OSCR

Neural signatures of human psychological resilience driven by acute stress.

Code ↔ Paper

8 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 8 matches
  1. [1] § Materials and Methods › Statistical Analyses. › fMRI. › Resting-state fMRI. ↔ fMRI/FC ROI-to-ROI analysis/CONN_script_Prep_1stLevel_MRI14.m, lines 143 to 213 · score 0.90 · subject motion parameters, band pass filtering, outlier scans, pitch, roll, yaw · Report a mismatch
  2. [2] § Results › Spatial Features Support Human-Specific Resilience. ↔ fMRI/FC ROI-to-ROI analysis/CONN_script_Prep_1stLevel_MRI14.m, lines 657 to 777 · score 0.89 · inferior frontal gyrus, middle frontal gyrus, cingulate, precuneus, salience, superior · Report a mismatch
  3. [3] § Materials and Methods › EEG. › Resting state. ↔ EEG/EEG_Ana_Prep_1stLevel_06.m, lines 141 to 157 · score 0.85 · abnormal spectra, 0–40 Hz, abnormal trends, abnormal distribution, improbable, epoch · Report a mismatch
  4. [4] § Materials and Methods › Statistical Tests. › Machine learning. ↔ Elastic-net/Elastic-net-52Factors_13x4.R, lines 1 to 63 · score 0.73 · explanatory variables, 0–1.5, optimized, lambda, Alpha · Report a mismatch
  5. [5] § Materials and Methods › Statistical Analyses. › fMRI. › Resting-state fMRI. ↔ fMRI/FC ROI-to-ROI analysis/CONN_script_Prep_1stLevel_MRI14.m, lines 514 to 597 · score 0.66 · kernel, space, ART, scrubbing, CSF, CONN · Report a mismatch
  6. [6] § Materials and Methods › Statistical Analyses. › fMRI. › fALFF. ↔ fMRI/FC ROI-to-ROI analysis/CONN_script_Prep_1stLevel_MRI14.m, lines 657 to 777 · score 0.62 · frequency band, accumbens, anterior, dorsal, hippocampus, amygdala · Report a mismatch
  7. [7] § Materials and Methods › Statistical Analyses. › fMRI. › Resting-state fMRI. ↔ fMRI/FC ROI-to-ROI analysis/CONN_script_Prep_1stLevel_MRI14.m, lines 606 to 655 · score 0.57 · 0.008–0.09 Hz, 0.008 Hz, linear, filter, temporal · Report a mismatch
  8. [8] § Materials and Methods › EEG. › EEG source localization analysis. ↔ fMRI/FC ROI-to-ROI analysis/CONN_script_Prep_1stLevel_MRI14.m, lines 514 to 597 · score 0.56 · space, surface, template, seeds, segmented, ROI · Report a mismatch

