OSCR

Network-based near-scalp personalized brain stimulation targets.

Code ↔ Paper

6 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 6 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Preprocessing ↔ stable_projects/disorder_subtypes/Zhang2016_ADFactors/step3_analyses_internalUse/validateFactorsWithFSStats/CBIG_assignFactorsToStructures.m, the whole file · a weak match · score 0.66 · FSL MNI152, FreeSurfer, ventricular, filtering, temporal, volumes
  2. [2] § Methods › Tree-based MS-HBM personalized anxiosomatic target localization ↔ targeting/CBIG_TMS_MSHBM_parcellation_workflow.m, lines 65–198 · score 0.58 · MNI template, DLPFC mask, dorsal, sphere, mm, ROI
  3. [3] § Methods › DLPFC mask and sACC time course for depression target localization ↔ targeting/CBIG_TMS_MSHBM_parcellation_workflow.m, lines 1–64 · score 0.54 · MNI space, T1 space, TMS, DLPFC, stimulation
  4. [4] § Methods › Tree-based MS-HBM personalized depression target localization ↔ targeting/CBIG_TMS_MSHBM_parcellation_workflow.m, lines 1–64 · score 0.54 · dorsal attention, attention networks, salience, fMRI, DLPFC, ROI
  5. [5] § Methods › Distance-to-scalp and sulcal depth maps ↔ external_packages/SD/SDv1.5.1-svn593/SphericalDemons/ReleaseSampleCode/CoregisterSurfaces.m, lines 128–271 · score 0.52 · sulcal depth, FreeSurfer, surfaces, space
  6. [6] § Methods › Preprocessing ↔ targeting/CBIG_TMS_MSHBM_parcellation_workflow.m, lines 65–198 · score 0.50 · FSL MNI152, censored, ANTs, volumes, template, fsaverage6

