OSCR

Test-retest reliability and symptom association of personalized depression TMS targets: A comparative study of refined seed-based (RSA) and hierarchical clustering (HCA) approaches.

Code ↔ Paper

3 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 3 matches
  1. [1] § Materials and methods › The implementation of RSA ↔ Cash_Targeting.m, lines 107–151 · score 0.66 · functional connectivity map, AFNI, sgACC, command, brain, weighting
  2. [2] § Materials and methods › The implementation of HCA ↔ Cole_Targeting.m, lines 93–144 · score 0.66 · Spearman correlations, distance matrix, hierarchical, subunits, median, voxel
  3. [3] § Materials and methods › The implementation of RSA ↔ Cole_Targeting.m, lines 1–15 · score 0.60 · voxels outside, DLPFC mask, sgACC, map, weighting, preprocessed

Paper

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The authors' code

MATLAB · 198 lines · 9.8 KB · no license · 2 matches

  1. clc;
  2. clear;
  3. % Folder organization:
  4. % Data_folder: (1) preprocessed_data [preprocessed nifti files for each subject]
  5. % (2) censor_file [files containing 0 and 1 values]
  6. % Mask: SGC_mask; DLPFC mask
  7. % Interm_folder (output folder):
  8. % Sub_sgcFCmap [sgACC FC maps for each subject; mean sgACC FC map; weights]
  9. % sub_timeseries [timeseries of each voxel outside the DLPFC]
  10. % WeightedTimeseries [weighted timeseries representing SGC]
  11. % Mask_timeseries [extracted timeseries for each voxel within the ROI]
  12. % Mask_clusterres [clustering information, representative timeseries and coordinates]
  15. wkdir = 'TargetCalc';
  17. %%% Define mask
  18. Mask46_name = 'DLPFC';
  19. Mask25_name = 'BA25';
  20. Mask_BA46 = fullfile(wkdir, 'Mask', [Mask46_name, '_mask_GMres.nii']);
  21. Mask_BA25 = fullfile(wkdir, 'Mask', [Mask25_name, '_resp.nii']);
  23. %%% Define folders
  24. Data_folder = fullfile(wkdir, 'Data_folder', 'preprocessed_data'); %% preprocessed fMRI data
  25. censor_folder = fullfile(wkdir, 'Data_folder', 'censor_file'); %% censor txt file
  26. out_folder = fullfile(wkdir, 'Interm_folder'); %% output folder
  28. %%% Whether to censor timeseries, 1 = censor, 0 = no censor
  29. censor = 1;
  31. %%% Create output directories
  32. output_dirs = {fullfile(out_folder, [Mask46_name, '_timeseries']), ...
  33. fullfile(out_folder, [Mask25_name, '_timeseries']), ...
  34. fullfile(out_folder, [Mask46_name, '_clusterres']), ...
  35. fullfile(out_folder, [Mask25_name, '_clusterres']), ...
  36. fullfile(out_folder, 'Parameters'), ...
  37. fullfile(out_folder, 'Decision')};
  38. for dir = output_dirs
  39. if exist(dir{1}, 'dir')
  40. rmdir(dir{1},'s');
  41. end
  42. mkdir(dir{1});
  43. end
  45. %%% Load sublist
  46. sublist = importdata(fullfile(wkdir, 'sublist_depression.txt'));
  48. for i = 1:numel(sublist)
  49. %%%%% Get timeseries from masks %%%%%
  50. sub = sublist{i};
  51. preprocessed_data = fullfile(Data_folder, [sub, '_rbs5nwra_rest.nii']);
  53. ExtractTimeSeries(preprocessed_data, sub, Mask_BA46, Mask46_name, fullfile(out_folder, [Mask46_name, '_timeseries']));
  54. ExtractTimeSeries(preprocessed_data, sub, Mask_BA25, Mask25_name, fullfile(out_folder, [Mask25_name, '_timeseries']));
  56. %%%%% Clustering %%%%%
  57. BA46_timeseries = load(fullfile(out_folder, [Mask46_name, '_timeseries'], [sub, '_', Mask46_name, '_timeseries.txt']));
  58. BA25_timeseries = load(fullfile(out_folder, [Mask25_name, '_timeseries'], [sub, '_', Mask25_name, '_timeseries.txt']));
  59. censor_file = load(fullfile(censor_folder, [sub, '_bs5wra_censor0.5.txt']));
  61. calc_medialseries(BA46_timeseries, Mask46_name, sub, censor_file, censor, fullfile(out_folder, [Mask46_name, '_clusterres']));