Paper

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The authors' code

MATLAB · 781 lines · 51 KB · no license · 6 matches

  1. %% 0 Check file directories %%
  2. for sbj = 1:SBJ %
  3. cd(PATH1(sbj,:));
  4. CurrentD = pwd;
  6. D1_full = '';
  7. D2_full = '';
  8. D3_full = '';
  9. D4_full = '';
  10. D5_full = '';
  11. %EPI1
  12. cd([PATH1(sbj,:),'/',FOLDERNAME1(sbj,:),'/EPI1']);
  13. D1 = dir('Sub*_4d.nii');
  14. D1_path(sbj,:) = pwd;
  15. D1_size = length(D1);
  16. fprintf('%s EPI1 has %d *_4d.nii files\n',FOLDERNAME1(sbj,:),D1_size);
  17. for epi1 = 1:D1_size
  18. D1_full(epi1,:) = [D1_path(sbj,:),'/',D1(epi1).name];
  19. end
  21. %EPI2
  22. cd([PATH1(sbj,:),'/',FOLDERNAME1(sbj,:),'/EPI2']);
  23. D2 = dir('Sub*_4d.nii');
  24. D2_path(sbj,:) = pwd;
  25. D2_size = length(D2);
  26. fprintf('%s EPI2 has %d *_4d.nii files\n',FOLDERNAME1(sbj,:),D2_size);
  27. for epi2 = 1:D2_size
  28. D2_full(epi2,:) = [D2_path(sbj,:),'/',D2(epi2).name];
  29. end
  31. %EPI3
  32. cd([PATH1(sbj,:),'/',FOLDERNAME1(sbj,:),'/EPI3']);
  33. D3 = dir('Sub*_4d.nii');
  34. D3_path(sbj,:) = pwd;
  35. D3_size = length(D3);
  36. fprintf('%s EPI3 has %d *_4d.nii files\n',FOLDERNAME1(sbj,:),D3_size);
  37. for epi3 = 1:D3_size
  38. D3_full(epi3,:) = [D3_path(sbj,:),'/',D3(epi3).name];
  39. end
  41. %EPI4
  42. cd([PATH1(sbj,:),'/',FOLDERNAME2(sbj,:),'/EPI4']);
  43. D4 = dir('Sub*_4d.nii');
  44. D4_path(sbj,:) = pwd;
  45. D4_size = length(D4);
  46. fprintf('%s EPI4 has %d *_4d.nii files\n',FOLDERNAME2(sbj,:),D4_size);
  47. for epi4 = 1:D4_size
  48. D4_full(epi4,:) = [D4_path(sbj,:),'/',D4(epi4).name];
  49. end
  51. %EPI5
  52. cd([PATH1(sbj,:),'/',FOLDERNAME2(sbj,:),'/EPI5']);
  53. D5 = dir('Sub*_4d.nii');
  54. D5_path(sbj,:) = pwd;
  55. D5_size = length(D5);
  56. fprintf('%s EPI5 has %d *_4d.nii files\n',FOLDERNAME2(sbj,:),D5_size);
  57. for epi5 = 1:D5_size
  58. D5_full(epi5,:) = [D5_path(sbj,:),'/',D5(epi5).name];
  59. end
  61. %Str1 (EPI1~3))
  62. cd([PATH1(sbj,:),'/',FOLDERNAME1(sbj,:),'/Str1']);
  63. S1 = dir('s*.nii');
  64. S1_path(sbj,:) = pwd;
  65. S1_size = length(S1);
  66. fprintf('%s Str1 has %d s*.nii files\n',FOLDERNAME1(sbj,:),S1_size);
  67. for str1 = 1:S1_size
  68. S1_full(str1,:) = [S1_path(sbj,:),'/',S1(str1).name];
  69. end
  71. %Str2 (EPI4~5)
  72. cd([PATH1(sbj,:),'/',FOLDERNAME2(sbj,:),'/Str2']);
  73. S2 = dir('s*.nii');
  74. S2_path(sbj,:) = pwd;
  75. S2_size = length(S2);
  76. fprintf('%s Str2 has %d s*.nii files\n',FOLDERNAME2(sbj,:),S2_size);
  77. for str2 = 1:S2_size
  78. S2_full(str2,:) = [S2_path(sbj,:),'/',S2(str2).name];
  79. end
  82. % NAME4D(1,:,sbj) = [D1_path(sbj,:),'/',FOLDERNAME1(sbj,:),'_epi1_4d.nii'];
  83. % NAME4D(2,:,sbj) = [D2_path(sbj,:),'/',FOLDERNAME1(sbj,:),'_epi2_4d.nii'];
  84. % NAME4D(3,:,sbj) = [D3_path(sbj,:),'/',FOLDERNAME1(sbj,:),'_epi3_4d.nii'];
  85. NAME4D(1,:,sbj) = D1_full;
  86. NAME4D(2,:,sbj) = D2_full;
  87. NAME4D(3,:,sbj) = D3_full;
  88. NAME4D(4,:,sbj) = D4_full;
  89. NAME4D(5,:,sbj) = D5_full;
  90. NAMET1(1,:,sbj) = S1_full; %EPI1->Str1
  91. NAMET1(2,:,sbj) = S1_full; %EPI2->Str1
  92. NAMET1(3,:,sbj) = S1_full; %EPI3->Str1
  93. NAMET1(4,:,sbj) = S2_full; %EPI4->Str2
  94. NAMET1(5,:,sbj) = S2_full; %EPI5->Str2
  96. end
  97. cd(CurrentD);
  99. %% STEP1: PREPROCESSING %%
  100. PATH = '/data3/SRR/MRI12'; %for Alpha
  101. BATCH.filename = [PATH,'/',FILENAME]; %<required>
  102. BATCH.Setup.isnew = 1; %1/0: is this a new conn project
  103. BATCH.Setup.done = 1; %<required>1/0: 0 defines fields only; 1 runs SETUP processing steps
  104. BATCH.Setup.overwrite= 'Yes'; %<required>(for done=1) 1/0 overwrites target files if they exist
  105. %BATCH.Setup.parallel = 0;
  108. % %% Pararel process %%
  109. BATCH.parallel.N=SBJ; % One process per subject
  110. %
  112. %% 1 Basic %%
  113. BATCH.Setup.nsubjects= SBJ; %Number of subjects
  114. BATCH.Setup.RT = 2.0;%Repetition time (seconds)
  115. BATCH.Setup.acquisitiontype = 1; %1 is Continuous acquisition, 0 is Sparse acquisition of functional volumes
  116. %% 2&3 Original Functional and Structual data %%
  117. for sbj = 1:SBJ %1 to 18
  118. BATCH.Setup.structurals{sbj}{1} = cellstr(NAMET1(1,:,sbj));
  119. BATCH.Setup.structurals{sbj}{2} = cellstr(NAMET1(2,:,sbj));
  120. BATCH.Setup.structurals{sbj}{3} = cellstr(NAMET1(3,:,sbj));
  121. BATCH.Setup.structurals{sbj}{4} = cellstr(NAMET1(4,:,sbj));
  122. BATCH.Setup.structurals{sbj}{5} = cellstr(NAMET1(5,:,sbj));
  124. BATCH.Setup.functionals{sbj}{1} = cellstr(NAME4D(1,:,sbj));
  125. BATCH.Setup.functionals{sbj}{2} = cellstr(NAME4D(2,:,sbj));
  126. BATCH.Setup.functionals{sbj}{3} = cellstr(NAME4D(3,:,sbj));
  127. BATCH.Setup.functionals{sbj}{4} = cellstr(NAME4D(4,:,sbj));
  128. BATCH.Setup.functionals{sbj}{5} = cellstr(NAME4D(5,:,sbj));
  130. fprintf('Registored Str1 %s as structual data of EPI1 \n',NAMET1(1,:,sbj));
  131. fprintf('Registored Str1 %s as structual data of EPI2 \n',NAMET1(2,:,sbj));
  132. fprintf('Registored Str1 %s as structual data of EPI3 \n',NAMET1(3,:,sbj));
  133. fprintf('Registored Str2 %s as structual data of EPI4 \n',NAMET1(4,:,sbj));
  134. fprintf('Registored Str2 %s as structual data of EPI5 \n',NAMET1(5,:,sbj));
  136. fprintf('Registored EPI1 %s as functional data \n',NAME4D(1,:,sbj));
  137. fprintf('Registored EPI2 %s as functional data \n',NAME4D(2,:,sbj));
  138. fprintf('Registored EPI3 %s as functional data \n',NAME4D(3,:,sbj));
  139. fprintf('Registored EPI4 %s as functional data \n',NAME4D(4,:,sbj));
  140. fprintf('Registored EPI5 %s as functional data \n',NAME4D(5,:,sbj));
  141. end
  143. %% 4 Preprocessing %%
  144. BATCH.Setup.preprocessing.steps = 'default_mni'; %<required>Ddefault pipeline. Check help documents for more details
  145. BATCH.Setup.preprocessing.affreg= 'mni'; % :<required> (normalization) affine registration before normalization ['mni']
  147. %: (functional_art) ART thresholds for identifying outlier scans
  148. BATCH.Setup.preprocessing.art_thresholds(1)= 3; %art_thresholds(1): threshold value for global-signal (z-value; default 5)
  149. BATCH.Setup.preprocessing.art_thresholds(2)= 0.5; %art_thresholds(2): threshold value for subject-motion (mm; default .9)
  150. %note: the default art_thresholds(1:2) [5 .9] values correspond to the "intermediate" (97th percentile) settings
  151. % to use the "conservative" (95th percentile) settings use [3 .5]
  152. % to use the "liberal" (99th percentile) settings use [9 2] values instead
  153. %
  154. % additional options: art_thresholds(3): 1/0 global-signal threshold based on scan-to-scan changes
  155. % in global-BOLD measure (default 1)
  156. % art_thresholds(4): 1/0 subject-motion threshold based on scan-to-scan changes
  157. % in subject-motion measure (default 1)
  158. % art_thresholds(5): 1/0 subject-motion threhsold based on composite-movement
  159. % measure (default 1)
  160. % art_thresholds(6): 1/0 force interactive mode (ART gui) (default 0)
  161. % art_thresholds(7): [only when art_threshold(5)=0] subject-motion threshold
  162. % based on rotation measure
  163. % art_thresholds(8): N number of initial scans to be flagged for removal
  164. % (default 0)
  165. % note: when art_threshold(5)=0, art_threshold(2) defines the threshold based on the translation
  166. % measure, and art_threhsold(7) defines the threshold based on the rotation measure;
  167. % otherwise
  168. % art_threshold(2) defines the (single) threshold based on the composite-motion measure
  169. %
  170. % note: art needs subject-motion files to estimate possible outliers. If a 'realignment'
  171. % first-level covariate exists it will load the subject-motion parameters from that first-
  172. % level covariate; otherwise it will look for a rp_*.txt file (SPM format) in the same
  173. % folder as the functional data
  174. %
  175. % note: subject-motion files can be in any of the following formats: a) *.txt file (SPM
  176. % format; three translation parameters in mm followed by pitch/roll/yaw in radians);
  177. % b) *.par (FSL format; three Euler angles in radians followed by translation parameters
