OSCR

Hierarchical brain dynamics supporting visual perceptual transitions.

Code ↔ Paper

19 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 19 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › Regions of interest ↔ brainstorm_analysis.zip/UI_prep_motorcortex.m, the whole file · a weak match · score 0.93 · precentral gyrus, 1–80 Hz, 30–50 ms, right hemisphere, band pass, 30 ms
  2. [2] § RESULTS › Task and behavior ↔ behavior.zip/analyze_group_data.m, the whole file · a weak match · score 0.84 · Wilcoxon signed rank, replay FTs, Replay trials, illusion trial, report filling, S1
  3. [3] § MATERIALS AND METHODS › Data acquisition ↔ paradigm.zip/UI_master.m, lines 2–129 · score 0.79 · eye tracker, eyeLink, eye tracking, fixation point, Post, restarts
  4. [4] § MATERIALS AND METHODS › MEG preprocessing ↔ brainstorm_analysis.zip/UI_preproc.m, lines 409–449 · score 0.79 · 0.05–200 Hz, notch filtered, band pass, attenuated, preprocessing, ECG
  5. [5] § MATERIALS AND METHODS › Whole-brain analysis of high-alpha activity ↔ brainstorm_analysis.zip/UI_source_timefreq.m, lines 455–507 · score 0.77 · 0.5–0.75 s, 10–15 Hz, Hilbert transformed, high alpha, brain, 0.5 s
  6. [6] § MATERIALS AND METHODS › Stimuli and behavioral tasks ↔ paradigm.zip/UI_master.m, lines 2–129 · score 0.72 · DKL azimuth, isoluminant, radius, calibrations, physical, RGB
  7. [7] § MATERIALS AND METHODS › Stimuli and behavioral tasks ↔ paradigm.zip/UI_openScreen.m, the whole file · a weak match · score 0.69 · ProPixx, visual angle, gamma, calibrations, smoothly, RGB
  8. [8] § MATERIALS AND METHODS › Data acquisition ↔ brainstorm_analysis.zip/UI_import_anat.m, the whole file · a weak match · score 0.66 · fiducial points, FreeSurfer, warp, MRI, Brainstorm, surfaces
  9. [9] § MATERIALS AND METHODS › Phase-locking of cortical time series with photodiode or template sinusoid signals ↔ brainstorm_edited_code.zip/bst_connectivity_MGL.m, lines 850–930 · score 0.64 · Hilbert transform, band pass, frequency band, fc, phase, cross
  10. [10] § MATERIALS AND METHODS › RIFT presentation ↔ paradigm.zip/UI_openScreen.m, the whole file · a weak match · score 0.62 · ProPixx, double, gamma, quadrants, RGB, frame
  11. [11] § MATERIALS AND METHODS › Statistical analysis ↔ brainstorm_analysis.zip/UI_source_timefreq.m, lines 455–507 · score 0.59 · spatial smoothing, mm, FWHM, template, brain, maps
  12. [12] § MATERIALS AND METHODS › Statistical analysis ↔ statistics.zip/timeseries_permutation_zero.m, lines 18–60 · score 0.59 · cluster defining threshold, zero, ROI, PLVt, permutation, epoch
  13. [13] § MATERIALS AND METHODS › Microsaccade detection ↔ eye_analysis.zip/UI_extractMicrosaccades.m, lines 173–243 · score 0.57 · eye movements, arc, velocity, min, angle, binocular
  14. [14] § RESULTS › Microsaccades restore both visual inhibition and motor high-alpha activity ↔ statistics.zip/timeseries_permutation_zero.m, lines 135–236 · score 0.56 · Aperiodic exponent, Aperiodic corrected, baseline corrected, uncorrected, permutation, offset
  15. [15] § MATERIALS AND METHODS › Regions of interest ↔ brainstorm_analysis.zip/UI_ret_analysis.m, lines 12–153 · score 0.55 · phase locking, FWHM, kernel, occipital, smoothed, PLV
  16. [16] § RESULTS › Excitation-inhibition balance in visual cortex before filling-in ↔ statistics.zip/timeseries_permutation_zero.m, lines 135–236 · score 0.55 · Aperiodic exponent, Aperiodic corrected, baseline period, permutation, offset, cluster
  17. [17] § RESULTS › Task and behavior ↔ behavior.zip/analyze_group_data.m, the whole file · a weak match · score 0.54 · Wilcoxon signed rank, Replay trials, FT, illusion, uniform, behavior
  18. [18] § MATERIALS AND METHODS › Phase-locking of cortical time series with photodiode or template sinusoid signals ↔ brainstorm_analysis.zip/UI_source_timefreq.m, lines 928–1032 · score 0.54 · Hilbert transform, sinusoids, intermodulation, magnitude, PLVt, phase
  19. [19] § MATERIALS AND METHODS › RIFT presentation ↔ paradigm.zip/generateAlphaSinusoids.m, the whole file · a weak match · score 0.54 · sinusoidal luminance, perceived, fp, fc, frame, modulations