Paper

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The authors' code

MATLAB · 247 lines · 11 KB · other · 4 matches

  1. function CBIG_TMS_MSHBM_parcellation_workflow(params)
  3. % CBIG_TMS_MSHBM_parcellation_workflow(params)
  4. %
  5. % This function generates the 17-network MSHBM parcellation for a given subject.
  6. % The parcellation is projected to the individual T1 space and binarized to
  7. % keep only the relevant network components within DLPFC. Relevant components of the
  8. % parcellation are saved as target_ROI.nii.gz, which will be used to identify
  9. % the DLPFC target region for TMS stimulation.
  10. %
  11. % Inputs:
  12. % params.TMS_out_dir:
  13. % The directory to save the output files
  14. % params.subname:
  15. % The subject name
  16. % params.parcellation_flag:
  17. % 0: use pre-defined parcellation file
  18. % 1: use fMRI data to generate parcellation (default)
  19. % params.parcellation_file:
  20. % The pre-defined parcellation file.
  21. % This field is required if parcellation_flag is 0
  22. % params.lh_fMRI_list:
  23. % The list of fMRI files for the left hemisphere for data in fsaverage6.
  24. % The list of fMRI files for both hemisphere for data in fs_LR_32k.
  25. % If parcellation_flag is 0, this field is not necessary.
  26. % params.rh_fMRI_list: (no need if the data is in fs_LR_32k)
  27. % The list of fMRI files for the right hemisphere for data in fsaverage6.
  28. % If parcellation_flag is 0, this field is not necessary.
  29. % params.censor_list:
  30. % The list of censor files
  31. % If parcellation_flag is 0, this field is not necessary.
  32. % params.project_dir:
  33. % The directory to save the MSHBM parcellation.
  34. % If parcellation_flag is 0, this field is not necessary.
  35. % 1: project the parcellation to MNI space instead of individual T1 space
  36. % 0: project the parcellation to individual T1 space (Default)
  37. % params.anat_dir:
  38. % The parent directory of the recon-all results
  39. % If MNI_flag is 1, this field is not necessary.
  40. % params.anat_id:
  41. % The subject name in the recon-all results. The recon-all results are saved in
  42. % anat_dir/anat_id. If not specified, subname is used as default.
  43. % params.target_mesh:
  44. % 'fsaverage' or 'fs_LR_32k'. The target mesh for the parcellation. Default is fsaverage6.
  45. % params.parcellation_name:
  46. % The name of the parcellation. Default is MSHBM17net.
  47. % params.netids:
  48. % The network ids to be extracted. Default is [1,5,9,15].
  49. % 1: salience network B, 5: dorsal attention network A, 9: salience netwrok B, 15: dorsal attention network B
  50. % params.netname:
  51. % The name of the network. Default is SalDorsal.
  52. %
  53. % Output:
  54. % The parameters are saved in TMS_out_dir/params/subname/MSHBM_parcellation_workflow_params.mat
  55. % The DLPFC target ROI is saved in TMS_out_dir/MSHBM_networks_ind_vol/subname/target_ROI.nii.gz
  56. %
  57. % Written by Ru(by) Kong and CBIG under CBIG TMS license
  60. CBIG_CODE_DIR = getenv('CBIG_CODE_DIR');
  61. if (~isdeployed)
  62. addpath(genpath(fullfile(CBIG_CODE_DIR,'stable_projects','brain_parcellation','Kong2019_MSHBM')));
  63. addpath(genpath(fullfile(CBIG_CODE_DIR,'stable_projects','brain_parcellation','Kong2022_ArealMSHBM')));
  64. end
  65. %% check input
  66. % check if all the necessary fields are specified
  67. if(~isfield(params,'TMS_out_dir'))
  68. error('TMS_out_dir is not specified')
  69. else
  70. TMS_out_dir = params.TMS_out_dir;
  71. end
  72. if(~isfield(params,'subname'))
  73. error('subname is not specified')
  74. else
  75. subname = params.subname;
  76. end
  78. if(~isfield(params,'parcellation_flag'))
  79. params.parcellation_flag = 1;
  80. end
  81. if(~isfield(params,'MNI_flag'))
  82. params.MNI_flag = 0;
  83. end
  84. if(params.parcellation_flag == 0)
