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Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.

Code ↔ Paper

10 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 10 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Material and Methods › Datasets › The Genomics Superstruct Project (GSP) Dataset ↔ stable_projects/preprocessing/CBIG_fMRI_Preproc2016/utilities/CBIG_preproc_censor_wrapper.m, lines 1–119 · score 0.72 · bandpass filtering, 0.08 Hz, fMRI, censoring, outlier, motion
  2. [2] § Material and Methods › Datasets › The Genomics Superstruct Project (GSP) Dataset ↔ stable_projects/preprocessing/CBIG_fMRI_Preproc2016/utilities/CBIG_preproc_censor.m, lines 1–112 · score 0.71 · bandpass filtering, 0.08 Hz, fMRI, censoring, outlier, motion
  3. [3] § Material and Methods › Tensor‐Based Identification of Brain Networks Using NASCAR and BrainSync ↔ stable_projects/brain_parcellation/Kong2019_MSHBM/step3_generate_ind_parcellations/CBIG_MSHBM_parameters_validation.m, lines 1–142 · score 0.68 · inter subject, surface space, fMRI, variability, locations, fsaverage6
  4. [4] § Material and Methods › Tensor‐Based Identification of Brain Networks Using NASCAR and BrainSync ↔ stable_projects/brain_parcellation/Kong2022_ArealMSHBM/step3_generate_ind_parcellations/CBIG_ArealMSHBM_gMSHBM_generate_individual_parcellation.m, lines 1–149 · score 0.63 · inter subject, surface space, variability, locations, fsaverage6, gradient
  5. [5] § Material and Methods › Graph Construction From NASCAR Spatial Maps ↔ utilities/matlab/predictive_models/KernelRidgeRegression/CBIG_KRR_generate_kernels_LITE.m, the whole file · a weak match · score 0.58 · Gaussian kernel, feature matrix, inter, Pearson, algorithm, vectors
  6. [6] § Material and Methods › Comparison With Existing Parcellations ↔ stable_projects/brain_parcellation/Kong2022_ArealMSHBM/lib/CBIG_ArealMSHBM_component_distance.m, the whole file · a weak match · score 0.57 · contiguous parcels, fs lr, surface space, Parcellations
  7. [7] § Material and Methods › Datasets › The Human Connectome Project (HCP) Dataset ↔ src/utils/BDP_tools/EPI_correct_files_registration_INVERSION.m, lines 35–84 · score 0.56 · phase encoding direction, isotropic, resolution, space
  8. [8] § Material and Methods › Datasets › The Human Connectome Project (HCP) Dataset ↔ utilities/matlab/transforms/CBIG_Projectfsaverage2MNI_Ants.m, lines 1–85 · score 0.55 · MNI space, cortical surface, smoothing, pipeline, MRI, resolution
  9. [9] § Material and Methods › Comparison With Existing Parcellations ↔ stable_projects/brain_parcellation/Kong2022_ArealMSHBM/step3_generate_ind_parcellations/CBIG_ArealMSHBM_cMSHBM_generate_individual_parcellation.m, lines 1–142 · score 0.51 · fs lr, surface space, fsaverage, contiguous, hemispheres, networks
  10. [10] § Material and Methods › Datasets › The Genomics Superstruct Project (GSP) Dataset ↔ stable_projects/preprocessing/Li2019_GSR/VarianceComponentModel/scripts/CBIG_LiGSR_LME_workflowGSP.m, the whole file · a weak match · score 0.50 · Genomics Superstruct Project, GSP

