OSCR

Lifestyle and Transcriptional Signatures Associated with Ethnicity/Race-Related Variations in the Functional Connectome.

Code ↔ Paper

14 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 14 matches · 5 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Ethnicity/race-related variability in brain functional topography and connectome ↔ Code/Split_match/HCP_AA_WA_match_diff.m, lines 1 to 55 · score 0.83 · AA WA, framewise displacement, matched WA, intracranial volume, randomly split, fold · Report a mismatch
  2. [2] § Methods › Defining individual-specific cortical parcellations and the functional connectome ↔ stable_projects/brain_parcellation/Kong2022_ArealMSHBM/step3_generate_ind_parcellations/CBIG_ArealMSHBM_gMSHBM_generate_individual_parcellation.m, lines 1 to 149 · score 0.76 · diffusion embedding matrices, spatial mask, connectivity profiles, probability, Schaefer400, gradients · Report a mismatch
  3. [3] § Results › Ethnicity/race-related variability in brain functional topography and connectome ↔ stable_projects/brain_parcellation/Lim2026_MSHBM_epilepsy/examples/CBIG_MSHBM_Epilepsy_wrapper.m, the whole file · a weak match · score 0.63 · MS HBM, fMRI, cortical parcellations, rs, locations, network · Report a mismatch
  4. [4] § Results › Ethnicity/race-related variability in brain functional topography and connectome ↔ Code/Split_match/HCP_AA_WA_match_diff.m, lines 1 to 55 · score 0.63 · AA WA, framewise displacement, intracranial volume, HCP, age · Report a mismatch
  5. [5] § Methods › Datasets ↔ external_packages/matlab/non_default_packages/palm/palm-alpha109/fileio/@nifti/private/nifti1.h, lines 1070 to 1158 · score 0.63 · phase encoding directions, duration, scanning, fMRI, dimension · Report a mismatch
  6. [6] § Results › Ethnicity/race-related variability in brain functional topography and connectome ↔ Code/Split_match/HCP_match_WA_with_AAfolds.m, lines 148 to 292 · score 0.60 · Hungarian matching, matched WA, family, AA, split, fold · Report a mismatch
  7. [7] § Results › Ethnicity/race-related variability in brain functional topography and connectome ↔ stable_projects/brain_parcellation/Lim2026_MSHBM_epilepsy/CBIG_MSHBM_Epilepsy_LI.m, the whole file · a weak match · score 0.58 · MS HBM, fMRI, cortical parcellations, network, brain · Report a mismatch
  8. [8] § Results › Lifestyle influences ethnicity/race-related variability in brain functional connectome ↔ Code/KRR_lifestyle_prediction/KRR_confounds_prediction.m, lines 86 to 140 · score 0.56 · social relationships, physical activity, smoking, sleep, Lifestyle, HCP · Report a mismatch
  9. [9] § Methods › Predictive modeling ↔ Code/Split_match/HCP_split_AA_rm_hardtomatch.m, the whole file · a weak match · score 0.55 · model training, cross validated, loop, fold, predict, correlation · Report a mismatch
  10. [10] § Methods › Predictive modeling ↔ Code/Split_match/HCP_match_WA_with_AAfolds.m, lines 148 to 292 · score 0.54 · Hungarian matching, WA, family, AA, split, fold · Report a mismatch
  11. [11] § Methods › Defining individual-specific cortical parcellations and the functional connectome ↔ stable_projects/brain_parcellation/Kong2022_ArealMSHBM/step3_generate_ind_parcellations/CBIG_ArealMSHBM_cMSHBM_generate_individual_parcellation.m, lines 1 to 142 · score 0.54 · spatial mask, connectivity profiles, probability, Schaefer400, parcellations, vertex · Report a mismatch
  12. [12] § Results › Lifestyle influences ethnicity/race-related variability in brain functional connectome ↔ Code/KRR_lifestyle_prediction/KRR_confounds_prediction.m, lines 86 to 140 · score 0.54 · social relationships, physical activity, sleep, KRR, lifestyle, HCP · Report a mismatch
  13. [13] § Methods › Image acquisition and processing ↔ stable_projects/brain_parcellation/Lim2026_MSHBM_epilepsy/examples/CBIG_MSHBM_Epilepsy_wrapper.m, the whole file · a weak match · score 0.53 · motion censoring, fMRI, rs, cortical · Report a mismatch
  14. [14] § Methods › Image acquisition and processing ↔ stable_projects/brain_parcellation/Yan2023_homotopic/code/step1_generate_fmri_input/CBIG_hMRF_build_time_matrix.m, the whole file · a weak match · score 0.52 · cortical vertices, fMRI, censoring, frames, volumes · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 280 lines · 11 KB · no license · 2 matches