  85. if(~isfield(params,'parcellation_file'))
  86. error('parcellation_file is not specified')
  87. end
  88. end
  89. if(params.parcellation_flag == 1)
  90. if(~isfield(params,'lh_fMRI_list'))
  91. error('lh_fMRI_list is not specified')
  92. end
  93. if(~isfield(params,'censor_list'))
  94. error('censor_list is not specified')
  95. end
  96. if(~isfield(params,'project_dir'))
  97. error('project_dir is not specified')
  98. end
  99. end
  100. if(params.MNI_flag == 0)
  101. if(~isfield(params,'anat_dir'))
  102. error('anat_dir is not specified')
  103. else
  104. anat_dir = params.anat_dir;
  105. end
  106. if(~isfield(params,'anat_id'))
  107. anat_id = params.subname;
  108. disp('anat_id is not specified. Using subname as default')
  109. else
  110. anat_id = params.anat_id;
  111. end
  112. end
  114. % set default parameters if not specified
  115. if(~isfield(params,'target_mesh'))
  116. target_mesh = 'fsaverage6';
  117. disp('target mesh is not specified. Using fsaverage6 as default')
  118. else
  119. if(strcmp(params.target_mesh, 'fs_LR_32k'))
  120. disp('target mesh is fs_LR_32k.')
  121. elseif(strcmp(params.target_mesh, 'fsaverage6'))
  122. disp('target mesh is fsaverage6.')
  123. else
  124. error('target_mesh should be either fs_LR_32k or fsaverage6.')
  125. end
  126. target_mesh = params.target_mesh;
  127. end
  129. if(~isfield(params,'netids'))
  130. netids = [1,5,9,15];
  131. disp('netids is not specified. Using [1,9,5,15] as default.')
  132. disp('1: salience network B, 5: dorsal attention network A, 9: salience netwrok B, 15: dorsal attention network B')
  133. else
  134. netids = params.netids;
  135. end
  136. if(~isfield(params, 'parcellation_name'))
  137. parcellation_name = 'MSHBM17net';
  138. else
  139. parcellation_name = params.parcellation_name;
  140. end
  141. if(~isfield(params,'netname'))
  142. netname = 'SalDorsal';
  143. else
  144. netname = params.netname;
  145. end
  147. if(params.parcellation_flag == 1)
  148. [lh_labels, rh_labels] = CBIG_MSHBM_parcellation_single_subject(params);
  149. else
  150. disp(['loading parcellation for ' subname])
  151. load(params.parcellation_file);
  152. end
  154. % save params to replicate results
  155. if ~exist(fullfile(TMS_out_dir, subname,'params'), 'dir')
  156. mkdir(fullfile(TMS_out_dir, subname, 'params'));
  157. end
  158. save(fullfile(TMS_out_dir, subname, 'params', 'MSHBM_parcellation_workflow_params.mat'), 'params');
  160. mkdir(fullfile(TMS_out_dir, subname, 'MSHBM_networks_ind_vol'));
  163. if(strcmp(target_mesh, 'fs_LR_32k'))
  164. disp(['projecting parcellation from fs_LR_32k to fsaverage for ' subname])
  165. [lh_labels, rh_labels] = CBIG_project_fsLR2fsaverage(lh_labels, rh_labels,'fs_LR_32k','label',fullfile(TMS_out_dir, 'tmp_par'),'20170508');
  166. DLPFC = load(fullfile(getenv('B1N_LIB_DIR'), 'lib_data', 'group_data', 'DLPFC_fs', 'DLPFC_mask_fsaverage.mat'));
  167. lh_FS_mesh = CBIG_ReadNCAvgMesh('lh', 'fsaverage', 'sphere', 'cortex');
  168. rh_FS_mesh = CBIG_ReadNCAvgMesh('rh', 'fsaverage', 'sphere', 'cortex');
  169. else
  170. DLPFC = load(fullfile(getenv('B1N_LIB_DIR'), 'lib_data', 'group_data', 'DLPFC_fs', ['DLPFC_mask_' target_mesh '.mat']));
  171. lh_FS_mesh = CBIG_ReadNCAvgMesh('lh', target_mesh, 'sphere', 'cortex');
  172. rh_FS_mesh = CBIG_ReadNCAvgMesh('rh', target_mesh, 'sphere', 'cortex');
  173. end
  175. if(params.MNI_flag == 1)
  177. par_extend = CBIG_TMS_MSHBM_extract_networks(lh_labels, rh_labels, lh_FS_mesh, rh_FS_mesh, DLPFC, netids);
  178. save(fullfile(TMS_out_dir, subname, 'MSHBM_networks_ind_vol', 'target_ROI_surf.mat'), 'par_extend');
  180. disp(['projecting parcellation to MNI for ' subname])
  181. output = CBIG_Projectfsaverage2MNI_Ants(par_extend.lh_labels, par_extend.rh_labels,'nearest');
