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Partially Different Mechanisms of Social and Nonsocial Attention: Evidence From Changes in Cueing Effects and Underlying Frontal Cortex Processing Over Time.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Methods › fNIRS Data Preprocessing ↔ SOT_fnirs.m, lines 382–424 · score 0.79 · distribution repair, signal improvement, Hz, component, global, shift
  2. [2] § Methods › fNIRS Data Preprocessing ↔ SOT_fnirs.m, lines 467–540 · score 0.59 · optical density changes, Inc, rejected, fNIRS, channels, SD

Paper

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The authors' code

MATLAB · 621 lines · 28 KB · no license · 2 matches

  1. clear; clc
  3. % Selects and reads in the data file.
  4. Drive = 'E:\';
  5. offset = 'y'; %delete the 2nd stimuli
  6. subj_list = [19:45 47:59 61]; %subj46-SOT1 marker problem
  7. rep_list = 1;
  8. task_selection = 1; %1:SOT
  10. for task_num = task_selection;
  11. switch task_num
  12. case 1
  13. Task = 'SOT';
  14. trial_tp = 240;
  15. trial_amp = 1;
  16. nCond = 6;
  17. end
  19. %Path setup
  20. posfilename = '0001.pos';
  21. dir_file = [Drive 'From SD Card\Project_C_test_retest\'];
  22. output_dir = [Drive 'From SD Card\Matlab_C\' Task '_fNIRS_output\'];
  23. pospath = [Drive 'From SD Card\Matlab_C\PosForHomerConversion\'];
  25. % Load subject information
  26. [~,~,c] = xlsread([Drive 'From SD Card\Matlab_C\fNIRS_subj_info.xlsx']);
  27. SubjINFO_header = c(1,:);
  28. SubjINFO_data = c(2:end,:);
  30. %Transform csv to nirs
  31. for subj = subj_list
  32. Age = cell2mat(SubjINFO_data(subj,find(cellfun(@(x)isequal(x,'Age'),SubjINFO_header)==1)));
  33. CapSize = cell2mat(SubjINFO_data(subj,find(cellfun(@(x)isequal(x,'cap_size'),SubjINFO_header)==1)));
  34. Folder_name = char(SubjINFO_data(subj,find(cellfun(@(x)isequal(x,'Folder_name'),SubjINFO_header)==1)));
  35. for rep = rep_list;
  36. for probe = 1:2;
  37. Probe_num = num2str(probe);
  39. %correction for 2nd test-retest batch
  40. if subj<=43 & rep==1
  41. filename = [Task '_MES_Probe' Probe_num '.csv'];
  42. else
  43. filename = [Task num2str(rep) '_MES_Probe' Probe_num '.csv'];
  44. end
  46. if rep==2
  47. filename = [Task num2str(rep) '_MES_Probe' Probe_num '.csv'];
  48. end
  49. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  50. % Open raw data file
  51. fid = fopen([dir_file Folder_name '\' filename]);
  52. disp('Loading data...');
  53. while 1
  54. tline = fgetl(fid);
  56. if isempty(strfind(tline, 'Mode')) == 0
  57. rindex = find(tline == ',');
  58. tline(rindex) = ' ';
  59. text_array = tline(rindex(1)+1:end);
  60. end
  61. if isempty(strfind(tline, 'Wave[nm]')) == 0
  62. windex = find(tline == ',');
  63. tline(windex) = ' ';
  64. text_lambda = tline(windex(1)+1:end);
  65. wavelengths = str2num(text_lambda);
  66. end
  67. if isempty(strfind(tline, 'Sampling Period[s]')) == 0
  68. nindex = find(tline == ',');
  69. tline(nindex) = ' ';
  70. txt_fs = tline(nindex(1)+1:end);
  71. fs = 1./mean(str2num(txt_fs));
  72. end
  74. if isempty(strfind(tline, 'Data')) == 0
  75. tline = fgetl(fid);
  76. nch = length(strfind(tline, 'CH'));
  77. nindex = find(tline == ',');
  78. try
  79. col_mark = strfind(tline, 'Mark');
  80. col_mark = col_mark(1);
  81. col_mark = find(nindex == col_mark - 1) + 1;
  82. end
  83. try
  84. col_prescan = strfind(tline, 'PreScan');
  85. col_prescan = col_prescan(1);
