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How Standardized Is Transcranial Magnetic Stimulation Treatment for Depression? Large-Cohort Modeling Reveals Systematic Dosimetric Variability.

Code ↔ Paper

2 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.

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  1. [1] § Methods › E-Field Simulation ↔ run_efield_modeling.py, lines 60–101 · score 0.70 · SimNIBS, positions F3, EEG, volume, simulation, head
  2. [2] § Methods › Statistical Analysis ↔ get_intracranial_volume.m, the whole file · a weak match · score 0.52 · intracranial volume, ICV, variable

Paper

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The authors' code

Python · 101 lines · 3 KB · no license · 1 match

  1. import csv
  2. from pathlib import Path
  3. from simnibs import sim_struct, run_simnibs
  5. # A very large value used here to define the coil y-direction.
  6. # Replace this with a more explicit anatomical or electrode reference if needed.
  7. HUGE_FLOAT = 1e100
  9. # Replace this with your local CHARM directory before running the script.
  10. # Example: Path("/path/to/charm/hcp")
  11. charm_path = Path("path/to/charm/hcp")
  13. # Relative path to the coil model file.
  14. # Update this if your coil file is stored elsewhere.
  15. coil_file = Path("Drakaki_BrainStim_2022") / "MagVenture_Cool-B65.ccd"
  17. # Output directory for simulation results.
  18. output_root = Path("simulation_outputs")
  21. def get_coords(subject_path):
  22. """
  23. Read C3 and F3 coordinates from the subject-specific EEG position file.
  25. Parameters
  26. ----------
  27. subject_path : Path
  28. Path to the subject's m2m directory.
  30. Returns
  31. -------
  32. tuple
  33. ((c3_x, c3_y, c3_z), (f3_x, f3_y, f3_z))
  34. """
  35. csv_file = subject_path / "eeg_positions" / "EEG10-10_UI_jurak_2007.csv"
  37. with csv_file.open(newline="") as csvfile:
  38. reader = csv.reader(csvfile)
  39. rows = list(reader)
  41. # C3 coordinates (row 34, index 33)
  42. c3_x = float(rows[33][1])
  43. c3_y = float(rows[33][2])
  44. c3_z = float(rows[33][3])
  46. # F3 coordinates (row 12, index 11)
  47. f3_x = float(rows[11][1])
  48. f3_y = float(rows[11][2])
  49. f3_z = float(rows[11][3])
  51. return (c3_x, c3_y, c3_z), (f3_x, f3_y, f3_z)
  54. # Create the output directory if it does not already exist.
  55. output_root.mkdir(exist_ok=True)
  57. # Automatically detect subject folders inside the CHARM directory.
  58. subjects = [folder.name for folder in charm_path.iterdir() if folder.is_dir()]
  60. # Run one TMS session per subject.
  61. for sub in subjects:
  62. print(f"Running simulation for subject: {sub}")
  64. # Initialize a new SimNIBS session for the current subject.
  65. s = sim_struct.SESSION()
  66. s.map_to_surf = True
  67. s.map_to_fsavg = True
  68. s.map_to_vol = True
  69. s.map_to_MNI = True
  70. s.fields = "eEjJ"
  72. # Path to the subject-specific head model directory.
  73. s.subpath = str(charm_path / sub / f"m2m_{sub}")
  75. # Subject-specific output directory.
  76. s.pathfem = str(output_root / sub)
  77. s.open_in_gmsh = False
  79. # Create a TMS simulation list and assign the coil model.
  80. tmslist = s.add_tmslist()
  81. tmslist.fnamecoil = str(coil_file)
  83. # Read C3 and F3 coordinates from the EEG file.
  84. (c3_x, c3_y, c3_z), (f3_x, f3_y, f3_z) = get_coords(Path(s.subpath))
  86. # Add TMS position at C3.
  87. pos_c3 = tmslist.add_position()
  88. pos_c3.centre = [c3_x, c3_y, c3_z]
  89. pos_c3.pos_ydir = [HUGE_FLOAT, HUGE_FLOAT, 0.0]
  90. pos_c3.didt = 75e6
  92. # Add TMS position at F3.
  93. pos_f3 = tmslist.add_position()
  94. pos_f3.centre = [f3_x, f3_y, f3_z]
  95. pos_f3.pos_ydir = [HUGE_FLOAT, HUGE_FLOAT, 0.0]
  96. pos_f3.didt = 89e6
  98. # Run the simulation for the current subject.
  99. run_simnibs(s)
  101. print(f"Finished simulation for subject: {sub}")

