OSCR

Multimodal imaging-based targeting approach for network-level brain stimulation.

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] § Materials and methods › Statistical analysis › Confound time series removal ↔ scripts/denoising_pipeline.m, lines 1–62 · score 0.73 · denoising pipeline, fMRIPrep, preprocessing, confound
  2. [2] § Materials and methods › Statistical analysis › Preprocessing ↔ scripts/denoising_pipeline.m, lines 1–62 · score 0.72 · MNI152NLin6Asym, fMRIPrep, pipeline, preprocessing, spaces
  3. [3] § Materials and methods › Statistical analysis › Spatial overlap analysis ↔ scripts/spatial_overlap_analysis.py, lines 1–23 · score 0.57 · Pearson correlation, connectivity maps, coefficient, Dice, activation, overlap
  4. [4] § Materials and methods › Statistical analysis › Task-based fMRI analysis ↔ scripts/denoising_pipeline.m, lines 354–408 · score 0.56 · design matrix, GLMs, regressors, models, motion, error

Paper

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

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The authors' code

MATLAB · 574 lines · 21 KB · MIT · 3 matches

  1. % DENOISING_PIPELINE.M
  2. % Main script for resting-state fMRI denoising using CONN toolbox.
  3. % Input: Path to pre-processed data with fMRIPrep
  4. % Output: Denoised volumes in CONN's results folder.
  5. % Reference: Wang et al. (2024)
  6. % Author: Your Name
  7. %Author Alireza Shahbabaie and Filip Niemann
  9. % prepare data.
  10. % data must be in BIDS format
  12. % use unziped version of GM, WM and CSF masks conducted by fmriprep during
  14. % fresh start( no variables and values)in MATLAB
  15. clear
  16. clc
  17. %setpaths of CONN and SPM
  18. addpath /usr/local/MATLAB/MATLAB_TOOLBOX/conn_la
  19. addpath /usr/local/MATLAB/MATLAB_TOOLBOX/spm12
  20. % 1. Project name (fixed)
  21. Project = 'MeMoSLAP';
  23. % 2. Define root paths (user must customize these)
  24. % - Replace with relative paths or variables that auto-detect location
  25. repo_root = fileparts(mfilename('fullpath')); % Auto-detects script location
  27. % Default BIDS/derivatives structure (relative to repo root)
  28. root_fmriprep = fullfile(repo_root, 'derivatives', 'fMRIPrep');
  29. root_conn = fullfile(repo_root, 'derivatives', 'Conn_script_based');
  31. % 3. Mask paths (store masks in repo's /masks/ folder)
  32. mask_dir = fullfile(repo_root, 'masks');
  33. lvifg_mask_path = fullfile(mask_dir, 'resampled_res-2_lvIFG_seed_6mm.nii');
  34. rotc_mask_path = fullfile(mask_dir, 'resampled_rOTC_res-02.nii');
  35. hippo_mask_path = fullfile(mask_dir, 'resampled_hippocampus_4mm_mask_res-02.nii');
  37. % 4. Filename filters (shared across users)
  38. struct_filter_name = '_acq-mprage_space-MNI152NLin6Asym*_desc-preproc_T1w.nii.gz';
  39. func_filter_name = '_task-resting_dir-AP_space-MNI152NLin6Asym*_desc-preproc_bold.nii.gz';
  40. timeseries_filter_name = '_task-resting_dir-AP_merg_desc-confounds_timeseries.tsv';
  41. GM_filter_name = '_acq-mprage_space-MNI152NLin6Asym*_label-GM_probseg.nii';
  42. WM_filter_name = '_acq-mprage_space-MNI152NLin6Asym*_label-WM_probseg.nii';
  43. CSF_filter_name = '_acq-mprage_space-MNI152NLin6Asym*_label-CSF_probseg.nii';
  44. % 5. Session naming (user may need to modify)
  45. ses_1 = 'ses-1'; % Change to 'ses-01' if needed
  46. ses_2 ='ses-2'; % Change to 'ses-01' if needed
  47. ses_1_str ='ses_1'; % Change to 'ses_01' if needed
  48. ses_2_str ='ses_2';% Change too 'ses-02' if needed
  50. % ==============================================
  51. batch.Setup.RT = 1;
  52. % (Rest of your pipeline code here)
  54. % Verify paths exist before proceeding
  55. if ~exist(root_fmriprep, 'dir')
  56. error('fmriprep directory not found: %s', root_fmriprep);
  57. end
  58. if ~exist(root_conn, 'dir')
  59. mkdir(root_conn); % Create CONN directory if needed
  60. end
  62. batch.filename = fullfile(root_conn,'conn_rsfmri_denoising.mat');