Paper

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The authors' code

MATLAB · 1,417 lines · 99 KB · CC-BY-4.0 · 3 matches

  1. clear
  2. SubjectNames = string([301:321 323:330]); % all
  3. %SubjectNames = string([301:321 323 326 328 329]); % all that have mris
  4. %SubjectNames = string(301);
  5. %SubjectNames = string([328:330]);
  6. nSubjects = numel(SubjectNames);
  7. opts = UI_load_params(3);
  9. runwholebrainhighalpha = 1; % whole brain high alpha activity, taken from motor cortex result, avg'd in one time window
  10. runplvt = 1;
  11. runfiplvt = 1; % PLVt at intermodulation, 8 Hz and 16 Hz
  12. runsprintcustom = 1; sprint_winlength = 1; sprint_winstep = 0.1; sprint_maxFreq = 50; % SPRiNT but for each moving window, average PSD across trials before computing FOOOF. Then recombine to form the SPRiNT timeseries.
  13. runpsds = 1; % initial psds for visualization?
  14. combinecontrasts = 1; % usually 1, just means we run the normal analyses combined across both contrasts
  15. splitcontrasts = 0;
  16. powerscoutfunc = 1; % 1 = mean, 3 = PCA
  17. first_run = 1; % if 1, also run some initial things
  18. runret = 0; % analyze ret microsaccades
  20. bl_method = 'ersd'; % zscore, ersd, or bl - what baseline-correction method to use
  22. % rl_rowidx: each element contains the two scouts that are R L of the same
  23. all_atlases(1).name = 'motor'; all_atlases(1).rl_rownames = {'RL_motor'}; all_atlases(1).scouts2use = {'R_motor_cortex', 'L_motor_cortex'}; all_atlases(1).rl_rowidx = {'1, 2'}; all_atlases(1).label = 'motor';
  24. all_atlases(2).name = 'localizer'; all_atlases(2).rl_rownames = {'periphery', 'center'}; all_atlases(2).scouts2use = {'R_periphery', 'R_center', 'L_periphery', 'L_center'}; all_atlases(2).rl_rowidx = {'1, 3', '2, 4'}; all_atlases(2).label = 'flicker';
  26. for iAtlas = 1:numel(all_atlases)
  27. all_atlases(iAtlas).rl_label = ['RL_', all_atlases(iAtlas).label];
  28. end
  29. mergerl = [0 1]; % motor is not merged across hemispheres; localizer is.
  31. use_atlases = 1; % just motor
  32. %use_atlases = 2; % just flicker
  33. %use_atlases = [1 2]; % both
  36. % set processing options for each epoch type
  37. epoch_types = {'stimon', 'button', 'button_noms', 'ms_instim', 'ms_infix'};
  39. % Have to run retinotopy part first (UI_ret_analysis), then manually define scouts,
  40. % then run this script.
  42. for iSubj = 1:nSubjects
  43. sname = ['s', char(SubjectNames(iSubj))];
  45. % Start a new report
  46. bst_report('Start');
  48. % get 8 Hz file
  49. f8Files = bst_process('CallProcess', 'process_select_files_matrix', [], [], ...
  50. 'subjectname', sname, ...
  51. 'condition', 'sin_8Hz', ...
  52. 'tag', [], ...
  53. 'includebad', 0, ...
  54. 'includeintra', 0, ...
  55. 'includecommon', 0);
  56. % get 16 Hz file
  57. f16Files = bst_process('CallProcess', 'process_select_files_matrix', [], [], ...
  58. 'subjectname', sname, ...
  59. 'condition', 'sin_16Hz', ...
  60. 'tag', [], ...
  61. 'includebad', 0, ...
  62. 'includeintra', 0, ...
  63. 'includecommon', 0);
  64. f11Files = bst_process('CallProcess', 'process_select_files_matrix', [], [], ...
  65. 'subjectname', sname, ...
  66. 'condition', 'sin_11Hz', ...
  67. 'tag', [], ...
  68. 'includebad', 0, ...
  69. 'includeintra', 0, ...
  70. 'includecommon', 0);
  71. % get 60 Hz file
  72. f60Files = bst_process('CallProcess', 'process_select_files_matrix', [], [], ...
  73. 'subjectname', sname, ...
  74. 'condition', 'sin_60Hz', ...
  75. 'tag', [], ...
  76. 'includebad', 0, ...
  77. 'includeintra', 0, ...
  78. 'includecommon', 0);
  80. %% retinotopy microsaccades within scouts
  81. if runret
  82. % Process: Select data files in: SUBJ
  83. retFiles = bst_process('CallProcess', 'process_select_files_results', [], [], ...
  84. 'subjectname', sname, ...
  85. 'condition', ['ret_sss_notch_band'], ...
  86. 'tag', [], ...
  87. 'includebad', 0, ...
  88. 'includeintra', 0, ...
  89. 'includecommon', 0);
  90. % Process: Ignore file names with tag: Avg
  91. retFiles = bst_process('CallProcess', 'process_select_tag', retFiles, [], ...
  92. 'tag', 'average', ...
  93. 'search', 1, ... % Search the file paths
  94. 'select', 2); % Ignore the files with the tag
  96. for iEpoch_type = 1:numel(epoch_types)
  98. epochname = epoch_types{iEpoch_type};
  99. curr_opts = opts.epochs.(epochname);
  101. if strcmp(epochname, 'button_noms') && sum(strcmp(sname, {'s318', 's320', 's324'}))
  102. continue
  103. end
  105. epochFiles = bst_process('CallProcess', 'process_select_tag', retFiles, [], ...
  106. 'tag', curr_opts.tag, ...
  107. 'search', 3, ... % Search the parent file names
  108. 'select', 1); % Select the files with the tag
  110. % Process: Ignore file names with tag: Avg
  111. epochFiles = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  112. 'tag', 'Avg', ...
  113. 'search', 3, ... % Search the parent file name
  114. 'select', 2); % Ignore the files with the tag
  116. % don't take the left / right / out / in specific epochs (these are copies)
  117. ignoreTags = {'left', 'right', 'outward', 'inward', 'noms'};
  118. for iTag = 1:numel(ignoreTags)
  119. if ~contains(curr_opts.tag, ignoreTags{iTag})
  120. epochFiles = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  121. 'tag', ignoreTags{iTag}, ...
  122. 'search', 1, ... % Search the file paths
  123. 'select', 2); % Ignore the files with the tag
  124. epochFiles = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  125. 'tag', ignoreTags{iTag}, ...
  126. 'search', 3, ... % Search the parent file name
  127. 'select', 2); % Ignore the files with the tag
  128. end
  129. end
  130. if isempty(epochFiles) | numel(epochFiles) < 2
  131. continue
  132. end
  134. for iAtlas = use_atlases
  135. curr_atlas = all_atlases(iAtlas);
  137. %% scout plvt
  138. if runplvt
  140. conn_metric = 'PLVt';
  141. % PHASE LOCKING WITH PHOTODIODE
  143. % Process: Select data files
  144. sensorepochFiles = bst_process('CallProcess', 'process_select_files_data', [], [], ...
  145. 'subjectname', sname, ...
  146. 'condition', ['ret_sss_notch_band'], ...
  147. 'tag', curr_opts.tag, ...
  148. 'includebad', 0, ...
  149. 'includeintra', 0, ...
  150. 'includecommon', 0);
  152. % Process: Ignore file names with tag: bl.mat (don't use baseline-corrected)
  153. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  154. 'tag', 'bl.mat', ...
  155. 'search', 1, ... % Search the file paths
  156. 'select', 2); % Ignore the files with the tag
  158. % don't take the wrong epochs
  159. ignoreTags = {'noms'};
  160. for iTag = 1:numel(ignoreTags)
  161. if ~contains(curr_opts.tag, ignoreTags{iTag})
  162. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  163. 'tag', ignoreTags{iTag}, ...
  164. 'search', 1, ... % Search the file paths
  165. 'select', 2); % Ignore the files with the tag
  166. end
  167. end
  169. % Process: Ignore file names with tag: Avg
  170. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  171. 'tag', 'Avg', ...
  172. 'search', 2, ... % Search the file names
  173. 'select', 2); % Ignore the files with the tag
  175. for iDiode = 1:2
  176. diode = opts.diode_channels{iDiode};
  177. useband = opts.flicker_bands{iDiode};
  178. % PLV in localizer scouts
  179. % Process: PLV: Phase locking value
  180. connectivityScout(iDiode) = bst_process('CallProcess', 'process_plv2', sensorepochFiles, epochFiles, ...
  181. 'timewindow', curr_opts.fullwindow, ...
  182. 'src_channel', diode, ...
  183. 'dest_scouts', {curr_atlas.name, curr_atlas.scouts2use}, ...
  184. 'scoutfunc', 'mean', ... % Mean
  185. 'scouttime', 'after', ... % After
  186. 'scoutfuncaft', 'mean', ... % Mean
  187. 'tfedit', struct(...
  188. 'Comment', 'Complex', ...
  189. 'TimeBands', [], ...
  190. 'Freqs', {useband}, ...
  191. 'ClusterFuncTime', 'none', ...
  192. 'Measure', 'none', ...
  193. 'Output', 'all', ...
  194. 'SaveKernel', 0), ...
  195. 'plvmethod', 'plv', ... % PLV: Phase locking value
  196. 'tfmeasure', 'hilbert', ... % Hilbert transform
  197. 'timeres', 'full', ... % Full (requires epochs)
  198. 'plvmeasure', 2, ... % Magnitude
  199. 'outputmode', 'avg'); % Save average connectivity matrix (one file)
  201. % Process: Set name:
  202. connectivityScout(iDiode) = bst_process('CallProcess', 'process_set_comment', connectivityScout(iDiode), [], ...
  203. 'tag', ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ': ', diode, ' ', curr_opts.label, extralabel], ...
  204. 'isindex', 0);
  205. end
  208. % Process: PLV: Phase locking value with 60 Hz sin (control)
  209. f60FilesMulti = repmat(f60Files, 1, numel(epochFiles));
  210. connectivity60Scout = bst_process('CallProcess', 'process_plv2', f60FilesMulti, epochFiles, ...
  211. 'timewindow', curr_opts.fullwindow, ...
  212. 'src_channel', '', ...
  213. 'dest_scouts', {curr_atlas.name, curr_atlas.scouts2use}, ...
  214. 'scoutfunc', 'mean', ... % Mean
  215. 'scouttime', 'after', ... % After
  216. 'scoutfuncaft', 'mean', ... % Mean
  217. 'tfedit', struct(...
  218. 'Comment', 'Complex', ...
  219. 'TimeBands', [], ...
  220. 'Freqs', {{'fcontrol', '59, 61', 'mean'}}, ...
  221. 'ClusterFuncTime', 'none', ...
  222. 'Measure', 'none', ...
  223. 'Output', 'all', ...
  224. 'SaveKernel', 0), ...
  225. 'plvmethod', 'plv', ... % PLV: Phase locking value
  226. 'tfmeasure', 'hilbert', ... % Hilbert transform
  227. 'timeres', 'full', ... % Full (requires epochs)
  228. 'plvmeasure', 2, ... % Magnitude
  229. 'outputmode', 'avg'); % Save average connectivity matrix (one file)
  231. % Process: Set name:
  232. connectivity60Scout = bst_process('CallProcess', 'process_set_comment', connectivity60Scout, [], ...
  233. 'tag', ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ' 60 ', curr_opts.label, extralabel], ...
  234. 'isindex', 0);
  236. % combine into one file (not merging R/L yet)
  237. mat56 = in_bst_timefreq(connectivityScout(1).FileName);
  238. mat64 = in_bst_timefreq(connectivityScout(2).FileName);
  239. mat60 = in_bst_timefreq(connectivity60Scout.FileName);
  240. matall = mat56;
  241. matall.Freqs = {'fp', '55,57', 'mean'; 'fc', '63,65', 'mean'; 'fcontrol', '59, 61', 'mean'};
  242. matall.RefRowNames = {'MISC'};
