OSCR

Task-dependent increases and decreases of BOLD signal in theory of mind brain regions during strategic social interaction.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Statistical analysis of fMRI data ↔ TMFC_denoise.m, lines 1–60 · score 0.63 · general linear model, motion parameters, BOLD signal, GLM, temporal, regressors
  2. [2] § Methods › Statistical analysis of fMRI data ↔ functions/tmfc_BSC.m, lines 1–60 · score 0.58 · task modulated functional, beta series, BSC, correlation, LSS, ROIs
  3. [3] § Methods › Statistical analysis of fMRI data ↔ functions/tmfc_BSC_after_FIR.m, lines 1–60 · score 0.58 · task modulated functional, beta series, BSC, correlation, LSS, ROIs
  4. [4] § Methods › Statistical analysis of fMRI data ↔ functions/tmfc_LSS_after_FIR.m, lines 1–60 · score 0.53 · regressor modeling, nuisance, canonical, HRF, motion, GLM

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 408 lines · 19 KB · GPL-3.0 · 1 match

  1. function output_paths = TMFC_denoise(SPM_paths,subject_paths,options,anat_paths,func_paths,display_FD,estimate_GLMs,clear_all,seg_paths)
  3. % =[Task-Modulated Functional Connectivity (TMFC) Denoise Toolbox v1.5.0]=
  4. %
  5. % The TMFC denoise toolbox updates the selected general linear model with
  6. % the addition of noise regressors. It can be used prior to TMFC analysis
  7. % (gPPI or BSC) or standard task activation analysis. The general linear model
  8. % must be specified and estimated in the SPM8/12/25 software (user needs
  9. % to select the corresponding SPM.mat files).
  10. %
  11. % Extraction of BOLD signals from whole-brain, GM, WM, and CSF masks
  12. % requires structural T1 images in native space and unsmoothed, realigned
  13. % functional images in MNI space. If the source SPM.mat files specify paths
  14. % to smoothed functional images, then unsmoothed functional images should
  15. % be stored in the same folders without the smoothing prefix.
  16. %
  17. % NOTE: All regressors specified in the original general linear model will
  18. % be included in the updated model along with noise regressors. That is,
  19. % if the original model already contains expansions of the six motion parameters
  20. % or physiological regressors, they may be duplicated in the updated model.
  21. % Thus, it is necessary to select models that include six standard motion
  22. % regressors and other confound regressors that will not be calculated by
  23. % the TMFC denoise toolbox.
  24. %
  25. % Functionality of the TMFC denoise toolbox:
  26. %
  27. % (1) Calculates head motion parameters (temporal derivatives and quadratic
  28. % terms). Temporal derivatives are computed as backward differences
  29. % (Van Dijk et al., 2012). Quadratic terms represent 6 squared motion
  30. % parameters and 6 squared temporal derivatives (Satterthwaite et al., 2012).
  31. %
  32. % (2) Calculates framewise displacement (FD) as the sum of the absolute values
  33. % of the derivatives of translational and rotational motion parameters
  34. % (Power et al., 2012).
  35. %
  36. % (3) Creates spike regressors based on a user-defined FD threshold. For each
  37. % flagged time point, a unit impulse function is included in the general
  38. % linear model; it has the value 1 at that time point and 0 elsewhere.
  39. % (Lemieux et al., 2007; Satterthwaite et al., 2012).
  40. %
  41. % (4) Creates aCompCor regressors (Behzadi et al., 2007). Calculates a fixed
  42. % number of principal components (PCs) or variable number of PCs
  43. % explaining 50% of the signal variability separately for the eroded WM
  44. % and CSF masks (Muschelli et al., 2014).
  45. %
  46. % (5) Creates WM/CSF regressors (Fox et al., 2005). Calculates average
  47. % BOLD signals separately for eroded WM and CSF masks. Optionally
  48. % calculates derivatives and quadratic terms (Parkes et al., 2017).
  49. %
  50. % (6) Creates GSR regressors (Fox et al., 2005, 2009). Calculates the average
  51. % BOLD signal for the whole-brain mask. Optionally calculates
  52. % derivatives and quadratic terms (Parkes et al., 2017).
  53. %