  86. col_prescan = find(nindex == col_prescan - 1)+1;
  87. end
  88. while 1
  89. tline = fgetl(fid);
  90. if ischar(tline) == 0, break, end,
  91. nindex = find(tline == ',');
  92. tline_data = tline(nindex(1)+1:nindex(nch+1)-1);
  93. nindex_d = find(tline_data == ',');
  94. tline_data(nindex_d) = ' ';
  95. tline_data = str2num(tline_data);
  96. count = str2num(tline(1:nindex(1)-1));
  97. nirs_data.rawData(count, :) = tline_data;
  98. try
  99. vector_onset(count) = str2num(tline(nindex(col_mark-1)+1:nindex(col_mark)-1));
  100. end
  101. try
  102. baseline(count) = str2num(tline(nindex(col_prescan-1)+1:nindex(col_prescan)-1));
  103. end
  104. end
  105. break;
  106. end
  107. end
  109. disp('Data loaded... Getting more information...');
  110. % Asks if you want to remove the marker at the end of the stimulus (i.e. if you
  111. % have a block design and your stimuli are marked at both beginning and end (as
  112. % is required by the ETG4000) rather than just at the beginning (as is required
  113. % by HomER2. To hard-code this, replace the next line with offset = 'y' or 'n'.
  115. % offset = input('Do you want to remove the marker at the end of each stimulus? y/n: ','s');
  117. % Constructs the arrays that are required in the .nirs file
  118. t = transpose((0:count-1)*(1/fs));
  119. d = nirs_data.rawData;
  120. switch probe
  121. case 1
  122. d1 = d;
  123. case 2
  124. d2 = d(:,[43:48 35:42 29:34 21:28 15:20 7:14 1:6]); %Homologous channels of probe 1
  125. end
  126. SD.Lambda = transpose(wavelengths);
  127. SD.MeasList = [];
  128. end % probe end
  129. d = [d1 d2]; %Combine Probe 1 and Probe 2
  131. channel_pos = importdata(strcat(pospath,posfilename));
  132. channel_pos_tmp = char(channel_pos);
  134. text_array = '4x4';
  136. % Calculation of MeasList for two 4x4 optode arrays
  137. if strcmp('4x4', text_array)
  138. names = {'[LeftEar]','[RightEar]','[Nasion]','[Back]','[Top]',...
  139. '[Probe1-ch1]','[Probe1-ch2]','[Probe1-ch3]','[Probe1-ch4]',...
  140. '[Probe1-ch5]','[Probe1-ch6]','[Probe1-ch7]','[Probe1-ch8]',...
  141. '[Probe1-ch9]','[Probe1-ch10]','[Probe1-ch11]','[Probe1-ch12]',...
  142. '[Probe1-ch13]','[Probe1-ch14]','[Probe1-ch15]','[Probe1-ch16]',...
  143. '[Probe2-ch13]','[Probe2-ch14]','[Probe2-ch15]','[Probe2-ch16]',...
  144. '[Probe2-ch9]','[Probe2-ch10]','[Probe2-ch11]','[Probe2-ch12]',...
  145. '[Probe2-ch5]','[Probe2-ch6]','[Probe2-ch7]','[Probe2-ch8]',...
  146. '[Probe2-ch1]','[Probe2-ch2]','[Probe2-ch3]','[Probe2-ch4]'};
  148. optodes = 32;
  149. x = zeros(optodes,1);
  150. y = zeros(optodes,1);
  151. z = zeros(optodes,1);
  152. for i=1:optodes
  153. ind = find(strcmp(channel_pos,names{i+5}));
  154. x(i) = str2num(channel_pos_tmp(ind+1,3:end));
  155. y(i) = str2num(channel_pos_tmp(ind+2,3:end));
  156. z(i) = str2num(channel_pos_tmp(ind+3,3:end));
  157. end
  158. SD.SpatialUnit = 'mm';
  159. SD.nSrcs = 16;
  160. SD.nDets = 16;
  161. SD.SrcPos = [x(1), y(1), z(1); x(3), y(3), z(3); x(6), y(6), z(6); x(8), y(8), z(8);...
  162. x(9), y(9), z(9); x(11), y(11), z(11); x(14), y(14), z(14); x(16), y(16), z(16);...
  163. x(1+16), y(1+16), z(1+16); x(3+16), y(3+16), z(3+16); x(6+16), y(6+16), z(6+16); x(8+16), y(8+16), z(8+16);...
  164. x(9+16), y(9+16), z(9+16); x(11+16), y(11+16), z(11+16); x(14+16), y(14+16), z(14+16); x(16+16), y(16+16), z(16+16)];
  165. SD.DetPos = [x(2), y(2), z(2); x(4), y(4), z(4); x(5), y(5), z(5); x(7), y(7), z(7);...
