+1),zeros(size(block_rval_nov_rstay,1),1))<pthr && ranksum(block_rval_nov_rstay(:,i+2),zeros(size(block_rval_nov_rstay,1),1))<pthr
  322. plot([bn_x(i)-w_bin/2 bn_x(i+2)+w_bin/2],[yrange_psth(2)*0.95 yrange_psth(2)*0.95],'r-','linewidth',4);
  323. end
  324. end
  325. if ranksum(block_rval_nov_rswitch(:,i),zeros(size(block_rval_nov_rswitch,1),1))<pthr
  326. if ranksum(block_rval_nov_rswitch(:,i+1),zeros(size(block_rval_nov_rswitch,1),1))<pthr && ranksum(block_rval_nov_rswitch(:,i+2),zeros(size(block_rval_nov_rswitch,1),1))<pthr
  327. plot([bn_x(i)-w_bin/2 bn_x(i+2)+w_bin/2],[yrange_psth(2)*0.9 yrange_psth(2)*0.9],'m-','linewidth',4);
  328. end
  329. end
  330. end
  331. plot([ts_win(1) ts_win(2)],[yrange_psth(1) yrange_psth(1)],'k-');
  332. plot([0 0],[yrange_psth(1) yrange_psth(2)],'k--'); plot([psth_window(1) psth_window(2)],[0 0],'k-'); ylabel("PPI"); xlabel("Time from rew"); title('Novel');
  333. xlim(psth_window); ylim(yrange_psth);
  334. subplot(2,4,3); hold on;
  335. boundedline(bn_x,smooth(nanmean(block_rval_fam_rstay,1),sm_param),smooth(nanstd(block_rval_fam_rstay)/(bnum_fam^0.5),sm_param),'transparency',0.1,'r');
  336. boundedline(bn_x,smooth(nanmean(block_rval_fam_rswitch,1),sm_param),smooth(nanstd(block_rval_fam_rswitch)/(bnum_fam^0.5),sm_param),'transparency',0.1,'m');
  337. for i = 1:nstep-3
  338. if ranksum(block_rval_fam_rstay(:,i),zeros(size(block_rval_fam_rstay,1),1))<pthr
  339. if ranksum(block_rval_fam_rstay(:,i+1),zeros(size(block_rval_fam_rstay,1),1))<pthr && ranksum(block_rval_fam_rstay(:,i+2),zeros(size(block_rval_fam_rstay,1),1))<pthr
  340. plot([bn_x(i)-w_bin/2 bn_x(i+2)+w_bin/2],[yrange_psth(2)*0.95 yrange_psth(2)*0.95],'r-','linewidth',4);
  341. end
  342. end
  343. if ranksum(block_rval_fam_rswitch(:,i),zeros(size(block_rval_fam_rswitch,1),1))<pthr
  344. if ranksum(block_rval_fam_rswitch(:,i+1),zeros(size(block_rval_fam_rswitch,1),1))<pthr && ranksum(block_rval_fam_rswitch(:,i+2),zeros(size(block_rval_fam_rswitch,1),1))<pthr
  345. plot([bn_x(i)-w_bin/2 bn_x(i+2)+w_bin/2],[yrange_psth(2)*0.9 yrange_psth(2)*0.9],'m-','linewidth',4);
  346. end
  347. end
  348. end
  349. plot([ts_win(1) ts_win(2)],[yrange_psth(1) yrange_psth(1)],'k-');
  350. plot([0 0],[yrange_psth(1) yrange_psth(2)],'k--'); plot([psth_window(1) psth_window(2)],[0 0],'k-'); ylabel("PPI"); xlabel("Time from rew"); title('Familiar');
  351. xlim(psth_window); ylim(yrange_psth);
  352. subplot(2,4,5); hold on;
  353. boundedline(bn_x,smooth(nanmean(block_rval_nov_nrstay,1),sm_param),smooth(nanstd(block_rval_nov_nrstay)/(bnum_nov^0.5),sm_param),'transparency',0.1,'b');
  354. boundedline(bn_x,smooth(nanmean(block_rval_nov_nrswitch,1),sm_param),smooth(nanstd(block_rval_nov_nrswitch)/(bnum_nov^0.5),sm_param),'transparency',0.1,'c');
  355. for i = 1:nstep-3
  356. if ranksum(block_rval_nov_nrstay(:,i),zeros(size(block_rval_nov_nrstay,1),1))<pthr
  357. if ranksum(block_rval_nov_nrstay(:,i+1),zeros(size(block_rval_nov_nrstay,1),1))<pthr && ranksum(block_rval_nov_nrstay(:,i+2),zeros(size(block_rval_nov_nrstay,1),1))<pthr
  358. plot([bn_x(i)-w_bin/2 bn_x(i+2)+w_bin/2],[yrange_psth(2)*0.95 yrange_psth(2)*0.95],'b-','linewidth',4);