run_efield_modeling.py at commit 1371962, no license · at the source

Overview

Authors: Bella Bingbing Zhang1, Tim Tianze Lin1, Penny Ping Qin1, Rebecca Laidi Kan1, Adam Weili Xia1, Minxia Jin1,2, Yinghu Peng3, Daniel Keeser4,5,6, on Behalf of CDP Working Group, Frank Padberg4,7, Martin Tik8,9, Tifei Yuan10, Benjamin Becker11,12, Frank Fan Zhu13, Georg S. Kranz1,14,15
ORCID iDs: Penny Ping Qin, Daniel Keeser, Frank Padberg, Martin Tik, Benjamin Becker
15 affiliations
  1. Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
  2. Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
  3. Shenzhen Institutes of Advanced Technology, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China
  4. Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
  5. Max Planck Institute of Psychiatry (MPIP), Munich, Germany
  6. Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, University of Augsburg, Augsburg, Germany
  7. DZPG (German Center for Mental Health), Partner Site Munich-Augsburg, Munich/Augsburg, Germany
  8. Center for Medical Physics and BME, Medical University of Vienna, Vienna, Austria
  9. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
  10. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  11. State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
  12. Department of Psychology, The University of Hong Kong, Hong Kong, China
  13. HealthyMind Neuro-Wellness Centre, Hong Kong, China
  14. Mental Health Research Center (MHRC), The Hong Kong Polytechnic University, Hong Kong, China
  15. University Research Facility in Behavioral and Systems Neuroscience (UBSN), The Hong Kong Polytechnic University, Hong Kong, China
Institutions: Hong Kong Polytechnic University (Hong Kong SAR China); Tongji University (China); Shanghai Sunshine Rehabilitation Center (China); Chinese Academy of Sciences (China); Shenzhen Institutes of Advanced Technology (China); University of Augsburg (Germany); LMU Klinikum (Germany); Max Planck Institute of Psychiatry (Germany); Ludwig-Maximilians-Universität München (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); Stanford Medicine (United States); Medical University of Vienna (Austria); Stanford University (United States); Shanghai Jiao Tong University (China); Shanghai Mental Health Center (China); University of Hong Kong (Hong Kong SAR China)
Journal: Psychotherapy and psychosomatics, pages 1-17
Dates: received 17 October 2025; accepted 31 May 2026; published online 8 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1159/000552942 · PMID 42258597 · PMCID PMC13299130 · OpenAlex W7163873613
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), depression (population), clinical / translational (subfield)
Methods: Statistics, Connectivity, fMRI & imaging
Keywords: Transcranial magnetic stimulation, Electric field modeling, Depression, Dosing strategies, Population variability
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: Hong Kong Polytechnic University
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

Introduction: Repetitive transcranial magnetic stimulation (rTMS) is a widely utilized, noninvasive brain stimulation technique for treating neurological and psychiatric disorders worldwide. The efficacy of rTMS is influenced by individual head morphology, which shapes the induced electric field (E-field) in the brain. The standard practice for dosing rTMS involves adjusting the intensity to a defined percentage of the motor threshold (MT), a method exemplified by the globally adopted, US FDA-approved protocol of using 120% MT for depression. Since head morphology varies systematically across ethnic groups, current dosing protocols may lead to inconsistent cortical dosing.

Methods: Here, we used large-scale computational modeling (N = 1,085) to systematically evaluate TMS dosage across “White,” “Han Chinese,” and “Black or African Am.” populations.

Results: Our findings reveal striking ethnic disparities in the E-field ratio between the therapeutic target (left dorsolateral prefrontal cortex, DLPFC) and the calibration site (left primary motor cortex, M1). “Han Chinese” individuals exhibited a significantly higher E-field ratio (E-fieldDLPFC/E-fieldM1 = 1.226) compared to “White” individuals (E-fieldDLPFC/E-fieldM1 = 1.096). These differences are primarily driven by variations in scalp-to-cortex distance (SCD). Based on these findings, we derived both simplified population-level adjustment formulas and individualized SCD-based dosing equations and implemented them in a web-based tool for practical use.