  166. x(10), y(10), z(10); x(12), y(12), z(12); x(13), y(13), z(13); x(15), y(15), z(15);...
  167. x(2+16), y(2+16), z(2+16); x(4+16), y(4+16), z(4+16); x(5+16), y(5+16), z(5+16); x(7+16), y(7+16), z(7+16);...
  168. x(10+16), y(10+16), z(10+16); x(12+16), y(12+16), z(12+16); x(13+16), y(13+16), z(13+16); x(15+16), y(15+16), z(15+16)];
  169. SD.MeasList(1,:) = [1 1 1 1];
  170. SD.MeasList(2,:) = [1 1 1 2];
  171. SD.MeasList(3,:) = [2 1 1 1];
  172. SD.MeasList(4,:) = [2 1 1 2];
  173. SD.MeasList(5,:) = [2 2 1 1];
  174. SD.MeasList(6,:) = [2 2 1 2];
  175. SD.MeasList(7,:) = [1 3 1 1];
  176. SD.MeasList(8,:) = [1 3 1 2];
  177. SD.MeasList(9,:) = [3 1 1 1];
  178. SD.MeasList(10,:) = [3 1 1 2];
  179. SD.MeasList(11,:) = [2 4 1 1];
  180. SD.MeasList(12,:) = [2 4 1 2];
  181. SD.MeasList(13,:) = [4 2 1 1];
  182. SD.MeasList(14,:) = [4 2 1 2];
  183. SD.MeasList(15,:) = [3 3 1 1];
  184. SD.MeasList(16,:) = [3 3 1 2];
  185. SD.MeasList(17,:) = [3 4 1 1];
  186. SD.MeasList(18,:) = [3 4 1 2];
  187. SD.MeasList(19,:) = [4 4 1 1];
  188. SD.MeasList(20,:) = [4 4 1 2];
  189. SD.MeasList(21,:) = [5 3 1 1];
  190. SD.MeasList(22,:) = [5 3 1 2];
  191. SD.MeasList(23,:) = [3 5 1 1];
  192. SD.MeasList(24,:) = [3 5 1 2];
  193. SD.MeasList(25,:) = [6 4 1 1];
  194. SD.MeasList(26,:) = [6 4 1 2];
  195. SD.MeasList(27,:) = [4 6 1 1];
  196. SD.MeasList(28,:) = [4 6 1 2];
  197. SD.MeasList(29,:) = [5 5 1 1];
  198. SD.MeasList(30,:) = [5 5 1 2];
  199. SD.MeasList(31,:) = [6 5 1 1];
  200. SD.MeasList(32,:) = [6 5 1 2];
  201. SD.MeasList(33,:) = [6 6 1 1];
  202. SD.MeasList(34,:) = [6 6 1 2];
  203. SD.MeasList(35,:) = [5 7 1 1];
  204. SD.MeasList(36,:) = [5 7 1 2];
  205. SD.MeasList(37,:) = [7 5 1 1];
  206. SD.MeasList(38,:) = [7 5 1 2];
  207. SD.MeasList(39,:) = [6 8 1 1];
  208. SD.MeasList(40,:) = [6 8 1 2];
  209. SD.MeasList(41,:) = [8 6 1 1];
  210. SD.MeasList(42,:) = [8 6 1 2];
  211. SD.MeasList(43,:) = [7 7 1 1];
  212. SD.MeasList(44,:) = [7 7 1 2];
  213. SD.MeasList(45,:) = [7 8 1 1];
  214. SD.MeasList(46,:) = [7 8 1 2];
  215. SD.MeasList(47,:) = [8 8 1 1];
  216. SD.MeasList(48,:) = [8 8 1 2];
  217. SD.MeasList(1+48,:) = [1+8 1+8 1 1];
  218. SD.MeasList(2+48,:) = [1+8 1+8 1 2];
  219. SD.MeasList(3+48,:) = [2+8 1+8 1 1];
  220. SD.MeasList(4+48,:) = [2+8 1+8 1 2];
  221. SD.MeasList(5+48,:) = [2+8 2+8 1 1];
  222. SD.MeasList(6+48,:) = [2+8 2+8 1 2];
  223. SD.MeasList(7+48,:) = [1+8 3+8 1 1];
  224. SD.MeasList(8+48,:) = [1+8 3+8 1 2];
  225. SD.MeasList(9+48,:) = [3+8 1+8 1 1];
  226. SD.MeasList(10+48,:) = [3+8 1+8 1 2];
  227. SD.MeasList(11+48,:) = [2+8 4+8 1 1];
  228. SD.MeasList(12+48,:) = [2+8 4+8 1 2];
  229. SD.MeasList(13+48,:) = [4+8 2+8 1 1];
  230. SD.MeasList(14+48,:) = [4+8 2+8 1 2];
  231. SD.MeasList(15+48,:) = [3+8 3+8 1 1];
  232. SD.MeasList(16+48,:) = [3+8 3+8 1 2];
  233. SD.MeasList(17+48,:) = [3+8 4+8 1 1];
  234. SD.MeasList(18+48,:) = [3+8 4+8 1 2];
  235. SD.MeasList(19+48,:) = [4+8 4+8 1 1];
  236. SD.MeasList(20+48,:) = [4+8 4+8 1 2];
  237. SD.MeasList(21+48,:) = [5+8 3+8 1 1];
  238. SD.MeasList(22+48,:) = [5+8 3+8 1 2];
  239. SD.MeasList(23+48,:) = [3+8 5+8 1 1];
  240. SD.MeasList(24+48,:) = [3+8 5+8 1 2];
  241. SD.MeasList(25+48,:) = [6+8 4+8 1 1];
  242. SD.MeasList(26+48,:) = [6+8 4+8 1 2];
  243. SD.MeasList(27+48,:) = [4+8 6+8 1 1];
  244. SD.MeasList(28+48,:) = [4+8 6+8 1 2];
  245. SD.MeasList(29+48,:) = [5+8 5+8 1 1];
  246. SD.MeasList(30+48,:) = [5+8 5+8 1 2];