  359. end
  360. end
  361. if ranksum(block_rval_nov_nrswitch(:,i),zeros(size(block_rval_nov_nrswitch,1),1))<pthr
  362. if ranksum(block_rval_nov_nrswitch(:,i+1),zeros(size(block_rval_nov_nrswitch,1),1))<pthr && ranksum(block_rval_nov_nrswitch(:,i+2),zeros(size(block_rval_nov_nrswitch,1),1))<pthr
  363. plot([bn_x(i)-w_bin/2 bn_x(i+2)+w_bin/2],[yrange_psth(2)*0.9 yrange_psth(2)*0.9],'c-','linewidth',4);
  364. end
  365. end
  366. end
  367. plot([ts_win(1) ts_win(2)],[yrange_psth(1) yrange_psth(1)],'k-');
  368. plot([0 0],[yrange_psth(1) yrange_psth(2)],'k--'); plot([psth_window(1) psth_window(2)],[0 0],'k-'); ylabel("PPI"); xlabel("Time from rew");
  369. xlim(psth_window); ylim(yrange_psth);
  370. subplot(2,4,7); hold on;
  371. boundedline(bn_x,smooth(nanmean(block_rval_fam_nrstay,1),sm_param),smooth(nanstd(block_rval_fam_nrstay)/(bnum_fam^0.5),sm_param),'transparency',0.1,'b');
  372. boundedline(bn_x,smooth(nanmean(block_rval_fam_nrswitch,1),sm_param),smooth(nanstd(block_rval_fam_nrswitch)/(bnum_fam^0.5),sm_param),'transparency',0.1,'c');
  373. for i = 1:nstep-3
  374. if ranksum(block_rval_fam_nrstay(:,i),zeros(size(block_rval_fam_nrstay,1),1))<pthr
  375. if ranksum(block_rval_fam_nrstay(:,i+1),zeros(size(block_rval_fam_nrstay,1),1))<pthr && ranksum(block_rval_fam_nrstay(:,i+2),zeros(size(block_rval_fam_nrstay,1),1))<pthr
  376. plot([bn_x(i)-w_bin/2 bn_x(i+2)+w_bin/2],[yrange_psth(2)*0.95 yrange_psth(2)*0.95],'b-','linewidth',4);
  377. end
  378. end
  379. if ranksum(block_rval_fam_nrswitch(:,i),zeros(size(block_rval_fam_nrswitch,1),1))<pthr
  380. if ranksum(block_rval_fam_nrswitch(:,i+1),zeros(size(block_rval_fam_nrswitch,1),1))<pthr && ranksum(block_rval_fam_nrswitch(:,i+2),zeros(size(block_rval_fam_nrswitch,1),1))<pthr
  381. plot([bn_x(i)-w_bin/2 bn_x(i+2)+w_bin/2],[yrange_psth(2)*0.9 yrange_psth(2)*0.9],'c-','linewidth',4);
  382. end
  383. end
  384. end
  385. plot([ts_win(1) ts_win(2)],[yrange_psth(1) yrange_psth(1)],'k-');
  386. plot([0 0],[yrange_psth(1) yrange_psth(2)],'k--'); plot([psth_window(1) psth_window(2)],[0 0],'k-'); ylabel("PPI"); xlabel("Time from rew");
  387. xlim(psth_window); ylim(yrange_psth);
  389. % averaged value
  390. pthr2 = 0.05
  391. subplot(2,4,2); hold on;
  392. errorbar(1,nanmean(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),nanstd(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1)/bnum_nov^0.5,'r');
  393. errorbar(2,nanmean(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),nanstd(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1)/bnum_nov^0.5,'m');
  394. plot(1,nanmean(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),'ro');
  395. plot(2,nanmean(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),'mo');
  396. pval_nov_r(1) = ranksum(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),zeros(sum(~isnan(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1));
  397. pval_nov_r(2) = ranksum(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),zeros(sum(~isnan(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1));
  398. for i=1:2
  399. if pval_nov_r(i)<pthr2
  400. plot(i,0.18,'k*');
  401. end
  402. text(i,0.1,sprintf('%.2f',pval_nov_r(i)));