Paper

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The authors' code

MATLAB · 374 lines · 14 KB · MIT · 1 match

  1. function CBIG_preproc_censor_wrapper(BOLD_in, outlier_file, TR, BOLD_interm_out, BOLD_final_out, loose_mask, max_mem, low_f, high_f)
  3. % CBIG_preproc_censor_wrapper(BOLD_in, outlier_file, TR, BOLD_interm_out, BOLD_final_out, loose_mask, max_mem, low_f, high_f)
  4. %
  5. % Motion scrubbing for fMRI preprocessing. Users can perform bandpass
  6. % filtering simultaneously by specifying low_f and high_f, where [low_f,
  7. % high_f] (inclusive) is the passband. If low_f and high_f are not passed
  8. % in, then bandpass filtering will not be performed.
  9. %
  10. % This function uses the same censoring interpolation method as Power et
  11. % al. 2014. Given the input fMRI file (BOLD_in), the outlier file
  12. % (outlier_file), and TR, this function calls CBIG_preproc_censor.m to
  13. % perform signal recovery and interpolation, where Lomb-Scargle Periodogram
  14. % method is used. The intermediate signals and the final signals are stored
  15. % in nifti files BOLD_interm_out and BOLD_final_out respectively.
  16. %
  17. % Note: At the beginning, this function detrend on the uncensored frames.
  18. % This trend is not added back at the end. Moreover, no matter what option
  19. % combination does the user pass in, the mean of the signal is not added
  20. % back, which means if low_f = 0, 0 is NOT included in the passband.
  21. %
  22. % Input:
  23. % - BOLD_in:
  24. % the BOLD file name before motion scrubbing (full path), e.g.
  25. % 'subject_dir/subject_name/bold/subject_name_bld002_rest_skip4_stc_mc.nii.gz'
  26. %
  27. % - outlier_file:
  28. % file name of outliers (full path). In this file, each line is a
  29. % number of 0 or 1, where 0 indicates high motion frames that the
  30. % users want to censor, while 1 indicates low motion frames. e.g.
  31. % 'subject_dir/subject_name/qc/subject_name_bld002_FDRMS0.2_DVARS50_motion_outliers.txt'
  32. %
  33. % - TR:
  34. % a string, the repetion time of fMRI data. The unit is milisecond.
  35. % e.g. '3000'
  36. %
  37. % - BOLD_interm_out:
  38. % the BOLD file name of intermediate result (full path). The
  39. % intermediate result means the signal of each time point (including
  40. % low motion time points) recovered by Lomb-Scargle Periodogram.
  41. % e.g. '<subject_dir>/<subject_name>/bold/<subject_name>_bld002_rest_skip4_stc_mc_interp_inter_FDRMS0.2_DVARS50.nii.gz'
  42. %
  43. % - BOLD_out:
  44. % the final BOLD file name after motion scrubbing (full path).
  45. % (a) If bandpass filtering is not performed (low_f and high_f are not
  46. % passed in), the final signals are the interpolationed signal with
  47. % uncensored (low-motion) frames replaced by original signals.
  48. % (b) If bandpass filtering is performed (both low_f and high_f are
  49. % passed in), the coefficients of frequency components outside the
  50. % passband are set to be 0, and then we use the masked coefficients
  51. % to recover the final signals.
  52. % e.g. '<subject_dir>/<subject_name>/bold/<subject_name>_bld002_rest_skip4_stc_mc_interp_FDRMS0.2_DVARS50.nii.gz'
  53. %
  54. % - loose_mask:
  55. % the filename of a loose whole brain mask (full path). The
  56. % interpolation will only be done for the voxels within this loose
  57. % mask to save time (if passed in). The voxels outside the mask will
  58. % be set to 0.
  59. % e.g. '<subject_dir>/<subject_name>/bold/mask/<subject_name>.loosebrainmask.bin.nii.gz'
  60. % In the cases that you are not able to pass in a loose mask (for
  61. % instance, "BOLD_in" is a CIFTI dtseries file), you have two options:
  62. % (1) if you still want to pass in the latter two parameters "low_f"
  63. % and "high_f", you need to pass 'NONE' to "loose_mask" argument.