  178. % in mm); c) *.siemens.txt (Siemens MotionDetectionParameter.txt format); d) *.deg.txt (same
  179. % as SPM format but rotations in degrees instead of radians)
  181. %BATCH.Setup.preprocessing.boundingbox %: (normalization) target bounding box for resliced volumes (mm) [-90,-126,-72;90,90,108]
  183. %BATCH.Setup.preprocessing.bp_filter %: (functional_bandpass) Low- and High- frequency thresholds (in Hz)
  185. %BATCH.Setup.preprocessing.bp_keep0 %: (functional_bandpass) [1]
  186. % 0: removes average BOLD signal (freq=0Hz component)
  187. % 1: keeps average BOLD signal in output independent of band-pass filter values;
  189. %BATCH.Setup.preprocessing.coregtomean %: (functional_coregister/segment/normalize) [1]
  190. % 0: use first volume
  191. % 1: use mean volume (computed during realignment)
  192. % 2: use user-defined source volume (see Setup.coregsource_functionals field)
  193. %BATCH.Setup.preprocessing.diffusionsteps %: (surface_smooth) number of diffusion steps
  194. BATCH.Setup.preprocessing.fwhm = 8; %: (functional_smooth) Smoothing factor (mm) [8]
  195. %BATCH.Setup.preprocessing.interp = 4; %: (normalization) target voxel interpolation method [4]
  196. % 0:nearest neighbor
  197. % 1:trilinear
  198. % 2 or higher:n-order spline)
  199. %BATCH.Setup.preprocessing.label %: (functional_label) label of secondary dataset [datestr(now)]
  200. %BATCH.Setup.preprocessing.reg_names %: (functional_regression) list of first-level covariates to use as model regressors / design matrix
  201. % (valid entries are first-level covariate names or ROI names)
  202. %BATCH.Setup.preprocessing.reg_dimensions %: (functional_regression) list of maximum number of dimensions (one vlaue for each model regressor in reg_names)
  203. %BATCH.Setup.preprocessing.reg_deriv %: (functional_regression) list of 0/1/2 values (one value for each model regressorr in reg_names):
  204. %: add first- or second- order derivatives to each model regressor
  205. %BATCH.Setup.preprocessing.reg_detrend = 1; %: (functional_regression) 1: adds a linear/detrending term to model regressors [1]
  206. %BATCH.Setup.preprocessing.reg_skip %: (functional_regression) [0]
  207. % 1: does not create output functional files, only creates session-specific dp_*.txt files with covariate timeseries to be included later in an arbitrary first-level model
  208. %BATCH.Setup.preprocessing.removescans %: (functional_removescans) number of initial scans to remove
  209. %BATCH.Setup.preprocessing.reorient %: (functional/structural_manualorient) 3x3 or 4x4 transformation matrix or filename containing corresponding matrix
  210. %BATCH.Setup.preprocessing.respatialdef %: (functional/structural_manualspatialdef) nifti deformation file (e.g. y_*.nii or *seg_sn.mat files)
  211. %BATCH.Setup.preprocessing.rtm %: (functional_realign) 0: use first volume; 1: use mean volume [0]
  213. BATCH.Setup.preprocessing.sliceorder = 'descending'; %<required>[36:-1:1]
  214. %%%%%%%%%%%%%%%%%%%% CAUTION %%%%%%%%%%%%%%%%%%%%
  215. %In the case of Siemens interleave,
  216. %if Slice number has EVEN size (e.g.36,70),
  217. %Order is 2,4,6,8,10,…,34,36,1,3,5,7,10,….,35
  218. %if Slice number has ODD size (e.g.35,43),
  219. %Order is 1,3,5,7,9,…,33,35,2,4,6,8,10,….,32,34
  220. %%%%%%%%%%%%%%%%%%%% CAUTION %%%%%%%%%%%%%%%%%%%%
  221. %: (functional_slicetime) acquisition order (vector of indexes; 1=first slice in image; note: use cell array for subject-specific vectors)
  222. % alternatively sliceorder may also be defined as one of the following strings: 'ascending',
  223. % 'descending','interleaved (middle-top)','interleaved (bottom-up)','interleaved (top-down)',
  224. % 'interleaved (Siemens)','interleaved (Philips)','BIDS' (this option reads slice timing information from .json files)
  225. % alternatively sliceorder may also be defined as a vector containing the acquisition time in
  226. % milliseconds for each slice (e.g. for multi-band sequences)
  228. %BATCH.Setup.preprocessing.ta %: (functional_slicetime) acquisition time (TA) in seconds
  229. % (used to determine slice times when sliceorder is defined by a vector of slice indexes;
  230. % note: use vector for subject-specific values).
  231. % Defaults to (1-1/nslices)*TR where nslices is the number of slices
  232. %BATCH.Setup.preprocessing.template_structural %: (structural_normalize SPM8 only) anatomical template file for approximate coregistration
  233. %[spm/template/T1.nii]
  234. %BATCH.Setup.preprocessing.template_functional:%(functional_normalize SPM8 only) functional template file for normalization
  235. %[spm/template/EPI.nii]
  236. %BATCH.Setup.preprocessing.tpm_template %: (structural_segment, structural_segment&normalize in SPM8, and any segment/normalize option in SPM12) tissue probability map
  237. % [spm/tpm/TPM.nii]
  238. %BATCH.Setup.preprocessing.tpm_ngaus %: (structural_segment, structural_segment&normalize in SPM8&SPM12) number of gaussians for each tissue probability map
  239. %BATCH.Setup.preprocessing.vdm_et1 %: (functional_vdm_create) ET1 (Echo Time first echo in fieldmap sequence) (default [] : read from .json file / BIDS)
  240. %BATCH.Setup.preprocessing.vdm_et2 %: (functional_vdm_create) ET2 (Echo Time second echo in fieldmap sequence) (default [] : read from .json file / BIDS)
  241. %BATCH.Setup.preprocessing.vdm_ert %: (functional_vdm_create) ERT (Effective Readout Time in funcional data) (default [] : read from .json file / BIDS)
  242. %BATCH.Setup.preprocessing.vdm_blip %: (functional_vdm_create) k-space traversal blip direction (+1 or -1; default -1)
  243. %BATCH.Setup.preprocessing.vdm_type %: (functional_vdm_create only) type of fieldmap sequence files
  244. % []: automatically detect;
  245. % 1: magnitude+phasediff (or magnitude1+magnitude2+phasediff)
  246. % 2: real1+imag1+real2+imag2; 3: fieldmapHz)
  247. %BATCH.Setup.preprocessing.vdm_fmap %: (functional_vdm_create only) location of fieldmap sequence files
  248. % (secondary functional dataset number or label containing fieldmap sequence files) ['fmap']
  249. %BATCH.Setup.preprocessing.voxelsize_anat %: (structural normalization) target voxel size for resliced volumes (mm) [2]
  250. %BATCH.Setup.preprocessing.voxelsize_func %: (functional normalization) target voxel size for resliced volumes (mm) [2]
  251. %BATCH.Setup.preprocessing.sets %: defines functional dataset to preprocess (0 for Primary Dataset; [1-N] or labels for Secondary Datasets) [0]
  254. % STEP2: ROIS, CONDITIONS, COVARIATES(1st,2nd), OPTIONS %%
  255. % 5 ROIs %%
  256. % BATCH.Setup.secondarydatasets...
  257. % BATCH.Setup.add...
  258. % BATCH.Setup.masks... % Specifies the Grey/White/CSF ROIs for each subject. If any of these fields are left unset,
  259. % the conn toolbox will perform segmentation on the anatomical volumes (for each subject) in
  260. % order to define the missing masks
  262. % for sbj = 1:SBJ %1 to 18
  263. % Gray = dir([S1_path(sbj,:),'/c1c*.nii']);
  264. % White= dir([S1_path(sbj,:),'/c2c*.nii']);
  265. % CSF = dir([S1_path(sbj,:),'/c3c*.nii']);
  266. % BATCH.Setup.masks.Gray{sbj} = Gray.name;
  267. % BATCH.Setup.masks.White{sbj}= White.name;
  268. % BATCH.Setup.masks.CSF{sbj} = CSF.name;
  269. % end
  271. % % BATCH.Setup.rois... % Specifies the ROI files for connectivity analyses.
  272. % If this field is left unset, the toolbox will by
  273. % default use all the ROI files in the conn/rois directory
  274. % If you set some rois by yourself, please take care
  275. % whether BATCH.Setup.rois.add = 1 or 0.
  276. % BATCH.Setup.rois.add= 1 %: 1/0; use 0 (default) to define the full set of ROIs to be used in your analyses; use 1 to define
  277. % an additional set of ROIs (to be added to any already-existing ROIs in your project) [0]
  279. % % [Tips] If you want to simply add ROI set (such as Power's atlas), save
  280. % % one .nii file including ROI images and one .txt file including lists of
  281. % % seed names in tha folder 'XX:/spm12/toolbox/conn/rois'
  282. % % Conn indentifys the files as default atlas and automatically adds the
  283. % % ROI set.