  403. end
  404. plot([0 3],[0 0],'k-');
  405. xlim([0 3]); ylim([-0.1 0.2]); title(roi_name_tmp,'interpreter','none');
  406. subplot(2,4,4); hold on;
  407. errorbar(1,nanmean(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),nanstd(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1)/bnum_fam^0.5,'r');
  408. errorbar(2,nanmean(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),nanstd(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1)/bnum_fam^0.5,'m');
  409. plot(1,nanmean(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),'ro');
  410. plot(2,nanmean(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),'mo');
  411. pval_fam_r(1) = ranksum(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),zeros(sum(~isnan(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1));
  412. pval_fam_r(2) = ranksum(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),zeros(sum(~isnan(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1));
  413. for i=1:2
  414. if pval_fam_r(i)<pthr2
  415. plot(i,0.18,'k*');
  416. end
  417. text(i,0.1,sprintf('%.2f',pval_fam_r(i)));
  418. end
  419. plot([0 3],[0 0],'k-');
  420. xlim([0 3]); ylim([-0.1 0.2]);
  421. subplot(2,4,6); hold on;
  422. errorbar(1,nanmean(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),nanstd(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1)/bnum_nov^0.5,'b');
  423. errorbar(2,nanmean(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),nanstd(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1)/bnum_nov^0.5,'c');
  424. plot(1,nanmean(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),'bo');
  425. plot(2,nanmean(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),'co');
  426. pval_nov_nr(1) = ranksum(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),zeros(sum(~isnan(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1));
  427. pval_nov_nr(2) = ranksum(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),zeros(sum(~isnan(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1));
  428. for i=1:2
  429. if pval_nov_nr(i)<pthr2
  430. plot(i,0.18,'k*');
  431. end
  432. text(i,0.1,sprintf('%.2f',pval_nov_nr(i)));
  433. end
  434. plot([0 3],[0 0],'k-');
  435. xlim([0 3]); ylim([-0.1 0.2]);
  436. subplot(2,4,8); hold on;
  437. errorbar(1,nanmean(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),nanstd(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1)/bnum_fam^0.5,'b');
  438. errorbar(2,nanmean(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),nanstd(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1)/bnum_fam^0.5,'c');
  439. plot(1,nanmean(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),'bo');
  440. plot(2,nanmean(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),1),'co');
  441. pval_fam_nr(1) = ranksum(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),zeros(sum(~isnan(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1));
  442. pval_fam_nr(2) = ranksum(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2),zeros(sum(~isnan(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1));
  443. for i=1:2
  444. if pval_fam_nr(i)<pthr2
  445. plot(i,0.18,'k*');