  247. SD.MeasList(31+48,:) = [6+8 5+8 1 1];
  248. SD.MeasList(32+48,:) = [6+8 5+8 1 2];
  249. SD.MeasList(33+48,:) = [6+8 6+8 1 1];
  250. SD.MeasList(34+48,:) = [6+8 6+8 1 2];
  251. SD.MeasList(35+48,:) = [5+8 7+8 1 1];
  252. SD.MeasList(36+48,:) = [5+8 7+8 1 2];
  253. SD.MeasList(37+48,:) = [7+8 5+8 1 1];
  254. SD.MeasList(38+48,:) = [7+8 5+8 1 2];
  255. SD.MeasList(39+48,:) = [6+8 8+8 1 1];
  256. SD.MeasList(40+48,:) = [6+8 8+8 1 2];
  257. SD.MeasList(41+48,:) = [8+8 6+8 1 1];
  258. SD.MeasList(42+48,:) = [8+8 6+8 1 2];
  259. SD.MeasList(43+48,:) = [7+8 7+8 1 1];
  260. SD.MeasList(44+48,:) = [7+8 7+8 1 2];
  261. SD.MeasList(45+48,:) = [7+8 8+8 1 1];
  262. SD.MeasList(46+48,:) = [7+8 8+8 1 2];
  263. SD.MeasList(47+48,:) = [8+8 8+8 1 1];
  264. SD.MeasList(48+48,:) = [8+8 8+8 1 2];
  265. end
  267. % Sort SD.MeasList by lambda
  268. [SD.MeasList, I] = sortrows(SD.MeasList,4); % Version 3
  270. % Re-arrange the measurement signals in the data matrix accordingly
  271. d = d(:,I); % Version 3
  273. % Trim irrelevant time points
  274. Exp_timestamp = find(vector_onset==10);
  275. t = t(Exp_timestamp(1):Exp_timestamp(2)) - t(Exp_timestamp(1));
  276. d = d(Exp_timestamp(1):Exp_timestamp(2),:);
  277. vector_onset = vector_onset(Exp_timestamp(1):Exp_timestamp(2));
  278. count = length(t);
  281. % Reading vector of stimulus markers and arranging this into the format
  282. % required by Homer2 and storing in the variable "aux"
  283. markertimes = [find(vector_onset>0) find(vector_onset<0)];
  284. markers = vector_onset(markertimes);
  285. unique_markers = unique(markers);
  286. aux = zeros(count, length(unique_markers));
  287. for stimuli=1:length(unique_markers)
  288. if offset == 'y' || offset == 'Y' % Version 3
  289. stim_on_off = find(vector_onset==(unique_markers(stimuli))); % Version 3
  290. stim_markers = stim_on_off(1:2:length(stim_on_off)-1); % Version 3
  291. aux(stim_markers,stimuli) = 1; % Version 3
  292. else % Version 3
  293. stim_markers = find(vector_onset==(unique_markers(stimuli)));
  294. aux(stim_markers,stimuli) = 1;
  295. end % Version 3
  296. end
  297. ml = SD.MeasList;
  299. % As the stimulus markers are stored in "aux", the stimulus matrix "s" still
  300. % needs to be created. This is set to zeroes for now.
  301. s = zeros(size(t));
  303. % Add all task conditions
  304. switch Task
  305. case 'SOT'
  306. [row col] = find(aux(:,1:6)==1);
  307. case 'NBT'
  308. [row col] = find(aux(:,1:4)==1);
  309. case 'FMT'
  310. [row col] = find(aux(:,1:3)==1);
  311. end
  312. aux(row,end+1)=1;
  315. % Finished rearranging information...
  316. disp('I have all the information I need... Saving...');
  317. save(strcat(output_dir,['C' num2str(subj,'%03d')],'_',Task,'_run',num2str(rep),'.nirs')...
  318. ,'t', 'd', 'SD', 's', 'ml', 'aux');
  319. disp('Done!');
  321. save(strcat(output_dir,['C' num2str(subj,'%03d')],'_',Task,'_run',num2str(rep),'.mat')...
  322. ,'t', 'd', 'SD', 's', 'ml', 'aux','vector_onset','Age','CapSize','output_dir');
  324. %Condition setup
  325. snirf = SnirfClass(load(strcat(output_dir,['C' num2str(subj,'%03d')],'_',Task,'_run',num2str(rep),'.nirs'),'-mat'));
  327. %Create StimClass from aux
  328. for i = 1:nCond
  329. Cond{i}.idx = find(snirf.aux(i).dataTimeSeries==1);
  330. Cond{i}.tp =zeros(length(snirf.data.dataTimeSeries),1);
  331. Cond{i}.tp(Cond{i}.idx)=1;
  332. Cond{i}.dur = repmat(round(trial_tp/fs),length(Cond{i}.idx),1);