  65. %% Get files
  66. % get all subjects in fmriprep folder, subjects are saved in list_sub.name
  67. list_sub = dir(fullfile(root_fmriprep,'sub-*'));
  69. list_sub = list_sub([list_sub.isdir]); % Only folders
  70. %for debugging use only one subject
  71. %list_sub=list_sub(1:2); %uncomment for debugging
  73. % get anatomical
  74. list_anat = dir(fullfile(root_fmriprep,'**',['sub*',struct_filter_name]));
  75. anat_path = fullfile({list_anat.folder},{list_anat.name});
  76. anat_path = sort(anat_path);
  78. %anat_path = anat_path(1:2); %uncomment for debugging
  79. % get gm mask
  80. gm_mask = dir(fullfile(root_fmriprep,'**',['sub*',GM_filter_name]));
  81. gm_path = fullfile({gm_mask.folder},{gm_mask.name});
  82. gm_path = sort(gm_path);
  83. %gm_path = gm_path(1:2); %uncomment for debugging
  85. % get gm mask
  86. wm_mask = dir(fullfile(root_fmriprep,'**',['sub*',WM_filter_name]));
  87. wm_path = fullfile({wm_mask.folder},{wm_mask.name});
  88. wm_path = sort(wm_path);
  89. %wm_path = wm_path(1:2); %uncomment for debugging
  91. % get csf mask
  92. csf_mask = dir(fullfile(root_fmriprep,'**',['sub*',CSF_filter_name]));
  93. csf_path = fullfile({csf_mask.folder},{csf_mask.name});
  94. csf_path = sort(csf_path);
  95. %csf_path = csf_path(1:2); %uncomment for debugging
  97. % get functional
  98. list_func_1 = dir(fullfile(root_fmriprep,'**',['sub*',ses_1,'*',func_filter_name]));
  99. list_func_2 = dir(fullfile(root_fmriprep,'**',['sub*',ses_2,'*',func_filter_name]));
  100. list_func = [list_func_1; list_func_2];
  101. func_path = fullfile({list_func.folder},{list_func.name});
  102. func_path = sort(func_path);
  103. %func_path = func_path(1:4); %uncomment for debugging
  105. % get timeseries
  106. list_timeseries = dir(fullfile(root_fmriprep,'**',['sub*',timeseries_filter_name]));
  107. cov_path = fullfile({list_timeseries.folder},{list_timeseries.name});
  108. cov_path = sort(cov_path);
  109. %cov_path = cov_path(1:4); %uncomment for debugging
  111. %% set basic parameters for batch and check if data are complete
  112. % Basic parameters
  113. batch.Setup.isnew = 1;
  114. batch.Setup.nsubjects = numel(list_sub);
  115. batch.Setup.nsessions = 2;
  116. batch.Setup.overwrite =1;
  117. batch.Setup.analysis =1;
  119. % number anatomical image verification
  120. if ~isequal(numel(list_sub),numel(anat_path))
  121. disp('anatomical images does not match subject count')
  122. end
  124. % number gm image verification
  125. if ~isequal(numel(list_sub),numel(gm_path))
  126. disp('grey matter images does not match subject count')
  127. end
  129. % number wm image verification
  130. if ~isequal(numel(list_sub),numel(wm_path))
  131. disp('white matter images does not match subject count')
  132. end
  134. % number csf image verification
  135. if ~isequal(numel(list_sub),numel(csf_path))
  136. disp('csf images does not match subject count')
  137. end
  139. % number functional image verification
  140. if ~isequal(numel(anat_path)*batch.Setup.nsessions,numel(func_path))
  141. disp('functional images does not match anatomical image count')
  142. end
  144. % number covariate file verification
  145. if ~isequal(numel(cov_path),numel(func_path))
  146. disp('timeseries images does not match functional image count')
  147. end
  149. % session verification
  150. for sub_idx = 1:numel(list_sub)
  151. subject_id = list_sub(sub_idx).name;
  152. ses_count = sum(contains(func_path, subject_id));
  153. if ses_count ~= 2
  154. warning('Subject %s has %d sessions (expected 2)', subject_id, ses_count);
  155. end
  156. end
  158. %% Initialize batch struct
  159. batch.Setup.structurals = cell(1, numel(list_sub));
  160. batch.Setup.functionals = cell(1, numel(list_sub));
  163. % Initialize covariates
  164. covariate_names = {'Global', 'csf', 'Motion','white_matter'};
  166. % Initialize covariates files structure properly