  446. end
  447. text(i,0.1,sprintf('%.2f',pval_fam_nr(i)));
  448. end
  449. plot([0 3],[0 0],'k-');
  450. xlim([0 3]); ylim([-0.1 0.2]);
  452. % anova, for each block type
  453. tmp_nov_ppi = [mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);
  454. mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2)];
  455. an_nov_ppi = tmp_nov_ppi(~isnan(tmp_nov_ppi));
  456. an_nov_stsw = [ones(sum(~isnan(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);
  457. ones(sum(~isnan(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1)];
  458. an_nov_rew = [ones(sum(~isnan(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);ones(sum(~isnan(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);
  459. 2*ones(sum(~isnan(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1)];
  460. [~,tbl_nov,stats_nov] = anovan(an_nov_ppi,{an_nov_stsw,an_nov_rew},'model','interaction','varnames',{'StaySwitch','Reward'},'display','off');
  461. tmp_fam_ppi = [mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);
  462. mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2)];
  463. an_fam_ppi = tmp_fam_ppi(~isnan(tmp_fam_ppi));
  464. an_fam_stsw = [ones(sum(~isnan(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);
  465. ones(sum(~isnan(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1)];
  466. an_fam_rew = [ones(sum(~isnan(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);ones(sum(~isnan(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);
  467. 2*ones(sum(~isnan(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1)];
  468. [~,tbl_fam,stats_fam] = anovan(an_fam_ppi,{an_fam_stsw,an_fam_rew},'model','interaction','varnames',{'StaySwitch','Reward'},'display','off');
  470. % anova, for rewarded trials and unrewarded trials
  471. tmp_r_ppi = [mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2)];
  472. an_r_ppi = tmp_r_ppi(~isnan(tmp_r_ppi));
  473. tmp_nr_ppi = [mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2);mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2)];
  474. an_nr_ppi = tmp_nr_ppi(~isnan(tmp_nr_ppi));
  475. an_r_novfam = [ones(sum(~isnan(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2)))+sum(~isnan(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1); 2*ones(sum(~isnan(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2)))+sum(~isnan(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1)];
  476. an_nr_novfam = [ones(sum(~isnan(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2)))+sum(~isnan(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1); 2*ones(sum(~isnan(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2)))+sum(~isnan(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1)];
  477. an_r_stsw = [ones(sum(~isnan(mean(block_rval_nov_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_nov_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);ones(sum(~isnan(mean(block_rval_fam_rstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_fam_rswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1)];