  183. MSHBM_MNI1mm_file = fullfile(TMS_out_dir, subname, 'MSHBM_networks_ind_vol', [parcellation_name '_MNI1mm.nii.gz']);
  184. MSHBM_MNI2mm_file = fullfile(TMS_out_dir, subname, 'MSHBM_networks_ind_vol', [parcellation_name '_MNI2mm.nii.gz']);
  185. MRIwrite(output, MSHBM_MNI1mm_file);
  187. disp(['resampling parcellation to MNI2mm for ' subname])
  188. MNItemplates_dir = fullfile(getenv('CBIG_CODE_DIR'), 'data', 'templates', 'volume', 'FSL5.0.8_MNI_templates');
  189. system(['mri_vol2vol --targ ', fullfile(MNItemplates_dir, 'MNI152_T1_2mm_brain.nii.gz'), ...
  190. ' --regheader --mov ', MSHBM_MNI1mm_file,' --o ', MSHBM_MNI2mm_file]);
  192. MSHBM = MRIread(MSHBM_MNI2mm_file);
  193. MNI_gmmask = fullfile(CBIG_CODE_DIR,'data','templates','volume','FSL_MNI152_masks','GM_Mask_MNI1mm_MNI2mm_91x109x91.nii.gz');
  194. gm_mask = MRIread(MNI_gmmask);
  196. MSHBM.vol(gm_mask.vol == 0) = 0;
  197. MRIwrite(MSHBM, fullfile(TMS_out_dir, subname, 'MSHBM_networks_ind_vol', 'target_ROI.nii.gz'));
  198. else
  200. %% project parcellation to T1
  201. disp(['projecting parcellation to T1 for ' subname])
  202. % project to individual surface
  203. ct = load(fullfile(CBIG_CODE_DIR,'stable_projects','brain_parcellation',...
  204. 'Kong2019_MSHBM','lib','group_priors','HCP_40_fs6','group.mat'));
  206. lh_sess_mesh = CBIG_TMS_read_ind_surf_mesh('lh', anat_id, 'sphere.reg', anat_dir);
  207. rh_sess_mesh = CBIG_TMS_read_ind_surf_mesh('rh', anat_id, 'sphere.reg', anat_dir);
  209. lh_data_ind = MARS_NNInterpolate_kdTree(lh_sess_mesh.vertices, lh_FS_mesh, lh_labels');
  210. rh_data_ind = MARS_NNInterpolate_kdTree(rh_sess_mesh.vertices, rh_FS_mesh, rh_labels');
  212. lh_filename = fullfile(anat_dir, anat_id, 'label', ['lh.' parcellation_name '.annot']);
  213. rh_filename = fullfile(anat_dir, anat_id, 'label', ['rh.' parcellation_name '.annot']);
  214. CBIG_TMS_create_annot(lh_data_ind',rh_data_ind',ct.colors,lh_filename,rh_filename);
  216. % project individual surface to individual volume
  217. % this is the original MSHBM networks
  218. system(['export SUBJECTS_DIR=' anat_dir '; mri_aparc2aseg --s ' anat_id ' --o ' TMS_out_dir '/' subname '/MSHBM_networks_ind_vol/' parcellation_name '_' subname '_T1.nii.gz --annot ' parcellation_name]);
  220. %% extract relevant networks in DLPFC
  221. par_extend = CBIG_TMS_MSHBM_extract_networks(lh_labels, rh_labels, lh_FS_mesh, rh_FS_mesh, DLPFC, netids);
  222. save(fullfile(TMS_out_dir, subname, 'MSHBM_networks_ind_vol', 'target_ROI_fs.mat'), 'par_extend');
  224. % project to individual surface
  225. lh_data_ind_net = MARS_NNInterpolate_kdTree(lh_sess_mesh.vertices, lh_FS_mesh, par_extend.lh_labels');
  226. rh_data_ind_net = MARS_NNInterpolate_kdTree(rh_sess_mesh.vertices, rh_FS_mesh, par_extend.rh_labels');
  228. lh_filename = fullfile(anat_dir, anat_id, 'label',['lh.' parcellation_name '_' netname '.annot']);
  229. rh_filename = fullfile(anat_dir, anat_id, 'label',['rh.' parcellation_name '_' netname '.annot']);
  230. CBIG_TMS_create_annot(lh_data_ind_net',rh_data_ind_net',ct.colors,lh_filename,rh_filename);
  232. % project individual surface to individual volume
  233. % this is the relevant networks within DLPFC
  234. system(['export SUBJECTS_DIR=' anat_dir '; mri_aparc2aseg --s ' anat_id ' --o ' TMS_out_dir '/' subname '/MSHBM_networks_ind_vol/' parcellation_name '_' netname '_' subname '_T1.nii.gz --annot ' parcellation_name '_' netname]);
  236. %% binarize the parcellation file. Only relevant network components within DLPFC are kept as 1
  237. MSHBM_file = fullfile(TMS_out_dir, subname, 'MSHBM_networks_ind_vol', [parcellation_name '_' netname '_' subname '_T1.nii.gz']);
  238. MSHBM = MRIread(MSHBM_file);
  239. MSHBM.vol(MSHBM.vol <= 1000) = 0;
  240. MSHBM.vol(MSHBM.vol >= 2000) = 0;
  241. MSHBM.vol(MSHBM.vol ~= 0) = 1;
  243. MRIwrite(MSHBM, fullfile(TMS_out_dir, subname, 'MSHBM_networks_ind_vol', 'target_ROI.nii.gz'));
  244. end