  167. batch.Setup.covariates.files = cell(1, numel(covariate_names));
  168. for c = 1:numel(covariate_names)
  169. batch.Setup.covariates.files{c} = cell(1, numel(list_sub));
  170. end
  172. % get timeseries and create temporary csv files
  173. % Create temporary directory for filtered CSV files
  174. temp_dir = fullfile(root_conn, 'temp_covariates');
  175. if ~exist(temp_dir, 'dir')
  176. mkdir(temp_dir);
  177. end
  179. %% set basic parameters for batch and check if data are complete
  180. % Basic parameters
  181. batch.Setup.isnew = 1;
  182. batch.Setup.nsubjects = numel(list_sub);
  183. batch.Setup.nsessions = 2;
  184. batch.Setup.overwrite =1;
  186. % number anatomical image verification
  187. if ~isequal(numel(list_sub),numel(anat_path))
  188. disp('anatomical images does not match subject count')
  189. end
  191. % number gm image verification
  192. if ~isequal(numel(list_sub),numel(gm_path))
  193. disp('grey matter images does not match subject count')
  194. end
  196. % number wm image verification
  197. if ~isequal(numel(list_sub),numel(wm_path))
  198. disp('white matter images does not match subject count')
  199. end
  201. % number csf image verification
  202. if ~isequal(numel(list_sub),numel(csf_path))
  203. disp('csf images does not match subject count')
  204. end
  206. % number functional image verification
  207. if ~isequal(numel(anat_path)*batch.Setup.nsessions,numel(func_path))
  208. disp('functional images does not match anatomical image count')
  209. end
  211. % number covariate file verification
  212. if ~isequal(numel(cov_path),numel(func_path))
  213. disp('timeseries images does not match functional image count')
  214. end
  216. % session verification
  217. for sub_idx = 1:numel(list_sub)
  218. subject_id = list_sub(sub_idx).name;
  219. ses_count = sum(contains(func_path, subject_id));
  220. if ses_count ~= 2
  221. warning('Subject %s has %d sessions (expected 2)', subject_id, ses_count);
  222. end
  223. end
  227. %% Process each subject
  228. for sub_idx = 1:numel(list_sub)
  230. subject_id = list_sub(sub_idx).name;
  232. % filter path that contain subject
  233. % Anatomical path
  234. idx_anat = contains(anat_path,subject_id);
  235. anat_path_subj = anat_path(idx_anat);
  237. % GM path
  238. idx_gm = contains(gm_path,subject_id);
  239. gm_path_subj = gm_path(idx_gm);
  241. % WM path
  242. idx_wm = contains(wm_path,subject_id);
  243. wm_path_subj = wm_path(idx_wm);
  245. % CSF path
  246. idx_csf = contains(csf_path,subject_id);
  247. csf_path_subj = csf_path(idx_csf);
  250. % functional path
  251. idx_func_ses1 = contains(func_path,subject_id) & contains(func_path,ses_1);
  252. func_path_subj_ses_1 = func_path(idx_func_ses1);
  254. idx_func_ses2 = contains(func_path,subject_id) & contains(func_path,ses_2);
  255. func_path_subj_ses_2 = func_path(idx_func_ses2);
  257. % covariate path
  258. idx_cov = contains(cov_path,subject_id);
  259. cov_path_subj = cov_path(idx_cov);
  262. % Get indices from timeseries.tsv file and create filtered CSV files
  263. [motion_indices, motion_csv_files] = get_indices_and_write_csv(Project, cov_path_subj, subject_id, "motion", temp_dir);
  264. [csf_indices, csf_csv_files] = get_indices_and_write_csv(Project, cov_path_subj, subject_id, "csf", temp_dir);
  265. [global_indices, global_csv_files] = get_indices_and_write_csv(Project, cov_path_subj, subject_id, "global", temp_dir);
  266. [wm_indices, wm_csv_files] = get_indices_and_write_csv(Project, cov_path_subj, subject_id,'white_matter', temp_dir);
  268. %% batch.Setup
  270. % Structural file
  271. batch.Setup.structurals{sub_idx} = {anat_path_subj};
  273. % Functional files
  274. batch.Setup.functionals{sub_idx} = { ...
  275. func_path_subj_ses_1, ...
  276. func_path_subj_ses_2 ...
  277. };
  279. % Covariates setup - now using the filtered CSV files