  478. an_nr_stsw = [ones(sum(~isnan(mean(block_rval_nov_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_nov_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);ones(sum(~isnan(mean(block_rval_fam_nrstay(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1);2*ones(sum(~isnan(mean(block_rval_fam_nrswitch(:,min(find(bn_x>ts_win(1))):min(find(bn_x>ts_win(2)))),2))),1)];
  479. [~,tbl_r,stats_r] = anovan(an_r_ppi,{an_r_novfam,an_r_stsw},'model','interaction','varnames',{'NovFam','StaySwitch'},'display','off');
  480. [~,tbl_nr,stats_nr] = anovan(an_nr_ppi,{an_nr_novfam,an_nr_stsw},'model','interaction','varnames',{'NovFam','StaySwitch'},'display','off');
  482. % anova, all trials
  483. an_all_ppi = [an_r_ppi;an_nr_ppi];
  484. an_all_novfam = [an_r_novfam;an_nr_novfam];
  485. an_all_stsw = [an_r_stsw;an_nr_stsw];
  486. an_all_rew = [ones(length(an_r_stsw),1);2*ones(length(an_nr_stsw),1)];
  487. [p,tbl_all,stats_all] = anovan(an_all_ppi,{an_all_novfam,an_all_stsw,an_all_rew},'model','full','varnames',{'NovFam','StaySwitch','Rew'},'display','off');
  488. an_table_all{roi_cnt,1} = tbl_r;
  489. an_table_all{roi_cnt,2} = tbl_nr;
  490. an_table_all{roi_cnt,3} = tbl_all;
  491. % c = multcompare(stats_all);
  492. %% PPI-performance correlation analysis
  493. sc_range = [-1 1];
  494. ts_win2 = [0 4000];
  495. % ts_win2 = ts_win;
  496. figure(2);
  497. block_rval_nov_rew_mean = mean(block_rval_nov_rew(:,min(find(bn_x>ts_win2(1))):min(find(bn_x>ts_win2(2)))),2);
  498. block_rval_fam_rew_mean = mean(block_rval_fam_rew(:,min(find(bn_x>ts_win2(1))):min(find(bn_x>ts_win2(2)))),2);
  499. block_rval_nov_nrew_mean = mean(block_rval_nov_nrew(:,min(find(bn_x>ts_win2(1))):min(find(bn_x>ts_win2(2)))),2);
  500. block_rval_fam_nrew_mean = mean(block_rval_fam_nrew(:,min(find(bn_x>ts_win2(1))):min(find(bn_x>ts_win2(2)))),2);
  501. block_rval_nov_stay_mean = mean(block_rval_nov_stay(:,min(find(bn_x>ts_win2(1))):min(find(bn_x>ts_win2(2)))),2);
  502. block_rval_fam_stay_mean = mean(block_rval_fam_stay(:,min(find(bn_x>ts_win2(1))):min(find(bn_x>ts_win2(2)))),2);
  503. block_rval_nov_switch_mean = mean(block_rval_nov_switch(:,min(find(bn_x>ts_win2(1))):min(find(bn_x>ts_win2(2)))),2);
  504. block_rval_fam_switch_mean = mean(block_rval_fam_switch(:,min(find(bn_x>ts_win2(1))):min(find(bn_x>ts_win2(2)))),2);
  505. subplot(2,2,1); hold on;
  506. plot(pfm_allmk_each_block_nov,block_rval_nov_rew_mean,'r.');
  507. [R,Pval] = corrcoef(pfm_allmk_each_block_nov,block_rval_nov_rew_mean);
  508. plot(pfm_allmk_each_block_nov,polyval(polyfit(pfm_allmk_each_block_nov,block_rval_nov_rew_mean,1),pfm_allmk_each_block_nov),'r-');
  509. xlim([-3 3]); ylim([sc_range(1) sc_range(2)]); if Pval(1,2)<0.1 text(0,-0.1,string(Pval(1,2)),'color','g'); else text(0,-0.1,string(Pval(1,2)),'color','k'); end
  510. title("Novel block"); xlabel("Correct pfm (Z)"); ylabel("win trials PPI");
  511. subplot(2,2,2); hold on;
  512. plot(pfm_allmk_each_block_fam,block_rval_fam_rew_mean,'b.');
  513. [R,Pval] = corrcoef(pfm_allmk_each_block_fam,block_rval_fam_rew_mean);
  514. plot(pfm_allmk_each_block_fam,polyval(polyfit(pfm_allmk_each_block_fam,block_rval_fam_rew_mean,1),pfm_allmk_each_block_fam),'b-');
  515. xlim([-3 3]); ylim([sc_range(1) sc_range(2)]); if Pval(1,2)<0.1 text(0,-0.1,string(Pval(1,2)),'color','g'); else text(0,-0.1,string(Pval(1,2)),'color','k'); end
