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An algorithmic procedure for measuring deep brain stimulation-induced capsular activation using motor evoked

Eric Richard Cole1,2,3, Enrico Opri4, Seyyed Bahram Borgheai2

  • 1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.

Journal of Neural Engineering
|December 17, 2025
PubMed
Summary

This study introduces an automated method to detect motor evoked potentials (mEPs) using electromyography. This objective biomarker aids in precise deep brain stimulation (DBS) programming for Parkinson's disease, improving treatment efficiency.

Keywords:
DBS side effectscorticospinal tractinternal capsuleprogrammingsignal processing

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • Deep brain stimulation (DBS) for Parkinson's disease requires precise targeting to avoid motor side effects from internal capsule activation.
  • Current methods rely on subjective patient reporting, leading to inconsistent and potentially suboptimal treatment programming.

Purpose of the Study:

  • To develop and validate an automated algorithm for detecting and quantifying motor evoked potentials (mEPs) as an objective biomarker for DBS-induced motor side effects.
  • To establish a reliable method for improving surgical targeting and device programming in DBS therapy.

Main Methods:

  • Signal processing techniques were designed to detect mEPs while minimizing noise and stimulation artifacts.
  • A multi-channel electromyography (EMG) approach was used to create a unified side effect biomarker, the mEP score.
  • Intraoperative recordings from 54 subthalamic nucleus (STN) leads were analyzed to characterize mEP physiological features.

Main Results:

  • The automated algorithm accurately detected mEPs and quantified their features (frequency, latency, amplitude, waveform similarity).
  • The mEP score demonstrated a clear relationship with DBS amplitude and contact configuration, consistent with STN-M2 capsule anatomy.
  • The developed method provides an end-to-end approach for objective side effect measurement.

Conclusions:

  • Automated mEP detection offers a promising, objective biomarker for real-time assessment of motor side effects during DBS procedures.
  • This approach can enhance the precision and efficiency of surgical targeting and programming for DBS therapy in Parkinson's disease.
  • The validated mEP score facilitates more consistent and effective DBS treatment optimization.