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Related Experiment Video

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Deep Brain Stimulation with Simultaneous fMRI in Rodents
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Patient-specific and interpretable deep brain stimulation optimisation using MRI and clinical review data.

Apostolos Mikroulis1, Andrej Lasica2, Pavel Filip1,2

  • 1Analysis and Interpretation of Biomedical Data, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Prague, Czechia.

Frontiers in Neuroscience
|November 7, 2025
PubMed
Summary

This study introduces a geometry-based method for optimizing Deep Brain Stimulation (DBS) settings in Parkinson's disease. The automated approach improves target coverage and reduces side effects compared to expert settings.

Keywords:
MRIParkinson’s diseasecomputational modellingdeep brain stimulationoptimisationsubthalamic nucleus

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Area of Science:

  • Neurosurgery
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Optimizing Deep Brain Stimulation (DBS) is crucial for treating movement disorders like Parkinson's disease.
  • Current data-intensive methods add complexity to clinical workflows.
  • A geometry-based approach using MRI data offers a streamlined alternative.

Purpose of the Study:

  • To develop and validate an automated, geometry-based method for optimizing DBS electrode contact and current selection.
  • To improve the precision and efficiency of DBS parameter tuning.
  • To integrate seamlessly with existing clinical practices and tools.

Main Methods:

  • Utilized lead reconstruction data and Volume of Tissue Activated (VTA) simulations.
  • Developed a cross-platform tool for automated contact and current selection.
  • Incorporated optional inclusion of existing electrode contact evaluations for fine-tuning.

Main Results:

  • The algorithm demonstrated superior target coverage (p < 5e-13) and minimized electric field leakage (p < 2e-10) compared to expert settings in 174 electrode reconstructions.
  • Retrospective analysis predicted comparable motor outcomes to expert settings (g = 0.05-0.08, p = 0.09-1).
  • Algorithmically selected contacts outperformed manual selections in electric field calculations.

Conclusions:

  • The automated DBS optimization method shows significant promise for clinical application.
  • The approach enhances electric field distribution compared to manual selection without iterative procedures.
  • This method is readily applicable to existing clinical workflows for improved DBS therapy.