  284. % % Otherwise, if you want to mannualy add ROIs for with some weights,
  285. % % use the following commands,
  286. % BATCH.Setup.rois.add= 1;
  287. % BATCH.Setup.rois.names={'XXXX','YYYY'};
  288. % BATCH.Setup.rois.dimensions={1,1};
  289. % BATCH.Setup.rois.files{1}='D:/spm12/toolbox/conn/utils/otherrois/XXXX.nii';
  290. % BATCH.Setup.rois.files{2}='D:/spm12/toolbox/conn/utils/otherrois/YYYY.nii';
  295. %% 6 Conditions %%
  296. % Description: Specifies the (taks or rest) conditions characterizing the block-design acquisition of
  297. % the functional volumes. Note that if the BATCH.New initialization step has been run this field will
  298. % default to defining a single condition encompassing the entire functional data. Alternatively if
  299. % BATCH.Setup.spmfiles has been specified this field will be defined by the modeled conditions of
  300. % interest in the corresponding SPM.mat files. Otherwise this field needs to be defined (e.g. use the
  301. % example below for rest-state functional data)
  302. for sbj = 1:SBJ %Loop through subjects <required>
  303. BATCH.Setup.conditions.names {1} ='All runs';
  304. BATCH.Setup.conditions.onsets {1}{sbj}{1}= 0;
  305. BATCH.Setup.conditions.onsets {1}{sbj}{2}= 0;
  306. BATCH.Setup.conditions.onsets {1}{sbj}{3}= 0;
  307. BATCH.Setup.conditions.onsets {1}{sbj}{4}= 0;
  308. BATCH.Setup.conditions.onsets {1}{sbj}{5}= 0;
  309. BATCH.Setup.conditions.durations{1}{sbj}{1}=inf;
  310. BATCH.Setup.conditions.durations{1}{sbj}{2}=inf;
  311. BATCH.Setup.conditions.durations{1}{sbj}{3}=inf;
  312. BATCH.Setup.conditions.durations{1}{sbj}{4}=inf;
  313. BATCH.Setup.conditions.durations{1}{sbj}{5}=inf;
  315. BATCH.Setup.conditions.names {2} ='First half';
  316. BATCH.Setup.conditions.onsets {2}{sbj}{1}= 0;
  317. BATCH.Setup.conditions.onsets {2}{sbj}{2}= 0;
  318. BATCH.Setup.conditions.onsets {2}{sbj}{3}= 0;
  319. BATCH.Setup.conditions.onsets {2}{sbj}{4}= [];
  320. BATCH.Setup.conditions.onsets {2}{sbj}{5}= [];
  321. BATCH.Setup.conditions.durations{2}{sbj}{1}=inf;
  322. BATCH.Setup.conditions.durations{2}{sbj}{2}=inf;
  323. BATCH.Setup.conditions.durations{2}{sbj}{3}=inf;
  324. BATCH.Setup.conditions.durations{2}{sbj}{4}= [];
  325. BATCH.Setup.conditions.durations{2}{sbj}{5}= [];
  327. BATCH.Setup.conditions.names {3} ='Second half';
  328. BATCH.Setup.conditions.onsets {3}{sbj}{1}= [];
  329. BATCH.Setup.conditions.onsets {3}{sbj}{2}= [];
  330. BATCH.Setup.conditions.onsets {3}{sbj}{3}= [];
  331. BATCH.Setup.conditions.onsets {3}{sbj}{4}= 0;
  332. BATCH.Setup.conditions.onsets {3}{sbj}{5}= 0;
  333. BATCH.Setup.conditions.durations{3}{sbj}{1}= [];
  334. BATCH.Setup.conditions.durations{3}{sbj}{2}= [];
  335. BATCH.Setup.conditions.durations{3}{sbj}{3}= [];
  336. BATCH.Setup.conditions.durations{3}{sbj}{4}=inf;
  337. BATCH.Setup.conditions.durations{3}{sbj}{5}=inf;
  339. BATCH.Setup.conditions.names {4} ='Stress all';
  340. BATCH.Setup.conditions.onsets {4}{sbj}{1}= [];
  341. BATCH.Setup.conditions.onsets {4}{sbj}{2}= 0;
  342. BATCH.Setup.conditions.onsets {4}{sbj}{3}= 0;
  343. BATCH.Setup.conditions.onsets {4}{sbj}{4}= [];
  344. BATCH.Setup.conditions.onsets {4}{sbj}{5}= [];
  345. BATCH.Setup.conditions.durations{4}{sbj}{1}= [];
  346. BATCH.Setup.conditions.durations{4}{sbj}{2}=inf;
  347. BATCH.Setup.conditions.durations{4}{sbj}{3}=inf;
  348. BATCH.Setup.conditions.durations{4}{sbj}{4}= [];
  349. BATCH.Setup.conditions.durations{4}{sbj}{5}= [];
  351. BATCH.Setup.conditions.names {5} ='Baseline';
  352. BATCH.Setup.conditions.onsets {5}{sbj}{1}= 0;
  353. BATCH.Setup.conditions.onsets {5}{sbj}{2}= [];
  354. BATCH.Setup.conditions.onsets {5}{sbj}{3}= [];
  355. BATCH.Setup.conditions.onsets {5}{sbj}{4}= [];
  356. BATCH.Setup.conditions.onsets {5}{sbj}{5}= [];
  357. BATCH.Setup.conditions.durations{5}{sbj}{1}=inf;
  358. BATCH.Setup.conditions.durations{5}{sbj}{2}= [];
  359. BATCH.Setup.conditions.durations{5}{sbj}{3}= [];
  360. BATCH.Setup.conditions.durations{5}{sbj}{4}= [];
  361. BATCH.Setup.conditions.durations{5}{sbj}{5}= [];
  363. BATCH.Setup.conditions.names {6} ='Stress1';
  364. BATCH.Setup.conditions.onsets {6}{sbj}{1}= [];
  365. BATCH.Setup.conditions.onsets {6}{sbj}{2}= 0;
  366. BATCH.Setup.conditions.onsets {6}{sbj}{3}= [];
  367. BATCH.Setup.conditions.onsets {6}{sbj}{4}= [];
  368. BATCH.Setup.conditions.onsets {6}{sbj}{5}= [];
  369. BATCH.Setup.conditions.durations{6}{sbj}{1}= [];
  370. BATCH.Setup.conditions.durations{6}{sbj}{2}=inf;
  371. BATCH.Setup.conditions.durations{6}{sbj}{3}= [];
  372. BATCH.Setup.conditions.durations{6}{sbj}{4}= [];
  373. BATCH.Setup.conditions.durations{6}{sbj}{5}= [];
  375. BATCH.Setup.conditions.names {7} ='Stress2';
  376. BATCH.Setup.conditions.onsets {7}{sbj}{1}= [];
  377. BATCH.Setup.conditions.onsets {7}{sbj}{2}= [];
  378. BATCH.Setup.conditions.onsets {7}{sbj}{3}= 0;
  379. BATCH.Setup.conditions.onsets {7}{sbj}{4}= [];
  380. BATCH.Setup.conditions.onsets {7}{sbj}{5}= [];
  381. BATCH.Setup.conditions.durations{7}{sbj}{1}= [];
  382. BATCH.Setup.conditions.durations{7}{sbj}{2}= [];
  383. BATCH.Setup.conditions.durations{7}{sbj}{3}=inf;
  384. BATCH.Setup.conditions.durations{7}{sbj}{4}= [];
  385. BATCH.Setup.conditions.durations{7}{sbj}{5}= [];
  387. BATCH.Setup.conditions.names {8} ='Recov1';
  388. BATCH.Setup.conditions.onsets {8}{sbj}{1}= [];
  389. BATCH.Setup.conditions.onsets {8}{sbj}{2}= [];
  390. BATCH.Setup.conditions.onsets {8}{sbj}{3}= [];
  391. BATCH.Setup.conditions.onsets {8}{sbj}{4}= 0;
  392. BATCH.Setup.conditions.onsets {8}{sbj}{5}= [];
  393. BATCH.Setup.conditions.durations{8}{sbj}{1}= [];
  394. BATCH.Setup.conditions.durations{8}{sbj}{2}= [];
  395. BATCH.Setup.conditions.durations{8}{sbj}{3}= [];
  396. BATCH.Setup.conditions.durations{8}{sbj}{4}=inf;
  397. BATCH.Setup.conditions.durations{8}{sbj}{5}= [];
  399. BATCH.Setup.conditions.names {9} ='Recov2';
  400. BATCH.Setup.conditions.onsets {9}{sbj}{1}= [];
  401. BATCH.Setup.conditions.onsets {9}{sbj}{2}= [];
  402. BATCH.Setup.conditions.onsets {9}{sbj}{3}= [];
  403. BATCH.Setup.conditions.onsets {9}{sbj}{4}= [];
  404. BATCH.Setup.conditions.onsets {9}{sbj}{5}= 0;
  405. BATCH.Setup.conditions.durations{9}{sbj}{1}= [];
  406. BATCH.Setup.conditions.durations{9}{sbj}{2}= [];
  407. BATCH.Setup.conditions.durations{9}{sbj}{3}= [];
  408. BATCH.Setup.conditions.durations{9}{sbj}{4}= [];
  409. BATCH.Setup.conditions.durations{9}{sbj}{5}=inf;
  411. end
  412. %NumOfCond = 5;
  413. %BATCH.Setup.conditions.param = zeros(1,NumOfCond);
  414. %BATCH.Setup.conditions.filter= [];
  415. %BATCH.Setup.conditions.missingdata= 0;
  416. %BATCH.Setup.conditions.model= []; %optional
  417. %BATCH.Setup.conditions.importfile= []; %optional
  418. %BATCH.Setup.conditions.importfile= 0;
  420. %% 7 Covariates 1st level %%
  421. % % Description: Specifies the first level covariates (timeseries possibly associated with the BOLDsignal).
  422. % % Note that if the new initialization step has been run(BATCH.Setup.isnew = 1),
  423. % % this field will default to THREE covariates representing the estimated subject motion for each subject/session.
  425. % % BATCH.Setup.covariate.files{ncovariate}{nsub}{nses} element contains a char array pointing to a
  426. % % .txt or .mat file defining the time series first level covariate ncovariate, for subject nsub, during
  427. % % session nses . Note that each covariate can in turn be multivariate (e.g. the ‘movement’ covariate
  428. % % contains 6 time series, each representing one aspect of the estimated subject movement
  429. % % translation and rotation parameters --).
  431. % % Valid first level covariate files are .txt files with a row of
  432. % % numbers for each scan, and .mat files containing a single two dimensional variable (arbitrarily
  433. % % named) with as many rows as scans.
  435. %OPTION:Put physiological data, if you have
  438. BATCH.Setup.covariates.add = 1; %1/0 1: Add 1sr level covariates in addition to default
  439. % (= 'realignment', 'QC_timeseries', 'scrubbing').
  440. % 0: Make original set of 1st level covariates.