  62. calc_medialseries(BA25_timeseries, Mask25_name, sub, censor_file, censor, fullfile(out_folder, [Mask25_name, '_clusterres']));
  64. %%%%% Calculate three parameters %%%%%
  65. BA46_sbunittime = load(fullfile(out_folder, [Mask46_name, '_clusterres'], [sub, '_', Mask46_name, '_cluster_representts.mat']));
  66. BA25_sbunittime = load(fullfile(out_folder, [Mask25_name, '_clusterres'], [sub, '_', Mask25_name, '_cluster_representts.mat']));
  67. BA46_sbunitinfo = load(fullfile(out_folder, [Mask46_name, '_clusterres'], [sub, '_', Mask46_name, '_subunit_inform.mat']));
  68. BA25_sbunitinfo = load(fullfile(out_folder, [Mask25_name, '_clusterres'], [sub, '_', Mask25_name, '_subunit_inform.mat']));
  70. para_cal(BA25_sbunittime.cor_medial_series, BA46_sbunittime.cor_medial_series, BA25_sbunitinfo.subunit_inform, BA46_sbunitinfo.subunit_inform, sub, fullfile(out_folder, 'Parameters'));
  72. %%%%% Decision Making %%%%%
  73. BA46_sbmedialtime = load(fullfile(out_folder, [Mask46_name, '_clusterres'], [sub, '_', Mask46_name, '_cluster_medialts.mat']));
  74. BA46_sbunittime = load(fullfile(out_folder, [Mask46_name, '_clusterres'], [sub, '_', Mask46_name, '_cluster_representts.mat']));
  75. BA46_sbunitcoor = load(fullfile(out_folder, [Mask46_name, '_clusterres'], [sub, '_', Mask46_name, '_cluster_coor.mat']));
  76. BA46_sbunitinfo = load(fullfile(out_folder, [Mask46_name, '_clusterres'], [sub, '_', Mask46_name, '_subunit_inform.mat']));
  77. p1 = load(fullfile(out_folder, 'Parameters', [sub, '_subunitcorsize.mat']));
  78. p2 = load(fullfile(out_folder, 'Parameters', [sub, '_subunitsize.mat']));
  79. p3 = load(fullfile(out_folder, 'Parameters', [sub, '_subunitconcentraion.mat']));
  81. decisionMaking(BA46_sbunitcoor.cor_medial_coord, BA46_sbunittime.cor_medial_series, BA46_sbmedialtime.medial_series, BA46_sbunitinfo.subunit_inform, p1.parameter1_cor_size, p2.parameter2_dlPFCsize, p3.parameter3_concentration, sub, fullfile(out_folder, 'Decision'));
  82. end
  84. %%%%%%%%%%%%%%%%%%%%%%%%%%
  85. %%%%%%% Extract timeseries from BA46 and BA25 %%%%%%%
  86. function ExtractTimeSeries(data, sub, mask, mask_name, outfolder)
  87. outfilename = fullfile(outfolder, [sub, '_', mask_name, '_timeseries.txt']);
  88. unix(['3dmaskdump -mask ', mask, ' -noijk -xyz ', data, ' > ', outfilename]);
  89. end
  91. %%%%%%%%%%%%%%%%%%%%%%%%%%
  92. %%%%%%%% Calculate Medial Series %%%%%%%%
  93. function calc_medialseries(data, mask_name, sub, rm_tmfile, cens, outfolder)
  94. % Delete rows (voxels) with all zero values in timeseries
  95. rowsToDelete = all(data(:, 4:end) == 0, 2);
  96. nozeros_rawdata = data;
  97. nozeros_rawdata(rowsToDelete, :) = [];
  99. % Remove censored time points
  100. if cens == 1
  101. rm_tmpoint = find(rm_tmfile == 0);
  102. nozeros_rawdata(:, rm_tmpoint + 3) = [];
  103. end
  105. % Compute Spearman correlation matrix
  106. n_voxels = size(nozeros_rawdata, 1);
  107. colmatrix = corr(nozeros_rawdata(:, 4:end)', 'Type', 'Spearman');
  109. % Hierarchical clustering
  110. X = 1 - colmatrix; % change correlation matrix to distance matrix
  111. X(eye(size(X)) == 1) = 0; % Set diagonal to 0
  112. Y_square = squareform(X);
  113. Z = linkage(Y_square, 'complete');
  114. clusters = cluster(Z, 'cutoff', 0.5, 'criterion', 'distance');
  115. subunit_inform = [nozeros_rawdata(:, 1:3), clusters];
  117. % Calculate representative time series
  118. clustersize = max(clusters);
  119. medial_series = zeros(clustersize, size(nozeros_rawdata, 2) - 3);
  120. cor_medial_series = zeros(clustersize, size(nozeros_rawdata, 2) - 3);
  121. cor_medial_coord = zeros(clustersize, 3);
  123. for i = 1:clustersize
