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Related Experiment Video

Updated: Jul 27, 2025

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
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Single-Subject TMS Pulse Visualization on MRI-Based Brain Model: A precise method for mapping TMS pulses on cortical

Nikolay Syrov1, Alfiia Mustafina2, Artemiy Berkmush-Antipova3

  • 1Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia.

Methodsx
|June 9, 2023
PubMed
Summary

This study presents a novel method for visualizing transcranial magnetic stimulation (TMS) application points on brain models. This technique enhances the anatomical specificity of TMS analysis for improved research and clinical applications.

Keywords:
BlenderMotor-evoked potentialsNavigated transcranial magnetic stimulationNeuronavigationNeurovisualizationPhosphenesTMSTMS-affected Cortical points Visualization on MRI-based Brain Model. The software tool is implemented using the Python language and runs on the Blender software platform. It uses a subject-specific 3D model of the brain's cortical surface reconstructed from MRI data. The software marks the points of TMS application on the cortical surface

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Anatomy

Background:

  • Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique used for research and therapy.
  • Accurate targeting of cortical areas is essential for understanding TMS effects and optimizing treatment.
  • Current neuronavigation methods allow site-specific TMS but precise visualization of application points can be improved.

Purpose of the Study:

  • To develop and validate a method for highly accurate visualization of TMS application points on individual brain cortical surfaces.
  • To enable anatomy-specific analysis of TMS effects by precisely mapping stimulation sites.
  • To create a customizable tool for researchers and clinicians to analyze TMS-induced cortical activation.

Main Methods:

  • Utilized magnetic resonance imaging (MRI) data to construct personalized 3D brain models.
  • Segmented MRI data to generate and optimize detailed 3D brain models using specialized software.
  • Developed a Python script integrated with Blender software to process TMS coil orientation data and brain models.
  • Defined and marked specific cortical sites impacted by TMS pulses based on coil position and orientation.

Main Results:

  • Successfully visualized precise points of TMS application on 3D brain models.
  • Demonstrated the capability to link stimulation points to specific anatomical locations on the cortical surface.
  • The developed Python script allows for task-specific visualization of TMS points, enhancing analytical flexibility.

Conclusions:

  • The proposed method offers a significant advancement in visualizing TMS application points with high anatomical accuracy.
  • This technique facilitates detailed, site-specific analysis of TMS effects, potentially leading to more effective therapeutic strategies.
  • The customizable nature of the Python script supports diverse research applications in neurostimulation and brain mapping.