  54. % (7) Calculates the temporal Derivative of root mean square VARiance over voxelS (DVARS).
  55. % DVARS is computed as the root mean square (RMS) of the differentiated
  56. % BOLD time series within the GM mask (Muschelli et al., 2014).
  57. % Also computes FD–DVARS correlations.
  58. % DVARS is computed both before and after noise regression
  59. % (for the original and updated GLM, respectively).
  60. %
  61. % (8) Adds noise regressors to the original model and estimates the updated model.
  62. % The noise regressors and the updated model will be stored in the TMFC_denoise subfolder.
  63. %
  64. % (9) Optionally applies robust weighted least squares (rWLS) for model estimation
  65. % (Diedrichsen & Shadmehr, 2005).
  66. % It assumes that each image has its own variance parameter; some scans
  67. % may be disrupted by noise (high variance). In the first pass, SPM
  68. % estimates the noise variances; in the second pass, each image
  69. % is reweighted by the inverse of its variance.
  70. %
  71. % -------------------------------------------------------------------------
  72. % FORMAT: output_paths = TMFC_denoise
  73. % Will call GUIs to select SPM.mat files, define denoising options, select
  74. % structural and functional files, define FD threshold for spike regression.
  75. %
  76. % FORMAT: output_paths = TMFC_denoise(SPM_paths,subject_paths,options,anat_paths,func_paths)
  77. % FORMAT: output_paths = TMFC_denoise(SPM_paths,subject_paths,options,anat_paths,func_paths,display_FD,estimate_GLMs,clear_all)
  78. % FORMAT: output_paths = TMFC_denoise(SPM_paths,subject_paths,options,anat_paths,func_paths,display_FD,estimate_GLMs,clear_all,seg_paths)
  79. % Performs noise regression without calling the GUI.
  80. %
  81. % INPUTS:
  82. % SPM_paths - Cell array containing paths to SPM.mat files that
  83. % need to be re-estimated with noise regressors
  84. % (e.g., C:\fMRI_project\sub-01\stat\GLM-01\SPM.mat)
  85. %
  86. % subject_paths - Cell array of subject folders corresponding to SPM_paths
  87. % (e.g., C:\fMRI_project\sub-01)
  88. %
  89. % options.motion - '6HMP' : do not add additional motion regressors
  90. % - '12HMP': add 6 temporal derivatives
  91. % - '24HMP': add 6 temporal derivatives and 12 quadratic terms
  92. %
  93. % Order of motion regressors in SPM.Sess.C structure:
  94. % options.translation_idx - [1 2 3] (default)
  95. % options.rotation_idx - [4 5 6] (default)
  96. % In SPM, HCP, and fMRIPrep the first three regressors are translations;
  97. % in FSL and AFNI the first three are rotations.
  98. %
  99. % options.rotation_unit - Rotation units:
  100. % - 'rad' (radians, e.g., SPM, FSL, fMRIPrep)
  101. % - 'deg' (degrees, e.g., HCP, AFNI)
  102. %
  103. % options.head_radius - Approximate head radius in mm (Default: 50)
  104. %
  105. % options.DVARS - 0 (none) or 1 (calculate)
  106. %
  107. % options.aCompCor - Number of aCompCor regressors for the WM mask
  108. % and CSF masks (Default: [5 5]; None: [0 0];
  109. % aCompCor50%: [0.5 0.5])
  110. % options.aCompCor_ort - Pre-orthogonalize WM and CSF signals w.r.t. high-pass
  111. % filter (HPF) regressors and head motion
  112. % regressors prior to PC calculation (Default: 1)
  113. %
  114. % options.rWLS - 0 (none) or 1 (apply rWLS)
  115. %
  116. % options.spikereg - 0 (none) or 1 (add spike regressors)
  117. % options.spikeregFDthr - FD threshold for creating spike regressors in mm (Default: 0.5)
  118. %
  119. % options.WM_CSF - 'none' : do not add WM and CSF regressors
  120. % - '2Phys': add WM and CSF signals
  121. % - '4Phys': add WM and CSF signals and 2 temporal derivatives
  122. % - '8Phys': add WM and CSF signals, 2 temporal derivatives, and 4 quadratic terms
  123. %
  124. % options.GSR - 'none': do not add whole-brain signal
  125. % - 'GSR' : add whole-brain signal
  126. % - '2GSR': add whole-brain signal and its temporal derivative
  127. % - '4GSR': add whole-brain signal, its temporal derivative, and 2 quadratic terms
  128. %
  129. % options.parallel - 0 or 1: Sequential or parallel computation
  130. %
  131. % options.GMmask.prob - Probability threshold for the liberal GM mask (Default: 0.95)