  64. % (2) if you do not need "low_f" and "high_f", you can skip
  65. % "loose_mask" argument as well.
  66. %
  67. % - max_mem:
  68. % a string of numbers to specify the maximal memory usage, or 'NONE'
  69. % (does not specify maximal memory usage). The unit is in Gigabyte.
  70. % In our code, we use a parameter k to adjust the maximal memory
  71. % usage, which is calculated according to this equation (concluded
  72. % from our tests)
  73. % max_mem (G) = 1 + (8e-4) * k * T
  74. % k is defined to determine the number of voxels processed each
  75. % time under this equation
  76. % V0 = floor(k * V / T / (oversample_fac/2))
  77. % where V0 is the number of voxels processed each time, V is the
  78. % total number of voxels within the whole brain (or within grey
  79. % matter, if "loose_mask" is passed in), T is the number of frames,
  80. % oversample_fac is an oversampling factor used in the Lomb-Scargle
  81. % algorithm, which is set to be 8.
  82. % We define this complicated equation is because we found the maximal
  83. % memory ussage is linearly proportional to the number of voxels
  84. % processed each time, and quadractically proportional to the number
  85. % of frames.
  86. % We suggest the users to pass in a number that is 1G less than the
  87. % memory you will require from your job scheduler.
  88. % If 'NONE' is passed in, we use the default k = 20.
  89. %
  90. % - low_f:
  91. % a string, low cut-off frequency. The passband includes cut-off
  92. % frequencies.
  93. % e.g. if the passband is [0, 0.08], then low_f is '0'.
  94. %
  95. % - high_f:
  96. % a string, high cut-off frequency. The passband includes cut-off
  97. % frequencies. If the user wants to do highpass filtering, then
  98. % high_f is 'Inf'.
  99. % e.g. if the passband is [0, 0.08], then high_f is '0.08'.
  100. %
  101. % Example:
  102. % CBIG_preproc_censor_wrapper('subject_dir/subject_name/bold/subject_name_bld002_rest_skip4_stc_mc.nii.gz',
  103. % 'subject_dir/subject_name/qc/subject_name_bld002_FDRMS0.2_DVARS50_motion_outliers.txt',
  104. % '3000',
  105. % 'subject_dir/subject_name/bold/subject_name_bld002_rest_skip4_stc_mc_interp_inter_FDRMS0.2_DVARS50.nii.gz',
  106. % 'subject_dir/subject_name/bold/subject_name_bld002_rest_skip4_stc_mc_interp_FDRMS0.2_DVARS50.nii.gz',
  107. % 'subject_dir/subject_name/bold/mask/subject_name.loosebrainmask.nii.gz'
  108. % '5'
  109. % '0', '0.08')
  110. %
  111. % Reference:
  112. % 1) Power, Jonathan D., et al. "Methods to detect, characterize, and
  113. % remove motion artifact in resting state fMRI." Neuroimage 84 (2014):
  114. % 320-341.
  115. %
  116. % Date: Jun.3, 2016
  117. %
  118. % Written by Jingwei Li.
  119. % Written by CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
  122. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  123. % check loose mask
  124. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  125. mask_flag = 1;
  126. if(~exist('loose_mask', 'var') || strcmp(loose_mask, 'NONE'))
  127. mask_flag = 0;
  128. end
  131. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  132. % check low_f and high_f
  133. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  134. if(~exist('low_f', 'var') && ~exist('high_f', 'var'))
  135. bandpass_flag = 0;
  136. fprintf('Do not perform bandpass filtering in censoring.\n');
  137. elseif(exist('low_f', 'var') && exist('high_f', 'var'))
  138. bandpass_flag = 1;
  139. fprintf('Perform bandpass filtering in censoring.\n');
  140. fprintf('Passband is [%s, %s] (inclusive).\n', low_f, high_f);
  141. else
  142. error('low_f or high_f does not exist! Please check the input arguments.\n');
  143. end
  146. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  147. % Read BOLD data and outliers