  333. Cond{i}.amp = repmat(trial_amp,length(Cond{i}.idx),1);
  335. % stim{i} = Cond{i}.idx;
  336. obj = StimClass();
  337. obj.data = [find(Cond{i}.tp>0)/fs repmat(trial_tp,length(find(Cond{i}.tp>0)),1)/fs repmat(1,length(find(Cond{i}.tp>0)),1)];
  338. obj.states = [find(Cond{i}.tp>0)/fs repmat(1,length(find(Cond{i}.tp>0)),1)];
  339. obj.name =['Cond' num2str(i)];
  340. snirf.stim(i) = obj;
  341. % stimRuns(:,i) = Cond{i}.tp
  342. end
  344. if strcmp(Task,'SOT')
  345. for i = 4:6
  346. snirf.stim(i).data(:,2)= 1/fs;
  347. end
  349. blk_markers_pre = markers(markers>1 & markers<4);
  350. blk_markers= blk_markers_pre(1:2:end);
  351. Nonsoc_valid = [];
  352. Nonsoc_invalid = [];
  353. Soc_valid = [];
  354. Soc_invalid = [];
  355. aaa = find(blk_markers==2);
  356. for i = aaa
  357. Nonsoc_valid = [Nonsoc_valid;snirf.stim(5).data((i-1)*6+1:(i-1)*6+6,:)];
  358. Nonsoc_invalid = [Nonsoc_invalid;snirf.stim(6).data((i-1)*6+1:(i-1)*6+6,:)];
  359. end
  360. bbb = find(blk_markers==3);
  361. for i = bbb
  362. Soc_valid = [Soc_valid;snirf.stim(5).data((i-1)*6+1:(i-1)*6+6,:)];
  363. Soc_invalid = [Soc_invalid;snirf.stim(6).data((i-1)*6+1:(i-1)*6+6,:)];
  364. end
  365. snirf.stim(7).data = Nonsoc_valid; snirf.stim(7).states = Nonsoc_valid(:,[1 3]); snirf.stim(7).name = 'Nonsoc_valid';
  366. snirf.stim(8).data = Nonsoc_invalid; snirf.stim(8).states = Nonsoc_invalid(:,[1 3]); snirf.stim(8).name = 'Nonsoc_invalid';
  367. snirf.stim(9).data = Soc_valid; snirf.stim(9).states = Soc_valid(:,[1 3]); snirf.stim(9).name = 'Soc_valid';
  368. snirf.stim(10).data = Soc_invalid; snirf.stim(10).states = Soc_valid(:,[1 3]); snirf.stim(10).name = 'Soc_invalid';
  369. end
  371. %convert from .nirs to .snirf
  372. snirf.Save(strcat(output_dir,['C' num2str(subj,'%03d')],'_',Task,'_run',num2str(rep),'.snirf'));
  374. end %rep end
  375. ['Subj ' num2str(subj) ' - completed']
  376. end %subj end
  378. %Channel locations corrected for Probe 2; d:ch1-24(695nm); ch25-48(830nm)
  379. end %task end
  382. %% Preprocessing
  383. clear; clc;
  384. % Selects and reads in the data file.
  385. Drive = 'E:\';
  386. fs = 10; % sampling rate (Hz)
  387. age_correction = 1; %Correct DPF for the subject's age
  388. MAmp_criterion = [0 4.9];%OD
  389. SNR_criterion = [20 65]; %db
  390. glm = 1;
  391. TDDR = 1; %Temporal Deriative Distribution Repair
  392. PCA = 1;nSV_option= 1; %PCA to remove the global spatial covariance. nSV = no. of components to remove
  393. Freqfilt = 1;Freqfilt_freq = [0.005 0.5];
  394. CBSI = 0; %Correlation-Based Signal Improvement
  395. fNIRS_idx = 1; %1:HbO; 2:HbR: 3:HbT
  396. plot_OD = 0;
  398. task_num = 1; %1:SOT
  399. subj_list = [1:17 19:45 47:59 61];
  401. rep_list = 1;
  403. for task_num = task_num;
  404. switch task_num
  405. case 1
  406. Task = 'SOT';
  407. er_flag = 1; %1: event-related (use stim 7-10)
  408. target_flag = 1; %1: shift from cue onset to target onset
  409. max_rep = 1;
  410. Task_duration = 24;
  411. Pretask_bsline = 2;
  412. Posttask_plot = 10;
  413. Pretask_lag = 0;
  414. Posttask_lag = 0;
  415. linearfit = 0; %0:no; 1:yes;
  416. TaskBlk_marker = [1];%1-3)Congruent and no,Center,SpatualCue; 4-6)Incongruent and no,Center,SpatualCue
  417. c_vector = [1/3 1/3 1/3];
  418. end
  420. %Path setup
  421. posfilename = '0001.pos';
  422. dir_file = [Drive 'From SD Card\'];
  423. output_dir = [Drive 'From SD Card\Matlab_C\' Task '_fNIRS_output\'];
  424. pospath = [Drive 'From SD Card\Matlab_C\PosForHomerConversion\'];