  1. function HCP_AA_WA_match_diff(split_AAdir, split_WAdir, full_subj_ls, FD_txt, bhvr_ls_rstr, ...
  2. bhvr_ls_unrstr, num_seeds, mat_out, restricted_csv, unrestricted_csv, match_ls)
  4. % HCP_AA_WA_match_diff(split_AAdir, split_WAdir, full_subj_ls, FD_txt, DV_txt, bhvr_ls_rstr, ...
  5. % bhvr_ls_unrstr, num_seeds, mat_out, restricted_csv, unrestricted_csv, FS_csv)
  6. %
  7. % Compare the demographic & behavioral distributions between selected African Americans and
  8. % white Americans using two-sample t test and K-S test. Usually K-S test was more conservative
  9. % (i.e. bigger p values) than t test, based on the author's experiences in this data. Hence the
  10. % final significance was based on the t test.
  11. %
  12. % Inputs:
  13. % - split_AAdir
  14. % Full path of the directory storing the split folds of African Americans.
  15. % It is the output of `HCP_split_AA_rm_hardtomatch.m`.
  16. % - split_WAdir
  17. % Full path of the directory storing the white Americans that were matched to African Americans.
  18. % It is the output of `HCP_match_WA_with_AAfolds.sh`.
  19. % - full_subj_ls
  20. % Full path of the list of all subject IDs that were used for this project.
  21. % - FD_txt
  22. % Full path of the list of framewise displacement. Each row in this list corresponds to
  23. % the FD of a subject in `full_subj_ls`.
  24. % - DV_txt
  25. % Full path of the list of DVARS. Each row in this list corresponds to the DVARS of a subject
  26. % in `full_subj_ls`.
  27. % - bhvr_ls_rstr
  28. % Full path of a list of behavioral measures that were contained in the HCP restricted CSV.
  29. % Pass 'NONE' if all behavioral measure you want to study were in the unrestricted CSV.
  30. % - bhvr_ls_unrstr
  31. % Full path of a list of behavioral measures that were contained in the HCP unrestricted CSV.
  32. % - num_seeds
  33. % Total number of random splitting repetitions. It needs to be consistent with the maximal
  34. % seed used for `HCP_split_AA_rm_hardtomatch.m`.
  35. % - mat_out
  36. % Full path of output .mat file.
  37. % - restricted_csv (optional)
  38. % Full path of the HCP restricted CSV file. Default (on CSC HPC):
  39. % '/mnt/isilon/CSC1/Yeolab/Data/HCP/S1200/scripts/restricted_hcp_data/...
  40. % RESTRICTED_jingweili_4_12_2017_1200subjects_fill_empty_zygosityGT_by_zygositySR.csv'
  41. % - unrestricted_csv (optional)
  42. % Full path of the HCP unrestricted CSV file. Default (on CSC HPC):
  43. % '/mnt/isilon/CSC1/Yeolab/Data/HCP/S1200/scripts/subject_measures/...
  44. % unrestricted_jingweili_12_7_2017_21_0_16_NEO_A_corrected.csv'
  45. % - FS_csv (optional)
  46. % Full path of the HCP FreeSurfer stats CSV file. This is used to calculate matching
  47. % cost of intracranial volume. Default (on CSC HPC):
  48. % '/mnt/isilon/CSC1/Yeolab/Data/HCP/S1200/scripts/Morphometricity/Anat_Sim_Matrix/...
  49. % FS_jingweili_5_9_2017_2_2_24.csv'
  50. % - match_ls (optional)
  51. % Full path of the list of confounds to be matched. If not passed in, the confounds to
  52. % be checked include age, gender, FD. If 'NONE' is passed in,
  53. % only the difference in behavioral distributions will be checked.
  54. %
  55. % Author: Jingwei Li
  57. %% set default parameters
  58. HCP_dir = '/mnt/isilon/CSC1/Yeolab/Data/HCP/S1200';
  59. if(~exist('restricted_csv', 'var') || isempty(restricted_csv))
  60. restricted_csv = fullfile(HCP_dir, 'scripts', 'restricted_hcp_data', ...
  61. 'RESTRICTED_jingweili_4_12_2017_1200subjects_fill_empty_zygosityGT_by_zygositySR.csv');
  62. end
  63. if(~exist('unrestricted_csv', 'var') || isempty(unrestricted_csv))
  64. unrestricted_csv = fullfile(HCP_dir, 'scripts', 'subject_measures', ...
  65. 'unrestricted_jingweili_12_7_2017_21_0_16_NEO_A_corrected.csv');
  66. end
  68. if(~exist('match_ls', 'var') || isempty(match_ls))