  148. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  149. [input, in_vol, in_size] = read_fmri(BOLD_in);
  151. outliers = dlmread(outlier_file); % N (number of timepoints) x 1 binary vector, 0 means censored (high-motion) frames.
  152. outliers = ~outliers; % N x 1 binary vector, 0 means uncensored (low-motion) frames.
  153. % if no frame to be censored, skip whole procedure
  154. if(~any(outliers==1))
  155. write_fmri(BOLD_interm_out, input, in_vol, in_size);
  156. write_fmri(BOLD_final_out, input, in_vol, in_size);
  157. return
  158. end
  160. % Read loose mask and apply it, if there is one.
  161. if(mask_flag == 1)
  162. [~, mask_vol, ~] = read_fmri(loose_mask);
  163. mask_ind = find(mask_vol ~= 0);
  164. in_vol = in_vol(mask_ind, :);
  165. end
  167. % remove voxels with 0 signal
  168. zero_ind = sum(abs(in_vol(:, outliers==0)), 2)==0;
  169. if(~isempty(zero_ind==1))
  170. in_vol(zero_ind==1, :) = [];
  171. end
  174. % detrend, trend is computed from uncensored frames
  175. [in_vol, ~, ~, retrend] = CBIG_glm_regress_matrix(in_vol', [], 1, ~outliers);
  176. in_vol = in_vol';
  178. if(length(outliers)~=in_size(end))
  179. error('length of outlier file is not the same as the number of timepoints of input volume');
  180. end
  184. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  185. % parameters setup
  186. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  187. N = size(in_vol, 2);
  189. % check maximal memory usage
  190. if(~exist('max_mem') || strcmp(max_mem, 'NONE'))
  191. k = 20;
  192. else
  193. max_mem = str2num(max_mem);
  194. k = (max_mem - 1) / (8e-4) / N; % Tests show that 1 + (8e-4) * k * num_frames = max_mem (G)
  195. end
  196. fprintf('The factor used to split voxel batches is k = %f.\n', k);
  198. TR = str2num(TR);
  199. TR = TR/1000;
  200. t = (1:N)' * TR; % N x 1 vector, time of all frames
  201. t_uncen = t(outliers==0); % (N-M) x 1 vector, time of uncensored frames
  203. oversample_fac = 8; % oversampling factor for Lomb-Scargle periodogram
  205. if(bandpass_flag == 1)
  206. low_f = str2num(low_f);
  207. high_f = str2num(high_f);
  208. end
  211. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  212. % Lomb-Scargle Periodogram
  213. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  215. % divide voxels into branches, to reduce memory usage
  216. voxbinsize = floor(k * size(in_vol, 1) / N / (oversample_fac/2));
  217. voxbin = 1:voxbinsize:size(in_vol,1);
  218. voxbin = [voxbin size(in_vol,1)+1]; % voxbin is the starting voxels in each branch
  220. for v = 1:length(voxbin)-1
  221. fprintf('Dealing with voxels from %d to %d ...\n', voxbin(v), voxbin(v+1)-1);
  223. uncen_series = in_vol(voxbin(v):(voxbin(v+1)-1), outliers==0)'; % grab uncensored frames and the voxels in the voxel branch
  225. % interpolation, where interm_out_series is always the signal only with interppolation
  226. if(bandpass_flag == 0)
  227. % if no bandpass, out_series is the same signal with interm_out_series
  228. [out_vol(:, voxbin(v):(voxbin(v+1)-1)), interm_out_vol(:, voxbin(v):(voxbin(v+1)-1))] = CBIG_preproc_censor(uncen_series, t_uncen, t, oversample_fac, outliers);
  229. else
  230. % if bandpass, out_series is the interpolated signal within passband
  231. [out_vol(:, voxbin(v):(voxbin(v+1)-1)), interm_out_vol(:, voxbin(v):(voxbin(v+1)-1))] = CBIG_preproc_censor(uncen_series, t_uncen, t, oversample_fac, outliers, low_f, high_f);
  232. end
  234. end
  237. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  238. % save output
  239. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  241. interm_out_vol = single(interm_out_vol');
  242. out_vol = single(out_vol');
  243. if(bandpass_flag == 0)
  244. % if no bandpass, replace the uncensored frames with the original signal.