Conclusion: Our results suggest that applying a uniform multiplier of MT to DLPFC stimulation may introduce systematic differences in delivered cortical dose across ethnic populations, highlighting that universal dosing conventions derived predominantly from specific demographic cohorts may embed bias, such that established safety and efficacy profiles may not be uniformly applicable across diverse global populations.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

nuan750/2026-zhang-TMS-dosimetric-variability

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1371962b1c5a7d241d06a74a1e21a778dce4292c, 24 March 2026
Languages: MATLAB (6), Python (2)
Size: 9 files, 8 scripts
Software Heritage: not archived
Found in: the text, “E-Field Simulation”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/nuan750/2026-zhang-TMS-dosimetric-variability

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 2 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 Statement

The data that support the findings of this study include in-house proprietary datasets and publicly available datasets. The three in-house datasets are not publicly available due to participant privacy and the data sharing policies of the originating institutions. However, access to these datasets can be requested through the corresponding author. Any data sharing is subject to a formal data use agreement and requires the approval of the respective principal investigators who collected the data (G.S.K. for the in-house Chinese dataset; M.T. and D.K. for the in-house White dataset). The public datasets analyzed in this study were sourced from the Human Connectome Project (HCP) and the Chinese Human Connectome Project (CHCP). The head models generated from all datasets (both in-house and public) are available from the corresponding author upon reasonable request. Data acquired at the Medical University of Vienna, Austria, will be made available via author M.T. upon reasonable request and in accordance with the local IRB. The interactive tool is available at https://calculator-dosage.vercel.app/.

Reproduced under the paper's license (CC BY-NC), 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

  • Publisher: n/a → Karger Publishers
  • Authors: added Benjamin Becker (0000-0002-9014-9671); removed Benjamin Becker
  • Funding: added Hong Kong Polytechnic University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, pages, dates, 15 authors, 5 keywords, 95 references.

Cite

This paper

Zhang, B. B., Lin, T. T., Qin, P. P., Kan, R. L., Xia, A. W., Jin, M., Peng, Y., Keeser, D., on Behalf of CDP Working Group, Padberg, F., Tik, M., Yuan, T., Becker, B., Zhu, F. F., & Kranz, G. S. (2026). How Standardized Is Transcranial Magnetic Stimulation Treatment for Depression? Large-Cohort Modeling Reveals Systematic Dosimetric Variability. Psychotherapy and psychosomatics, 1-17. https://doi.org/10.1159/000552942

BibTeX

@article{zhang2026how,
author = {Zhang, Bella Bingbing and Lin, Tim Tianze and Qin, Penny Ping and Kan, Rebecca Laidi and Xia, Adam Weili and Jin, Minxia and Peng, Yinghu and Keeser, Daniel and {on Behalf of CDP Working Group} and Padberg, Frank and Tik, Martin and Yuan, Tifei and Becker, Benjamin and Zhu, Frank Fan and Kranz, Georg S.},
title = {{How Standardized Is Transcranial Magnetic Stimulation Treatment for Depression? Large-Cohort Modeling Reveals Systematic Dosimetric Variability}},
journal = {Psychotherapy and psychosomatics},
year = {2026},
month = jun,
pages = {1--17},
publisher = {Karger Publishers},
issn = {0033-3190},
doi = {10.1159/000552942},
url = {https://doi.org/10.1159/000552942},
pmid = {42258597},
pmcid = {PMC13299130}
}

RIS

TY - JOUR
AU - Zhang, Bella Bingbing
AU - Lin, Tim Tianze
AU - Qin, Penny Ping
AU - Kan, Rebecca Laidi
AU - Xia, Adam Weili
AU - Jin, Minxia
AU - Peng, Yinghu
AU - Keeser, Daniel
AU - on Behalf of CDP Working Group
AU - Padberg, Frank
AU - Tik, Martin
AU - Yuan, Tifei
AU - Becker, Benjamin
AU - Zhu, Frank Fan
AU - Kranz, Georg S.
TI - How Standardized Is Transcranial Magnetic Stimulation Treatment for Depression? Large-Cohort Modeling Reveals Systematic Dosimetric Variability
T2 - Psychotherapy and psychosomatics
J2 - Psychother Psychosom
PY - 2026
DA - 2026/06/08
SP - 1
EP - 17
SN - 0033-3190
PB - Karger Publishers
DO - 10.1159/000552942
UR - https://doi.org/10.1159/000552942
LA - en
ER -

CSL-JSON

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"container-title": "Psychotherapy and psychosomatics",
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"family": "Zhang",
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8
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