  69. match_var = {'age', 'FD',};
  70. elseif(strcmpi(match_ls, 'none'))
  71. match_var = [];
  72. else
  73. match_var = CBIG_text2cell(match_ls);
  74. end
  76. %% read demographics and behavioral scores
  77. [subjects, ~] = CBIG_text2cell(full_subj_ls);
  78. subjects = subjects';
  80. if(~strcmpi(bhvr_ls_rstr, 'none'))
  81. [bhvr_rstr, n_rstr] = CBIG_text2cell(bhvr_ls_rstr);
  82. else
  83. bhvr_rstr = [];
  84. n_rstr = 0;
  85. end
  86. [bhvr_unrstr, n_unrstr] = CBIG_text2cell(bhvr_ls_unrstr);
  87. behaviors = [bhvr_rstr bhvr_unrstr];
  88. nbehav = n_rstr + n_unrstr;
  90. [~, age_Educ] = CBIG_parse_delimited_txtfile(restricted_csv, {'Race', 'Family_ID'}, ...
  91. {'Age_in_Yrs', 'SSAGA_Educ'}, 'Subject', subjects, ',');
  93. if(~strcmpi(bhvr_ls_rstr, 'none'))
  94. [~, bhvr_rstr_val] = CBIG_parse_delimited_txtfile(restricted_csv, [], ...
  95. bhvr_rstr, 'Subject', subjects, ',');
  96. else
  97. bhvr_rstr_val = [];
  98. end
  99. [sex, ~] = CBIG_parse_delimited_txtfile(unrestricted_csv, {'Gender'}, ...
  100. bhvr_unrstr, 'Subject', subjects, ',');
  102. sex = strcmp(sex, 'M');
  103. bhvr_val = [bhvr_rstr_val sex];
  105. FD = dlmread(FD_txt);
  106. %DV = dlmread(DV_txt);
  108. metric = [];
  109. weigh_idx = [];
  110. for mv = 1:length(match_var)
  111. switch match_var{mv}
  112. case 'age'
  113. metric = [metric age_Educ(:,1)];
  114. % case 'gender'
  115. % metric = [metric sex];
  116. case 'FD'
  117. metric = [metric FD];
  118. otherwise
  119. error('Unknown variable name to be matched')
  120. end
  121. end
  123. metric = [metric bhvr_val];
  124. no_match_idx = weigh_idx;
  125. if(~isempty(match_var))
  126. weigh_idx = [weigh_idx length(match_var)+1];
  127. end
  128. metric = metric - mean(metric,1);
  129. metric = metric ./ sqrt(sum(metric.^2,1));
  131. %% statistical testing for each random seed. Correct for multiple comparisons across all seeds.
  132. H_ks = []; p_ks = []; ksstat = [];
  133. H_tt = []; p_tt = [];
  134. meanA = []; meanW = []; varA = []; varW = [];
  135. outlier_AA = {};
  136. all_mAA = cell(nbehav,1);
  137. all_mWA = cell(nbehav,1);
  138. all_mAA_seed = cell(nbehav,num_seeds);
  139. all_mWA_seed = cell(nbehav,num_seeds);
  140. for seed = 1:num_seeds
  141. load(fullfile(split_AAdir, ['split_AA_5folds_seed' num2str(seed) '.mat']))
  142. load(fullfile(split_WAdir, ['match_WA_seed' num2str(seed) '.mat']))
  143. %%%%%%%%%%%%%%%% for my own debugging purpose
  144. if(~exist('AA_fold', 'var'))
  145. AA_fold = Afr_fold;
  146. end
  147. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  149. H_ks_seed = []; p_ks_seed = []; ksstat_seed = [];
  150. H_tt_seed = []; p_tt_seed = [];
  151. meanA_seed = []; meanW_seed = []; varA_seed = []; varW_seed = [];
  153. for b = 1:nbehav
  154. curr_m = metric(:,[1:length(match_var) b+length(match_var)]);
  156. A_metric{b} = [];
  157. W_metric{b} = [];
  158. for f = 1:length(AA_fold.sub_perfold)
  159. [~,Aind] = intersect(subjects, AA_fold.sub_perfold{f});
  160. [~,Wind] = intersect(subjects, best_assign{b}{f});
  162. A_metric{b} = [A_metric{b}; curr_m(Aind, :)];
  163. W_metric{b} = [W_metric{b}; curr_m(Wind, :)];
  165. % the calculation of Hungarian matching cost should be the same as in `HCP_match_WA_with_AAfolds.m`
  166. cost_mat = bsxfun(@minus, reshape(curr_m(Aind,:), [length(Aind) 1 size(curr_m,2)]), ...
  167. reshape(curr_m(Wind,:), [1 size(curr_m(Wind,:))]));
  168. cost_mat(:,:,weigh_idx) = cost_mat(:,:,weigh_idx) .*2;
  169. cost_mat = sum(abs(cost_mat), 3);
  171. [assign, ~] = munkres(cost_mat);
  173. all_mAA{b} = [all_mAA{b}; curr_m(Aind, :)];
  174. all_mWA{b} = [all_mWA{b}; curr_m(Wind(assign), :)];
  175. all_mAA_seed{b,seed} = [all_mAA_seed{b,seed}; curr_m(Aind, :)];
  176. all_mWA_seed{b,seed} = [all_mWA_seed{b,seed}; curr_m(Wind(assign), :)];