  124. current_series = nozeros_rawdata(clusters == i, 4:end);
  125. current_coord = nozeros_rawdata(clusters == i, 1:3);
  127. % Find median series
  128. medial_series(i, :) = median(current_series, 1);
  130. % Find most correlated series
  131. cor_medial_value = corr(current_series', medial_series(i, :)');
  132. [~, max_index] = max(cor_medial_value);
  133. cor_medial_series(i, :) = current_series(max_index, :);
  134. cor_medial_coord(i, :) = current_coord(max_index, :);
  135. end
  137. % Save results
  138. save(fullfile(outfolder, [sub, '_', mask_name, '_cluster_coor']), 'cor_medial_coord');
  139. save(fullfile(outfolder, [sub, '_', mask_name, '_cluster_representts']), 'cor_medial_series');
  140. save(fullfile(outfolder, [sub, '_', mask_name, '_subunit_inform']), 'subunit_inform');
  141. save(fullfile(outfolder, [sub, '_', mask_name, '_cluster_medialts']), 'medial_series');
  142. end
  144. %%%%% calculate three parameters for decision making%%%%%
  145. function para_cal(sgACC_subunittime, dlpfc_subunittime, sgACC_subunitinfo, dlpfc_subunitinfo, sub, outfolder)
  146. sgACC_cormedial_tmseries = sgACC_subunittime;
  147. dlpfc_cormedial_tmseries = dlpfc_subunittime;
  149. % Calculate functional connectivity matrix
  150. subunitFC = corr(sgACC_cormedial_tmseries', dlpfc_cormedial_tmseries');
  152. % parameter1: correlation * size of sgACC
  153. subunit_sgACC_tb = tabulate(sgACC_subunitinfo(:, 4));
  154. subunit_sgACC_size = subunit_sgACC_tb(:, 2);
  155. parameter1_cor_size = -subunitFC' * subunit_sgACC_size;
  157. % parameter2: size of dlPFC
  158. subunit_dlPFC_tb = tabulate(dlpfc_subunitinfo(:, 4));
  159. parameter2_dlPFCsize = subunit_dlPFC_tb(:, 2);
  161. % parameter3: spatial concentration
  162. parameter3_concentration = zeros(max(dlpfc_subunitinfo(:, 4)), 1);
  163. for i = 1:max(dlpfc_subunitinfo(:, 4))
  164. clustermatrix = dlpfc_subunitinfo(dlpfc_subunitinfo(:, 4) == i, :);
  165. cluster_coord_matrix = clustermatrix(:, 1:3);
  166. if size(cluster_coord_matrix, 1) == 1
  167. parameter3_concentration(i) = 0;
  168. else
  169. distancematrix = pdist2(cluster_coord_matrix, cluster_coord_matrix);
  170. upper_triangle_values = nonzeros(triu(distancematrix, 1));
  171. mean_distance = mean(upper_triangle_values);
  172. parameter3_concentration(i) = parameter2_dlPFCsize(i) / mean_distance;
  173. end
  174. end
  176. % Save parameters
  177. save(fullfile(outfolder, [sub, '_subunitcorsize']), 'parameter1_cor_size');
  178. save(fullfile(outfolder, [sub, '_subunitsize']), 'parameter2_dlPFCsize');
  179. save(fullfile(outfolder, [sub, '_subunitconcentraion']), 'parameter3_concentration');
  180. end
  182. function decisionMaking(dlpfc_cormedial_coord, dlpfc_cormedial_tmseries, dlpfc_medial_series, dlpfc_subunit, parameter1_cor_size, parameter2_dlPFCsize, parameter3_concentration, sub, outfolder)
  183. parameters = [parameter1_cor_size, parameter2_dlPFCsize, parameter3_concentration];
  184. z_parameters = zscore(parameters);
  185. z_final = sum(z_parameters, 2);
  186. [~, max_target] = max(z_final);
  188. % Optimal target
  189. target_coord = dlpfc_cormedial_coord(max_target, :);
  190. target_timeseries = dlpfc_cormedial_tmseries(max_target, :);
  191. target_median_timeseries = dlpfc_medial_series(max_target, :);
  192. corr_with_median = corr(target_median_timeseries', target_timeseries');
  193. target_info = parameters(max_target, :);
  194. target_subunit = dlpfc_subunit(dlpfc_subunit(:, 4) == max_target, 1:3);
  196. % Save target information
  197. save(fullfile(outfolder, [sub, '_OptimalTarget']), 'target_coord', 'target_timeseries', 'target_median_timeseries', 'corr_with_median', 'target_info', 'target_subunit');
  198. end