  441. %note: 'rp_**.txt', art_regression_timeseries_**.mat,
  442. %'art_regression_outliers_**.mat have not been made
  443. % until running preprocessig process.
  444. for sbj = 1:SBJ %Loop through subjects
  445. BATCH.Setup.covariates.names{1} ='retroicor';
  446. BATCH.Setup.covariates.files{1}{sbj}{1}=cellstr([PATH2,'/',PHYSIONAME(sbj,:),'/retroicor_hrv_rvt1.txt']);
  447. BATCH.Setup.covariates.files{1}{sbj}{2}=cellstr([PATH2,'/',PHYSIONAME(sbj,:),'/retroicor_hrv_rvt2.txt']);
  448. BATCH.Setup.covariates.files{1}{sbj}{3}=cellstr([PATH2,'/',PHYSIONAME(sbj,:),'/retroicor_hrv_rvt3.txt']);
  449. BATCH.Setup.covariates.files{1}{sbj}{4}=cellstr([PATH2,'/',PHYSIONAME(sbj,:),'/retroicor_hrv_rvt4.txt']);
  450. BATCH.Setup.covariates.files{1}{sbj}{5}=cellstr([PATH2,'/',PHYSIONAME(sbj,:),'/retroicor_hrv_rvt5.txt']);
  451. end
  453. % % THIS IS A MISTAKE OF THE SCRIPTS. YOU DO NOT NEED TO PUT THESE COVARIATES MANUALLY AS CONN MAKES IT AUTOMATICALLY.
  454. % % % BATCH.Setup.covariates.names{1} ='realignment';
  455. % % % BATCH.Setup.covariates.files{1}{sbj}{1}=cellstr([D1_path(sbj,:),'/rp_',FOLDERNAME1(sbj,:),'_epi1_4d.txt']);
  456. % % % BATCH.Setup.covariates.files{1}{sbj}{2}=cellstr([D2_path(sbj,:),'/rp_',FOLDERNAME1(sbj,:),'_epi2_4d.txt']);
  457. % % % BATCH.Setup.covariates.files{1}{sbj}{3}=cellstr([D3_path(sbj,:),'/rp_',FOLDERNAME1(sbj,:),'_epi3_4d.txt']);
  458. % % %
  459. % % % BATCH.Setup.covariates.names{2} ='QC_timeseries';
  460. % % % BATCH.Setup.covariates.files{2}{sbj}{1}=cellstr([D1_path(sbj,:),'/art_regression_timeseries','_au',FOLDERNAME1(sbj,:),'_epi1_4d.mat']);
  461. % % % BATCH.Setup.covariates.files{2}{sbj}{2}=cellstr([D2_path(sbj,:),'/art_regression_timeseries','_au',FOLDERNAME1(sbj,:),'_epi2_4d.mat']);
  462. % % % BATCH.Setup.covariates.files{2}{sinishing 59 job(s) running
  463. % % %
  464. % % % BATCH.Setup.covariates.names{3} ='scrubbing';
  465. % % % BATCH.Setup.covariates.files{3}{sbj}{1}=cellstr([D1_path(sbj,:),'/art_regression_outliers','_au',FOLDERNAME1(sbj,:),'_epi1_4d.mat']);
  466. % % % BATCH.Setup.covariates.files{3}{sbj}{2}=cellstr([D2_path(sbj,:),'/art_regression_outliers','_au',FOLDERNAME1(sbj,:),'_epi2_4d.mat']);
  467. % % % BATCH.Setup.covariates.files{3}{sbj}{3}=cellstr([D3_path(sbj,:),'/art_regression_outliers','_au',FOLDERNAME1(sbj,:),'_epi3_4d.mat']);
  469. %% 8 Covariates 2nd level %%
  470. %
  474. % BATCH.Setup.subjects
  475. % Description: Specifies the second level covariates (subject level variables, such as behavioral or
  476. % demographic measures as well as subject group definitions). By default a single second level
  477. % covariate is defined named ‘All’ characterizing a single group that includes all subjects. Note that
  478. % second level covariates can be redundant as different subsets of second level covariates can be
  479. % used in different second level analyses (see BATCH.Results for more details)
  481. %BATCH.Setup.subjects.add= 1 ; %1/0 1: Add 2nd level regressor in addition to default (AllSubjects).
  482. % 0: Make original set of 2nd level regressors.
  483. BATCH.Setup.subjects.effect_names= IDinf.name ;
  484. BATCH.Setup.subjects.effects{ 1}=IDinf.val(1:SBJ, 1);
  485. BATCH.Setup.subjects.effects{ 2}=IDinf.val(1:SBJ, 2);
  486. BATCH.Setup.subjects.effects{ 3}=IDinf.val(1:SBJ, 3);
  487. BATCH.Setup.subjects.effects{ 4}=IDinf.val(1:SBJ, 4);
  488. BATCH.Setup.subjects.effects{ 5}=IDinf.val(1:SBJ, 5);
  489. BATCH.Setup.subjects.effects{ 6}=IDinf.val(1:SBJ, 6);
  490. BATCH.Setup.subjects.effects{ 7}=IDinf.val(1:SBJ, 7);
  491. BATCH.Setup.subjects.effects{ 8}=IDinf.val(1:SBJ, 8);
  492. BATCH.Setup.subjects.effects{ 9}=IDinf.val(1:SBJ, 9);
  493. BATCH.Setup.subjects.effects{10}=IDinf.val(1:SBJ,10);
  494. BATCH.Setup.subjects.effects{11}=IDinf.val(1:SBJ,11);
  495. BATCH.Setup.subjects.effects{12}=IDinf.val(1:SBJ,12);
  496. BATCH.Setup.subjects.effects{13}=IDinf.val(1:SBJ,13);
  497. BATCH.Setup.subjects.effects{14}=IDinf.val(1:SBJ,14);
  498. BATCH.Setup.subjects.effects{15}=IDinf.val(1:SBJ,15);
  499. BATCH.Setup.subjects.effects{16}=IDinf.val(1:SBJ,16);
  500. BATCH.Setup.subjects.effects{17}=IDinf.val(1:SBJ,17);
  501. BATCH.Setup.subjects.effects{18}=IDinf.val(1:SBJ,18);
  502. BATCH.Setup.subjects.effects{19}=IDinf.val(1:SBJ,19);
  503. BATCH.Setup.subjects.effects{20}=IDinf.val(1:SBJ,20);
  504. BATCH.Setup.subjects.effects{21}=IDinf.val(1:SBJ,21);
  505. BATCH.Setup.subjects.effects{22}=IDinf.val(1:SBJ,22);
  506. BATCH.Setup.subjects.effects{23}=IDinf.val(1:SBJ,23);
  507. BATCH.Setup.subjects.effects{24}=IDinf.val(1:SBJ,24);
  508. BATCH.Setup.subjects.effects{25}=IDinf.val(1:SBJ,25);
  509. BATCH.Setup.subjects.effects{26}=IDinf.val(1:SBJ,26);
  510. BATCH.Setup.subjects.effects{27}=IDinf.val(1:SBJ,27);
  511. BATCH.Setup.subjects.effects{28}=IDinf.val(1:SBJ,28);
  512. BATCH.Setup.subjects.effects{29}=IDinf.val(1:SBJ,29);
  514. %% 9 Options %%
  515. BATCH.Setup.analyses = [1,2,3,4];% Vector of index to analysis types (1: ROI-to-ROI; 2: Seed-to-voxel; 3: Voxel-to-voxel; 4: Dynamic FC)
  516. % Defaults to vector [1,2,3,4] (all analyses)
  517. BATCH.Setup.voxelmask= 1; %Analysis mask (voxel-level analyses):
  518. %1: Explicit mask (brainmask.nii); 2: Implicit mask (subject-specific) [1]
  519. %BATCH.Setup.voxelmaskfile=[]; %Explicit mask file (only when voxelmask=1) [fullfile(fileparts(which('spm')),'apriori','brainmask.nii')]
  520. BATCH.Setup.voxelresolution=1;
  521. %Analysis space (voxel-level analyses):
  522. %1: Volume-based template (SPM; default 2mm isotropic or same as explicit mask if specified)
  523. %2: Same as structurals
  524. %3: Same as functionals;
  525. %4: Surface-based template (Freesurfer) [1]
  526. BATCH.Setup.analysisunits =1;%BOLD signal units. 1: PSC(percent signal change) units ; 2: raw units [1]
  527. BATCH.Setup.outputfiles =[1,1,0,0,0,0]; %Optional output files [0,0,0,0,0,0]
  528. %outputfiles(1): 1/0 creates confound beta-maps
  529. %outputfiles(2): 1/0 creates confound-corrected timeseries
  530. %outputfiles(3): 1/0 creates seed-to-voxel r-maps
  531. %outputfiles(4): 1/0 creates seed-to-voxel p-maps)
  532. %outputfiles(5): 1/0 creates seed-to-voxel FDR-p-maps;
  533. %outputfiles(6): 1/0 creates ROI-extraction REX files;
  535. % BATCH.Setup.spmfiles
  536. %Optionally, spmfiles{nsub} is a char array pointing to the 'SPM.mat' source file to extract Setup
  537. %information from for each subject (use alternatively spmfiles{nsub}{nses} for session-specific SPM.mat files)
  538. % BATCH.Setup.spmfiles_options
  539. %(for Setup.spmfiles procedure) Cell array containing additional options to pass to conn_importspm
  540. %when importing experiment info from spmfiles (see help conn_importspm)
  541. % BATCH.Setup.vdm_functionals
  542. %(for Setup.preprocessing.steps=='realign&unwarp&fieldmap') vdm_functionals{nsub}{nses} char
  543. %array of voxel-displacement volumes (vdm* file; explicitly entering these volumes here superceeds CONN's
  544. %default option to search for/use vdm* files in same directory as functional data)
  545. % BATCH.Setup.fmap_functionals
  546. %(for Setup.preprocessing.steps=='vdm_create') fmap_functionals{nsub}{nses} char
  547. %array of fieldmap sequence files (magnitude1+phasediff or real1+imag1+real2+imag2 or fieldmap (Hz) volumes)
  548. % BATCH.Setup.coregsource_functionals
  549. %(for Setup.preprocessing.steps=='functional_coregister/segment/normalize')
  550. %coregsource_functionals{nsub} char array of source volume for coregistration/normalization/
  551. %segmentation (used only when preprocessing "coregtomean" field is set to 2, user-defined source
  552. %volumes are used in this case instead of either the first functional volume (coregtomean=0) or the
  553. %mean functional volume (coregtomean=1) for coregistration/normalization/segmentation)
  554. % BATCH.Setup.localcopy
  555. %(for Setup.structural, Setup.functional, Setup.secondarydatasets, and Setup.rois) 1/0 : copies structural/
  556. %functional files into conn_*/data/BIDS folder before importing into CONN [0]
  557. % BATCH.Setup.binary_threshold = [.5 .5 .5]; %default [.5 .5 .5]