  178. % Outlier AAs based on the matching cost
  179. % It helps to decide which AA to be excluded when using `HCP_split_AA_rm_hardtomatch.m` :
  180. % the AA IDs appeared as outliers for multiple behavioral measures can be considered to be ignored.
  181. pair_ind = sub2ind(size(cost_mat), 1:length(Aind), assign);
  182. outlier = isoutlier(cost_mat(pair_ind));
  183. fprintf('Outlier African: %s\n', subjects{Aind(outlier==1)})
  184. outlier_AA = [outlier_AA; subjects(Aind(outlier==1))];
  185. end
  187. curr_H_ks = []; curr_p_ks = []; curr_ksstats = [];
  188. curr_H_tt = []; curr_p_tt = [];
  189. curr_meanA = []; curr_meanW = []; curr_varA = []; curr_varW = [];
  190. for m = 1:size(curr_m,2)
  191. [curr_H_ks(m), curr_p_ks(m), curr_ksstats(m)] = kstest2(A_metric{b}(:,m), W_metric{b}(:,m));
  192. [curr_H_tt(m), curr_p_tt(m)] = ttest2(A_metric{b}(:,m), W_metric{b}(:,m), 'Vartype', 'unequal');
  194. curr_meanA(m) = mean(A_metric{b}(:,m));
  195. curr_meanW(m) = mean(W_metric{b}(:,m));
  196. curr_varA(m) = var(A_metric{b}(:,m));
  197. curr_varW(m) = var(W_metric{b}(:,m));
  198. end
  200. H_ks_seed = [H_ks_seed curr_H_ks];
  201. p_ks_seed = [p_ks_seed curr_p_ks];
  202. ksstat_seed = [ksstat_seed curr_ksstats];
  204. H_tt_seed = [H_tt_seed curr_H_tt];
  205. p_tt_seed = [p_tt_seed curr_p_tt];
  207. meanA_seed = [meanA_seed curr_meanA];
  208. meanW_seed = [meanW_seed curr_meanW];
  209. varA_seed = [varA_seed curr_varA];
  210. varW_seed = [varW_seed curr_varW];
  211. end
  212. H_ks = [H_ks; H_ks_seed];
  213. p_ks = [p_ks; p_ks_seed];
  214. ksstat = [ksstat; ksstat_seed];
  216. H_tt = [H_tt; H_tt_seed];
  217. p_tt = [p_tt; p_tt_seed];
  220. meanA = [meanA; meanA_seed];
  221. meanW = [meanW; meanW_seed];
  222. varA = [varA; varA_seed];
  223. varW = [varW; varW_seed];
  224. end
  226. alpha = 0.05;
  227. H_ks_FDR = FDR(p_ks(:), alpha);
  228. H_tt_FDR = FDR(p_tt(:), alpha);
  230. %% check which (seed, behavior) combinations have significant difference
  231. % in demographic and behavioral distributions between AA and WA
  232. sig_bool = zeros(size(p_tt));
  233. sig_bool(H_tt_FDR) = 1;
  234. matched_mtx = zeros(num_seeds, nbehav);
  235. for b = 1:nbehav
  236. curr_bool = sig_bool(:, ((b-1)*(length(match_var)+1)+1):(b*(length(match_var)+1)) );
  237. if(any(no_match_idx))
  238. curr_bool(:,no_match_idx) = []; % Educ and ICV can't be matched in this data
  239. end
  240. sum_bool = sum(curr_bool, 2);
  241. matched = find(sum_bool == 0);
  242. if(~isempty(matched))
  243. for i = 1:length(matched)
  244. matched_mtx(matched(i),b) = 1;
  245. fprintf('Random seed %d for behavior %s has matched AA & WA in age, FD)\n', ...
  246. matched(i), behaviors{b});
  247. end
  248. end
  249. end
  251. save(mat_out, 'p_ks', 'H_ks', 'H_ks_FDR', 'p_tt', 'H_tt', 'H_tt_FDR', 'matched_mtx', 'outlier_AA')
  253. %% save the statistics into a csv file
  254. header = {'Random split #'};
  255. for b = 1:nbehav
  256. header = [header {[behaviors{b} ': Age'], ...
  257. [behaviors{b} ': FD'], behaviors{b}}];
  258. end
  260. data = [meanA(1,:); varA(1,:)];
  261. data(3+(0:2:(num_seeds-1)*2),:) = meanW;
  262. data(3+(1:2:(num_seeds-1)*2+1),:) = varW;
  264. for row = 1:size(data,1)
  265. for col = 1:size(data,2)
  266. data_rows{col}{row} = num2str(data(row, col));
  267. end
  268. end
  270. data_rows = [cell(1), data_rows];
  271. data_rows{1}{1} = 'All selected African Am (mean)';
  272. data_rows{1}{2} = 'All selected African Am (var)';
  273. for seed = 1:num_seeds
  274. data_rows{1}{3+(seed-1)*2} = sprintf('Seed %d: matched White (mean)', seed);
  275. data_rows{1}{4+(seed-1)*2} = sprintf('Seed %d: matched White (var)', seed);
  276. end
  277. CBIG_write_delimited_txtfile(fullfile(split_WAdir, 'mean_var_AA_vs_WA.csv'), header, data_rows);
  280. end