Cole_Targeting.m at commit f96b673, no license · at the source

Overview

Authors: Hui Zhou1,2, Yanmeng Bao2, Jiasheng Xu2, Dan Wang3, Fengji Geng4, Wanjun Guo1,5,6, Yuzheng Hu1,2,6,7
  1. The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310058, China
  2. Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China
  3. Department of Endocrinology, Children's Hospital of Zhejiang University, School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310058, China
  4. Department of Curriculum and Learning Sciences, College of Education, Zhejiang University, Hangzhou 310007, China
  5. Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
  6. MOE Frontiers Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310058, China
  7. Nanhu Brain-Computer Interface Institute, Hangzhou 311121, China
Institutions: Zhejiang University (China); Children's Hospital of Zhejiang University (China); Hangzhou Seventh Peoples Hospital (China)
Journal: Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics, volume 23, issue 2, article e00884
Dates: received 1 December 2025; accepted 26 February 2026; published online 12 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.neurot.2026.e00884 · PMID 41825227 · PMCID PMC12996647 · OpenAlex W7135079096
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), depression (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: Personalized TMS targeting, Depression, Neuromodulation
MeSH: Depression*, Precision Medicine*, Transcranial Magnetic Stimulation*, Adult, Brain Mapping, Cluster Analysis, Clustering Algorithms, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Reproducibility of Results, Young Adult (* major topic)
Topic: Treatment of Major Depression (Pharmacology, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China
Citations: cited by 1 paper (Europe PMC); 34 references in the paper