  558. %(for BOLD extraction from Grey/White/CSF ROIs) Threshold value # for binarizing Grey/White/CSF masks
  559. % BATCH.Setup.binary_threshold_type
  560. %(for BOLD extraction from Grey/White/CSF ROIs)
  561. %1: absolute threshold (keep voxels with values above x)
  562. %2: percentile threshold (keep x% of voxels with the highest values) [1 1 1]
  563. % BATCH.Setup.exclude_grey_matter
  564. %(for BOLD extration from White/CSF ROIs) threhsold for excluding Grey matter voxels (nan for no threshold) [nan nan nan]
  565. % BATCH.Setup.erosion_steps = [0 1 1]; %default [0 1 1]
  566. %(for BOLD extraction from Grey/White/CSF ROIs) integer numbers are interpreted as erosion kernel
  567. %size for Grey/White/CSF mask erosion after binarization; non-integer numbers are interpreted as
  568. %percentile voxels kept after erosion
  569. % BATCH.Setup.erosion_neighb
  570. %(for BOLD extraction from Grey/White/CSF ROIs; only when using integer erosion_steps/ kernel sizes,
  571. %this field is disregarded otherwise) Neighborhood size for Grey/White/CSF mask erosion after
  572. %binarization (a voxel is eroded if there are more than masks_erosion_neighb zeros within the
  573. %(2*masks_erosionsteps+1)^3-neighborhood of each voxel) [1 1 1]
  576. % %% RUN POINT1 %%
  577. % % conn_batch(BATCH);
  578. % % You can run this script in this point, if you want to stop by end of preprocessing.
  579. % % Results of STEP1&2 %%
  580. % % In functional data folder
  581. % % 'swauXXXXX.nii'
  582. % % 'rp_XXXXX.txt' is for realign parameters.
  583. % % 'art_movement_stats_auXXXXX.mat'
  584. % % 'art_regression_timeseries_auXXXXX.mat' is for QC_timeseries.
  585. % % 'art_regression_outliers_auXXXXX.mat' is for Scrubbing.
  586. % % 'art_regression_outliers_and_movement_auXXXXX.mat'
  587. % % also you will see 'c1art_mean_auXXXX.nii',...'c5art_mean_auXXXX.nii' etc...
  588. % % Those are for segmentaion.
  589. % % In structual data folder
  590. % % all new files are related to segmentation process
  591. % % 'centering_XXXXX.mat
  592. % % 'icentering_XXXXX.mat
  593. % % 'c1cs_XXXXX.nii'...'c5cs_XXXXX.nii'
  594. % % 'wc0csXXXXX.nii'...'wc3csXXXXX.nii'
  595. % % 'wcsXXXXX.nii'
  596. % % 'y_csXXXXX.nii'
  597. % % 'iy_csXXXXX.nii'
  600. %% STEP3: DENOISING %%
  601. % % If you already finished the prepreprocessing, please read following commands
  602. % clear BATCH
  603. % BATCH.filename = [PATH1,'/',FILENAME];
  604. % %
  606. %% 10 Denoising %%
  607. BATCH.Denoising.done = 1; %<required>: 1/0: 0 defines fields only; 1 runs DENOISING processing steps [0]
  608. BATCH.Denoising.overwrite = 1; %<required>: (for done=1) 1/0: overwrites target files if they exist [1]
  609. BATCH.Denoising.filter = [0.008, 0.09]; %<required>: vector with two elements specifying band pass filter: low-frequency & high-frequency cutoffs (Hz)
  610. BATCH.Denoising.detrending = 1; %: 0/1/2/3: BOLD times-series polynomial detrending order
  611. % (0: no detrending; 1: linear detrending; 3: cubic detrending)
  612. BATCH.Denoising.despiking = 0; %: 0/1/2: temporal despiking with a hyperbolic tangent squashing function
  613. %(1:before regression; 2:after regression) [0]
  614. BATCH.Denoising.regbp = 1; %: 1/2: order of band-pass filtering step
  615. %(1 = RegBP: regression followed by band-pass; 2 = Simult: simultaneous regression&band-pass) [1]
  617. % % BATCH.Denoising.confounds... %You do not need to put follwing commands,
  618. % % %if you use default setting of Denoisinig.
  619. % % Description: Specifies the denoising confounds factors (timeseries possibly associated with the BOLDsignal).
  620. % % Note that this field will default to THREE factors ('White Matter','CSF','realignment','scrubbing') and Effect of XX.
  621. % % You need to describe only when you want to change the denosing factors.
  622. % % BATCH.Denoising.confounds.names = {'retroicor'};
  623. % % BATCH.Denoising.confounds.dimensions = {[]}; %regressor size of retroicor is different by subjects
  624. % % BATCH.Denoising.confounds.deriv = {0};
  625. % % BATCH.Denoising.confounds.power = {1};
  626. % % BATCH.Denoising.confounds.filter = {0};
  628. BATCH.Denoising.confounds.names = {'White Matter','CSF','realignment','scrubbing','retroicor',...
  629. 'Effect of All runs','Effect of First half','Effect of Second half','Effect of Stress all','Effect of Baseline','Effect of Stress1','Effect of Stress2','Effect of Recov1','Effect of Recov2'};
  630. %: confounds.names{nconfound} char array of confound name
  631. % (confound names can be: 'Grey Matter','White Matter','CSF',any ROI name, any covariate name,
  632. % or 'Effect of *' where * represents any condition name]
  633. BATCH.Denoising.confounds.dimensions = {5,5,[],[],[],...
  634. 2,2,2,2,2,2,2,2,2};
  635. %: confounds.dimensions{nconfound} number of confound dimensions [defaults to using all dimensions
  636. % available for each confound variable]
  637. % leave empty a particular value to set to the default value -maximum dimensions of the corresponding effect-
  638. BATCH.Denoising.confounds.deriv = {0,0,1,0,0,...
  639. 1,1,1,1,1,1,1,1,1};
  640. %: confounds.deriv{nconfound} include temporal derivatives up to n-th order of each effect
  641. % (0 for raw timeseries, 1 for raw+firstderivative timeseries, etc.) [0|1]
  642. BATCH.Denoising.confounds.power = {1,1,1,1,1,...
  643. 1,1,1,1,1,1,1,1,1};
  644. %: confounds.power{nconfound} include powers up to n-th order of each effect
  645. %(1 for linear effects, %2 for linear+quadratic effect, etc.) [1]
  646. BATCH.Denoising.confounds.filter = {0,0,0,0,0,...
  647. 0,0,0,0,0,0,0,0,0};
  648. %: (for regbp==1) confounds.filter{nconfound} band-pass filter confound regressors before entering
  650. % for roi = 1:ROIsize %in regression equation [0]
  653. % %% RUN POINT2 %%
  654. % conn_batch(BATCH);
  655. % % You can run this script in this point, if you want to stop by end of denoising.
  657. %% STEP4: ANALYSES(1ST LEVEL) %%
  658. % % % If you already finish the denoising, please read following commands
  659. % % clear BATCH
  660. % % BATCH.filename = [PATH1,'/',FILENAME];
  661. % % %
  662. %
  663. % %The list of ROInames is for excluding Cerebellum rois.
  664. % ROIname = {'atlas.FP r (Frontal Pole Right)','atlas.FP l (Frontal Pole Left)',...
  665. % 'atlas.IC r (Insular Cortex Right)','atlas.IC l (Insular Cortex Left)',...
  666. % 'atlas.SFG r (Superior Frontal Gyrus Right)','atlas.SFG l (Superior Frontal Gyrus Left)',...
  667. % 'atlas.MidFG r (Middle Frontal Gyrus Right)','atlas.MidFG l (Middle Frontal Gyrus Left)',...
  668. % 'atlas.IFG tri r (Inferior Frontal Gyrus, pars triangularis Right)','atlas.IFG tri l (Inferior Frontal Gyrus, pars triangularis Left)',...
  669. % 'atlas.IFG oper r (Inferior Frontal Gyrus, pars opercularis Right)','atlas.IFG oper l (Inferior Frontal Gyrus, pars opercularis Left)',...
  670. % 'atlas.PreCG r (Precentral Gyrus Right)','atlas.PreCG l (Precentral Gyrus Left)',...
  671. % 'atlas.TP r (Temporal Pole Right)','atlas.TP l (Temporal Pole Left)',...
  672. % 'atlas.aSTG r (Superior Temporal Gyrus, anterior division Right)','atlas.aSTG l (Superior Temporal Gyrus, anterior division Left)',...
  673. % 'atlas.pSTG r (Superior Temporal Gyrus, posterior division Right)','atlas.pSTG l (Superior Temporal Gyrus, posterior division Left)',...
  674. % 'atlas.aMTG r (Middle Temporal Gyrus, anterior division Right)','atlas.aMTG l (Middle Temporal Gyrus, anterior division Left)',...
  675. % 'atlas.pMTG r (Middle Temporal Gyrus, posterior division Right)','atlas.pMTG l (Middle Temporal Gyrus, posterior division Left)',...
  676. % 'atlas.toMTG r (Middle Temporal Gyrus, temporooccipital part Right)','atlas.toMTG l (Middle Temporal Gyrus, temporooccipital part Left)',...
  677. % 'atlas.aITG r (Inferior Temporal Gyrus, anterior division Right)','atlas.aITG l (Inferior Temporal Gyrus, anterior division Left)',...
  678. % 'atlas.pITG r (Inferior Temporal Gyrus, posterior division Right)','atlas.pITG l (Inferior Temporal Gyrus, posterior division Left)',...