CBIG_TMS_MSHBM_parcellation_workflow.m at commit d4ecea9, under other · at the source

Overview

Authors: Ru Kong1,2,3,4, Aihuiping Xue1,2,3,4, Jingwen Cheng1,2,3,4, Leon Qi Rong Ooi1,2,3,4,5, Christopher L Asplund1,3,4,6,7,8, Xiao Wei Tan9, Shih Ee Goh9, Jonathan Jie Lee9, Jovi Zheng Jie Koh9, Rachel Si Yun Tan9, Hasvinjit Kaur Gulwant Singh9, Trevor Wei Kiat Tan1,2,3,4,5, Alvin PH Wong4,8,10, Ryan D Webler11,12,13, Michael D Fox11,12, Shan Siddiqi11,12, Phern-Chern Tor9,13, BT Thomas Yeo1,2,3,4,5,14
ORCID iDs: Ru Kong, Aihuiping Xue, Jingwen Cheng, Leon Qi Rong Ooi, Christopher L Asplund, Xiao Wei Tan, Trevor Wei Kiat Tan, Alvin PH Wong, Ryan D Webler, Michael D Fox, Shan Siddiqi, Phern-Chern Tor, BT Thomas Yeo
14 affiliations
  1. Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
  3. Department of Medicine, Healthy Longevity Translational Research Programme, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  4. N.1 Institute for Health, National University of Singapore, Singapore, Singapore
  5. Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
  6. Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
  7. Division of Social Sciences, Yale-NUS College, National University of Singapore, Singapore, Singapore
  8. Department of Psychology, College of Humanities and Sciences, National University of Singapore, Singapore, Singapore
  9. Institute of Mental Health, Singapore, Singapore
  10. School of Psychology, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, St Lucia, QLD, Australia
  11. Department of Psychiatry, Mass General Brigham, Harvard Medical School, Boston, MA, United States
  12. Center for Brain Circuit Therapeutics, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, United States
  13. National University Hospital, Singapore, Singapore
  14. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
Institutions: National University of Singapore (Singapore); Yale-NUS College (Singapore); Institute of Mental Health (Singapore); The University of Queensland (Australia); Brigham and Women's Hospital (United States); Harvard University (United States); National University Hospital (Singapore); Mass General Brigham (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1222
Dates: received 21 May 2025; accepted 25 March 2026; published online 21 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1222 · PMID 42180178 · PMCID PMC13195926 · OpenAlex W4410542717
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.05.15.654391
Status: code verified
Categories: other (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: functional connectivity, individual brain networks, personalized target, transcranial magnetic stimulation, psychiatric disorders, brain stimulation
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: Temasek Foundation (TF2223-IMH-01); National Medical Research Council (STaR20nov-0003, OFLCG19MAY-0035, OFIRG24jul-0049, CTGIIT23jan-0001, OFIRG24jan-0006)
Citations: not cited yet (Europe PMC); 88 references in the paper

Abstract

Functional connectivity (FC) is often used to identify personalized targets for transcranial magnetic stimulation (TMS). However, existing methods often overlook individual differences in whole-cortex network organization. Furthermore, in some personalized TMS protocols, lower stimulation intensity is used for targets closer to the scalp, which may improve patient tolerance. Here, we develop an algorithm to simultaneously optimize FC and scalp proximity for target localization. We first use the multi-session hierarchical Bayesian model (MS-HBM) to estimate high-quality individual-specific cortical networks. A tree-based algorithm is then used to select the optimal target. With essentially no parameter to tune, our framework may potentially improve generalizability across populations. We compare our approach with existing “cluster” and “cone” algorithms. In two test–retest datasets of healthy individuals from the United States and Singapore, tree-based MS-HBM reliably identifies personalized TMS targets for depression near the scalp. Tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and FC to the subgenual anterior cingulate cortex (sACC) in new out-of-sample MRI sessions. To demonstrate versatility, the same algorithm identifies personalized anxiety targets without tuning any parameter. In patients with treatment-resistant depression, tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and sACC FC, hypothetically reducing stimulation intensity by 15% and 5%, respectively. MS-HBM also exhibits the best (most negative) electric-field hotspot sACC FC and highest reliability in induced electric fields. Overall, tree-based MS-HBM provides a robust, generalizable framework to estimate near-scalp personalized targets across populations.