  516. title("Familiar block"); xlabel("Correct pfm (Z)"); ylabel("win trials PPI");
  517. subplot(2,2,3); hold on;
  518. plot(pfm_allmk_each_block_nov,block_rval_nov_nrew_mean,'r.');
  519. [R,Pval] = corrcoef(pfm_allmk_each_block_nov,block_rval_nov_nrew_mean);
  520. plot(pfm_allmk_each_block_nov,polyval(polyfit(pfm_allmk_each_block_nov,block_rval_nov_nrew_mean,1),pfm_allmk_each_block_nov),'r-');
  521. xlim([-3 3]); ylim([sc_range(1) sc_range(2)]); if Pval(1,2)<0.1 text(0,-0.1,string(Pval(1,2)),'color','g'); else text(0,-0.1,string(Pval(1,2)),'color','k'); end
  522. title(roi_name_tmp,'interpreter','none'); xlabel("Correct pfm (Z)"); ylabel("loss trials PPI");
  523. subplot(2,2,4); hold on;
  524. plot(pfm_allmk_each_block_fam,block_rval_fam_nrew_mean,'b.');
  525. [R,Pval] = corrcoef(pfm_allmk_each_block_fam,block_rval_fam_nrew_mean);
  526. plot(pfm_allmk_each_block_fam,polyval(polyfit(pfm_allmk_each_block_fam,block_rval_fam_nrew_mean,1),pfm_allmk_each_block_fam),'b-');
  527. xlim([-3 3]); ylim([sc_range(1) sc_range(2)]); if Pval(1,2)<0.1 text(0,-0.1,string(Pval(1,2)),'color','g'); else text(0,-0.1,string(Pval(1,2)),'color','k'); end
  528. title(""); xlabel("Correct pfm (Z)"); ylabel("loss trials PPI");
  531. %Stay/Shift trials vs Performance
  532. figure(3);
  533. subplot(2,2,1); hold on;
  534. plot(pfm_allmk_each_block_nov,block_rval_nov_stay_mean,'r.');
  535. [R,Pval] = corrcoef(pfm_allmk_each_block_nov,block_rval_nov_stay_mean);
  536. plot(pfm_allmk_each_block_nov,polyval(polyfit(pfm_allmk_each_block_nov,block_rval_nov_stay_mean,1),pfm_allmk_each_block_nov),'r-');
  537. xlim([-3 3]); ylim([sc_range(1) sc_range(2)]); if Pval(1,2)<0.1 text(0,-0.1,string(Pval(1,2)),'color','g'); else text(0,-0.1,string(Pval(1,2)),'color','k'); end
  538. title("Novel block"); xlabel("Correct pfm (Z)"); ylabel("Stay trials PPI");
  539. subplot(2,2,2); hold on;
  540. plot(pfm_allmk_each_block_fam,block_rval_fam_stay_mean,'b.');
  541. [R,Pval] = corrcoef(pfm_allmk_each_block_fam,block_rval_fam_stay_mean);
  542. plot(pfm_allmk_each_block_fam,polyval(polyfit(pfm_allmk_each_block_fam,block_rval_fam_stay_mean,1),pfm_allmk_each_block_fam),'b-');
  543. xlim([-3 3]); ylim([sc_range(1) sc_range(2)]); if Pval(1,2)<0.1 text(0,-0.1,string(Pval(1,2)),'color','g'); else text(0,-0.1,string(Pval(1,2)),'color','k'); end
  544. title("Familiar block"); xlabel("Correct pfm (Z)"); ylabel("Stay trials PPI");
  545. subplot(2,2,3); hold on;
  546. plot(pfm_allmk_each_block_nov,block_rval_nov_switch_mean,'r.');
  547. [R,Pval] = corrcoef(pfm_allmk_each_block_nov,block_rval_nov_switch_mean);
  548. plot(pfm_allmk_each_block_nov,polyval(polyfit(pfm_allmk_each_block_nov,block_rval_nov_switch_mean,1),pfm_allmk_each_block_nov),'r-');
  549. xlim([-3 3]); ylim([sc_range(1) sc_range(2)]); if Pval(1,2)<0.1 text(0,-0.1,string(Pval(1,2)),'color','g'); else text(0,-0.1,string(Pval(1,2)),'color','k'); end
  550. title(roi_name_tmp,'interpreter','none'); xlabel("Correct pfm (Z)"); ylabel("Shift trials PPI");
  551. subplot(2,2,4); hold on;
  552. plot(pfm_allmk_each_block_fam,block_rval_fam_switch_mean,'b.');