  679. % 'atlas.toITG r (Inferior Temporal Gyrus, temporooccipital part Right)','atlas.toITG l (Inferior Temporal Gyrus, temporooccipital part Left)',...
  680. % 'atlas.PostCG r (Postcentral Gyrus Right)','atlas.PostCG l (Postcentral Gyrus Left)',...
  681. % 'atlas.SPL r (Superior Parietal Lobule Right)','atlas.SPL l (Superior Parietal Lobule Left)',...
  682. % 'atlas.aSMG r (Supramarginal Gyrus, anterior division Right)','atlas.aSMG l (Supramarginal Gyrus, anterior division Left)',...
  683. % 'atlas.pSMG r (Supramarginal Gyrus, posterior division Right)','atlas.pSMG l (Supramarginal Gyrus, posterior division Left)',...
  684. % 'atlas.AG r (Angular Gyrus Right)','atlas.AG l (Angular Gyrus Left)',...
  685. % 'atlas.sLOC r (Lateral Occipital Cortex, superior division Right)','atlas.sLOC l (Lateral Occipital Cortex, superior division Left)',...
  686. % 'atlas.iLOC r (Lateral Occipital Cortex, inferior division Right)','atlas.iLOC l (Lateral Occipital Cortex, inferior division Left)',...
  687. % 'atlas.ICC r (Intracalcarine Cortex Right)','atlas.ICC l (Intracalcarine Cortex Left)',...
  688. % 'atlas.MedFC (Frontal Medial Cortex)',...
  689. % 'atlas.SMA r (Juxtapositional Lobule Cortex -formerly Supplementary Motor Cortex- Right)','atlas.SMA L(Juxtapositional Lobule Cortex -formerly Supplementary Motor Cortex- Left)',...
  690. % 'atlas.SubCalC (Subcallosal Cortex)',...
  691. % 'atlas.PaCiG r (Paracingulate Gyrus Right)','atlas.PaCiG l (Paracingulate Gyrus Left)',...
  692. % 'atlas.AC (Cingulate Gyrus, anterior division)',...
  693. % 'atlas.PC (Cingulate Gyrus, posterior division)',...
  694. % 'atlas.Precuneous (Precuneous Cortex)',...
  695. % 'atlas.Cuneal r (Cuneal Cortex Right)','atlas.Cuneal l (Cuneal Cortex Left)',...
  696. % 'atlas.FOrb r (Frontal Orbital Cortex Right)','atlas.FOrb l (Frontal Orbital Cortex Left)',...
  697. % 'atlas.aPaHC r (Parahippocampal Gyrus, anterior division Right)','atlas.aPaHC l (Parahippocampal Gyrus, anterior division Left)',...
  698. % 'atlas.pPaHC r (Parahippocampal Gyrus, posterior division Right)','atlas.pPaHC l (Parahippocampal Gyrus, posterior division Left)',...
  699. % 'atlas.LG r (Lingual Gyrus Right)','atlas.LG l (Lingual Gyrus Left)',...
  700. % 'atlas.aTFusC r (Temporal Fusiform Cortex, anterior division Right)','atlas.aTFusC l (Temporal Fusiform Cortex, anterior division Left)',...
  701. % 'atlas.pTFusC r (Temporal Fusiform Cortex, posterior division Right)','atlas.pTFusC l (Temporal Fusiform Cortex, posterior division Left)',...
  702. % 'atlas.TOFusC r (Temporal Occipital Fusiform Cortex Right)','atlas.TOFusC l (Temporal Occipital Fusiform Cortex Left)',...
  703. % 'atlas.OFusG r (Occipital Fusiform Gyrus Right)','atlas.OFusG l (Occipital Fusiform Gyrus Left)',...
  704. % 'atlas.FO r (Frontal Operculum Cortex Right)','atlas.FO l (Frontal Operculum Cortex Left)',...
  705. % 'atlas.CO r (Central Opercular Cortex Right)','atlas.CO l (Central Opercular Cortex Left)',...
  706. % 'atlas.PO r (Parietal Operculum Cortex Right)','atlas.PO l (Parietal Operculum Cortex Left)',...
  707. % 'atlas.PP r (Planum Polare Right)','atlas.PP l (Planum Polare Left)',...
  708. % 'atlas.HG r (Heschl''s Gyrus Right)','atlas.HG l (Heschl''s Gyrus Left)',...
  709. % 'atlas.PT r (Planum Temporale Right)','atlas.PT l (Planum Temporale Left)',...
  710. % 'atlas.SCC r (Supracalcarine Cortex Right)','atlas.SCC l (Supracalcarine Cortex Left)',...
  711. % 'atlas.OP r (Occipital Pole Right)','atlas.OP l (Occipital Pole Left)',...
  712. % 'atlas.Thalamus r','atlas.Thalamus l',...
  713. % 'atlas.Caudate r','atlas.Caudate l',...
  714. % 'atlas.Putamen r','atlas.Putamen l',...
  715. % 'atlas.Pallidum r','atlas.Pallidum l',...
  716. % 'atlas.Hippocampus r','atlas.Hippocampus l',...
  717. % 'atlas.Amygdala r','atlas.Amygdala l',...
  718. % 'atlas.Accumbens r','atlas.Accumbens l',...
  719. % 'atlas.Brain-Stem',...
  720. % 'networks.DefaultMode.MPFC (1,55,-3)',...
  721. % 'networks.DefaultMode.LP (L) (-39,-77,33)','networks.DefaultMode.LP (R) (47,-67,29)',...
  722. % 'networks.DefaultMode.PCC (1,-61,38)',...
  723. % 'networks.SensoriMotor.Lateral (L) (-55,-12,29)','networks.SensoriMotor.Lateral (R) (56,-10,29)',...
  724. % 'networks.SensoriMotor.Superior (0,-31,67)',...
  725. % 'networks.Visual.Medial (2,-79,12)',...
  726. % 'networks.Visual.Occipital (0,-93,-4)',...
  727. % 'networks.Visual.Lateral (L) (-37,-79,10)','networks.Visual.Lateral (R) (38,-72,13)',...
  728. % 'networks.Salience.ACC (0,22,35)',...
  729. % 'networks.Salience.AInsula (L) (-44,13,1)','networks.Salience.AInsula (R) (47,14,0)',...
  730. % 'networks.Salience.RPFC (L) (-32,45,27)','networks.Salience.RPFC (R) (32,46,27)',...
  731. % 'networks.Salience.SMG (L) (-60,-39,31)','networks.Salience.SMG (R) (62,-35,32)',...
  732. % 'networks.DorsalAttention.FEF (L) (-27,-9,64)','networks.DorsalAttention.FEF (R) (30,-6,64)',...
  733. % 'networks.DorsalAttention.IPS (L) (-39,-43,52)','networks.DorsalAttention.IPS (R) (39,-42,54)',...
  734. % 'networks.FrontoParietal.LPFC (L) (-43,33,28)','networks.FrontoParietal.PPC (L) (-46,-58,49)',...
  735. % 'networks.FrontoParietal.LPFC (R) (41,38,30)','networks.FrontoParietal.PPC (R) (52,-52,45)',...
  736. % 'networks.Language.IFG (L) (-51,26,2)','networks.Language.IFG (R) (54,28,1)',...
  737. % 'networks.Language.pSTG (L) (-57,-47,15)','networks.Language.pSTG (R) (59,-42,13)'};
  738. %
  739. % ROIsize = size(ROIname,2);
  740. %
  741. BATCH.Analysis.done = 1; %:<required> 1/0: 0 defines fields only; 1 runs ANALYSIS processing steps [0]
  742. BATCH.Analysis.overwrite = 1; %:<required> (for done=1) 1/0: overwrites target files if they exist [1]
  743. % BATCH.Analysis.name = 1; %: analysis name (identifying each set of independent analysis)
  744. % %(alternatively sequential index identifying each set of independent analyses [1])
  745. %
  746. % BATCH.Analysis.measure = 1; %: connectivity measure used; [1]
  747. % % 1 = 'correlation (bivariate)',
  748. % % 2 = 'correlation (semipartial)',
  749. % % 3 = 'regression (bivariate)',
  750. % % 4 = 'regression (multivariate)'
  751. % BATCH.Analysis.weight = 2; %: within-condition weight; [2]
  752. % % 1 = 'none',
  753. % % 2 = 'hrf',
  754. % % 3 = 'hanning'
  755. % BATCH.Analysis.modulation = 0; %: temporal modulation ; [0]
  756. % % 0 = standard weighted GLM analyses;
  757. % % 1 = gPPI analyses of condition-specific temporal modulation factor,
  758. % % or a string for PPI analyses of other temporal modulation factor
  759. % % (same for all conditions; valid strings are ROI names and 1st-level covariate names)
  760. % % BATCH.Analysis.conditions =[]; %: (for modulation==1 only) list of task condition names to be simultaneously entered in gPPI
  761. % % % model (leave empty for default 'all existing conditions') []
  762. % BATCH.Analysis.type = 3; % : analysis type, 1 = 'ROI-to-ROI', 2 = 'Seed-to-Voxel', 3 = 'all'; [3]
  763. % %
  764. % % % BATCH.Analysis.sources
  765. % % % Description: Cell array of sources names (seeds) (source names can be: any ROI name)
  766. % % % (if this variable does not exist the toolbox will perform the analyses for all of the existing ROIs
  767. % % % which are not defined as confounds in the Denoising step)
  768. % % % note: partial ROI name matches are accepted (e.g. using 'networks.Language' will match all
  769. % % % ROIs starting with that token)
  770. % % % Alternatively sources can be a structure with fields
  771. %
  772. % BATCH.Analysis.sources.names = ROIname; % {'Left Amygdala', 'Right Amygdala'}; %: sources.names{nsource} char array of source names (seeds)
  773. % for roi = 1:ROIsize
  774. % BATCH.Analysis.sources.dimensions{roi} = 1; %: sources.dimensions{nsource} number of source dimensions [1]
  775. % BATCH.Analysis.sources.deriv{roi} = 0; %: sources.deriv{nsource} number of derivatives for each dimension [0]
  776. % BATCH.Analysis.sources.fbands{roi} = 1; %: sources.fbands{nsource} number of frequency bands for each dimension [1]
  777. % end
  779. %% RUN POINT3 %%
  780. conn_batch(BATCH);
  781. % You can run this script in this point, if you want to stop by end of Analyses(1st level).