HCP_AA_WA_match_diff.m at commit e69fd2c, no license · at the source

Overview

Authors: Ziteng Han1, Kexin Wang1, Tiantian Liu1, Fanghui Zhao1, Yunxiao Ma2, Wenrui Li3, Guoyuan Yang1,4, Tianyi Yan1
ORCID iDs: Ziteng Han, Tianyi Yan
  1. School of Medical Technology, Beijing Institute of Technology, Beijing, China
  2. School of Life Science, Beijing Institute of Technology, Beijing, China
  3. Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
  4. School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China
Institutions: Beijing Institute of Technology (China); Vanderbilt University (United States)
Journal: Research (Washington, D.C.), volume 9, article 1143
Dates: received 13 September 2025; accepted 22 January 2026; published online 24 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.34133/research.1143 · PMID 41884333 · PMCID PMC13009535 · OpenAlex W7125489114
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Preprocessing
Journal subjects: Health Science
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (62336002)
Citations: not cited yet (Europe PMC); 115 references in the paper

Abstract

Understanding the variation of functional architecture across individuals and populations is fundamental to advancing our knowledge of human health and behavior. Yet, while functional organization differences related to ethnicity/race are consistently reported, their underlying mechanisms remain poorly understood. Here, we apply precision individualized functional mapping to systematically investigate ethnicity/race-related differences between African American (AA) and white American (WA) cohorts in the brain’s intrinsic organization and their associations with lifestyle and transcriptional signatures. We show that variations in network topography and functional connectivity across these ethnic/racial groups follow a hierarchical sensorimotor–association axis and are constrained by brain morphology. Importantly, we identify lifestyle factors—particularly education and substance use—that significantly mediate these associations between ethnicity/race and functional connectivity. Leveraging human brain gene expression data, we further demonstrate that cortical transcriptional patterns are spatially aligned with ethnicity/race-related variability in functional connectivity. Gene ontology analyses of associated genes reveal significant enrichment in biological processes such as synaptic signaling and neuronal system development. Together, these findings uncover a multi-layered framework linking ethnicity/race-related differences in brain function to structural constraints, lifestyle influences, and molecular signatures, and point to the need for a more comprehensive and equitable understanding of human brain diversity.