  426. % Load subject information
  427. [~,~,c] = xlsread([Drive 'From SD Card\Matlab_C\fNIRS_subj_info.xlsx']);
  428. SubjINFO_header = c(1,:);
  429. SubjINFO_data = c(2:end,:);
  431. %Transform csv to nirs
  432. for subj = subj_list
  433. Age = cell2mat(SubjINFO_data(subj,find(cellfun(@(x)isequal(x,'Age'),SubjINFO_header)==1)));
  434. CapSize = cell2mat(SubjINFO_data(subj,find(cellfun(@(x)isequal(x,'cap_size'),SubjINFO_header)==1)));
  435. Folder_name = char(SubjINFO_data(subj,find(cellfun(@(x)isequal(x,'Folder_name'),SubjINFO_header)==1)));
  436. for rep = rep_list;
  437. output_dir = [Drive 'From SD Card\Matlab_C\' Task '_fNIRS_output\'];
  439. snirf = SnirfClass(strcat(output_dir,['C' num2str(subj,'%03d')],'_',Task,'_run' ,num2str(rep) ,'.snirf'));
  440. data_d = snirf.data;
  441. probe = snirf.probe;
  443. %Channel rejection
  444. mlActAuto = hmrR_PruneChannels_MKY(data_d, probe, [], [], MAmp_criterion, SNR_criterion, [0 99]); %function modified to use SNR in decibels
  445. SD.MeasListAct = mlActAuto{1}(:,3); %for TDDR
  447. %Convert d to dod
  448. d = hmrR_PreprocessIntensity_Negative_MKY(data_d, 'OPTION1: Add a dc offset'); %Automatic selection of Option 1
  449. data_dod = hmrR_Intensity2OD(data_d);
  451. %TDDR motion correction
  452. if TDDR==1
  453. dod = hmrMotionCorrectTDDR(data_dod.GetDataTimeSeries,SD,fs);
  454. data_dod.SetDataTimeSeries(dod);
  455. end
  457. %PCA systemic correction
  458. if PCA==1
  459. [data_dod, svs, nSV] = hmrR_PCAFilter(data_dod, mlActAuto, [],nSV_option);
  460. end
  462. % frequency filtering
  463. if Freqfilt ==1
  464. data_dod = hmrR_BandpassFilt(data_dod, Freqfilt_freq(1) , Freqfilt_freq(2))
  465. end
  467. % From optical density changes to [oxy-Hb] and [deoxy-Hb] changes
  468. %NaN for rejected channels
  469. data_dod.dataTimeSeries(:,find(cell2mat(mlActAuto)==0))=NaN;
  471. %Conversion from dod to dc
  472. switch CapSize
  473. case 54, rho = 29;
  474. case 56, rho = 30;
  475. case 58, rho = 31;
  476. end
  478. % General equation from Scholkmann and Wolf (2013) in the Journal of Biomedical Optics
  479. %recommended for the age of 0-70 years and for the wavelength of 690-832 nm
  480. if age_correction ==1
  481. lambda=probe.wavelengths;
  482. for i = 1:length(lambda)
  483. DPF(i) = 223.3 + 0.05624*(Age^0.8493)-(5.723*10^-7)*(lambda(i)^3)+0.001245*(lambda(i)^2)-0.9025*lambda(i);
  484. end
  485. else
  486. DPF(1:length(lambda)) = 1;
  487. end
  488. ppf = DPF;
  489. data_dc = hmrR_OD2Conc_Hitachi_EASYCAP(data_dod, probe, ppf, rho);
  491. % CBSI
  492. if CBSI ==1;
  493. data_dc = hmrR_MotionCorrectCbsi(data_dc,mlActAuto);
  494. end
  496. %glm
  497. if glm ==1;
  498. data_y = data_dc;
  499. stim = snirf.stim;
  500. if task_num==1
  501. if er_flag
  502. stim = snirf.stim([4 7:10]);
  503. for i = 1:length(stim)
  504. stim(i).data(:,1) = stim(i).data(:,1)+0.3; stim(i).states(:,1) = stim(i).data(:,1)+0.3;% 300ms SOA
  505. end
  506. else
  507. stim = snirf.stim(1:3);
  508. end
  509. end
  510. switch task_num
  511. case {1}
  512. trange = [0, 32];
  513. end
  514. Aaux = [];
  515. tIncAuto =[];
  516. mlActAuto = mlActAuto;
  517. rcMap =[];
  518. glmSolveMethod= 1;
  519. idxBasis= 5; %1: consecutive sequence of guassian functions; 2: modified gamma; 5: canonical hrf from spm
  520. paramsBasis= []; %idxBasis2_default:[0.1 3.0 1.8 3.0]
  521. rhoSD_ssThresh= 0;
  522. flagNuisanceRMethod= 0;