  243. matall.TF(:, :, 2) = mat64.TF;
  244. matall.TF(:, :, 3) = mat60.TF;
  245. matall.Comment = ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ' fpfc ', curr_opts.label, ' ret'];
  246. scout_allconnectivity = db_add(connectivityScout(1).iStudy, matall);
  248. % Process: Extract time
  249. scout_allconnectivityTime = bst_process('CallProcess', 'process_extract_time', scout_allconnectivity, [], ...
  250. 'timewindow', curr_opts.extracttime, ...
  251. 'overwrite', 1);
  253. % Process: Event-related perturbation (ERS/ERD)
  254. scout_allconnectivityERSD = bst_process('CallProcess', 'process_baseline_norm', scout_allconnectivityTime, [], ...
  255. 'baseline', curr_opts.baseline, ...
  256. 'method', bl_method, ... % Event-related perturbation (ERS/ERD): x_std = (x - &mu;) / &mu; * 100
  257. 'overwrite', 0);
  259. % Process: Delete selected files
  260. bst_process('CallProcess', 'process_delete', [connectivityScout, connectivity60Scout], [], ...
  261. 'target', 1); % Delete selected files
  263. % Process: combine RL hemispheres
  264. if mergerl(iAtlas)
  265. for iScout = 1:numel(curr_atlas.rl_rowidx)
  266. RL_scoutconnectivity(iScout) = bst_process('CallProcess', 'process_extract_values', scout_allconnectivityERSD, [], ...
  267. 'timewindow', [], ...
  268. 'freqrange', [0, 100], ...
  269. 'rows', curr_atlas.rl_rowidx{iScout}, ...
  270. 'isabs', 0, ...
  271. 'avgtime', 0, ...
  272. 'avgrow', 1, ...
  273. 'avgfreq', 0, ...
  274. 'matchrows', 0, ...
  275. 'dim', 2, ... % Concatenate time (dimension 2)
  276. 'Comment', ['RL_scout,Avg: ', conn_metric, ' ', curr_opts.label, ' ret']);
  277. end
  279. clear allmats
  280. allmats(1) = in_bst_timefreq(RL_scoutconnectivity(1).FileName);
  281. matall = allmats(1);
  282. matall.RowNames = curr_atlas.rl_rownames;
  283. matall.Comment = [curr_atlas.rl_label, ',Avg: ', conn_metric, ' fpfc ', curr_opts.label, ' ret'];
  284. for iScout = 2:numel(curr_atlas.rl_rowidx)
  285. allmats(iScout) = in_bst_timefreq(RL_scoutconnectivity(iScout).FileName);
  286. matall.TF(iScout, :, :) = allmats(iScout).TF;
  287. end
  288. RL_allconnectivity = db_add(RL_scoutconnectivity(1).iStudy, matall);
  290. % Process: Delete selected files
  291. bst_process('CallProcess', 'process_delete', [RL_scoutconnectivity], [], ...
  292. 'target', 1); % Delete selected files
  293. end
  294. end
  297. %% custom SPRiNT
  298. if runsprintcustom
  299. % fooofs each moving window after averaging SFFTs across
  300. % trials.
  301. % method: extract each time window, run FOOOF, then combine
  302. % outputs into a SPRiNT structure.
  304. % set windows. winlength = 1 second, but at intervals of
  305. % 100ms.
  307. % make sure we'll cover the full extracttime window, but
  308. % don't waste compute on extra windows
  309. fulltime = curr_opts.extracttime + [-sprint_winlength/2, sprint_winlength/2];
  310. winstarts = fulltime(1):sprint_winstep:fulltime(2)-sprint_winlength+.001;
  311. winends = fulltime(1)+sprint_winlength:sprint_winstep:fulltime(2);
  312. nWin = numel(winstarts);
  314. % extract time windows, FFT, average them, and FOOOF.
  315. clear winFiles
  317. for iAtlas = use_atlases
  318. curr_atlas = all_atlases(iAtlas);
  320. for iWin = 1:nWin
  322. % Process: Power spectrum density (Welch)
  323. fftWinFiles = bst_process('CallProcess', 'process_psd', epochFiles, [], ...
  324. 'timewindow', [winstarts(iWin), winends(iWin)], ...
  325. 'win_length', sprint_winlength, ...
  326. 'win_overlap', 0, ...
  327. 'units', 'physical', ... % Physical: U2/Hz
  328. 'clusters', {curr_atlas.name, curr_atlas.scouts2use}, ...
  329. 'scoutfunc', 1, ... % Mean
  330. 'win_std', 0, ...
  331. 'edit', struct(...
  332. 'Comment', 'Scout psd,Avg,Power', ...
  333. 'TimeBands', [], ...
  334. 'Freqs', [], ...
  335. 'ClusterFuncTime', 'after', ...
  336. 'Measure', 'power', ...
  337. 'Output', 'average', ...
  338. 'SaveKernel', 0));
  340. % Process: specparam: Fitting oscillations and 1/f
  341. fooofWinFiles(iWin) = bst_process('CallProcess', 'process_fooof', fftWinFiles, [], ...
  342. 'implementation', 'matlab', ... % Matlab
  343. 'freqrange', [1, sprint_maxFreq], ...
  344. 'powerline', '50', ... % 50 Hz
  345. 'peaktype', 'gaussian', ... % Gaussian
  346. 'peakwidth', [0.5, 12], ...
  347. 'maxpeaks', 3, ...
  348. 'minpeakheight', 1, ...
  349. 'proxthresh', 2, ...
  350. 'apermode', 'fixed', ... % Fixed
  351. 'guessweight', 'none', ... % None
  352. 'sorttype', 'param', ... % Peak parameters
  353. 'sortparam', 'frequency', ... % Frequency
  354. 'sortbands', {'delta', '2, 4'; 'theta', '5, 7'; 'alpha', '8, 12'; 'beta', '15, 29'; 'gamma1', '30, 59'; 'gamma2', '60, 90'});
  356. % import to MATLAB.
  357. fooofMats(iWin) = in_bst(fooofWinFiles(iWin).FileName);
  359. % delete extracted ffts
  360. bst_process('CallProcess', 'process_delete', fftWinFiles, [], ...
  361. 'target', 1); % Delete selected files
  362. clear fftWinFiles
  363. end
  365. % manually convert to a SPRiNT structure.
  366. sprintMat = fooofMats(1); % get template from first fooof file.
  367. sprintMat.Comment = ['Scouts_', curr_atlas.label, ',Avg: SPRiNT_custom ', curr_opts.label, ' ret'];
  368. sprintMat.Time = winstarts + sprint_winlength/2;
  369. sprintMat.Freqs = sprintMat.Freqs(1:sprint_maxFreq);
  370. sprintMat.Method = 'sprint';
  371. sprintMat.RowNames = sprintMat.RowNames';
  372. sprintMat.nAvg = numel(epochFiles); sprintMat.Leff = numel(epochFiles);
  373. sprintMat.TF = sprintMat.TF(:, :, 1:sprint_maxFreq); % init TF
  374. sprintMat.Options.Method = 'sprint';
  375. sprintopts = struct;
  376. sprintopts.freqs = sprintMat.Options.FOOOF.freqs;
  377. for iScout = 1:numel(sprintMat.RowNames)
  378. sprintopts.channel(iScout).name = sprintMat.RowNames{iScout};
  379. sprintopts.channel(iScout).peaks = struct('time', 'center_frequency', 'amplitude', 'st_dev');
  380. for iWin = 1:nWin
  381. sprintMat.TF(iScout, iWin, :) = fooofMats(iWin).TF(iScout, :, 1:sprint_maxFreq);
  382. fooofopts = fooofMats(iWin).Options.FOOOF;
  383. sprintopts.topography.exponent(iScout, iWin) = fooofopts.aperiodics(iScout).exponent;
  384. sprintopts.topography.offset(iScout, iWin) = fooofopts.aperiodics(iScout).offset;
  385. sprintopts.SPRiNT_models(iScout, iWin, :) = fooofopts.data(iScout).fooofed_spectrum;
  386. sprintopts.peak_models(iScout, iWin, :) = fooofopts.data(iScout).peak_fit;
  387. sprintopts.aperiodic_models(iScout, iWin, :) = fooofopts.data(iScout).ap_fit;
  388. sprintopts.channel(iScout).data(iWin).time = sprintMat.Time(iWin);
  389. sprintopts.channel(iScout).data(iWin).aperiodic_params = [sprintopts.topography.offset(iScout, iWin), sprintopts.topography.exponent(iScout, iWin)];
  390. sprintopts.channel(iScout).data(iWin).peak_params = fooofopts.data(iScout).peak_params;
  391. sprintopts.channel(iScout).data(iWin).peak_types = fooofopts.data(iScout).peak_types;
  392. sprintopts.channel(iScout).data(iWin).ap_fit = fooofopts.data(iScout).ap_fit;
  393. sprintopts.channel(iScout).data(iWin).peak_fit = fooofopts.data(iScout).peak_fit;
  394. sprintopts.channel(iScout).data(iWin).fooofed_spectrum = fooofopts.data(iScout).fooofed_spectrum;
  395. sprintopts.channel(iScout).data(iWin).power_spectrum = fooofMats(iWin).TF(iScout, :, 1:sprint_maxFreq);
  396. sprintopts.channel(iScout).data(iWin).error = fooofopts.data.error;
  397. sprintopts.channel(iScout).data(iWin).r_squared = fooofopts.data.r_squared;
  398. sprintopts.channel(iScout).aperiodics(iWin).time = sprintMat.Time(iWin);
  399. sprintopts.channel(iScout).aperiodics(iWin).offset = sprintopts.topography.offset(iScout, iWin);
  400. sprintopts.channel(iScout).aperiodics(iWin).exponent = sprintopts.topography.exponent(iScout, iWin);
  401. sprintopts.channel(iScout).stats(iWin).MSE = fooofopts.stats(iScout).MSE;
  402. sprintopts.channel(iScout).stats(iWin).r_squared = fooofopts.stats(iScout).r_squared;
  403. sprintopts.channel(iScout).stats(iWin).frequency_wise_error = fooofopts.stats(iScout).frequency_wise_error;
  404. nPeaks = 1:numel(fooofopts.peaks);
  405. for iPeak = 1:nPeaks
  406. if strcmp(fooofopts.peaks(iPeak).channel{1}, sprintMat.RowNames{iScout})
  407. sprintopts.channel(iScout).peaks(iPeak).time = sprintMat.Time(iWin);
  408. sprintopts.channel(iScout).peaks(iPeak).center_frequency = fooofopts.peaks(iPeak).center_frequency;
  409. sprintopts.channel(iScout).peaks(iPeak).amplitude = fooofopts.peaks(iPeak).amplitude;
  410. sprintopts.channel(iScout).peaks(iPeak).st_dev = fooofopts.peaks(iPeak).std_dev;
  411. end
  412. end
  413. end
  414. end
  415. sprintopts.options.winLen = sprint_winlength;
  416. sprintopts.options.Ovrlp = 0;
  417. sprintopts.options.nAverage = 1;
  418. sprintopts.options.hOT = 1; % not sure what this is
  419. sprintopts.options.rmoutliers = 'no';
  420. sprintopts.options.freq_range = fooofopts.options.freq_range;
  421. sprintopts.options.peak_width_limits = fooofopts.options.peak_width_limits;
  422. sprintopts.options.max_peaks = fooofopts.options.max_peaks;
  423. sprintopts.options.min_peak_height = fooofopts.options.min_peak_height;
  424. sprintopts.options.peak_threshold = fooofopts.options.peak_threshold;
  425. sprintopts.options.aperiodic_mode = fooofopts.options.aperiodic_mode;
  426. sprintopts.options.peak_type = fooofopts.options.peak_type;
  427. sprintopts.options.proximity_threshold = fooofopts.options.proximity_threshold;
  428. sprintopts.options.guess_weight = fooofopts.options.guess_weight;
  430. sprintMat.Options.SPRiNT = sprintopts;
  431. sprintMat.Options = rmfield(sprintMat.Options, 'FOOOF');
  433. % add to database
  434. sprintFiles = db_add(fooofWinFiles(1).iStudy, sprintMat);
  436. % delete the windowed FOOOFs
  437. bst_process('CallProcess', 'process_delete', fooofWinFiles, [], ...
  438. 'target', 1); % Delete selected files
  439. clear fooofWinFiles
  441. % Process: Extract time
  442. sprintFiles = bst_process('CallProcess', 'process_extract_time', sprintFiles, [], ...
  443. 'timewindow', curr_opts.extracttime, ...
  444. 'overwrite', 1);
  446. % don't average across R/L hemispheres, it's too
  447. % complicated here. Instead we'll baseline correct,
  448. % then average across subjects, and can average across