  280. batch.Setup.covariates.names = {'Global', 'csf', 'Motion','white_matter'};
  281. %% Process each subject - corrected covariates part
  282. % Corrected covariates files assignment
  283. for nses = 1:numel(global_csv_files)
  284. batch.Setup.covariates.files{1}{sub_idx}{nses} = global_csv_files{nses}; % Global
  285. batch.Setup.covariates.files{2}{sub_idx}{nses} = csf_csv_files{nses}; % CSF
  286. batch.Setup.covariates.files{3}{sub_idx}{nses} = motion_csv_files{nses}; % Motion
  287. batch.Setup.covariates.files{4}{sub_idx}{nses} = wm_csv_files{nses}; % WM
  288. end
  291. % Indices - now simple since we have 1 column per CSV
  292. batch.Setup.covariates.indices = { ...
  293. {1}, ... % Global (only 1 column in CSV)
  294. {1}, ... % CSF (only 1 column in CSV)
  295. {1:numel(motion_indices.ses_1.indices)}, ... % Motion (multiple columns)
  296. {1} ... % white matter (onley 1 column in csv
  297. };
  300. % Dimensions
  301. batch.Setup.covariates.dimensions = { ...
  302. 1, ... % Global
  303. 1, ... % CSF
  304. numel(motion_indices.ses_1.indices), ... % Motion
  305. 1, ... % WM
  306. };
  308. % ROIs setup
  309. %% ROIs setup (updated)
  310. batch.Setup.rois.names = {'Grey Matter', 'White Matter', 'CSF', 'IFG', 'OTC','Hippocampus'};
  311. batch.Setup.rois.files{1}{sub_idx} = gm_path_subj; % GM
  312. batch.Setup.rois.files{2}{sub_idx} = wm_path_subj; % WM
  313. batch.Setup.rois.files{3}{sub_idx} = csf_path_subj; % CSF
  314. batch.Setup.rois.files{4}{sub_idx} = lvifg_mask_path; % IFG (same file for all subjects)
  315. batch.Setup.rois.files{5}{sub_idx} = rotc_mask_path; % OTC (same file for all subjects)
  316. batch.Setup.rois.files{6}{sub_idx} = hippo_mask_path; % Hippocampus (same file for all subjects)
  317. batch.Setup.rois.multiplelabels = 0;
  318. batch.Setup.rois.dimensions = {1, 1, 1, 1, 1, 1};
  322. % Condition
  323. %% Condition setup - corrected version
  324. batch.Setup.conditions.names = {'rest'};
  325. for nsub = 1:numel(list_sub)
  326. for nses = 1:batch.Setup.nsessions
  327. % Get duration from this subject's data
  328. if nses == 1
  329. ses_field = 'ses_1';
  330. else
  331. ses_field = 'ses_02';
  332. end
  334. % Initialize onsets and durations for all subjects/sessions
  335. if nsub == 1 && nses == 1
  336. batch.Setup.conditions.onsets{1} = cell(1, numel(list_sub));
  337. batch.Setup.conditions.durations{1} = cell(1, numel(list_sub));
  338. end
  340. % Set onset to 0 and calculate duration based on RT and number of volumes
  341. batch.Setup.conditions.onsets{1}{nsub}{nses} = 0;
  343. % Use motion indices to get number of volumes (more reliable than CSF)
  344. if exist('motion_indices', 'var') && isfield(motion_indices, ses_field)
  345. batch.Setup.conditions.durations{1}{nsub}{nses} = batch.Setup.RT * motion_indices.(ses_field).table_length;
  346. else
  347. % Fallback to a default value if motion indices not available
  348. batch.Setup.conditions.durations{1}{nsub}{nses} = Inf; % or use a known value
  349. end
  350. end
  351. end
  352. end
  354. %% batch.Denoising
  355. % Debug: Verify denoising settings
  356. %Denoising steps for Debugging
  357. %Step 1/7: Expanding conditions - Sets up experimental conditions
  358. %Step 2/7: Importing conditions/covariates - Loads your regressors
  359. %Step 3/7: Updating Denoising variables (where you're stuck) - Prepares denoising parameters
  360. %Step 4/7: Creating Denoising design matrices - Builds the GLM model
  361. %Step 5/7: Estimating Denoising parameters (conn_process_5) - Computes the actual denoising
  362. %Step 6/7: Applying Denoising - Removes noise from data
  363. %Step 7/7: Saving results - Stores cleaned data
  365. % if you have problesm in Step 5/7 open conn_process for debugging (line
  366. % 1096)
  367. % this means ROIs where not created
  368. % line 657 Creates ROI_Subject###_Session###.mat files (activation timecourses for each roi)