  523. driftOrder= 0;
  525. [data_yavg, data_yavgstd, nTrials, data_ynew, data_yresid, data_ysum2, beta_blks, yR_blks, hmrstats] = ...
  526. hmrR_GLM_MKY(data_y, stim, probe, mlActAuto, Aaux, tIncAuto, rcMap, trange, glmSolveMethod, ...
  527. idxBasis, paramsBasis, rhoSD_ssThresh, flagNuisanceRMethod, driftOrder, c_vector, fs);
  529. if idxBasis ==1
  530. beta_master(:,:,:,rep,subj) = squeeze(mean(cell2mat(beta_blks),1));%idx, ch, cond, rep, subj
  531. else
  532. beta_master(:,:,:,rep,subj) = squeeze(cell2mat(beta_blks)); %idx, ch, cond, rep, subj
  533. end
  534. % set bad channels to NaN
  535. beta_master(:,mlActAuto{1}(1:48,3)==0,:,rep,subj) = NaN;
  536. beta_master(:,mlActAuto{1}(49:96,3)==0,:,rep,subj) = NaN;
  537. bad_ch_list = mlActAuto{1}(1:48,3)==0 |mlActAuto{1}(49:96,3)==0;
  538. BadChannel_N(:,subj) = sum(bad_ch_list);
  540. end% end GLM
  542. %Homer block averaging
  543. stim = snirf.stim;
  544. if task_num==1
  545. if er_flag
  546. stim = snirf.stim([4 7:10]);
  547. for i = 1:length(stim)
  548. stim(i).data(:,1) = stim(i).data(:,1)+0.3; stim(i).states(:,1) = stim(i).states(:,1)+0.3;% 300ms SOA
  549. end
  550. else
  551. stim = snirf.stim(1:3);
  552. end
  553. end
  555. % set bad channels to NaN
  556. fNIRS_master(:,bad_ch_list,:,:,:,subj) = NaN;
  558. clearvars dc
  559. end % rep end
  561. ['Preprocessing for ' num2str(subj) ' completed']
  562. end % subj end
  564. %correct output_dir
  565. output_dir = [Drive 'From SD Card\Matlab_C\' Task '_fNIRS_output\'];
  566. end % task end
  568. fNIRS_master_M = squeeze(mean(nanmean(fNIRS_master((Pretask_bsline+Pretask_lag)*fs+1:...
  569. (Pretask_bsline+Task_duration+Posttask_lag)*fs+1,:,:,fNIRS_idx,:,:),5))); %tp ch cond idx rep subj
  571. %BA Tal
  572. %unilateral
  573. ROI_config = {[1 4 8 11] [2 3 6 7 9 10 13] [5 12 15 16 20] [18 19 22 23] [14 17 21 24]...
  574. [25 28 32 35] [26 27 30 31 33 34 37] [29 36 39 40 44] [42 43 46 47] [38 41 45 48]};
  576. for i = 1:length(ROI_config)
  577. fNIRS_master_M_cluster(i,:,:) = squeeze(nanmean(fNIRS_master_M(ROI_config{i},:,:),1));
  578. end
  580. if glm==1
  581. Master_glm_beta = squeeze(nanmean(beta_master(fNIRS_idx,:,:,rep,:),4));
  582. for i = 1:length(ROI_config)
  583. fNIRS_master_M_beta_cluster(i,:,:) = squeeze(nanmean(Master_glm_beta(ROI_config{i},:,:),1));
  584. end
  585. end
  587. fNIRS_master_plot = squeeze(mean(nanmean(fNIRS_master(:,:,TaskBlk_marker,fNIRS_idx,:,:),4),3));
  589. ['Preprocessing for ' Task ' completed']
  591. %% SOT GLM - Export cluster-level beta values
  592. LR_combine =0;
  593. NROI = 5;
  594. filename = [output_dir 'SOT_output7.xlsx'];
  596. delete(filename)
  597. clear beta_export
  598. beta_export_pre = reshape(fNIRS_master_M_beta_cluster,[size(fNIRS_master_M_beta_cluster,1)*size(fNIRS_master_M_beta_cluster,2) ...
  599. size(fNIRS_master_M_beta_cluster,3)])'*10^6;
  601. %left and right combined
  602. if LR_combine==1
  603. for m = 1:3 %number of conditions
  604. for n = 1:NROI %no. of ROIs in each hemisphere
  605. beta_export(:,n+(m-1)*NROI) = mean(beta_export_pre(:,[n n+NROI]+(m-1)*(NROI*2)),2);
  606. end
  607. end
  608. else
  609. beta_export = beta_export_pre;
  610. end
  612. for i = 1:size(beta_export,1)
  613. for j = 1:size(beta_export,2)
  614. if beta_export(i,j)==0
  615. beta_export(i,j)= NaN;
  616. end
  617. end
  618. end
  620. writematrix([[1:subj]' beta_export BadChannel_N'], filename);
  621. winopen(filename)