  449. % R/L at group level.
  451. end
  453. end
  455. %% whole brain analyses (combined contrast)
  456. if runwholebrainhighalpha
  457. % extract only average of high alpha (10-15 Hz) from morlet
  458. % wavelets or hilbert transform.
  459. % project to template and smooth.
  461. % set times of interest
  462. buffer_time = [-0.4, 0.4]; % 4 cycles of 10Hz for edge effects
  463. switch epochname
  464. case 'button'
  465. target_time = [-1.25, -0.75];
  466. case 'ms_instim'
  467. target_time = [0.5 0.75];
  468. otherwise
  469. continue
  470. end
  471. timebands2use = {'baseline', num2str(curr_opts.baseline), 'mean'; 'target', num2str(target_time), 'mean'};
  473. % use hilbert transform, much faster
  474. mergedFiles = bst_process('CallProcess', 'process_hilbert', epochFiles, [], ...
  475. 'clusters', [], ...
  476. 'scoutfunc', 1, ...
  477. 'edit', struct(...
  478. 'Comment', ['Wholebrain,Avg,Power, highalpha ', curr_opts.label, extralabel], ...
  479. 'TimeBands', {timebands2use}, ...
  480. 'Freqs', {{'highalpha', '10,15', 'mean'}}, ...
  481. 'ClusterFuncTime', 'none', ...
  482. 'Measure', 'power', ...
  483. 'Output', 'average', ...
  484. 'RemoveEvoked', 0, ...
  485. 'SaveKernel', 0), ...
  486. 'normalize2020', 0, ...
  487. 'normalize', 'none', ... % None: Save non-standardized time-frequency maps
  488. 'mirror', 0);
  490. % remove timebands from file
  491. inMerge = in_bst(mergedFiles.FileName);
  492. inMerge.Time = [mean(curr_opts.baseline), mean(target_time)];
  493. inMerge = rmfield(inMerge, 'TimeBands');
  494. bst_save(file_fullpath(mergedFiles.FileName), inMerge, 'v7.3');
  495. db_reload_studies(mergedFiles.iStudy);
  497. % project to template & smooth
  498. % Process: Project on default anatomy: surface
  499. templateFiles = bst_process('CallProcess', 'process_project_sources', mergedFiles, [], ...
  500. 'headmodeltype', 'surface'); % Cortex surface
  502. % Process: Spatial smoothing (3.00)
  503. templateFiles = bst_process('CallProcess', 'process_ssmooth', templateFiles, [], ...
  504. 'fwhm', 3, ...
  505. 'method', 'geodesic_dist', ... % Geodesic (mm)(recommended)
  506. 'overwrite', 1);
  507. end
  509. %% Init Main illusion task (blocks 1-7)
  510. if ~runret % don't run main task if we set above to run retinotopy
  511. % epoch microsaccade files if analyzing them
  512. ms_epochnames = {'ms_instim', 'ms_infix'};
  513. msEpochFiles = [];
  514. for iMSEpoch = 1:numel(ms_epochnames)
  515. if sum(strcmp(epoch_types, ms_epochnames{iMSEpoch}))
  516. epoch_out = UI_epoch({ms_epochnames{iMSEpoch}}, SubjectNames(iSubj));
  517. % save files here to delete at the end
  518. msEpochFiles = [msEpochFiles, epoch_out{1}, epoch_out{2}];
  519. end
  520. end
  522. % Input files
  523. % Process: Select data files in: SUBJ
  524. allFiles = bst_process('CallProcess', 'process_select_files_results', [], [], ...
  525. 'subjectname', sname, ...
  526. 'condition', [], ...
  527. 'tag', [], ...
  528. 'includebad', 0, ...
  529. 'includeintra', 0, ...
  530. 'includecommon', 0);
  532. % Process: Ignore file names with tag: ret
  533. allFiles = bst_process('CallProcess', 'process_select_tag', allFiles, [], ...
  534. 'tag', 'ret', ...
  535. 'search', 1, ... % Search the file paths
  536. 'select', 2); % Ignore the files with the tag
  538. %% run blocktypes
  539. for blocktype = [2, 1] % first do main task blocks 1-6, then block 7
  541. % Process: Ignore file names with tag: block7
  542. blockFiles = bst_process('CallProcess', 'process_select_tag', allFiles, [], ...
  543. 'tag', 'block7', ...
  544. 'search', 1, ... % Search the file paths
  545. 'select', blocktype); % Select/Ignore the files with the tag
  547. if subset
  548. if blocktype == 1
  549. continue % don't analyze block7 when we're just running the main task subset
  550. elseif blocktype == 2
  551. % get block7 files to start
  552. block7Files = bst_process('CallProcess', 'process_select_tag', allFiles, [], ...
  553. 'tag', 'block7', ...
  554. 'search', 1, ... % Search the file paths
  555. 'select', 1); % Select
  556. end
  557. end
  559. %% run epochs
  560. for iEpoch_type = 1:numel(epoch_types)
  562. epochname = epoch_types{iEpoch_type};
  563. curr_opts = opts.epochs.(epochname);
  565. if strcmp(epochname, 'button_noms') && sum(strcmp(sname, {'s318', 's320', 's324'}))
  566. continue
  567. end
  569. epochFiles = bst_process('CallProcess', 'process_select_tag', blockFiles, [], ...
  570. 'tag', curr_opts.tag, ...
  571. 'search', 3, ... % Search the parent file names
  572. 'select', 1); % Select the files with the tag
  574. % Process: Ignore file names with tag: Avg
  575. epochFiles = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  576. 'tag', 'Avg', ...
  577. 'search', 3, ... % Search the parent file name
  578. 'select', 2); % Ignore the files with the tag
  580. epochFiles_bl = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  581. 'tag', 'bl.mat', ...
  582. 'search', 1, ... % Search the file paths
  583. 'select', 1); % Select the files with the tag
  585. % Process: Ignore file names with tag: bl.mat (don't use baseline-corrected)
  586. epochFiles = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  587. 'tag', 'bl.mat', ...
  588. 'search', 1, ... % Search the file paths
  589. 'select', 2); % Ignore the files with the tag
  591. % don't take the wrong epochs
  592. ignoreTags = {'noms'};
  593. for iTag = 1:numel(ignoreTags)
  594. if ~contains(curr_opts.tag, ignoreTags{iTag})
  595. epochFiles = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  596. 'tag', ignoreTags{iTag}, ...
  597. 'search', 3, ... % Search the parent filenames
  598. 'select', 2); % Ignore the files with the tag
  599. epochFiles = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  600. 'tag', ignoreTags{iTag}, ...
  601. 'search', 1, ... % Search the file paths
  602. 'select', 2); % Ignore the files with the tag
  603. end
  604. end
  606. if isempty(epochFiles) | numel(epochFiles) < 2
  607. continue
  608. end
  610. if subset % collect block7 trials too, to get subset of main task trials with same trial count.
  611. epoch7Files = bst_process('CallProcess', 'process_select_tag', block7Files, [], ...
  612. 'tag', curr_opts.tag, ...
  613. 'search', 3, ... % Search the parent file names
  614. 'select', 1); % Select the files with the tag
  616. % Process: Ignore file names with tag: Avg
  617. epoch7Files = bst_process('CallProcess', 'process_select_tag', epoch7Files, [], ...
  618. 'tag', 'Avg', ...
  619. 'search', 3, ... % Search the parent file name
  620. 'select', 2); % Ignore the files with the tag
  622. % Process: Ignore file names with tag: bl.mat (don't use baseline-corrected)
  623. epoch7Files = bst_process('CallProcess', 'process_select_tag', epoch7Files, [], ...
  624. 'tag', 'bl.mat', ...
  625. 'search', 1, ... % Search the file paths
  626. 'select', 2); % Ignore the files with the tag
  628. % don't take the wrong epochs
  629. ignoreTags = {'noms'};
  630. for iTag = 1:numel(ignoreTags)
  631. if ~contains(curr_opts.tag, ignoreTags{iTag})
  632. epoch7Files = bst_process('CallProcess', 'process_select_tag', epoch7Files, [], ...
  633. 'tag', ignoreTags{iTag}, ...
  634. 'search', 3, ... % Search the parent filenames
  635. 'select', 2); % Ignore the files with the tag
  636. epoch7Files = bst_process('CallProcess', 'process_select_tag', epoch7Files, [], ...
  637. 'tag', ignoreTags{iTag}, ...
  638. 'search', 1, ... % Search the file paths
  639. 'select', 2); % Ignore the files with the tag
  640. end
  641. end
  643. if isempty(epoch7Files) | numel(epoch7Files) < 2
  644. continue
  645. end
  648. % Process: Select uniform number of files [uniform]
  649. % so the files are uniformly distributed along the list of trials, which is
  650. % ordered block1,block2, etc...
  651. % i.e. a pseudo-random but fully reproducible selection, not necessarily biased towards
  652. % any particular task block.
  653. [epochFiles, epoch7Files] = bst_process('CallProcess', 'process_select_uniform2', epochFiles, epoch7Files, ...
  654. 'nfiles', 0, ...
  655. 'method', 4); % Uniformly distributed
  657. end
  659. for iAtlas = use_atlases
  660. curr_atlas = all_atlases(iAtlas);
  661. %% psds
  662. if strcmp(epochname, 'stimon') && runpsds % get avg FFTs or PSDs for quality control.
  664. psdscout = bst_process('CallProcess', 'process_psd', epochFiles, [], ...
  665. 'timewindow', [3, 5], ...
  666. 'win_length', 1, ...
  667. 'win_overlap', 50, ...
  668. 'units', 'physical', ... % Physical: U2/Hz
  669. 'clusters', {curr_atlas.name, curr_atlas.scouts2use}, ...
  670. 'scoutfunc', 1, ... % Mean
  671. 'win_std', 0, ...
  672. 'edit', struct(...
  673. 'Comment', 'Scouts,Avg,Power', ...
  674. 'TimeBands', [], ...
  675. 'Freqs', [], ...
  676. 'ClusterFuncTime', 'after', ...
  677. 'Measure', 'power', ...
  678. 'Output', 'average', ...
  679. 'SaveKernel', 0));
  681. bst_process('CallProcess', 'process_set_comment', psdscout, [], ...
  682. 'tag', ['PSD ', curr_atlas.label, ' stimon'], ...
  683. 'isindex', 0);
  685. psdscout = bst_process('CallProcess', 'process_psd', epochFiles, [], ...
  686. 'timewindow', [-2, 0], ...
  687. 'win_length', 1, ...
  688. 'win_overlap', 50, ...
  689. 'units', 'physical', ... % Physical: U2/Hz
  690. 'clusters', {curr_atlas.name, curr_atlas.scouts2use}, ...
  691. 'scoutfunc', 1, ... % Mean
  692. 'win_std', 0, ...
  693. 'edit', struct(...
  694. 'Comment', 'Scouts,Avg,Power', ...
  695. 'TimeBands', [], ...
  696. 'Freqs', [], ...
  697. 'ClusterFuncTime', 'after', ...
  698. 'Measure', 'power', ...
  699. 'Output', 'average', ...
  700. 'SaveKernel', 0));
  702. bst_process('CallProcess', 'process_set_comment', psdscout, [], ...
  703. 'tag', ['PSD ', curr_atlas.label, ' fix'], ...
  704. 'isindex', 0);
  707. elseif strcmp(epochname, 'button') && runpsds % get PSDs for button epoch to compare 1/f slope/intercept
  708. % button_baseline: t -4 to -3 s
  709. % button_filling: t -1.5 to -0.5 s
  711. psdscout = bst_process('CallProcess', 'process_psd', epochFiles, [], ...
  712. 'timewindow', [-4 -3], ...
  713. 'win_length', 1, ...
  714. 'win_overlap', 50, ...
  715. 'units', 'physical', ... % Physical: U2/Hz