  369. %this means DATA_Subject was not created
  370. % open conn_process
  372. % Denoising
  373. batch.Denoising.done = 1;
  374. batch.Denoising.overwrite = 1;
  375. batch.Denoising.filter = [0.01 Inf];
  376. batch.Denoising.detrending = 1;
  378. batch.Denoising.confounds.names = batch.Setup.covariates.names;
  379. %batch.Denoising.confounds.dimensions = batch.Setup.covariates.dimensions;
  380. batch.Denoising.confounds.dimensions = {1, 1, numel(motion_indices.ses_1.indices),1};
  381. % change derivative and power if needed
  382. batch.Denoising.confounds.deriv = {0, 0, 1, 0};
  383. batch.Denoising.confounds.power = {0, 0, 2, 0};
  385. %check if motion dimension match
  386. motion_dims_setup = batch.Setup.covariates.dimensions{3};
  387. motion_dims_denoise = batch.Denoising.confounds.dimensions{3};
  388. if ~isequal(motion_dims_setup, motion_dims_denoise)
  389. error('Motion dimensions mismatch! Setup: %d vs Denoising: %d',...
  390. motion_dims_setup, motion_dims_denoise);
  391. end
  393. disp('--- Denoising Configuration ---');
  394. disp(batch.Denoising)
  395. disp('Confounds:');
  396. disp(batch.Denoising.confounds)
  398. % Check if denoising is actually enabled
  399. if ~batch.Denoising.done
  400. warning('Denoising is set to done=0! Changing to done=1');
  401. batch.Denoising.done = 1;
  402. end
  404. %Verify filter settings
  405. if isempty(batch.Denoising.filter) || ~all(isfinite(batch.Denoising.filter))
  406. warning('Invalid filter settings! Using default [0.01 Inf]');
  407. batch.Denoising.filter = [0.01 Inf];
  408. end
  410. %% because incorrect covariate specification is the most common error for silent failure add verification code
  411. % Debug: Verify covariates
  412. disp('--- Covariates Implementation ---');
  413. for c = 1:numel(batch.Denoising.confounds.names)
  414. fprintf('Covariate %d (%s):\n', c, batch.Denoising.confounds.names{c});
  415. fprintf('Dimensions: %s\n', mat2str(batch.Denoising.confounds.dimensions{c}));
  416. fprintf('Derivatives: %d\n', batch.Denoising.confounds.deriv{c});
  417. fprintf('Powers: %d\n', batch.Denoising.confounds.power{c});
  419. % Verify files exist
  420. for s = 1:min(3,numel(list_sub)) % Check first 3 subjects
  421. if numel(batch.Setup.covariates.files) >= c && ...
  422. numel(batch.Setup.covariates.files{c}) >= s
  423. fprintf('Subject %d files exist: %d\n', s, ...
  424. exist(batch.Setup.covariates.files{c}{s}{1}, 'file'));
  425. end
  426. end
  427. end
  429. %% batch.Analysis
  430. %% Analysis
  431. % Analysis setup to run ALL steps
  432. batch.Analysis.done = 1;
  433. batch.Analysis.overwrite = 1;
  434. batch.Analysis.measure = 1; % Correlation
  435. batch.Analysis.type = 'all'; % Changed from 3 to 'ROI-to-ROI' for clarity
  436. batch.Analysis.sources = {'IFG', 'OTC','Hippocampus'}; % Seed ROI % Empty for ROI-to-ROI analysis
  437. batch.Analysis.ROI_files = {'GM', 'WM', 'CSF'}; % All ROIs for ROI-to-ROI
  439. % ADD THESE TWO LINES RIGHT HERE:
  440. batch.Analysis.save = 1; % Saves individual subject results
  441. batch.Analysis.keep = 1; % Keeps temporary analysis files
  443. % Set up the GUI interaction option for ROI-to-ROI analysis
  444. batch.Analysis.gui = 1; % Enable GUI interaction
  445. batch.Analysis.gui_prompt = 1; % Ask user on each step
  446. batch.Analysis.name = 'SBC_MeMoSLAP_rest'; % Name for this analysis
  448. % Additional analysis parameters
  449. batch.Analysis.weight = 2; % Fisher-transformed correlation coefficients
  450. batch.Analysis.modulation = 0; % No modulation
  451. batch.Analysis.symmetric = 1; % Symmetric matrices
  452. batch.Analysis.scale = 1; % Scale to correlation units
  455. %% Save and run
  456. batch_file = fullfile(root_conn,'conn_rsfmri_denoising.mat');
  457. save(batch_file, 'batch');
  459. % Display for debugging:
  460. disp('--- Analysis Configuration ---');
  461. disp(['Analysis type: ', batch.Analysis.type]);