  132. % options.WMmask.prob - Probability threshold for the WM mask (Default: 0.99)
  133. % options.CSFmask.prob - Probability threshold for the CSF mask (Default: 0.99)
  134. % options.GMmask.dilate - Dilation cycles for the GM mask (Default: 2 voxels)
  135. % options.WMmask.erode - Erosion cycles for the WM mask (Default: 3 voxels)
  136. % options.CSFmask.erode - Erosion cycles for the CSF mask (Default: 2 voxels)
  137. %
  138. % anat_paths{iSub}.fname - Cell array containing paths to structural T1 images
  139. %
  140. % func_paths{iSub}.fname - Cell array containing paths to realigned, normalized, and unsmoothed functional images
  141. % (NOTE: Structural images must be in the native space; functional images must be in MNI space)
  142. %
  143. % seg_paths - Optional existing SPM segmentation output.
  144. %
  145. % If empty, the user will be asked whether to search
  146. % automatically for existing segmentation files.
  147. %
  148. % If 'auto', the toolbox searches the folder of each
  149. % selected anatomical image for:
  150. % c1*.nii, c2*.nii, c3*.nii, y_*.nii, and m*.nii.
  151. %
  152. % If a structure array is provided, it should contain:
  153. % seg_paths(iSub).GM - c1 image
  154. % seg_paths(iSub).WM - c2 image
  155. % seg_paths(iSub).CSF - c3 image
  156. % seg_paths(iSub).def - y_ deformation field
  157. % seg_paths(iSub).m - optional bias-corrected T1
  158. %
  159. % If c1/c2/c3/y_ are complete, segmentation is skipped
  160. % for that subject. If m is missing, the raw anatomical
  161. % image is used for the skull-stripped QC image.
  162. %
  163. % display_FD - 1 or 0 : Display individual FD plots (Default: 1)
  164. % estimate_GLMs - 1 or 0 : Estimate GLMs with noise regressors (Default: 1)
  165. % clear_all - 1 or 0 : Delete any existing files in 'TMFC_denoise'
  166. % subfolders before creating new files (Default: 0)
  167. %
  168. % OUTPUT:
  169. % output_paths - Cell array containing paths to estimated GLMs
  170. % with added noise regressors.
  171. %
  172. % =========================================================================
  173. %
  174. % Copyright (C) 2026 Ruslan Masharipov
  175. %
  176. % This program is free software: you can redistribute it and/or modify
  177. % it under the terms of the GNU General Public License as published by
  178. % the Free Software Foundation, either version 3 of the License, or
  179. % (at your option) any later version.
  180. %
  181. % This program is distributed in the hope that it will be useful,
  182. % but WITHOUT ANY WARRANTY; without even the implied warranty of
  183. % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
  184. % GNU General Public License for more details.
  185. %
  186. % You should have received a copy of the GNU General Public License
  187. % along with this program. If not, see <https://www.gnu.org/licenses/>.
  188. %
  189. % Contact email: [email hidden]
  191. %-Check SPM version
  192. %--------------------------------------------------------------------------
  193. if exist('spm','file')
  194. spm_version = spm('Ver');
  195. if ~isequal(spm_version,'SPM12') && ~isequal(spm_version,'SPM25')
  196. warning('Your SPM version: %s. TMFC_denoise toolbox was tested only with SPM12 and SPM25.', spm_version)
  197. end
  198. else
  199. error('SPM not found on MATLAB path.');
  200. end
  202. %-Check TMFC_denoise version
  203. %--------------------------------------------------------------------------
  204. localVer = 'v1.5.0';
  205. try
  206. r = webread(sprintf('https://api.github.com/repos/%s/%s/releases/latest', ...
  207. 'IHB-IBR-department','TMFC_denoise'), ...
  208. weboptions('Timeout',5));
  209. latestVer = r.tag_name;
  210. catch
  211. latestVer = '';
  212. end
  213. disp(['==============[TMFC denoise ' localVer ']==============']);
  214. if ~isequal(localVer,latestVer)
  215. disp(['Update available: ' latestVer '. Please visit: https://github.com/IHB-IBR-department/TMFC_denoise']);
  216. end
  218. output_paths = [];
  220. %-Prepare inputs
  221. %--------------------------------------------------------------------------