  716. 'clusters', {curr_atlas.name, curr_atlas.scouts2use}, ...
  717. 'scoutfunc', 1, ... % Mean
  718. 'win_std', 0, ...
  719. 'edit', struct(...
  720. 'Comment', 'Scouts,Avg,Power', ...
  721. 'TimeBands', [], ...
  722. 'Freqs', [], ...
  723. 'ClusterFuncTime', 'after', ...
  724. 'Measure', 'power', ...
  725. 'Output', 'average', ...
  726. 'SaveKernel', 0));
  728. bst_process('CallProcess', 'process_set_comment', psdscout, [], ...
  729. 'tag', ['PSD ', curr_atlas.label, ' button_baseline'], ...
  730. 'isindex', 0);
  732. psdscout = bst_process('CallProcess', 'process_psd', epochFiles, [], ...
  733. 'timewindow', [-1.5 -0.5], ...
  734. 'win_length', 1, ...
  735. 'win_overlap', 50, ...
  736. 'units', 'physical', ... % Physical: U2/Hz
  737. 'clusters', {curr_atlas.name, curr_atlas.scouts2use}, ...
  738. 'scoutfunc', 1, ... % Mean
  739. 'win_std', 0, ...
  740. 'edit', struct(...
  741. 'Comment', 'Scouts,Avg,Power', ...
  742. 'TimeBands', [], ...
  743. 'Freqs', [], ...
  744. 'ClusterFuncTime', 'after', ...
  745. 'Measure', 'power', ...
  746. 'Output', 'average', ...
  747. 'SaveKernel', 0));
  749. bst_process('CallProcess', 'process_set_comment', psdscout, [], ...
  750. 'tag', ['PSD ', curr_atlas.label, ' button_filling'], ...
  751. 'isindex', 0);
  753. end
  754. %% scout combine contrasts
  755. if combinecontrasts % run power analyses combined across both contrasts
  757. %% scout plvt
  758. if runplvt
  760. conn_metric = 'PLVt';
  761. % PHASE LOCKING WITH PHOTODIODE
  763. % Process: Select data files
  764. sensorepochFiles = bst_process('CallProcess', 'process_select_files_data', [], [], ...
  765. 'subjectname', sname, ...
  766. 'condition', [], ...
  767. 'tag', curr_opts.tag, ...
  768. 'includebad', 0, ...
  769. 'includeintra', 0, ...
  770. 'includecommon', 0);
  772. % Process: Ignore file names with tag: bl.mat (don't use baseline-corrected)
  773. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  774. 'tag', 'bl.mat', ...
  775. 'search', 1, ... % Search the file paths
  776. 'select', 2); % Ignore the files with the tag
  778. % don't take the wrong epochs
  779. ignoreTags = {'noms'};
  780. for iTag = 1:numel(ignoreTags)
  781. if ~contains(curr_opts.tag, ignoreTags{iTag})
  782. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  783. 'tag', ignoreTags{iTag}, ...
  784. 'search', 1, ... % Search the file paths
  785. 'select', 2); % Ignore the files with the tag
  786. end
  787. end
  789. % Process: Ignore file names with tag: Avg
  790. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  791. 'tag', 'Avg', ...
  792. 'search', 2, ... % Search the file names
  793. 'select', 2); % Ignore the files with the tag
  795. % Process: Ignore file names with tag: ret
  796. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  797. 'tag', 'ret', ...
  798. 'search', 1, ... % Search the file paths
  799. 'select', 2); % Ignore the files with the tag
  801. % Process: Select/ignore file names with tag: block7
  802. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  803. 'tag', 'block7', ...
  804. 'search', 1, ... % Search the file paths
  805. 'select', blocktype); % Select/ignore the files with the tag
  807. for iDiode = 1:2
  808. diode = opts.diode_channels{iDiode};
  809. useband = opts.flicker_bands{iDiode};
  810. % PLV in localizer scouts
  811. % Process: PLV: Phase locking value
  812. connectivityScout(iDiode) = bst_process('CallProcess', 'process_plv2', sensorepochFiles, epochFiles, ...
  813. 'timewindow', curr_opts.fullwindow, ...
  814. 'src_channel', diode, ...
  815. 'dest_scouts', {curr_atlas.name, curr_atlas.scouts2use}, ...
  816. 'scoutfunc', 'mean', ... % Mean
  817. 'scouttime', 'after', ... % After
  818. 'scoutfuncaft', 'mean', ... % Mean
  819. 'tfedit', struct(...
  820. 'Comment', 'Complex', ...
  821. 'TimeBands', [], ...
  822. 'Freqs', {useband}, ...
  823. 'ClusterFuncTime', 'none', ...
  824. 'Measure', 'none', ...
  825. 'Output', 'all', ...
  826. 'SaveKernel', 0), ...
  827. 'plvmethod', 'plv', ... % PLV: Phase locking value
  828. 'tfmeasure', 'hilbert', ... % Hilbert transform
  829. 'timeres', 'full', ... % Full (requires epochs)
  830. 'plvmeasure', 2, ... % Magnitude
  831. 'outputmode', 'avg'); % Save average connectivity matrix (one file)
  833. % Process: Set name:
  834. connectivityScout(iDiode) = bst_process('CallProcess', 'process_set_comment', connectivityScout(iDiode), [], ...
  835. 'tag', ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ': ', diode, ' ', curr_opts.label, extralabel], ...
  836. 'isindex', 0);
  837. end
  840. % Process: PLV: Phase locking value with 60 Hz sin (control)
  841. f60FilesMulti = repmat(f60Files, 1, numel(epochFiles));
  842. connectivity60Scout = bst_process('CallProcess', 'process_plv2', f60FilesMulti, epochFiles, ...
  843. 'timewindow', curr_opts.fullwindow, ...
  844. 'src_channel', '', ...
  845. 'dest_scouts', {curr_atlas.name, curr_atlas.scouts2use}, ...
  846. 'scoutfunc', 'mean', ... % Mean
  847. 'scouttime', 'after', ... % After
  848. 'scoutfuncaft', 'mean', ... % Mean
  849. 'tfedit', struct(...
  850. 'Comment', 'Complex', ...
  851. 'TimeBands', [], ...
  852. 'Freqs', {{'fcontrol', '59, 61', 'mean'}}, ...
  853. 'ClusterFuncTime', 'none', ...
  854. 'Measure', 'none', ...
  855. 'Output', 'all', ...
  856. 'SaveKernel', 0), ...
  857. 'plvmethod', 'plv', ... % PLV: Phase locking value
  858. 'tfmeasure', 'hilbert', ... % Hilbert transform
  859. 'timeres', 'full', ... % Full (requires epochs)
  860. 'plvmeasure', 2, ... % Magnitude
  861. 'outputmode', 'avg'); % Save average connectivity matrix (one file)
  863. % Process: Set name:
  864. connectivity60Scout = bst_process('CallProcess', 'process_set_comment', connectivity60Scout, [], ...
  865. 'tag', ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ' 60 ', curr_opts.label, extralabel], ...
  866. 'isindex', 0);
  868. % combine into one file (not merging R/L yet)
  869. mat56 = in_bst_timefreq(connectivityScout(1).FileName);
  870. mat64 = in_bst_timefreq(connectivityScout(2).FileName);
  871. mat60 = in_bst_timefreq(connectivity60Scout.FileName);
  872. matall = mat56;
  873. matall.Freqs = {'fp', '55,57', 'mean'; 'fc', '63,65', 'mean'; 'fcontrol', '59, 61', 'mean'};
  874. matall.RefRowNames = {'MISC'};
  875. matall.TF(:, :, 2) = mat64.TF;
  876. matall.TF(:, :, 3) = mat60.TF;
  877. matall.Comment = ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ' fpfc ', curr_opts.label, extralabel];
  878. scout_allconnectivity = db_add(connectivityScout(1).iStudy, matall);
  880. % Process: Extract time
  881. scout_allconnectivityTime = bst_process('CallProcess', 'process_extract_time', scout_allconnectivity, [], ...
  882. 'timewindow', curr_opts.extracttime, ...
  883. 'overwrite', 1);
  885. % Process: Event-related perturbation (ERS/ERD)
  886. scout_allconnectivityERSD = bst_process('CallProcess', 'process_baseline_norm', scout_allconnectivityTime, [], ...
  887. 'baseline', curr_opts.baseline, ...
  888. 'method', bl_method, ... % Event-related perturbation (ERS/ERD): x_std = (x - &mu;) / &mu; * 100
  889. 'overwrite', 0);
  891. % Process: Delete selected files
  892. bst_process('CallProcess', 'process_delete', [connectivityScout, connectivity60Scout], [], ...
  893. 'target', 1); % Delete selected files
  895. % Process: combine RL hemispheres
  896. if mergerl(iAtlas)
  897. for iScout = 1:numel(curr_atlas.rl_rowidx)
  898. RL_scoutconnectivity(iScout) = bst_process('CallProcess', 'process_extract_values', scout_allconnectivityERSD, [], ...
  899. 'timewindow', [], ...
  900. 'freqrange', [0, 100], ...
  901. 'rows', curr_atlas.rl_rowidx{iScout}, ...
  902. 'isabs', 0, ...
  903. 'avgtime', 0, ...
  904. 'avgrow', 1, ...
  905. 'avgfreq', 0, ...
  906. 'matchrows', 0, ...
  907. 'dim', 2, ... % Concatenate time (dimension 2)
  908. 'Comment', ['RL_scout,Avg: ', conn_metric, ' ', curr_opts.label, extralabel]);
  909. end
  911. clear allmats
  912. allmats(1) = in_bst_timefreq(RL_scoutconnectivity(1).FileName);
  913. matall = allmats(1);
  914. matall.RowNames = curr_atlas.rl_rownames;
  915. matall.Comment = [curr_atlas.rl_label, ',Avg: ', conn_metric, ' fpfc ', curr_opts.label, extralabel];
  916. for iScout = 2:numel(curr_atlas.rl_rowidx)
  917. allmats(iScout) = in_bst_timefreq(RL_scoutconnectivity(iScout).FileName);
  918. matall.TF(iScout, :, :) = allmats(iScout).TF;
  919. end
  920. RL_allconnectivity = db_add(RL_scoutconnectivity(1).iStudy, matall);
  922. % Process: Delete selected files
  923. bst_process('CallProcess', 'process_delete', [RL_scoutconnectivity], [], ...
  924. 'target', 1); % Delete selected files
  925. end
  926. end
  928. %% scout plvt intermodulation
  929. if runfiplvt
  930. conn_metric = 'PLVt';
  932. % PHASE LOCKING WITH 8 Hz AND 16 Hz
  933. fi_freqs = [8 16 11];
  934. for iFreq = 1:numel(fi_freqs)
  935. curr_freq = fi_freqs(iFreq);
  936. if curr_freq == 8
  937. fiFiles = f8Files;
  938. elseif curr_freq == 16
  939. fiFiles = f16Files;
  940. elseif curr_freq == 11
  941. fiFiles = f11Files;
  942. end
  943. fiFilesMulti = repmat(fiFiles, 1, numel(epochFiles));
  944. useband = opts.fi_bands{iFreq};
  946. % Process: PLV: Phase locking value with sinusoid
  947. connectivityfiScout(iFreq) = bst_process('CallProcess', 'process_plv2', fiFilesMulti, epochFiles, ...
  948. 'timewindow', curr_opts.fullwindow, ...
  949. 'src_channel', '', ...
  950. 'dest_scouts', {curr_atlas.name, curr_atlas.scouts2use}, ...
  951. 'scoutfunc', 'mean', ... % Mean
  952. 'scouttime', 'after', ... % After
  953. 'scoutfuncaft', 'mean', ... % Mean
  954. 'tfedit', struct(...
  955. 'Comment', 'Complex', ...
  956. 'TimeBands', [], ...
  957. 'Freqs', {useband}, ...
  958. 'ClusterFuncTime', 'none', ...
  959. 'Measure', 'none', ...
  960. 'Output', 'all', ...
  961. 'SaveKernel', 0), ...
  962. 'plvmethod', 'plv', ... % PLV: Phase locking value
  963. 'tfmeasure', 'hilbert', ... % Hilbert transform
  964. 'timeres', 'full', ... % Full (requires epochs)
  965. 'plvmeasure', 2, ... % Magnitude
  966. 'outputmode', 'avg'); % Save average connectivity matrix (one file)
  968. % Process: Set name:
  969. connectivityfiScout(iFreq) = bst_process('CallProcess', 'process_set_comment', connectivityfiScout(iFreq), [], ...
  970. 'tag', ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ' ' num2str(curr_freq), ' ', curr_opts.label, extralabel], ...
  971. 'isindex', 0);
  972. end
  974. % combine into one file (not merging R/L yet)
  975. mat8 = in_bst_timefreq(connectivityfiScout(1).FileName);
  976. mat16 = in_bst_timefreq(connectivityfiScout(2).FileName);
  977. mat11 = in_bst_timefreq(connectivityfiScout(3).FileName);
  978. matall = mat8;
  979. matall.Freqs = {'fi', '7,9', 'mean'; 'fi2', '15,17', 'mean'; 'ficontrol', '10,12', 'mean'};
  980. matall.RefRowNames = {'MISC'};
  981. matall.TF(:, :, 2) = mat16.TF;
  982. matall.TF(:, :, 3) = mat11.TF;
  983. matall.Comment = ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ' fi ', curr_opts.label, extralabel];
  984. scout_allconnectivity = db_add(connectivityfiScout(1).iStudy, matall);
  986. % Process: Event-related perturbation (ERS/ERD)
  987. scout_allconnectivityERSD = bst_process('CallProcess', 'process_baseline_norm', scout_allconnectivity, [], ...
  988. 'baseline', curr_opts.baseline, ...
  989. 'method', bl_method, ... % Event-related perturbation (ERS/ERD): x_std = (x - &mu;) / &mu; * 100
  990. 'overwrite', 0);
  992. % Process: Extract time
  993. scout_allconnectivityERSD = bst_process('CallProcess', 'process_extract_time', scout_allconnectivityERSD, [], ...
  994. 'timewindow', curr_opts.extracttime, ...
  995. 'overwrite', 1);
  997. % Process: Delete selected files
  998. bst_process('CallProcess', 'process_delete', [connectivityfiScout], [], ...
  999. 'target', 1); % Delete selected files
  1001. % Process: combine RL hemispheres
  1002. if mergerl(iAtlas)
  1003. for iScout = 1:numel(curr_atlas.rl_rowidx)
  1004. RL_scoutconnectivity(iScout) = bst_process('CallProcess', 'process_extract_values', scout_allconnectivityERSD, [], ...
  1005. 'timewindow', [], ...
  1006. 'freqrange', [0, 100], ...
  1007. 'rows', curr_atlas.rl_rowidx{iScout}, ...
  1008. 'isabs', 0, ...
  1009. 'avgtime', 0, ...
  1010. 'avgrow', 1, ...
  1011. 'avgfreq', 0, ...
  1012. 'matchrows', 0, ...
  1013. 'dim', 2, ... % Concatenate time (dimension 2)
  1014. 'Comment', ['RL_scout,Avg: ', conn_metric, ' ', curr_opts.label, extralabel]);
  1015. end
  1017. clear allmats
  1018. allmats(1) = in_bst_timefreq(RL_scoutconnectivity(1).FileName);
  1019. matall = allmats(1);
  1020. matall.RowNames = curr_atlas.rl_rownames;
  1021. matall.Comment = [curr_atlas.rl_label, ',Avg: ', conn_metric, ' fi ', curr_opts.label, extralabel];
  1022. for iScout = 2:numel(curr_atlas.rl_rowidx)
  1023. allmats(iScout) = in_bst_timefreq(RL_scoutconnectivity(iScout).FileName);
  1024. matall.TF(iScout, :, :) = allmats(iScout).TF;
  1025. end
  1026. RL_allconnectivity = db_add(RL_scoutconnectivity(1).iStudy, matall);
  1028. % Process: Delete selected files
  1029. bst_process('CallProcess', 'process_delete', [RL_scoutconnectivity], [], ...
  1030. 'target', 1); % Delete selected files
  1031. end
  1032. end
  1035. %% split contrasts
  1036. if splitcontrasts
  1037. % REDO ANALYSES SPLIT BY STIMULUS COLOR CONTRAST
  1038. if blocktype == 2
  1039. for iContrast = 1:2 % low, high
  1040. blocks2use = find(opts.blockorders.(sname) == iContrast);
  1041. contrastEpochFiles = [];
  1042. for iBlock = blocks2use
  1043. % Process: select filenames
  1044. curr_blockFiles = bst_process('CallProcess', 'process_select_tag', epochFiles, [], ...
  1045. 'tag', ['block', num2str(iBlock)], ...
  1046. 'search', 1, ... % Search the file paths
  1047. 'select', 1); % Select the files with the tag
  1048. contrastEpochFiles = [contrastEpochFiles, curr_blockFiles];
  1049. end
  1051. if isempty(contrastEpochFiles)
  1052. continue
  1053. end
  1055. %% scout plvt
  1056. if runplvt
  1057. conn_metric = 'PLVt';
  1059. % PHASE LOCKING WITH PHOTODIODE
  1061. % Process: Select data files
  1062. sensorepochFiles = bst_process('CallProcess', 'process_select_files_data', [], [], ...
  1063. 'subjectname', sname, ...
  1064. 'condition', [], ...
  1065. 'tag', curr_opts.tag, ...
  1066. 'includebad', 0, ...
  1067. 'includeintra', 0, ...
  1068. 'includecommon', 0);
  1070. % Process: Ignore file names with tag: bl.mat (don't use baseline-corrected)
  1071. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  1072. 'tag', 'bl.mat', ...
  1073. 'search', 1, ... % Search the file paths
  1074. 'select', 2); % Ignore the files with the tag
  1076. % don't take the wrong epochs
  1077. ignoreTags = {'noms'};
  1078. for iTag = 1:numel(ignoreTags)
  1079. if ~contains(curr_opts.tag, ignoreTags{iTag})
  1080. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  1081. 'tag', ignoreTags{iTag}, ...
  1082. 'search', 1, ... % Search the file paths
  1083. 'select', 2); % Ignore the files with the tag
  1084. end
  1085. end
  1087. % Process: Ignore file names with tag: Avg
  1088. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  1089. 'tag', 'Avg', ...
  1090. 'search', 2, ... % Search the file names
  1091. 'select', 2); % Ignore the files with the tag
  1093. % Process: Ignore file names with tag: ret
  1094. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  1095. 'tag', 'ret', ...
  1096. 'search', 1, ... % Search the file paths
  1097. 'select', 2); % Ignore the files with the tag
  1099. % Process: Select/ignore file names with tag: block7
  1100. sensorepochFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  1101. 'tag', 'block7', ...
  1102. 'search', 1, ... % Search the file paths
  1103. 'select', blocktype); % Select/ignore the files with the tag
  1105. contrastSensorepochFiles = [];
  1106. for iBlock = blocks2use
  1107. % Process: select filenames
  1108. curr_blockFiles = bst_process('CallProcess', 'process_select_tag', sensorepochFiles, [], ...
  1109. 'tag', ['block', num2str(iBlock)], ...
  1110. 'search', 1, ... % Search the file paths
  1111. 'select', 1); % Select the files with the tag
  1112. contrastSensorepochFiles = [contrastSensorepochFiles, curr_blockFiles];
  1113. end
  1115. for iDiode = 1:2
  1116. diode = opts.diode_channels{iDiode};
  1117. useband = opts.flicker_bands{iDiode};
  1118. % PLV in localizer scouts
  1119. % Process: PLV: Phase locking value
  1120. connectivityScout(iDiode) = bst_process('CallProcess', 'process_plv2', contrastSensorepochFiles, contrastEpochFiles, ...
  1121. 'timewindow', curr_opts.fullwindow, ...
  1122. 'src_channel', diode, ...
  1123. 'dest_scouts', {curr_atlas.name, curr_atlas.scouts2use}, ...
  1124. 'scoutfunc', 'mean', ... % Mean
  1125. 'scouttime', 'after', ... % After
  1126. 'scoutfuncaft', 'mean', ... % Mean
  1127. 'tfedit', struct(...
  1128. 'Comment', 'Complex', ...
  1129. 'TimeBands', [], ...
  1130. 'Freqs', {useband}, ...
  1131. 'ClusterFuncTime', 'none', ...
  1132. 'Measure', 'none', ...
  1133. 'Output', 'all', ...
  1134. 'SaveKernel', 0), ...
  1135. 'plvmethod', 'plv', ... % PLV: Phase locking value
  1136. 'tfmeasure', 'hilbert', ... % Hilbert transform
  1137. 'timeres', 'full', ... % Full (requires epochs)
  1138. 'plvmeasure', 2, ... % Magnitude
  1139. 'outputmode', 'avg'); % Save average connectivity matrix (one file)
  1141. % Process: Set name:
  1142. connectivityScout(iDiode) = bst_process('CallProcess', 'process_set_comment', connectivityScout(iDiode), [], ...
  1143. 'tag', ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ': ', diode, ' ', curr_opts.label, ' contrast', num2str(iContrast)], ...
  1144. 'isindex', 0);
  1145. end
  1148. % Process: PLV: Phase locking value with 60 Hz sin (control)
  1149. f60FilesMulti = repmat(f60Files, 1, numel(contrastEpochFiles));
  1150. connectivity60Scout = bst_process('CallProcess', 'process_plv2', f60FilesMulti, contrastEpochFiles, ...
  1151. 'timewindow', curr_opts.fullwindow, ...
  1152. 'src_channel', '', ...
  1153. 'dest_scouts', {curr_atlas.name, curr_atlas.scouts2use}, ...
  1154. 'scoutfunc', 'mean', ... % Mean
  1155. 'scouttime', 'after', ... % After
  1156. 'scoutfuncaft', 'mean', ... % Mean
  1157. 'tfedit', struct(...
  1158. 'Comment', 'Complex', ...
  1159. 'TimeBands', [], ...
  1160. 'Freqs', {{'fcontrol', '59, 61', 'mean'}}, ...
  1161. 'ClusterFuncTime', 'none', ...
  1162. 'Measure', 'none', ...
  1163. 'Output', 'all', ...
  1164. 'SaveKernel', 0), ... 'plvmethod', 'plv', ... % PLV: Phase locking value
  1165. 'tfmeasure', 'hilbert', ... % Hilbert transform
  1166. 'timeres', 'full', ... % Full (requires epochs)
  1167. 'plvmeasure', 2, ... % Magnitude
  1168. 'outputmode', 'avg'); % Save average connectivity matrix (one file)
  1170. % Process: Set name:
  1171. connectivity60Scout = bst_process('CallProcess', 'process_set_comment', connectivity60Scout, [], ...
  1172. 'tag', ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ' 60 ', curr_opts.label, ' contrast', num2str(iContrast)], ...
  1173. 'isindex', 0);
  1175. % combine into one file (not merging R/L yet)
  1176. mat56 = in_bst_timefreq(connectivityScout(1).FileName);
  1177. mat64 = in_bst_timefreq(connectivityScout(2).FileName);
  1178. mat60 = in_bst_timefreq(connectivity60Scout.FileName);
  1179. matall = mat56;
  1180. matall.Freqs = {'fp', '55,57', 'mean'; 'fc', '63,65', 'mean'; 'fcontrol', '59, 61', 'mean'};
  1181. matall.RefRowNames = {'MISC'};
  1182. matall.TF(:, :, 2) = mat64.TF;
  1183. matall.TF(:, :, 3) = mat60.TF;
  1184. matall.Comment = ['Scouts_', curr_atlas.label, ',Avg: ', conn_metric, ' fpfc ', curr_opts.label, ' contrast', num2str(iContrast)];
  1185. scout_allconnectivity = db_add(connectivityScout(1).iStudy, matall);
  1187. % Process: Event-related perturbation (ERS/ERD)
  1188. scout_allconnectivityERSD = bst_process('CallProcess', 'process_baseline_norm', scout_allconnectivity, [], ...
  1189. 'baseline', curr_opts.baseline, ...
  1190. 'method', bl_method, ... % Event-related perturbation (ERS/ERD): x_std = (x - &mu;) / &mu; * 100
  1191. 'overwrite', 0);
  1193. % Process: Extract time
  1194. scout_allconnectivityERSD = bst_process('CallProcess', 'process_extract_time', scout_allconnectivityERSD, [], ...
  1195. 'timewindow', curr_opts.extracttime, ...
  1196. 'overwrite', 1);
  1198. % Process: Delete selected files
