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Related Experiment Video

Updated: Jan 24, 2026

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
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A watershed algorithm GUI for personalized fMRI-guided rTMS target.

Zi-Jian Feng1, Ziyu Wei2, Liquan Hong3

  • 1TMS Center, The Third People's Hospital of Deqing (Deqing Hospital of Hangzhou Normal University), Deqing, Hangzhou, Zhejiang, China; Methods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

Neuroimage
|January 22, 2026
PubMed
Summary
This summary is machine-generated.

A new watershed-based method improves targeting for personalized repetitive transcranial magnetic stimulation (rTMS) using functional MRI (fMRI). This approach offers more reliable localization of stimulation sites compared to traditional methods.

Keywords:
GUI toolboxPersonalized rTMSRS-fMRIWatershed segmentationfunctional connectivityrTMS target

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Personalized repetitive transcranial magnetic stimulation (rTMS) relies on functional MRI (fMRI) for precise target localization.
  • Current fMRI-based targeting methods often use subjective thresholds and atlas masks, leading to variability in individualized rTMS targets.
  • Developing objective and reproducible methods for fMRI-guided rTMS is crucial for clinical efficacy.

Purpose of the Study:

  • To introduce a novel watershed-based segmentation approach for threshold-independent functional image analysis.
  • To implement this method in a user-friendly graphical interface for rTMS target localization.
  • To evaluate the performance of this new approach against conventional methods using default mode network targets relevant to Alzheimer's disease.

Main Methods:

  • Development of a watershed-based image segmentation algorithm for fMRI data.
  • Implementation of the algorithm within a graphical user interface (GUI) toolbox.
  • Validation using resting-state fMRI data from 21 healthy participants, comparing posterior cingulate cortex (PCC) and inferior parietal lobule (IPL) targets with a conventional peak-finding strategy.

Main Results:

  • The watershed approach demonstrated high concordance for posterior cingulate cortex (PCC) targets.
  • A median spatial displacement of 6.0 mm was observed for inferior parietal lobule (IPL) targets compared to conventional methods.
  • Qualitative analysis indicated reduced bias from neighboring clusters, atlas boundaries, and multiple peaks.

Conclusions:

  • The proposed watershed-based method provides a generalizable and accessible tool for individualized fMRI-guided rTMS.
  • This threshold-independent approach enhances the reliability and objectivity of rTMS target selection.
  • The framework has the potential to improve the precision and effectiveness of rTMS therapies.