  223. % Select SPM.mat files
  224. if nargin<1 || isempty(SPM_paths)
  225. [SPM_paths,subject_paths] = tmfc_select_subjects_GUI(0);
  226. end
  227. if isempty(SPM_paths); warning('Select SPM.mat files.'); return, end
  229. % Check SPM.mat files
  230. for iSub = 1:length(SPM_paths)
  231. if ~exist(SPM_paths{iSub},'file')
  232. error(['SPM file not found: ' SPM_paths{iSub}]);
  233. end
  234. end
  236. % Subject paths
  237. if nargin<2 || isempty(subject_paths)
  238. if isempty(subject_paths)
  239. subject_paths = cellstr(spm_select(inf,'dir', ...
  240. 'Select ALL subject folders (same order as SPMs)'));
  241. end
  242. end
  243. if numel(subject_paths) ~= numel(SPM_paths)
  244. error(['The number of selected subject folders (' num2str(numel(subject_paths)) ...
  245. ') must match the number of SPM.mat files (' num2str(numel(SPM_paths)) ').']);
  246. end
  248. % Define denoising options
  249. if nargin<3 || isempty(options)
  250. options = tmfc_denoise_options_GUI;
  251. end
  252. if isempty(options); error('Denoising options not selected.'); end
  254. % Check whether tissue masks are needed
  255. need_masks = sum(options.aCompCor)~=0 || ~strcmpi(options.WM_CSF,'none') || ~strcmpi(options.GSR,'none') || options.DVARS == 1;
  257. % Existing SPM segmentation paths
  258. if nargin<9
  259. seg_paths = [];
  260. end
  262. % Select structural T1 images in native space
  263. if nargin<4 || isempty(anat_paths)
  264. if need_masks
  265. anat_paths = tmfc_select_anat_GUI(subject_paths);
  266. if isempty(anat_paths); error('Select structural T1 files.'); end
  267. else
  268. anat_paths = [];
  269. end
  270. end
  272. % Optionally reuse existing SPM segmentation output
  273. if need_masks && (nargin<9 || isempty(seg_paths))
  274. answer = questdlg(['Use existing SPM segmentation output if available?', newline, newline, ...
  275. 'The toolbox will automatically search the anatomical image folder ', ...
  276. 'for c1/c2/c3 tissue probability maps and the matching y_ deformation field. ', ...
  277. 'Subjects with missing files will be segmented as usual.'], ...
  278. 'TMFC denoise', ...
  279. 'Segment T1 images','Use existing if available','Segment T1 images');
  281. switch answer
  282. case 'Use existing if available'
  283. seg_paths = 'auto';
  284. otherwise
  285. seg_paths = [];
  286. end
  287. end
  289. % Select realigned and unsmoothed functional images in MNI space
  290. if nargin<5 || isempty(func_paths)
  291. if (sum(options.aCompCor)~=0 || ~strcmpi(options.WM_CSF,'none') || ~strcmpi(options.GSR,'none') || options.DVARS == 1)
  292. func_paths = tmfc_select_func_GUI(SPM_paths,subject_paths);
  293. if isempty(func_paths); error('Select unsmoothed functional files.'); end
  294. else
  295. func_paths = [];
  296. end
  297. end
  299. % Display individual FD plots
  300. if nargin<6 || isempty(display_FD), display_FD = 1; end
  302. % Estimate GLMs with noise regressors
  303. if nargin<7 || isempty(estimate_GLMs), estimate_GLMs = 1; end
  305. % Clear "TMFC_denoise" subfolders
  306. if nargin<8 || isempty(clear_all)
  307. answer = questdlg('Delete previously created TMFC_denoise files?', ...
  308. 'TMFC denoise', ...
  309. 'Do not delete','Delete','Do not delete');
  310. switch answer
  311. case 'Do not delete'
  312. clear_all = 0;
  313. case 'Delete'
  314. clear_all = 1;
  315. end
  316. end
  318. %-Create TMFC_denoise subfolders
  319. %--------------------------------------------------------------------------
  320. for iSub = 1:length(SPM_paths)
  321. GLM_subfolder = fileparts(SPM_paths{iSub});
  322. if clear_all == 1 && exist(fullfile(GLM_subfolder,'TMFC_denoise'),'dir')
  323. rmdir(fullfile(GLM_subfolder,'TMFC_denoise'),'s');
  324. end
  325. if ~exist(fullfile(GLM_subfolder,'TMFC_denoise'),'dir')
  326. mkdir(fullfile(GLM_subfolder,'TMFC_denoise'));
  327. end
  328. clear GLM_subfolder
  329. end
  331. %-Calculate head motion parameters (HMP) and framewise displacement (FD)
  332. %--------------------------------------------------------------------------
  333. if ~strcmpi(options.motion,'6HMP') || options.spikereg == 1 || display_FD == 1 || options.DVARS == 1