CONN_script_Prep_1stLevel_MRI14.m, no license · at the source

Overview

Authors: Noriya Watanabe1,2, Shinichi Yoshida1,3, Ruedeerat Keerativittayayut1,4, Masaki Takeda1,3
ORCID iDs: Noriya Watanabe, Ruedeerat Keerativittayayut, Masaki Takeda
  1. Research Center for Brain Communication, Kochi University of Technology, Kami, Kochi 782-8502, Japan
  2. Department of Informatics, Faculty of Informatics, Shizuoka Institute of Science and Technology, Fukuroi, Shizuoka 437-8555, Japan
  3. School of Informatics, Kochi University of Technology, Kami, Kochi 782-8502, Japan
  4. School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok 10210, Thailand
Institutions: Kochi University of Technology (Japan); Shizuoka Institute of Science and Technology (Japan); Chulabhorn Royal Academy (Thailand)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 13, article e2524075123
Dates: received 31 August 2025; accepted 25 February 2026; published online 25 March 2026; in print 31 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2524075123 · PMID 41880572 · PMCID PMC13037921 · OpenAlex W7140329876
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Evoked potentials, Physiology & signal measures
Keywords: stress resilience, resting state, multimodal measurements, salience network, default mode network
MeSH: Brain*, Resilience, Psychological*, Stress, Psychological*, Adult, Brain Mapping, Electroencephalography, Female, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Young Adult (* major topic)
Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex
Funding: MEXT | Japan Society for the Promotion of Science (JP21H00211, JP21K07262, JP24H00914, JP20H00521, JP22H05219, JP22K12786, JP21K18267); Naito Science and Engineering Foundation ( ) (N.A); Public Health Research Foundation (N.A); Takeda Science Foundation (TSF) (N.A)
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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OSF kvnh9

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (2), R (1)
Size: 31 files, 3 scripts
Software Heritage: not checked
Found in: “Data, Materials, and Software Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CONN (1 file), EEGLAB (1 file), glmnet (1 file), ICLabel (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/kvnh9/

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Tracing map

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Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1073/pnas.2524075123.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 13 MeSH terms, 4 funders, 78 references.

Cite

This paper

Watanabe, N., Yoshida, S., Keerativittayayut, R., & Takeda, M. (2026). Neural signatures of human psychological resilience driven by acute stress. Proceedings of the National Academy of Sciences of the United States of America, 123(13), e2524075123. https://doi.org/10.1073/pnas.2524075123

BibTeX

@article{watanabe2026neural,
author = {Watanabe, Noriya and Yoshida, Shinichi and Keerativittayayut, Ruedeerat and Takeda, Masaki},
title = {{Neural signatures of human psychological resilience driven by acute stress}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = mar,
volume = {123},
number = {13},
pages = {e2524075123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2524075123},
url = {https://doi.org/10.1073/pnas.2524075123},
pmid = {41880572},
pmcid = {PMC13037921}
}

RIS

TY - JOUR
AU - Watanabe, Noriya
AU - Yoshida, Shinichi
AU - Keerativittayayut, Ruedeerat
AU - Takeda, Masaki
TI - Neural signatures of human psychological resilience driven by acute stress
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/03/25
VL - 123
IS - 13
SP - e2524075123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2524075123
UR - https://doi.org/10.1073/pnas.2524075123
LA - en
ER -

CSL-JSON

{
"id": "10.1073/pnas.2524075123",
"type": "article-journal",
"title": "Neural signatures of human psychological resilience driven by acute stress",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
{
"family": "Watanabe",
"given": "Noriya"
},
{
"family": "Yoshida",
"given": "Shinichi"
},
{
"family": "Keerativittayayut",
"given": "Ruedeerat"
},
{
"family": "Takeda",
"given": "Masaki"
}
],
"container-title-short": "Proc Natl Acad Sci U S A",
"volume": "123",
"issue": "13",
"page": "e2524075123",
"DOI": "10.1073/pnas.2524075123",
"PMID": "41880572",
"PMCID": "PMC13037921",
"ISSN": "0027-8424",
"publisher": "National Academy of Sciences",
"URL": "https://doi.org/10.1073/pnas.2524075123",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
25
]
]
}
}

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