  553. [R,Pval] = corrcoef(pfm_allmk_each_block_fam,block_rval_fam_switch_mean);
  554. plot(pfm_allmk_each_block_fam,polyval(polyfit(pfm_allmk_each_block_fam,block_rval_fam_switch_mean,1),pfm_allmk_each_block_fam),'b-');
  555. xlim([-3 3]); ylim([sc_range(1) sc_range(2)]); if Pval(1,2)<0.1 text(0,-0.1,string(Pval(1,2)),'color','g'); else text(0,-0.1,string(Pval(1,2)),'color','k'); end
  556. title(""); xlabel("Correct pfm (Z)"); ylabel("Shift trials PPI");
  557. %% sliding window regression analysis (with permutation tests)
  558. figure(4);
  559. %Effect size
  560. yrange_beta_all = [-0.4 0.4];
  561. subplot(2,2,1); hold on;
  562. boundedline(bn_x,mean(ef_sff_nov_wsls,1),ci_sff_nov_wsls,'transparency',0.1,'y');
  563. plot(bn_x,mean(ef_block_nov_wsls,1),'k-'); %wsls
  564. for bn = 2:nstep-2
  565. if (ef_block_nov_wsls(bn)>(mean(ef_sff_nov_wsls(:,bn))+ci_sff_nov_wsls(bn,2)) && ef_block_nov_wsls(bn+1)>(mean(ef_sff_nov_wsls(:,bn+1))+ci_sff_nov_wsls(bn+1,2)) && ef_block_nov_wsls(bn+2)>(mean(ef_sff_nov_wsls(:,bn+2))+ci_sff_nov_wsls(bn+2,2))) || ...
  566. (ef_block_nov_wsls(bn)<(mean(ef_sff_nov_wsls(:,bn))-ci_sff_nov_wsls(bn,1)) && ef_block_nov_wsls(bn+1)<(mean(ef_sff_nov_wsls(:,bn+1))-ci_sff_nov_wsls(bn+1,1)) && ef_block_nov_wsls(bn+2)<(mean(ef_sff_nov_wsls(:,bn+2))-ci_sff_nov_wsls(bn+2,1)))
  567. plot([bn_x(bn-1) bn_x(bn)],[ef_block_nov_wsls(bn-1) ef_block_nov_wsls(bn)],'k-','linewidth',3);
  568. plot([bn_x(bn) bn_x(bn+1)],[ef_block_nov_wsls(bn) ef_block_nov_wsls(bn+1)],'k-','linewidth',3);
  569. plot([bn_x(bn+1) bn_x(bn+2)],[ef_block_nov_wsls(bn+1) ef_block_nov_wsls(bn+2)],'k-','linewidth',3);
  570. end
  571. end
  572. plot([0 0],[yrange_beta_all(1) yrange_beta_all(2)],'k--'); plot([psth_window(1) psth_window(2)],[0 0],'k-'); title("Novel block"); ylabel("Beta (WSLS coding)"); xlabel("Time from rew");
  573. xlim(psth_window); ylim(yrange_beta_all);
  574. subplot(2,2,2); hold on;
  575. boundedline(bn_x,mean(ef_sff_fam_wsls,1),ci_sff_fam_wsls,'transparency',0.1,'y');
  576. plot(bn_x,mean(ef_block_fam_wsls,1),'k-'); %wsls
  577. for bn = 2:nstep-2
  578. if (ef_block_fam_wsls(bn)>(mean(ef_sff_fam_wsls(:,bn))+ci_sff_fam_wsls(bn,2)) && ef_block_fam_wsls(bn+1)>(mean(ef_sff_fam_wsls(:,bn+1))+ci_sff_fam_wsls(bn+1,2)) && ef_block_fam_wsls(bn+2)>(mean(ef_sff_fam_wsls(:,bn+2))+ci_sff_fam_wsls(bn+2,2))) || ...
  579. (ef_block_fam_wsls(bn)<(mean(ef_sff_fam_wsls(:,bn))-ci_sff_fam_wsls(bn,1)) && ef_block_fam_wsls(bn+1)<(mean(ef_sff_fam_wsls(:,bn+1))-ci_sff_fam_wsls(bn+1,1)) && ef_block_fam_wsls(bn+2)<(mean(ef_sff_fam_wsls(:,bn+2))-ci_sff_fam_wsls(bn+2,1)))
  580. plot([bn_x(bn-1) bn_x(bn)],[ef_block_fam_wsls(bn-1) ef_block_fam_wsls(bn)],'k-','linewidth',3);
  581. plot([bn_x(bn) bn_x(bn+1)],[ef_block_fam_wsls(bn) ef_block_fam_wsls(bn+1)],'k-','linewidth',3);
  582. plot([bn_x(bn+1) bn_x(bn+2)],[ef_block_fam_wsls(bn+1) ef_block_fam_wsls(bn+2)],'k-','linewidth',3);
  583. end
  584. end
  585. plot([0 0],[yrange_beta_all(1) yrange_beta_all(2)],'k--'); plot([psth_window(1) psth_window(2)],[0 0],'k-'); title("Familiar block"); xlabel("Time from rew");
  586. xlim(psth_window); ylim(yrange_beta_all);
  587. %%
  588. end