  245. out_vol(:, outliers==0) = in_vol(:, outliers==0);
  246. end
  248. % check if output timeseries contain NaN
  249. if(any(isnan(interm_out_vol)))
  250. fprintf('ERROR: intermediate output volume contains NaN.\n'); return;
  251. end
  252. if(any(isnan(out_vol)))
  253. fprintf('ERROR: final output volume contains NaN.\n'); return;
  254. end
  256. % recover voxels with 0 signal
  257. if(~isempty(zero_ind==1))
  258. tmp_interm_out = interm_out_vol;
  259. interm_out_vol = zeros(length(zero_ind), in_size(4));
  260. interm_out_vol(zero_ind==0, :) = tmp_interm_out;
  262. tmp_out = out_vol;
  263. out_vol = zeros(length(zero_ind), in_size(4));
  264. out_vol(zero_ind==0, :) = tmp_out;
  265. end
  267. % If there is a loose mask, construct output volumes
  268. if(mask_flag == 1)
  269. tmp_interm_out = interm_out_vol;
  270. interm_out_vol = zeros(prod(in_size(1:3)), in_size(4));
  271. interm_out_vol(mask_ind, :) = tmp_interm_out;
  272. clear tmp_interm_out
  274. tmp_out = out_vol;
  275. out_vol = zeros(prod(in_size(1:3)), in_size(4));
  276. out_vol(mask_ind, :) = tmp_out;
  277. clear tmp_out
  278. end
  280. % write out output volumes
  281. write_fmri(BOLD_interm_out, input, interm_out_vol, in_size);
  283. write_fmri(BOLD_final_out, input, out_vol, in_size);
  287. end
  293. function [fmri, vol, vol_size] = read_fmri(fmri_name)
  295. % [fmri, vol] = read_fmri(fmri_name)
  296. % Given the name of functional MRI file (fmri_name), this function read in
  297. % the fmri structure and the content of signals (vol).
  298. %
  299. % Input:
  300. % - fmri_name:
  301. % The full path of input file name.
  302. %
  303. % Output:
  304. % - fmri:
  305. % The structure read in by MRIread() or ft_read_cifti(). To save
  306. % the memory, fmri.vol (for NIFTI) or fmri.dtseries (for CIFTI) is
  307. % set to be empty after it is transfered to "vol".
  308. %
  309. % - vol:
  310. % A num_voxels x num_timepoints matrix which is the content of
  311. % fmri.vol (for NIFTI) or fmri.dtseries (for CIFTI) after reshape.
  312. %
  313. % - vol_size:
  314. % The size of fmri.vol (NIFTI) or fmri.dtseries (CIFTI).
  316. if (isempty(strfind(fmri_name, '.dtseries.nii')))
  317. % if input file is NIFTI file
  318. fmri = MRIread(fmri_name);
  319. vol = single(fmri.vol);
  320. vol_size = size(vol);
  321. if(length(vol_size) < 4)
  322. vol = reshape(vol, prod(vol_size(1:3)), 1);
  323. else
  324. vol = reshape(vol, prod(vol_size(1:3)), vol_size(4));
  325. end
  326. fmri.vol = [];
  327. else
  328. % if input file is CIFTI file
  329. fmri = ft_read_cifti(fmri_name);
  330. vol = single(fmri.dtseries);
  331. vol_size = size(vol);
  332. fmri.dtseries = [];
  333. end
  336. end
  340. function write_fmri(fmri_name, fmri, vol, vol_size)
  342. % function write_fmri(fmri_name, fmri, vol)
  343. % This function write out a fmri strucure (fmri) with signal content (vol)
  344. % into fmri_name.
  345. %
  346. % Input:
  347. % - fmri_name:
  348. % The output fMRI file name (full path).
  349. %
  350. % - fmri:
  351. % The structure for MRIwrite() or ft_write_cifti() to write out.
  352. %
  353. % - vol:
  354. % The content of fMRI signals that need to be assgined to fmri.vol
  355. % (for NIFTI) or fmri.dtseries (for CIFTI).
  356. %
  357. % - vol_size:
  358. % The size of volume when it was initially read in.
  360. if(isempty(strfind(fmri_name, '.dtseries.nii')))
  361. % if output file is NIFTI file
  362. vol = reshape(vol, vol_size);
  363. fmri.vol = single(vol);
  364. MRIwrite(fmri, fmri_name);
  365. else
  366. % if output file is CIFTI file
  367. vol = reshape(vol, vol_size);
  368. fmri.dtseries = single(vol);
  369. fmri_name = regexprep(fmri_name, '.dtseries.nii', '');
  370. ft_write_cifti(fmri_name, fmri, 'parameter', 'dtseries');
  371. end
  374. end