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 14 matches between paragraphs and lines of code.

thomasyeolab/cbig

License: MIT
State: the link answers, verified on 27 October 2026
Evidence: files inventoried
Commit: 634f676630929a71297852d01dd92a287103e861, 1 October 2026
Languages: MATLAB (2110), Shell (652), Python (589), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,316 files, 3,621 scripts
Software Heritage: not archived
Found in: “Data Availability”
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 (94 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: 2 checks, the latest on 27 October 2026: the link answers
  • 27 October 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/Schaefer2018_LocalGlobal/Parcellations

Zenodo 7606653

License: other-open
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), ggplot2 (2 files), ggseg (2 files), cowplot (1 file), mgcv (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
12 files
At the source: doi.org/10.5281/zenodo.7606653

TianyiYanLab/Ethnicity_Race_Diversity

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: e69fd2c62749d0e5dbb2bca99361a37b750216e2, 7 January 2026
Languages: MATLAB (11), R (6), Jupyter (2), Python (1)
Size: 25 files, 20 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lavaan (6 files), Statistics and Machine Learning Toolbox (6 files), NumPy (2 files), pandas (2 files), SciPy (2 files), abagen (1 file), Matplotlib (1 file), PyTorch (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
21 files
At the source: github.com/TianyiYanLab/Ethnicity_Race_Diversity

pennlinc/spatiotemp_dev_plasticity

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 7f1fa0b151ea2542d58dd1c53004ad2a05e4dd49, 4 February 2023
Languages: R (11)
Size: 19 files, 11 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, 4 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), ggplot2 (2 files), ggseg (2 files), cowplot (1 file), mgcv (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
12 files
At the source: github.com/PennLINC/spatiotemp_dev_plasticity/tree/v.3.0.0

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;
  • 2,040 scripts, each with its path and the digest of its content;
  • 14 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

The raw and preprocessed data are available from the HCP (https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release; https://humanconnectome.org/study/hcp-lifespan-development). Human gene expression data are available from the Allen Brain Atlas (https://human.brain-map.org). In our analyses, we also utilized publicly available cortical atlases, including the Schaefer400 atlas (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations), the S–A axis (https://github.com/PennLINC/S-A_ArchetypalAxis), and environmental effect maps (https://doi.org/10.5281/zenodo.7606653). The subject lists for both the HCP-YA and HCP-D datasets, along with all predictive feature matrices, are available on our GitHub page (https://github.com/TianyiYanLab/Ethnicity_Race_Diversity). The PLS1 gene lists and Z score weights for both cohorts are provided in Data S1. All data supporting the findings of this study are included in the paper and its Supplementary Materials, and all additional information is available from the authors upon reasonable request. All the codes used to generate the results are available on GitHub (https://github.com/TianyiYanLab/Ethnicity_Race_Diversity).

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 1 funder, 107 references.

Cite

This paper

Han, Z., Wang, K., Liu, T., Zhao, F., Ma, Y., Li, W., Yang, G., & Yan, T. (2026). Lifestyle and Transcriptional Signatures Associated with Ethnicity/Race-Related Variations in the Functional Connectome. Research (Washington, D.C.), 9, 1143. https://doi.org/10.34133/research.1143

BibTeX

@article{han2026lifestyle,
author = {Han, Ziteng and Wang, Kexin and Liu, Tiantian and Zhao, Fanghui and Ma, Yunxiao and Li, Wenrui and Yang, Guoyuan and Yan, Tianyi},
title = {{Lifestyle and Transcriptional Signatures Associated with Ethnicity/Race-Related Variations in the Functional Connectome}},
journal = {Research (Washington, D.C.)},
year = {2026},
month = mar,
volume = {9},
pages = {1143},
publisher = {AAAS Science Partner Journal Program},
issn = {2639-5274},
doi = {10.34133/research.1143},
url = {https://doi.org/10.34133/research.1143},
pmid = {41884333},
pmcid = {PMC13009535}
}