Abstract

Personalized transcranial magnetic stimulation (TMS) targeting holds promise for improving depression treatment, but its clinical translation is hindered by limited open-source implementation and systematic comparisons of target reproducibility and clinical relevance. We implemented two leading personalized TMS-target generating approaches, namely refined seed-based (RSA) and hierarchical clustering (HCA) algorithms, and compared them on (1) test-retest reliability of derived targets, and (2) association of target-sgACC connectivity with depressive symptoms. Using resting-state fMRI data from healthy and depressed individuals, spatial reliability was quantified via inter-run Euclidean distances, and clinical relevance was assessed through correlations between depression severity and functional connectivity of targets with sgACC. Effects of global signal regression (GSR) were also evaluated. The results showed that RSA produced targets in more superior and postrior part of DLPFC and demonstrated significantly higher test-retest reliability than HCA (smaller inter-run Euclidean distances). Further, RSA-derived target-sgACC connectivity correlated positively with depression severity, which was absent in HCA-derived targets. In addition, GSR improved spatial reliability for RSA but not HCA. Our results indicate that RSA exhibits superior test-retest reliability and symptom association compared to HCA, yet large-scale clinical trials are warranted to determine which approach yields superior therapeutic efficacy, and open-sourced implementation may accelerate clinical adoption.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

HuiiiZ/PersonalizedTargeting_NI

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f96b673bb904e5d8d40fe496c4712a82c384cd7f, 17 October 2025
Languages: MATLAB (2)
Size: 4 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Data statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: AFNI (2 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: github.com/HuiiiZ/PersonalizedTargeting_NI

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:

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  • 2 scripts, each with its path and the digest of its content;
  • 3 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.

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Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 14 MeSH terms, 1 funder, 31 references.

Cite

This paper

Zhou, H., Bao, Y., Xu, J., Wang, D., Geng, F., Guo, W., & Hu, Y. (2026). Test-retest reliability and symptom association of personalized depression TMS targets: A comparative study of refined seed-based (RSA) and hierarchical clustering (HCA) approaches. Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics, 23(2), e00884. https://doi.org/10.1016/j.neurot.2026.e00884

BibTeX

@article{zhou2026test,
author = {Zhou, Hui and Bao, Yanmeng and Xu, Jiasheng and Wang, Dan and Geng, Fengji and Guo, Wanjun and Hu, Yuzheng},
title = {{Test-retest reliability and symptom association of personalized depression TMS targets: A comparative study of refined seed-based (RSA) and hierarchical clustering (HCA) approaches}},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
year = {2026},
month = mar,
volume = {23},
number = {2},
pages = {e00884},
publisher = {Elsevier},
issn = {1933-7213},
doi = {10.1016/j.neurot.2026.e00884},
url = {https://doi.org/10.1016/j.neurot.2026.e00884},
pmid = {41825227},
pmcid = {PMC12996647}
}

RIS

TY - JOUR
AU - Zhou, Hui
AU - Bao, Yanmeng
AU - Xu, Jiasheng
AU - Wang, Dan
AU - Geng, Fengji
AU - Guo, Wanjun
AU - Hu, Yuzheng
TI - Test-retest reliability and symptom association of personalized depression TMS targets: A comparative study of refined seed-based (RSA) and hierarchical clustering (HCA) approaches
T2 - Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
J2 - Neurotherapeutics
PY - 2026
DA - 2026/03/12
VL - 23
IS - 2
SP - e00884
SN - 1933-7213
PB - Elsevier
DO - 10.1016/j.neurot.2026.e00884
UR - https://doi.org/10.1016/j.neurot.2026.e00884
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.neurot.2026.e00884",
"type": "article-journal",
"title": "Test-retest reliability and symptom association of personalized depression TMS targets: A comparative study of refined seed-based (RSA) and hierarchical clustering (HCA) approaches",
"container-title": "Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics",
"author": [
{
"family": "Zhou",
"given": "Hui"
},
{
"family": "Bao",
"given": "Yanmeng"
},
{
"family": "Xu",
"given": "Jiasheng"
},
{
"family": "Wang",
"given": "Dan"
},
{
"family": "Geng",
"given": "Fengji"
},
{
"family": "Guo",
"given": "Wanjun"
},
{
"family": "Hu",
"given": "Yuzheng"
}
],
"container-title-short": "Neurotherapeutics",
"volume": "23",
"issue": "2",
"page": "e00884",
"DOI": "10.1016/j.neurot.2026.e00884",
"PMID": "41825227",
"PMCID": "PMC12996647",
"ISSN": "1933-7213",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.neurot.2026.e00884",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
12
]
]
}
}

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