CBIG_preproc_censor_wrapper.m at commit 685d721, under MIT · at the source

Overview

Authors: Yijun Liu1, Jian Li2,3, Jessica L. Wisnowski4,5, Richard M. Leahy1
ORCID iDs: Yijun Liu, Jian Li, Jessica L. Wisnowski, Richard M. Leahy
  1. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California USA
  2. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology Massachusetts General Hospital and Harvard Medical School Charlestown Massachusetts USA
  3. Center for Neurotechnology and Neurorecovery, Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA
  4. Radiology and Pediatrics, Division of Neonatology Children's Hospital Los Angeles Los Angeles California USA
  5. Keck School of Medicine University of Southern California Los Angeles California USA
Institutions: University of Southern California (United States); Harvard University (United States); Massachusetts General Hospital (United States); Athinoula A. Martinos Center for Biomedical Imaging (United States); Children's Hospital of Los Angeles (United States)
Journal: Human brain mapping, volume 47, issue 4, article e70483
Dates: received 4 August 2025; accepted 14 February 2026; published online 6 March 2026; in print March 2026
Type: Other · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/hbm.70483 · PMID 41787960 · PMCID PMC12963934 · OpenAlex W7134087590
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Statistics, Machine learning, fMRI & imaging
Keywords: cortical parcellation, graph representation learning, resting‐state fMRI, temporal synchronization, tensor decomposition
MeSH: Cerebral Cortex*, Connectome*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Nerve Net*, Humans (* major topic)
Journal subjects: Technical Report
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH (NIH R01NS074980, NIH R01EB026299)
Citations: not cited yet (Europe PMC); 58 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

untamed-atlas.github.io

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: untamed-atlas.github.io/

ThomasYeoLab/Standalone_CBIG_fMRI_Preproc2016

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 685d721e68e8a3405f9b7340108009ce9f550cc2, 18 March 2025
Languages: MATLAB (1048), Shell (109), C (88), C++ (65), C/C++ (45), Python (5)
Size: 4,704 files, 1,360 scripts
Software Heritage: not archived
Found in: the text, “The Genomics Superstruct Project (GSP) Dataset”
Holds: README, license file, tests, documentation
Not found: CITATION.cff, environment file, continuous integration
Tools: FreeSurfer (131 files), FieldTrip (41 files), Statistics and Machine Learning Toolbox (41 files), Connectome Workbench (13 files), GIfTI library for MATLAB (10 files), SPM (7 files), FSL (5 files), AFNI (4 files), Image Processing Toolbox (4 files), NumPy (2 files), SciPy (2 files), ANTs (1 file), cifti-matlab (1 file), Signal Processing Toolbox (1 file), NiBabel (1 file), Nilearn (1 file), PyTorch (1 file), scikit-learn (1 file), tedana (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
1,362 files
At the source: github.com/ThomasYeoLab/Standalone_CBIG_fMRI_Preproc2016

