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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

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

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The authors' code

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

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It can be read at the source: targeting/CBIG_TMS_MSHBM_parcellation_workflow.m.

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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

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, not copied: shown from their source

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At the source: github.com/ThomasYeoLab/Kong2026_TMSTree

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2,020 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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

Data 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.

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 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.

Cite

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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