  462. disp(['GUI enabled: ', num2str(batch.Analysis.gui)]);
  463. disp(['GUI prompts: ', num2str(batch.Analysis.gui_prompt)]);
  464. disp(['ROIs included: ', strjoin(batch.Analysis.ROI_files, ', ')]);
  466. % First run setup steps (0-5)
  467. setup_batch = batch;
  468. setup_batch.Analysis.done = 0; % Don't run analysis yet
  469. conn_batch(setup_batch);
  471. % Then run the ROI-to-ROI analysis with GUI interaction
  472. analysis_batch = batch;
  473. analysis_batch.Setup.done = 1; % Mark setup as already done
  474. analysis_batch.Denoising.done = 1; % Mark denoising as already done
  475. conn_batch(analysis_batch);
  478. %% Clean up temporary files
  479. % Move cleanup to the very end and add verification
  480. try
  481. conn_batch(batch,'setup');
  482. % Verify processing completed successfully before cleanup
  484. % csv file path is saved in batch so don't delete
  485. %if exist(fullfile(root_conn,'conn_rsfmri.mat'), 'file')
  486. % rmdir(temp_dir, 's');
  487. %end
  488. catch ME
  489. warning('Keeping temp files for debugging due to error');
  490. end
  492. %% define functions
  493. function [indices, csv_files] = get_indices_and_write_csv(Project, cov_path, subject_id, covariate_column, temp_dir)
  494. if strcmp(Project,'VerFlu')
  495. sessions = {'ses-01', 'ses-02'};
  496. elseif strcmp(Project,'MeMoSLAP')
  497. sessions = {'ses-1', 'ses-2'};
  498. end
  501. indices = struct();
  502. csv_files = cell(1, numel(sessions));
  504. for i = 1:numel(sessions)
  505. ses = sessions{i};
  506. ses_field = strrep(ses, '-', '_');
  507. idx = contains(cov_path,ses);
  508. confound_file = cov_path(idx);
  510. if exist(confound_file{1}, 'file')
  511. T = readtable(confound_file{1}, 'FileType', 'text', 'Delimiter', '\t');
  512. colnames = T.Properties.VariableNames;
  514. % Determine which columns to select
  515. if strcmp(covariate_column, 'motion')
  516. is_cov = startsWith(colnames, 'trans') | startsWith(colnames, 'rot');
  517. contains_deriv = contains(colnames, 'derivative') | contains(colnames, 'power') | contains(colnames, 'wm');
  518. select_cols = is_cov & ~contains_deriv;
  519. else
  520. is_cov = startsWith(colnames, covariate_column);
  521. contains_deriv = contains(colnames, 'derivative') | contains(colnames, 'power') | contains(colnames, 'wm');
  522. select_cols = is_cov & ~contains_deriv;
  523. end
  525. idx = find(select_cols);
  527. % Create filtered table with only selected columns
  528. filtered_table = T(:, idx);
  530. % Create CSV filename
  531. csv_filename = fullfile(temp_dir, sprintf('%s_%s_%s.csv', subject_id, ses, covariate_column));
  533. % Write to CSV
  534. writetable(filtered_table, csv_filename);
  535. fprintf('Created filtered CSV: %s\n', csv_filename);
  537. % Store CSV path for CONN
  538. csv_files{i} = csv_filename;
  540. % Check for NaN/Inf
  541. nan_or_inf = false(1, numel(idx));
  542. for j = 1:numel(idx)
  543. col_data = table2array(T(:, idx(j)));
  544. nan_or_inf(j) = any(isnan(col_data)) || any(isinf(col_data));
  545. end
  547. if any(nan_or_inf)
  548. fprintf('Session %s: WARNING: NaN or Inf detected in columns: %s\n', ...
  549. ses, strjoin(colnames(idx(nan_or_inf)), ', '));
  550. end
  552. % Get table length from CSF column if available
  553. if ismember('csf', colnames)
  554. table_length = height(T);
  555. else
  556. table_length = NaN;
  557. end
  559. % Store indices
  560. indices.(ses_field).indices = idx;
  561. indices.(ses_field).names = colnames(idx);
  562. indices.(ses_field).nan_or_inf = nan_or_inf;
  563. indices.(ses_field).table_length = table_length;
  565. else
  566. warning('File not found: %s', confound_file);
  567. indices.(ses_field).indices = [];
  568. indices.(ses_field).names = {};
  569. indices.(ses_field).nan_or_inf = [];
  570. indices.(ses_field).table_length = NaN;
  571. csv_files{i} = '';
  572. end
  573. end
  574. end