  1199. bst_process('CallProcess', 'process_delete', [connectivityScout, connectivity60Scout], [], ...
  1200. 'target', 1); % Delete selected files
  1202. % Process: combine RL hemispheres
  1203. if mergerl(iAtlas)
  1204. for iScout = 1:numel(curr_atlas.rl_rowidx)
  1205. RL_scoutconnectivity(iScout) = bst_process('CallProcess', 'process_extract_values', scout_allconnectivityERSD, [], ...
  1206. 'timewindow', [], ...
  1207. 'freqrange', [0, 100], ...
  1208. 'rows', curr_atlas.rl_rowidx{iScout}, ...
  1209. 'isabs', 0, ...
  1210. 'avgtime', 0, ...
  1211. 'avgrow', 1, ...
  1212. 'avgfreq', 0, ...
  1213. 'matchrows', 0, ...
  1214. 'dim', 2, ... % Concatenate time (dimension 2)
  1215. 'Comment', ['RL_scout,Avg: ', conn_metric, ' ', curr_opts.label, ' contrast', num2str(iContrast)]);
  1216. end
  1218. clear allmats
  1219. allmats(1) = in_bst_timefreq(RL_scoutconnectivity(1).FileName);
  1220. matall = allmats(1);
  1221. matall.RowNames = curr_atlas.rl_rownames;
  1222. matall.Comment = [curr_atlas.rl_label, ',Avg: ', conn_metric, ' fpfc ', curr_opts.label, ' contrast', num2str(iContrast)];
  1223. for iScout = 2:numel(curr_atlas.rl_rowidx)
  1224. allmats(iScout) = in_bst_timefreq(RL_scoutconnectivity(iScout).FileName);
  1225. matall.TF(iScout, :, :) = allmats(iScout).TF;
  1226. end
  1227. RL_allconnectivity = db_add(RL_scoutconnectivity(1).iStudy, matall);
  1229. % Process: Delete selected files
  1230. bst_process('CallProcess', 'process_delete', [RL_scoutconnectivity], [], ...
  1231. 'target', 1); % Delete selected files
  1232. end
  1233. end
  1235. end
  1236. end
  1237. end
  1239. %% custom SPRiNT
  1240. if runsprintcustom
  1241. % fooofs each moving window after averaging SFFTs across
  1242. % trials.
  1243. % method: extract each time window, run FOOOF, then combine
  1244. % outputs into a SPRiNT structure.
  1246. % set windows. winlength = 1 second, but at intervals of
  1247. % 100ms.
  1248. % make sure we'll cover the full extracttime window, but
  1249. % don't waste compute on extra windows
  1250. fulltime = curr_opts.extracttime + [-sprint_winlength/2, sprint_winlength/2];
  1251. winstarts = fulltime(1):sprint_winstep:fulltime(2)-sprint_winlength+.001;
  1252. winends = fulltime(1)+sprint_winlength:sprint_winstep:fulltime(2);
  1253. nWin = numel(winstarts);
  1255. % extract time windows, FFT, average them, and FOOOF.
  1256. clear winFiles
  1258. for iAtlas = use_atlases
  1259. curr_atlas = all_atlases(iAtlas);
  1261. for iWin = 1:nWin
  1263. % Process: Power spectrum density (Welch)
  1264. fftWinFiles = bst_process('CallProcess', 'process_psd', epochFiles, [], ...
  1265. 'timewindow', [winstarts(iWin), winends(iWin)], ...
  1266. 'win_length', sprint_winlength, ...
  1267. 'win_overlap', 0, ...
  1268. 'units', 'physical', ... % Physical: U2/Hz
  1269. 'clusters', {curr_atlas.name, curr_atlas.scouts2use}, ...
  1270. 'scoutfunc', 1, ... % Mean
  1271. 'win_std', 0, ...
  1272. 'edit', struct(...
  1273. 'Comment', 'Scout psd,Avg,Power', ...
  1274. 'TimeBands', [], ...
  1275. 'Freqs', [], ...
  1276. 'ClusterFuncTime', 'after', ...
  1277. 'Measure', 'power', ...
  1278. 'Output', 'average', ...
  1279. 'SaveKernel', 0));
  1281. % Process: specparam: Fitting oscillations and 1/f
  1282. fooofWinFiles(iWin) = bst_process('CallProcess', 'process_fooof', fftWinFiles, [], ...
  1283. 'implementation', 'matlab', ... % Matlab
  1284. 'freqrange', [1, sprint_maxFreq], ...
  1285. 'powerline', '50', ... % 50 Hz
  1286. 'peaktype', 'gaussian', ... % Gaussian
  1287. 'peakwidth', [0.5, 12], ...
  1288. 'maxpeaks', 3, ...
  1289. 'minpeakheight', 1, ...
  1290. 'proxthresh', 2, ...
  1291. 'apermode', 'fixed', ... % Fixed
  1292. 'guessweight', 'none', ... % None
  1293. 'sorttype', 'param', ... % Peak parameters
  1294. 'sortparam', 'frequency', ... % Frequency
  1295. 'sortbands', {'delta', '2, 4'; 'theta', '5, 7'; 'alpha', '8, 12'; 'beta', '15, 29'; 'gamma1', '30, 59'; 'gamma2', '60, 90'});
  1297. % import to MATLAB.
  1298. fooofMats(iWin) = in_bst(fooofWinFiles(iWin).FileName);
  1300. % delete extracted ffts
  1301. bst_process('CallProcess', 'process_delete', fftWinFiles, [], ...
  1302. 'target', 1); % Delete selected files
  1303. clear fftWinFiles
  1304. end
  1306. % manually convert to a SPRiNT structure.
  1307. sprintMat = fooofMats(1); % get template from first fooof file.
  1308. sprintMat.Comment = ['Scouts_', curr_atlas.label, ',Avg: SPRiNT_custom ', curr_opts.label, extralabel];
  1309. sprintMat.Time = winstarts + sprint_winlength/2;
  1310. sprintMat.Freqs = sprintMat.Freqs(1:sprint_maxFreq);
  1311. sprintMat.Method = 'sprint';
  1312. sprintMat.RowNames = sprintMat.RowNames';
  1313. sprintMat.nAvg = numel(epochFiles); sprintMat.Leff = numel(epochFiles);
  1314. sprintMat.TF = sprintMat.TF(:, :, 1:sprint_maxFreq); % init TF
  1315. sprintMat.Options.Method = 'sprint';
  1316. sprintopts = struct;
  1317. sprintopts.freqs = sprintMat.Options.FOOOF.freqs;
  1318. for iScout = 1:numel(sprintMat.RowNames)
  1319. sprintopts.channel(iScout).name = sprintMat.RowNames{iScout};
  1320. sprintopts.channel(iScout).peaks = struct('time', 'center_frequency', 'amplitude', 'st_dev');
  1321. for iWin = 1:nWin
  1322. sprintMat.TF(iScout, iWin, :) = fooofMats(iWin).TF(iScout, :, 1:sprint_maxFreq);
  1323. fooofopts = fooofMats(iWin).Options.FOOOF;
  1324. sprintopts.topography.exponent(iScout, iWin) = fooofopts.aperiodics(iScout).exponent;
  1325. sprintopts.topography.offset(iScout, iWin) = fooofopts.aperiodics(iScout).offset;
  1326. sprintopts.SPRiNT_models(iScout, iWin, :) = fooofopts.data(iScout).fooofed_spectrum;
  1327. sprintopts.peak_models(iScout, iWin, :) = fooofopts.data(iScout).peak_fit;
  1328. sprintopts.aperiodic_models(iScout, iWin, :) = fooofopts.data(iScout).ap_fit;
  1329. sprintopts.channel(iScout).data(iWin).time = sprintMat.Time(iWin);
  1330. sprintopts.channel(iScout).data(iWin).aperiodic_params = [sprintopts.topography.offset(iScout, iWin), sprintopts.topography.exponent(iScout, iWin)];
  1331. sprintopts.channel(iScout).data(iWin).peak_params = fooofopts.data(iScout).peak_params;
  1332. sprintopts.channel(iScout).data(iWin).peak_types = fooofopts.data(iScout).peak_types;
  1333. sprintopts.channel(iScout).data(iWin).ap_fit = fooofopts.data(iScout).ap_fit;
  1334. sprintopts.channel(iScout).data(iWin).peak_fit = fooofopts.data(iScout).peak_fit;
  1335. sprintopts.channel(iScout).data(iWin).fooofed_spectrum = fooofopts.data(iScout).fooofed_spectrum;
  1336. sprintopts.channel(iScout).data(iWin).power_spectrum = fooofMats(iWin).TF(iScout, :, 1:sprint_maxFreq);
  1337. sprintopts.channel(iScout).data(iWin).error = fooofopts.data.error;
  1338. sprintopts.channel(iScout).data(iWin).r_squared = fooofopts.data.r_squared;
  1339. sprintopts.channel(iScout).aperiodics(iWin).time = sprintMat.Time(iWin);
  1340. sprintopts.channel(iScout).aperiodics(iWin).offset = sprintopts.topography.offset(iScout, iWin);
  1341. sprintopts.channel(iScout).aperiodics(iWin).exponent = sprintopts.topography.exponent(iScout, iWin);
  1342. sprintopts.channel(iScout).stats(iWin).MSE = fooofopts.stats(iScout).MSE;
  1343. sprintopts.channel(iScout).stats(iWin).r_squared = fooofopts.stats(iScout).r_squared;
  1344. sprintopts.channel(iScout).stats(iWin).frequency_wise_error = fooofopts.stats(iScout).frequency_wise_error;
  1345. nPeaks = 1:numel(fooofopts.peaks);
  1346. for iPeak = 1:nPeaks
  1347. if strcmp(fooofopts.peaks(iPeak).channel{1}, sprintMat.RowNames{iScout})
  1348. sprintopts.channel(iScout).peaks(iPeak).time = sprintMat.Time(iWin);
  1349. sprintopts.channel(iScout).peaks(iPeak).center_frequency = fooofopts.peaks(iPeak).center_frequency;
  1350. sprintopts.channel(iScout).peaks(iPeak).amplitude = fooofopts.peaks(iPeak).amplitude;
  1351. sprintopts.channel(iScout).peaks(iPeak).st_dev = fooofopts.peaks(iPeak).std_dev;
  1352. end
  1353. end
  1354. end
  1355. end
  1356. sprintopts.options.winLen = sprint_winlength;
  1357. sprintopts.options.Ovrlp = 0;
  1358. sprintopts.options.nAverage = 1;
  1359. sprintopts.options.hOT = 1; % not sure what this is
  1360. sprintopts.options.rmoutliers = 'no';
  1361. sprintopts.options.freq_range = fooofopts.options.freq_range;
  1362. sprintopts.options.peak_width_limits = fooofopts.options.peak_width_limits;
  1363. sprintopts.options.max_peaks = fooofopts.options.max_peaks;
  1364. sprintopts.options.min_peak_height = fooofopts.options.min_peak_height;
  1365. sprintopts.options.peak_threshold = fooofopts.options.peak_threshold;
  1366. sprintopts.options.aperiodic_mode = fooofopts.options.aperiodic_mode;
  1367. sprintopts.options.peak_type = fooofopts.options.peak_type;
  1368. sprintopts.options.proximity_threshold = fooofopts.options.proximity_threshold;
  1369. sprintopts.options.guess_weight = fooofopts.options.guess_weight;
  1371. sprintMat.Options.SPRiNT = sprintopts;
  1372. sprintMat.Options = rmfield(sprintMat.Options, 'FOOOF');
  1374. % add to database
  1375. sprintFiles = db_add(fooofWinFiles(1).iStudy, sprintMat);
  1377. % delete the windowed FOOOFs
  1378. bst_process('CallProcess', 'process_delete', fooofWinFiles, [], ...
  1379. 'target', 1); % Delete selected files
  1380. clear fooofWinFiles
  1382. % Process: Extract time
  1383. sprintFiles = bst_process('CallProcess', 'process_extract_time', sprintFiles, [], ...
  1384. 'timewindow', curr_opts.extracttime, ...
  1385. 'overwrite', 1);
  1387. % don't average across R/L hemispheres, it's too
  1388. % complicated here. Instead we'll baseline correct,
  1389. % then average across subjects, and can average across
  1390. % R/L at group level.
  1392. end
  1394. end
  1396. % if analyzed microsaccades, delete the imported epochs
  1397. if exist('msEpochFiles', 'var') && ~isempty(msEpochFiles)
  1398. % Process: Delete selected files
  1399. bst_process('CallProcess', 'process_delete', [msEpochFiles], [], ...
  1400. 'target', 1); % Delete selected files
  1401. end
  1403. clear allmats
  1404. end
  1405. %% finish
  1407. % Save and display report
  1408. ReportFile = bst_report('Save');
  1409. bst_report('Open', ReportFile);
  1410. end
  1411. end
  1412. end
  1413. end
  1414. end
  1415. end
  1416. end
  1417. end