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

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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: “Data and Code 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 (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

ThomasYeoLab/Kong2026_TMSTree

License: other
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d4ecea907edc3da94b29478abe445f6f49d3b706, 1 September 2026
Languages: MATLAB (18), Shell (4)
Size: 34 files, 22 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FreeSurfer (13 files), FSL (4 files), Statistics and Machine Learning Toolbox (3 files), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
24 files
At the source: github.com/ThomasYeoLab/Kong2026_TMSTree

The paper's code and data availability statement is in the Data section.

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Data

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

Data and Code Availability

These raw data for HCP test–retest dataset are publicly available (https://www.humanconnectome.org/). Code for this work is freely available at the GitHub repository maintained by the Computational Brain Imaging Group (https://github.com/ThomasYeoLab/CBIG). Processing pipelines of the fMRI data can be found here (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/CBIG_fMRI_Preproc2016). The individual-specific MS-HBM parcellation approach can be found here (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2019_MSHBM). Code specific to the analyses in this study can be found here (https://github.com/ThomasYeoLab/Kong2026_TMSTree).

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

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Version 2, 28 September 2026

  • Funding: added Temasek Foundation: TF2223-IMH-01; National Medical Research Council: STaR20nov-0003, OFLCG19MAY-0035, OFIRG24jul-0049, CTGIIT23jan-0001, OFIRG24jan-0006

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 18 authors, 6 keywords, 88 references.

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This paper

Kong, R., Xue, A., Cheng, J., Ooi, L. Q. R., Asplund, C. L., Tan, X. W., Goh, S. E., Lee, J. J., Koh, J. Z. J., Tan, R. S. Y., Singh, H. K. G., Tan, T. W. K., Wong, A. P., Webler, R. D., Fox, M. D., Siddiqi, S., Tor, P.-C., & Yeo, B. T. (2026). Network-based near-scalp personalized brain stimulation targets. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1222. https://doi.org/10.1162/imag.a.1222

BibTeX

@article{kong2026network,
author = {Kong, Ru and Xue, Aihuiping and Cheng, Jingwen and Ooi, Leon Qi Rong and Asplund, Christopher L and Tan, Xiao Wei and Goh, Shih Ee and Lee, Jonathan Jie and Koh, Jovi Zheng Jie and Tan, Rachel Si Yun and Singh, Hasvinjit Kaur Gulwant and Tan, Trevor Wei Kiat and Wong, Alvin PH and Webler, Ryan D and Fox, Michael D and Siddiqi, Shan and Tor, Phern-Chern and Yeo, BT Thomas},
title = {{Network-based near-scalp personalized brain stimulation targets}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {4},
pages = {IMAG.a.1222},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1222},
url = {https://doi.org/10.1162/imag.a.1222},
pmid = {42180178},
pmcid = {PMC13195926}
}

RIS

TY - JOUR
AU - Kong, Ru
AU - Xue, Aihuiping
AU - Cheng, Jingwen
AU - Ooi, Leon Qi Rong
AU - Asplund, Christopher L
AU - Tan, Xiao Wei
AU - Goh, Shih Ee
AU - Lee, Jonathan Jie
AU - Koh, Jovi Zheng Jie
AU - Tan, Rachel Si Yun
AU - Singh, Hasvinjit Kaur Gulwant
AU - Tan, Trevor Wei Kiat
AU - Wong, Alvin PH
AU - Webler, Ryan D
AU - Fox, Michael D
AU - Siddiqi, Shan
AU - Tor, Phern-Chern
AU - Yeo, BT Thomas
TI - Network-based near-scalp personalized brain stimulation targets
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/05/21
VL - 4
SP - IMAG.a.1222
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1222
UR - https://doi.org/10.1162/imag.a.1222
LA - en
ER -

CSL-JSON

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"type": "article-journal",
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"author": [
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"family": "Kong",
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"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1222",
"DOI": "10.1162/imag.a.1222",
"PMID": "42180178",
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"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1222",
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"issued": {
"date-parts": [
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}
}

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