thomasyeolab/cbig

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 35b5664bec8822e2f77da5e090e96f91d0095be6, 31 August 2026
Languages: MATLAB (2100), Shell (651), Python (571), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,187 files, 3,592 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, environment (external_packages/python/mapalign-master/requirements.txt, external_packages/python/mapalign-master/setup.py, external_packages/python/yapf-master/setup.cfg, external_packages/python/yapf-master/setup.py), tests, documentation, 2 notebooks
Not found: CITATION.cff, continuous integration
Tools: NumPy (130 files), PyTorch (122 files), FreeSurfer (93 files), SciPy (61 files), FieldTrip (60 files), Statistics and Machine Learning Toolbox (50 files), FSL (44 files), SPM (40 files), GIfTI library for MATLAB (10 files), Image Processing Toolbox (8 files), Connectome Workbench (8 files), scikit-learn (5 files), AFNI (2 files), Matplotlib (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), ANTs (1 file), NiBabel (1 file), Nilearn (1 file), tedana (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files
At the source: github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation

ajoshiusc/bfp

License: GPL-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: ebc00a7dfd36d969120f874b05770b5aaac4c444, 13 November 2025
Languages: MATLAB (378), Python (41), Shell (13), C (11), Jupyter (2), C/C++ (2)
Size: 565 files, 447 scripts
Software Heritage: archived
Found in: the resources table
Holds: license file, documentation, 2 notebooks
Not found: README, CITATION.cff, environment file, tests, continuous integration
Tools: NumPy (41 files), SciPy (37 files), FieldTrip (27 files), scikit-learn (20 files), Tools for NIfTI and ANALYZE image (MATLAB) (14 files), statsmodels (12 files), AFNI (6 files), FSL (6 files), GIfTI library for MATLAB (6 files), Image Processing Toolbox (6 files), Matplotlib (6 files), SPM (6 files), FreeSurfer (5 files), h5py (3 files), Parallel Computing Toolbox (2 files), NiBabel (2 files), Statistics and Machine Learning Toolbox (1 file), Nilearn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
448 files
At the source: github.com/ajoshiusc/bfp/blob/main/supp_data/USCBrain_grayordinate_labels_clean.mat

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3,805 scripts, each with its path and the digest of its content;
  • 10 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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1002/hbm.70483.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 6 MeSH terms, 1 funder, 56 references.

Cite

This paper

Liu, Y., Li, J., Wisnowski, J. L., & Leahy, R. M. (2026). Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation. Human brain mapping, 47(4), e70483. https://doi.org/10.1002/hbm.70483

BibTeX

@article{liu2026untamed,
author = {Liu, Yijun and Li, Jian and Wisnowski, Jessica L. and Leahy, Richard M.},
title = {{Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation}},
journal = {Human brain mapping},
year = {2026},
month = mar,
volume = {47},
number = {4},
pages = {e70483},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70483},
url = {https://doi.org/10.1002/hbm.70483},
pmid = {41787960},
pmcid = {PMC12963934}
}

RIS

TY - JOUR
AU - Liu, Yijun
AU - Li, Jian
AU - Wisnowski, Jessica L.
AU - Leahy, Richard M.
TI - Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/03/01
VL - 47
IS - 4
SP - e70483
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70483
UR - https://doi.org/10.1002/hbm.70483
LA - en
ER -

CSL-JSON

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"id": "10.1002/hbm.70483",
"type": "article-journal",
"title": "Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation",
"container-title": "Human brain mapping",
"author": [
{
"family": "Liu",
"given": "Yijun"
},
{
"family": "Li",
"given": "Jian"
},
{
"family": "Wisnowski",
"given": "Jessica L."
},
{
"family": "Leahy",
"given": "Richard M."
}
],
"container-title-short": "Hum Brain Mapp",
"volume": "47",
"issue": "4",
"page": "e70483",
"DOI": "10.1002/hbm.70483",
"PMID": "41787960",
"PMCID": "PMC12963934",
"ISSN": "1065-9471",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/hbm.70483",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
1
]
]
}
}

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

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