  334. disp('Head motion assessment...'); tic;
  335. FD = tmfc_head_motion(SPM_paths,subject_paths,options);
  336. hms = fix(mod((toc), [0, 3600, 60]) ./ [3600, 60, 1]);
  337. disp(['Done in ' num2str(hms(1),'%02.f') ':' num2str(hms(2),'%02.f') ':' num2str(hms(3),'%02.f') ' [hr:min:sec].']);
  338. end
  340. %-Plot FD time series and select FDthr for spike regression
  341. %--------------------------------------------------------------------------
  342. if display_FD == 1
  343. FDthr = tmfc_plot_FD(FD,options,SPM_paths,subject_paths,anat_paths,func_paths);
  344. options.spikeregFDthr = FDthr;
  345. end
  347. %-Create spike regressors
  348. %--------------------------------------------------------------------------
  349. if options.spikereg == 1
  350. disp('----------------------------------------');
  351. disp('Creating spike regressors...'); tic;
  352. tmfc_spikereg(SPM_paths,options);
  353. hms = fix(mod((toc), [0, 3600, 60]) ./ [3600, 60, 1]);
  354. disp(['Done in ' num2str(hms(1),'%02.f') ':' num2str(hms(2),'%02.f') ':' num2str(hms(3),'%02.f') ' [hr:min:sec].']);
  355. end
  357. %-Create GM/WM/CSF and whole-brain masks
  358. %--------------------------------------------------------------------------
  359. if sum(options.aCompCor)~=0 || ~strcmpi(options.WM_CSF,'none') || ~strcmpi(options.GSR,'none') || options.DVARS == 1
  360. if isempty(anat_paths); error('Select structural T1 files.'); end
  361. if isempty(func_paths); error('Select unsmoothed functional files.'); end
  362. disp('----------------------------------------');
  363. if ~isfield(options,'GMmask') || ~isfield(options,'WMmask') || ~isfield(options,'CSFmask')
  364. [options.GMmask.prob, options.WMmask.prob, options.CSFmask.prob, ...
  365. options.GMmask.dilate, options.WMmask.erode, options.CSFmask.erode] = tmfc_masks_GUI();
  366. end
  367. disp('Creating binary masks...'); tic;
  368. masks = tmfc_create_masks(SPM_paths,subject_paths,anat_paths,func_paths,options,seg_paths);
  369. hms = fix(mod((toc), [0, 3600, 60]) ./ [3600, 60, 1]);
  370. disp(['Done in ' num2str(hms(1),'%02.f') ':' num2str(hms(2),'%02.f') ':' num2str(hms(3),'%02.f') ' [hr:min:sec].']);
  371. end
  373. %-Calculate physiological regressors
  374. %--------------------------------------------------------------------------
  375. if sum(options.aCompCor)~=0 || ~strcmpi(options.WM_CSF,'none') || ~strcmpi(options.GSR,'none')
  376. disp('----------------------------------------');
  377. disp('Calculating physiological regressors...'); tic;
  378. tmfc_physioreg(SPM_paths,subject_paths,func_paths,masks,options);
  379. hms = fix(mod((toc), [0, 3600, 60]) ./ [3600, 60, 1]);
  380. disp(['Done in ' num2str(hms(1),'%02.f') ':' num2str(hms(2),'%02.f') ':' num2str(hms(3),'%02.f') ' [hr:min:sec].']);
  381. end
  383. %-Estimate updated GLMs with noise regressors
  384. %--------------------------------------------------------------------------
  385. if estimate_GLMs == 1
  386. disp('----------------------------------------');
  387. disp('Estimating GLMs with noise regressors...'); tic;
  388. if ~exist('masks','var'); masks = []; end
  389. output_paths = tmfc_estimate_updated_GLMs(SPM_paths,masks,options);
  390. hms = fix(mod((toc), [0, 3600, 60]) ./ [3600, 60, 1]);
  391. disp(['Done in ' num2str(hms(1),'%02.f') ':' num2str(hms(2),'%02.f') ':' num2str(hms(3),'%02.f') ' [hr:min:sec].']);
  392. end
  394. %-Calculate and plot DVARS
  395. %--------------------------------------------------------------------------
  396. if options.DVARS == 1
  397. disp('----------------------------------------');
  398. disp('Calculating DVARS...'); tic;
  399. if ~exist('masks','var'); masks = []; end
  400. [preDVARS,postDVARS] = tmfc_calculate_DVARS(FD,SPM_paths,options,masks,output_paths);
  401. tmfc_plot_DVARS(preDVARS,postDVARS,FD,options,SPM_paths,subject_paths,anat_paths,func_paths,masks);
  402. hms = fix(mod((toc), [0, 3600, 60]) ./ [3600, 60, 1]);
  403. disp(['Done in ' num2str(hms(1),'%02.f') ':' num2str(hms(2),'%02.f') ':' num2str(hms(3),'%02.f') ' [hr:min:sec].']);
  404. end
  406. end