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

Overview

Authors: Max Levinson1,2, Alice E Waitt3, Katharina Duecker3,4, Syanah C Wynn3, Ole Jensen3,5,6, Sylvain Baillet1,7,8
ORCID iDs: Max Levinson, Alice E Waitt, Katharina Duecker, Syanah C Wynn, Ole Jensen, Sylvain Baillet
  1. Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
  2. Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
  3. Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
  4. Department of Neuroscience, Brown University, Providence, RI, USA
  5. Oxford Centre for Human Brain Activity, Oxford Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
  6. Department of Experimental Psychology, University of Oxford, Oxford, UK
  7. Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Quebec, Canada
  8. Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
Institutions: Montreal Neurological Institute and Hospital (Canada); McGill University (Canada); New York University (United States); University of Birmingham (United Kingdom); Brown University (United States); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom); Centre Hospitalier de l’Université de Montréal (Canada); Université de Montréal (Canada)
Journal: Science advances, volume 12, issue 19, article eaea3919
Dates: received 7 July 2025; accepted 3 April 2026; published online 8 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aea3919 · PMID 42102217 · PMCID PMC13155359 · OpenAlex W7160710247
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, fMRI & imaging, Physiology & signal measures
MeSH: Brain*, Visual Cortex*, Visual Perception*, Brain Mapping, Consciousness, Humans, Illusions, Magnetoencephalography, Male, Photic Stimulation, Saccades (* major topic)
Topic: Multisensory perception and integration (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: National Institute for Health Research (NIHR) (NIHR203316); Wellcome Trust Discovery Award (227420); Wellcome Trust (227420); National Institutes of Health (R01-EB026299); Canadian Institutes of Health Research Canada Research Chair Tier 1 (CRC-2017-00311); National Sciences and Engineering Research Council of Canada (RGPIN-2020-06889); Quebec Bio imaging Network (12.49)
Citations: not cited yet (Europe PMC); 111 references in the paper

Abstract

A longstanding debate in consciousness research concerns whether subjective perceptual experiences arise primarily from activity in sensory cortices or rely critically on inferences made in higher-order brain regions. We address this question using a compelling visual illusion (perceptual filling-in) that isolates neural processes underlying transitions from veridical to illusory conscious experience. Using whole-brain magnetoencephalographic imaging and rapid invisible frequency tagging, we tracked cortical dynamics during filling-in and assessed their modulation by microsaccadic eye movements, which are known to delay the illusion. We found that transitions in conscious perception involved two dissociable mechanisms: (i) boundary fading in visual cortex, reflected by increased excitability and reduced alpha-band activity, consistent with a shift in excitation-inhibition balance, and (ii) higher-order perceptual monitoring processes involving motor cortex, indexed by decreased high-alpha and beta-band activity. Microsaccades selectively reset both processes. These findings support a hierarchical framework in which visual and motor systems jointly shape transitions in conscious visual experience.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

Zenodo 18793257

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (1)
Size: 11 files, 1 script
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Brainstorm (18 files), Statistics and Machine Learning Toolbox (11 files), Psychtoolbox (8 files), boundedline (3 files), Signal Processing Toolbox (2 files), Matplotlib (2 files), NumPy (2 files), fdr_bh (Benjamini-Hochberg FDR) (1 file), ggplot2 (1 file), MNE-Python (1 file), pandas (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
123 files
At the source: doi.org/10.5281/zenodo.18793257

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

Tracing map

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

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 120 scripts, each with its path and the digest of its content;
  • 19 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

Datasets cited

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. Deidentified raw data (MEG, MRI, eye-tracking, and behavior) have been deposited in a publicly available Zenodo repository located at https://doi.org/10.5281/zenodo.18825563. All analyses were conducted using open-source software toolboxes. Custom MATLAB, Python, and R scripts used for preprocessing, analysis, and figure generation are available at a second dedicated Zenodo repository located at https://doi.org/10.5281/zenodo.18793257. This study did not generate new materials.

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

Versions

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

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 11 MeSH terms, 7 funders, 104 references.

Cite

This paper

Levinson, M., Waitt, A. E., Duecker, K., Wynn, S. C., Jensen, O., & Baillet, S. (2026). Hierarchical brain dynamics supporting visual perceptual transitions. Science advances, 12(19), eaea3919. https://doi.org/10.1126/sciadv.aea3919

BibTeX

@article{levinson2026hierarchical,
author = {Levinson, Max and Waitt, Alice E and Duecker, Katharina and Wynn, Syanah C and Jensen, Ole and Baillet, Sylvain},
title = {{Hierarchical brain dynamics supporting visual perceptual transitions}},
journal = {Science advances},
year = {2026},
month = may,
volume = {12},
number = {19},
pages = {eaea3919},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aea3919},
url = {https://doi.org/10.1126/sciadv.aea3919},
pmid = {42102217},
pmcid = {PMC13155359}
}

RIS

TY - JOUR
AU - Levinson, Max
AU - Waitt, Alice E
AU - Duecker, Katharina
AU - Wynn, Syanah C
AU - Jensen, Ole
AU - Baillet, Sylvain
TI - Hierarchical brain dynamics supporting visual perceptual transitions
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/05/08
VL - 12
IS - 19
SP - eaea3919
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aea3919
UR - https://doi.org/10.1126/sciadv.aea3919
LA - en
ER -

CSL-JSON

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"id": "10.1126/sciadv.aea3919",
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"container-title": "Science advances",
"author": [
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"family": "Levinson",
"given": "Max"
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{
"family": "Waitt",
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{
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"given": "Katharina"
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"given": "Syanah C"
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],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "19",
"page": "eaea3919",
"DOI": "10.1126/sciadv.aea3919",
"PMID": "42102217",
"PMCID": "PMC13155359",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aea3919",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
8
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]
}
}

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