RIS

TY - JOUR
AU - Han, Ziteng
AU - Wang, Kexin
AU - Liu, Tiantian
AU - Zhao, Fanghui
AU - Ma, Yunxiao
AU - Li, Wenrui
AU - Yang, Guoyuan
AU - Yan, Tianyi
TI - Lifestyle and Transcriptional Signatures Associated with Ethnicity/Race-Related Variations in the Functional Connectome
T2 - Research (Washington, D.C.)
J2 - Research (Wash D C)
PY - 2026
DA - 2026/03/24
VL - 9
SP - 1143
SN - 2639-5274
PB - AAAS Science Partner Journal Program
DO - 10.34133/research.1143
UR - https://doi.org/10.34133/research.1143
LA - en
ER -

CSL-JSON

{
"id": "10.34133/research.1143",
"type": "article-journal",
"title": "Lifestyle and Transcriptional Signatures Associated with Ethnicity/Race-Related Variations in the Functional Connectome",
"container-title": "Research (Washington, D.C.)",
"author": [
{
"family": "Han",
"given": "Ziteng"
},
{
"family": "Wang",
"given": "Kexin"
},
{
"family": "Liu",
"given": "Tiantian"
},
{
"family": "Zhao",
"given": "Fanghui"
},
{
"family": "Ma",
"given": "Yunxiao"
},
{
"family": "Li",
"given": "Wenrui"
},
{
"family": "Yang",
"given": "Guoyuan"
},
{
"family": "Yan",
"given": "Tianyi"
}
],
"container-title-short": "Research (Wash D C)",
"volume": "9",
"page": "1143",
"DOI": "10.34133/research.1143",
"PMID": "41884333",
"PMCID": "PMC13009535",
"ISSN": "2639-5274",
"publisher": "AAAS Science Partner Journal Program",
"URL": "https://doi.org/10.34133/research.1143",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
24
]
]
}
}

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:10.1038/s42003-025-08509-7 [code]
Development of areal-level individualized homologous functional parcellations in youth.
Journal: Communications biology
In common: tedana, Connectome Workbench, AFNI, 17 other tools, 15 references, author Tianyi Yan
[2] doi:10.1038/s41467-025-62812-9 [code]
Mapping the coupling between tract reachability and cortical geometry of the human brain.
Journal: Nature communications
In common: tedana, mgcv, Connectome Workbench, 20 other tools, humanconnectome.org/study/hcp-lifespan-development, 8 references
[3] doi:10.1038/s41467-026-73668-y [code]
Convergent and divergent brain-cognition development in early adolescence.
Journal: Nature communications
In common: tedana, Connectome Workbench, AFNI, 19 other tools, 9 references
[4] doi:10.1186/s12916-025-04099-7 [code]
Normative structural connectome constrains spreading transient brain activity in generalized epilepsy.
Journal: BMC medicine
In common: tedana, abagen, mgcv, 19 other tools, 6 references
[5] doi:10.1371/journal.pbio.3003619 [code]
Five energy metabolism pathways show distinct regional distributions and lifespan trajectories in the human brain.
Journal: PLoS biology
In common: tedana, abagen, Connectome Workbench, 19 other tools, cellular / molecular, 7 references
[6] doi:10.1038/s41467-026-76011-7 [code]
Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.
Journal: Nature communications
In common: ggseg, mgcv, Connectome Workbench, 16 other tools, 10 references
[7] doi:10.1038/s41467-023-41689-6 [code]
Neurophysiological signatures of cortical micro-architecture.
Journal: Nature communications
In common: tedana, abagen, Connectome Workbench, 18 other tools, cellular / molecular, 7 references
[8] doi:10.1038/s41586-025-09250-1 [code]
Longer scans boost prediction and cut costs in brain-wide association studies.
Journal: Nature
In common: tedana, Connectome Workbench, AFNI, 16 other tools, 8 references
[9] doi:10.1038/s42003-025-08561-3 [code]
Network spreading and local biological vulnerability in amyotrophic lateral sclerosis.
Journal: Communications biology
In common: tedana, abagen, Connectome Workbench, 18 other tools, 6 references
[10] doi:10.1162/imag_a_00233 [code]
Multilayer meta-matching: Translating phenotypic prediction models from multiple datasets to small data.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: tedana, Connectome Workbench, AFNI, 16 other tools, 8 references

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.

Add the badge to the code's README

OSCR: the badge of this paper

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown
<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.34133/research.1143.svg)](https://openscicode.org/badge/doi/10.34133/research.1143)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.34133/research.1143.svg
   :target: https://openscicode.org/badge/doi/10.34133/research.1143
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.34133/research.1143"><img src="https://openscicode.org/badge/doi/10.34133/research.1143.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge 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: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

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