TMFC_denoise.m at commit feec54f, under GPL-3.0 · at the source

Overview

Authors: Maya Zheltyakova1, Maxim Kireev1,2, Irina Knyazeva1, Artem Myznikov1, Vladimir Kiselev1, Mikhail Didur1, Denis Cherednichenko1, Alexander Korotkov1
  1. N.P. Bechtereva Institute of the Human Brain, Russian Academy of Science, Saint Petersburg, Russia
  2. Institute for Cognitive Studies, Saint Petersburg State University, Saint Petersburg, Russia
Institutions: Russian Academy of Sciences (Russia); Institute of the Human Brain (Russia); St Petersburg University (Russia)
Journal: Frontiers in neural circuits, volume 20, article 1741762
Dates: received 7 November 2025; accepted 13 March 2026; published online 8 April 2026
Type: Brief report · Language: English
License: CC BY
Identifiers: DOI 10.3389/fncir.2026.1741762 · PMID 42028227 · PMCID PMC13099858 · OpenAlex W7152012994
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Statistics, fMRI & imaging
Keywords: affective ToM, cognitive ToM, functional MRI, mentalizing, rock-paper-scissors, strategic game, task-modulated functional connectivity
MeSH: Brain*, Social Interaction*, Theory of Mind*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministry of Science and Higher Education of the Russian Federation; Russian Science Foundation (23–18-00521)
Citations: not cited yet (Europe PMC); 67 references in the paper