denoising_pipeline.m at commit 71fdc65, under MIT · at the source

Overview

Authors: Alireza Shahbabaie1, Mohamed Abdelmotaleb1, Harun Kocataş1, Filip Niemann1, Daria Antonenko1, Agnes Flöel1,2, Marcus Meinzer1
  1. Department of Neurology, University Medicine Greifswald, Greifswald, Germany
  2. German Center for Neurodegenerative Diseases (DZNE Site Greifswald), Greifswald, Germany
Institutions: Universitätsmedizin Greifswald (Germany); Universität Greifswald (Germany); German Center for Neurodegenerative Diseases (Germany)
Journal: Frontiers in neuroscience, volume 20, article 1803897
Dates: received 4 February 2026; accepted 8 May 2026; published online 29 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1803897 · PMID 42292341 · PMCID PMC13260067 · OpenAlex W7162752289
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), other (modality), human (organism), systems (subfield)
Methods: Statistics, Connectivity, Machine learning, fMRI & imaging
Keywords: concurrent tDCS-fMRI, focal tDCS, functional connectivity, memory and language learning, multimodal magnetic resonance imaging, network-level brain stimulation, non-invasive brain stimulation (NIBS)
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 77 references in the paper
Research resources: 771) and the CONN toolbox RRID:SCR_009550

Abstract

Introduction: Neural network effects of transcranial direct current stimulation (tDCS) are poorly understood. Here, we introduce a prospective, empirically informed, multimodal functional magnetic resonance imaging (fMRI) framework for guiding target selection and hypothesis-based analysis in future focal tDCS-fMRI studies.

Methods: We illustrate our approach by using data of 37 healthy individuals (19 females; mean age ± SD = 25.8 ± 5.9) recruited from two tDCS-fMRI studies that were acquired at the same scanner and with placebo-tDCS. Participants completed two resting-state (RS) sessions and two task-fMRI sessions (object-location memory, OLM, or associative picture-pseudoword learning, APPL, experiments). Seed-based RS analysis identified functional networks originating from target regions for focal tDCS (right occipito-temporal cortex, rOTC; left ventral IFG, lvIFG) and established their test-retest reliability (TRR), using intraclass correlation coefficients (ICC). Dice coefficients quantified overlap between seeded RS networks and task-evoked activity to identify task-active regions potentially affected by downstream network effects from the target regions.