Fujimoto_NatComm_2026_gPPI.m at commit 3fcced8, no license · at the source

Overview

Authors: Atsushi Fujimoto1,2,3, Catherine Elorette1,2, Satoka H. Fujimoto1,2,3, Lazar Fleysher4, Brian E. Russ1,5,6, Peter H. Rudebeck1,2
ORCID iDs: Atsushi Fujimoto, Catherine Elorette, Satoka H. Fujimoto, Brian E. Russ, Peter H. Rudebeck
  1. Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai,New York, NY USA
  2. Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai,New York, NY USA
  3. Present Address: Department of Neuroscience and Center for Magnetic Resonance Research, University of Minnesota,Minneapolis, MN USA
  4. BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai,New York, NY USA
  5. Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute,Orangeburg, NY USA
  6. Department of Psychiatry, New York University at Langone,New York, NY USA
Institutions: Icahn School of Medicine at Mount Sinai (United States); University of Minnesota (United States); Nathan Kline Institute for Psychiatric Research (United States); New York University (United States)
Journal: Nature communications, volume 17, issue 1, article 7153
Dates: received 6 January 2025; accepted 20 April 2026; published online 4 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72782-1 · PMID 42236691 · PMCID PMC13396457 · OpenAlex W7163349172
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism)
Methods: Statistics, Connectivity, fMRI & imaging
Keywords: Neural circuits, Learning and memory
MeSH: Decision Making*, Learning*, Prefrontal Cortex*, Animals, Behavior, Animal, Brain Mapping, Female, Gyrus Cinguli, Macaca mulatta, Magnetic Resonance Imaging, Male, Receptors, Dopamine D2, Reward (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (R01MH117040, R01MH111439); Takeda Science Foundation; Brain and Behavior Research Foundation (28979); NINDS (R01NS109498)
Citations: cited by 1 paper (Europe PMC); 74 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.

Repositories

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

Fujimoto-Lab-UMN/Awake-fMRI-LRN

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3fcced843e70799d48f8845341acdac26fa5abe0, 25 March 2026
Languages: MATLAB (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files), boundedline (3 files), AFNI (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/Fujimoto-Lab-UMN/Awake-fMRI-LRN

Zenodo 19225305

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: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files), boundedline (3 files), AFNI (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
6 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:

Read it in the paper: doi.org/10.1038/s41467-026-72782-1.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 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

Datasets cited

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:

Read it in the paper: doi.org/10.1038/s41467-026-72782-1.

Versions

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

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 13 MeSH terms, 4 funders, 71 references.

Cite

This paper

Fujimoto, A., Elorette, C., Fujimoto, S. H., Fleysher, L., Russ, B. E., & Rudebeck, P. H. (2026). Ventrolateral prefrontal cortex in macaques guides decisions in different learning conditions. Nature communications, 17(1), 7153. https://doi.org/10.1038/s41467-026-72782-1

BibTeX

@article{fujimoto2026ventrolateral,
author = {Fujimoto, Atsushi and Elorette, Catherine and Fujimoto, Satoka H. and Fleysher, Lazar and Russ, Brian E. and Rudebeck, Peter H.},
title = {{Ventrolateral prefrontal cortex in macaques guides decisions in different learning conditions}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7153},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72782-1},
url = {https://doi.org/10.1038/s41467-026-72782-1},
pmid = {42236691},
pmcid = {PMC13396457}
}

RIS

TY - JOUR
AU - Fujimoto, Atsushi
AU - Elorette, Catherine
AU - Fujimoto, Satoka H.
AU - Fleysher, Lazar
AU - Russ, Brian E.
AU - Rudebeck, Peter H.
TI - Ventrolateral prefrontal cortex in macaques guides decisions in different learning conditions
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/04
VL - 17
IS - 1
SP - 7153
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72782-1
UR - https://doi.org/10.1038/s41467-026-72782-1
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-72782-1",
"type": "article-journal",
"title": "Ventrolateral prefrontal cortex in macaques guides decisions in different learning conditions",
"container-title": "Nature communications",
"author": [
{
"family": "Fujimoto",
"given": "Atsushi"
},
{
"family": "Elorette",
"given": "Catherine"
},
{
"family": "Fujimoto",
"given": "Satoka H."
},
{
"family": "Fleysher",
"given": "Lazar"
},
{
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"given": "Brian E."
},
{
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}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7153",
"DOI": "10.1038/s41467-026-72782-1",
"PMID": "42236691",
"PMCID": "PMC13396457",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72782-1",
"language": "en",
"issued": {
"date-parts": [
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2026,
6,
4
]
]
}
}

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

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