Abstract

Theory of Mind (ToM) is known as the capacity to infer others’ thoughts, intentions, and emotions, supported by a distributed neural brain network, including the medial prefrontal cortex (mPFC), temporoparietal junction (TPJ), inferior frontal gyrus (IFG), and precuneus. Although the Rock-Paper-Scissors (RPS) game is used to study the cognitive ToM domain, previous fMRI studies had methodological limitations, including lack of appropriate control conditions and the absence of analyses addressing the directionality of BOLD signal changes. The present fMRI study employed a modified RPS paradigm designed to overcome these limitations. Forty-six healthy adults performed the RPS game and a control task. Whole-brain analyses contrasted neural activity and task-modulated functional connectivity (TMFC) between these conditions and examined BOLD signal changes relative to baseline. In contrast to prior findings of BOLD signal suppression below baseline in affective ToM tasks, RPS elicited increased BOLD responses in canonical ToM regions, including the mPFC, bilateral TPJ, IFG, and precuneus, as well as additional frontal, cingulate and visual regions. TMFC analyses converged with these findings, demonstrating increased RPS-related functional interactions between the bilateral TPJ and precuneus with the left IFG, and between the mPFC and the right TPJ with the right IFG. Additionally, greater deactivation (negative BOLD deflection) below baseline during RPS was observed in the midcingulate cortex and opercular regions bilaterally. These findings extend current understanding of ToM network functioning by demonstrating that the engagement of its affective and cognitive domains manifest through TMFC changes and directionally distinct neural responses.

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 4 matches between paragraphs and lines of code.

IHB-IBR-department/TMFC_toolbox

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: feec54fe8986190ff95f027b8d61c042705b095f, 31 July 2026
Languages: MATLAB (82)
Size: 138 files, 82 scripts
Software Heritage: not archived
Found in: the text
Holds: README, license file, CITATION.cff
Not found: environment file, tests, continuous integration, documentation
Tools: SPM (34 files), Parallel Computing Toolbox (13 files), Tools for NIfTI and ANALYZE image (MATLAB) (4 files), Statistics and Machine Learning Toolbox (2 files), GIfTI library for MATLAB (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
84 files
At the source: github.com/IHB-IBR-department/TMFC_toolbox

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;
  • 82 scripts, each with its path and the digest of its content;
  • 4 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 availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 7 keywords, 10 MeSH terms, 2 funders, 67 references.

Cite

This paper

Zheltyakova, M., Kireev, M., Knyazeva, I., Myznikov, A., Kiselev, V., Didur, M., Cherednichenko, D., & Korotkov, A. (2026). Task-dependent increases and decreases of BOLD signal in theory of mind brain regions during strategic social interaction. Frontiers in neural circuits, 20, 1741762. https://doi.org/10.3389/fncir.2026.1741762

BibTeX

@article{zheltyakova2026task,
author = {Zheltyakova, Maya and Kireev, Maxim and Knyazeva, Irina and Myznikov, Artem and Kiselev, Vladimir and Didur, Mikhail and Cherednichenko, Denis and Korotkov, Alexander},
title = {{Task-dependent increases and decreases of BOLD signal in theory of mind brain regions during strategic social interaction}},
journal = {Frontiers in neural circuits},
year = {2026},
month = apr,
volume = {20},
pages = {1741762},
publisher = {Frontiers Media SA},
issn = {1662-5110},
doi = {10.3389/fncir.2026.1741762},
url = {https://doi.org/10.3389/fncir.2026.1741762},
pmid = {42028227},
pmcid = {PMC13099858}
}