Results: Seed-based analyses identified highly reliable ventral visual-limbic (rOTC) and language-related networks (lvIFG), with 72-77% of voxels showing good-to-excellent TRR (ICC ≥ 0.75). Only a subset of network voxels identified by the RS analyses overlapped with activity elicited by the experimental paradigms (ranging from 7.5-55%), with larger correspondence for the OLM (Dice: 0.249-0.349; APPL 0.065-0.106). Therefore, the degree of potential tDCS network effects varied substantially depending on the target region, the extent of its functional network and task-specific activity patterns. Degree of correspondence was further mediated by the selected contrasts-of-interest in the task-based analyses, with more conservative control conditions resulting in reduced overlap.

Conclusion: In sum, we established a principled multimodal fMRI framework bridging a critical gap in neuromodulation research. By integrating reliable intrinsic connectivity maps with task-evoked activity patterns, we provide a method to prospectively identify network-level targets for focal brain stimulation and generate hypotheses for tDCS-fMRI analyses. This approach shifts the rationale from stimulating isolated brain regions to strategically targeting key nodes within a predefined functional pathway.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

ShahAliR/memoslap-denoising-pipeline

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 71fdc65075ca839a95213c109532702584b9b2ff, 8 August 2025
Languages: MATLAB (1)
Size: 10 files, 1 script
Software Heritage: not archived
Found in: the text, “Confound time series removal”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CONN (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/ShahAliR/memoslap-denoising-pipeline

ShahAliR/NetworkOverlap-Task-RS-fMRI

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 842bca5478281657c8daad79bc9df09e492ff8bb, 23 January 2026
Languages: Jupyter (1), Python (1)
Size: 7 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NiBabel (2 files), Nilearn (2 files), NumPy (2 files), SciPy (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/ShahAliR/NetworkOverlap-Task-RS-fMRI

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 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

No dataset and no data link were found in the paper.

Data availability statement

The study was pre-registered with the Open Science Framework (OSF), and the protocol can be accessed through OSF Registries (https://osf.io/t37u2). To ensure full transparency and reproducibility, our analysis scripts are publicly available on GitHub (https://github.com/ShahAliR/NetworkOverlap-Task-RS-fMRI). Due to ethical and privacy concerns about the study’s participants, the row data are not publicly accessible. However, the corresponding author can provide the data supporting the study’s conclusions upon request.

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, pages, dates, 7 authors, 7 keywords, 75 references, 1 RRID.

Cite

This paper

Shahbabaie, A., Abdelmotaleb, M., Kocataş, H., Niemann, F., Antonenko, D., Flöel, A., & Meinzer, M. (2026). Multimodal imaging-based targeting approach for network-level brain stimulation. Frontiers in neuroscience, 20, 1803897. https://doi.org/10.3389/fnins.2026.1803897

BibTeX

@article{shahbabaie2026multimodal,
author = {Shahbabaie, Alireza and Abdelmotaleb, Mohamed and Kocataş, Harun and Niemann, Filip and Antonenko, Daria and Flöel, Agnes and Meinzer, Marcus},
title = {{Multimodal imaging-based targeting approach for network-level brain stimulation}},
journal = {Frontiers in neuroscience},
year = {2026},
month = may,
volume = {20},
pages = {1803897},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1803897},
url = {https://doi.org/10.3389/fnins.2026.1803897},
pmid = {42292341},
pmcid = {PMC13260067}
}

RIS

TY - JOUR
AU - Shahbabaie, Alireza
AU - Abdelmotaleb, Mohamed
AU - Kocataş, Harun
AU - Niemann, Filip
AU - Antonenko, Daria
AU - Flöel, Agnes
AU - Meinzer, Marcus
TI - Multimodal imaging-based targeting approach for network-level brain stimulation
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/05/29
VL - 20
SP - 1803897
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1803897
UR - https://doi.org/10.3389/fnins.2026.1803897
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnins.2026.1803897",
"type": "article-journal",
"title": "Multimodal imaging-based targeting approach for network-level brain stimulation",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Shahbabaie",
"given": "Alireza"
},
{
"family": "Abdelmotaleb",
"given": "Mohamed"
},
{
"family": "Kocataş",
"given": "Harun"
},
{
"family": "Niemann",
"given": "Filip"
},
{
"family": "Antonenko",
"given": "Daria"
},
{
"family": "Flöel",
"given": "Agnes"
},
{
"family": "Meinzer",
"given": "Marcus"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1803897",
"DOI": "10.3389/fnins.2026.1803897",
"PMID": "42292341",
"PMCID": "PMC13260067",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnins.2026.1803897",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
29
]
]
}
}

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

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