SOT_fnirs.m, no license · at the source

Overview

Authors: Michael K. Yeung1,2,3, Yvonne M. Y. Han4,5
ORCID iDs: Michael K. Yeung, Yvonne M. Y. Han
  1. Department of Psychology The Education University of Hong Kong Hong Kong SAR China
  2. University Research Facility of Human Behavioral Neuroscience The Education University of Hong Kong Hong Kong SAR China
  3. Centre for Psychosocial Health The Education University of Hong Kong Hong Kong SAR China
  4. Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hung Hom Hong Kong SAR China
  5. University Research Facility in Behavioral and Systems Neuroscience The Hong Kong Polytechnic University Hung Hom Hong Kong SAR China
Institutions: Education University of Hong Kong (Hong Kong SAR China); Hong Kong Polytechnic University (Hong Kong SAR China)
Journal: Psychophysiology, volume 63, issue 4, article e70286
Dates: received 11 October 2025; accepted 16 March 2026; published online 25 March 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/psyp.70286 · PMID 41882980 · PMCID PMC13018724 · OpenAlex W7140368631
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fNIRS (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: alerting, fNIRS, gaze, orienting, prefrontal cortex, social attention
MeSH: Attention*, Cues*, Frontal Lobe*, Psychomotor Performance*, Adult, Female, Fixation, Ocular, Humans, Male, Reaction Time, Spectroscopy, Near-Infrared, Young Adult (* major topic)
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Gaze conveys important information about one's intentions and likely object of reference. Because processes of attention may change over time, for reasons including fatigue or experience, this study aimed to compare mechanisms of gaze and arrow cueing effects by measuring across sessions. On two separate occasions, 39 young adults underwent a cueing paradigm with valid or invalid gaze or arrow cues, as well as neutral cues. Activation in frontal cortex regions implicated in the dorsal and ventral attention networks was examined during task performance using functional near‐infrared spectroscopy. Behavioral results showed comparable orienting (valid vs. neutral) and reorienting (invalid vs. valid) responses following gaze and arrow cues, which did not significantly change over sessions. However, the gaze cue elicited a significantly greater alerting effect (i.e., more benefits from the presence of the cue on reaction time) than the arrow cue in Session 2. Parallel to these behavioral findings, neuroimaging results indicated robust (de‐)activation during orienting and reorienting. Aligning with the greater alerting effect, target detection elicited significantly greater activation in the left posterior dorsomedial frontal cortex following gaze cues as opposed to arrow cues in Session 2. Therefore, insofar as changes over time are concerned, our findings offer converging evidence that gaze and arrow cues follow partially different attentional and neural mechanisms.

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

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OSF p7cx4

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State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (2)
Size: 106 files, 2 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/p7cx4

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

Tracing map

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Data

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Data Availability Statement

The dataset and data processing scripts that form the basis of the results are available on OSF (https://osf.io/p7cx4).

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

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 12 MeSH terms, 55 references.

Cite

This paper

Yeung, M. K., & Han, Y. M. Y. (2026). Partially Different Mechanisms of Social and Nonsocial Attention: Evidence From Changes in Cueing Effects and Underlying Frontal Cortex Processing Over Time. Psychophysiology, 63(4), e70286. https://doi.org/10.1111/psyp.70286

BibTeX

@article{yeung2026partially,
author = {Yeung, Michael K. and Han, Yvonne M. Y.},
title = {{Partially Different Mechanisms of Social and Nonsocial Attention: Evidence From Changes in Cueing Effects and Underlying Frontal Cortex Processing Over Time}},
journal = {Psychophysiology},
year = {2026},
month = apr,
volume = {63},
number = {4},
pages = {e70286},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70286},
url = {https://doi.org/10.1111/psyp.70286},
pmid = {41882980},
pmcid = {PMC13018724}
}

RIS

TY - JOUR
AU - Yeung, Michael K.
AU - Han, Yvonne M. Y.
TI - Partially Different Mechanisms of Social and Nonsocial Attention: Evidence From Changes in Cueing Effects and Underlying Frontal Cortex Processing Over Time
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/04/01
VL - 63
IS - 4
SP - e70286
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70286
UR - https://doi.org/10.1111/psyp.70286
LA - en
ER -

CSL-JSON

{
"id": "10.1111/psyp.70286",
"type": "article-journal",
"title": "Partially Different Mechanisms of Social and Nonsocial Attention: Evidence From Changes in Cueing Effects and Underlying Frontal Cortex Processing Over Time",
"container-title": "Psychophysiology",
"author": [
{
"family": "Yeung",
"given": "Michael K."
},
{
"family": "Han",
"given": "Yvonne M. Y."
}
],
"container-title-short": "Psychophysiology",
"volume": "63",
"issue": "4",
"page": "e70286",
"DOI": "10.1111/psyp.70286",
"PMID": "41882980",
"PMCID": "PMC13018724",
"ISSN": "0048-5772",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/psyp.70286",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

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