RIS

TY - JOUR
AU - Zheltyakova, Maya
AU - Kireev, Maxim
AU - Knyazeva, Irina
AU - Myznikov, Artem
AU - Kiselev, Vladimir
AU - Didur, Mikhail
AU - Cherednichenko, Denis
AU - Korotkov, Alexander
TI - Task-dependent increases and decreases of BOLD signal in theory of mind brain regions during strategic social interaction
T2 - Frontiers in neural circuits
J2 - Front Neural Circuits
PY - 2026
DA - 2026/04/08
VL - 20
SP - 1741762
SN - 1662-5110
PB - Frontiers Media SA
DO - 10.3389/fncir.2026.1741762
UR - https://doi.org/10.3389/fncir.2026.1741762
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fncir.2026.1741762",
"type": "article-journal",
"title": "Task-dependent increases and decreases of BOLD signal in theory of mind brain regions during strategic social interaction",
"container-title": "Frontiers in neural circuits",
"author": [
{
"family": "Zheltyakova",
"given": "Maya"
},
{
"family": "Kireev",
"given": "Maxim"
},
{
"family": "Knyazeva",
"given": "Irina"
},
{
"family": "Myznikov",
"given": "Artem"
},
{
"family": "Kiselev",
"given": "Vladimir"
},
{
"family": "Didur",
"given": "Mikhail"
},
{
"family": "Cherednichenko",
"given": "Denis"
},
{
"family": "Korotkov",
"given": "Alexander"
}
],
"container-title-short": "Front Neural Circuits",
"volume": "20",
"page": "1741762",
"DOI": "10.3389/fncir.2026.1741762",
"PMID": "42028227",
"PMCID": "PMC13099858",
"ISSN": "1662-5110",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fncir.2026.1741762",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
8
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.neuron.2026.04.011 [code]
Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex.
Journal: Neuron
In common: GIfTI library for MATLAB, Tools for NIfTI and ANALYZE image (MATLAB), Parallel Computing Toolbox, 2 other tools, fMRI, 1 reference
[2] doi:10.1038/s41467-026-71151-2 [code]
Common and distinct neural correlates of social interaction processing and theory of mind in narratives.
Journal: Nature communications
In common: GIfTI library for MATLAB, Parallel Computing Toolbox, SPM, 1 other tool, cognitive, 2 references
[3] doi:10.1002/hbm.70483 [code]
Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.
Journal: Human brain mapping
In common: GIfTI library for MATLAB, Tools for NIfTI and ANALYZE image (MATLAB), Parallel Computing Toolbox, 2 other tools, fMRI
[4] doi:10.1162/imag.a.1262 [code]
Frame-wise multi-echo distortion correction for superior functional MRI.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: GIfTI library for MATLAB, Tools for NIfTI and ANALYZE image (MATLAB), Parallel Computing Toolbox, 2 other tools, fMRI
[5] doi:10.1038/s41593-026-02219-x [code]
A neural signature of adaptive mentalization.
Journal: Nature neuroscience
In common: SPM, Statistics and Machine Learning Toolbox, fMRI, cognitive, 3 references
[6] doi:10.1162/netn.a.571 [code]
Regional activity and interregional functional connectivity uniquely contribute to social cognitive judgments during movie-watching.
Journal: Network neuroscience (Cambridge, Mass.)
In common: fMRI, 5 references
[7] doi:10.1016/j.cub.2026.05.068 [code]
An abstract relational map emerges in the human medial prefrontal cortex with consolidation.
Journal: Current biology : CB
In common: GIfTI library for MATLAB, Tools for NIfTI and ANALYZE image (MATLAB), SPM, 1 other tool, fMRI, 1 reference
[8] doi:10.1016/j.bbih.2026.101299 [code]
Multimodal approach to identify neuropsychophysiological subgroups in myalgic encephalomyelitis/chronic fatigue syndrome and their relevance for rehabilitation: protocol for a mechanistic cross-sectional and longitudinal study.
Journal: Brain, behavior, & immunity - health
In common: GIfTI library for MATLAB, Tools for NIfTI and ANALYZE image (MATLAB), Parallel Computing Toolbox, 2 other tools
[9] doi:10.1002/hbm.70602 [code]
Neuroimaging Correlates of Post-Stroke Pain After Ischemic Stroke: Secondary Analysis of the INSPiRE-TMS Trial.
Journal: Human brain mapping
In common: GIfTI library for MATLAB, Tools for NIfTI and ANALYZE image (MATLAB), Parallel Computing Toolbox, 2 other tools
[10] doi:10.1038/s41467-026-74099-5 [code]
Right amygdala ablation reduces maladaptive negative interpretation bias and symptoms in a patient with post-traumatic stress disorder.
Journal: Nature communications
In common: GIfTI library for MATLAB, Tools for NIfTI and ANALYZE image (MATLAB), Parallel Computing Toolbox, 2 other tools

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.