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Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation.

Bryan Howell1, Faical Isbaine2, Jon T Willie2

  • 1Department of Biomedical Engineering, Case Western Reserve University, USA.

Brain Stimulation
|March 24, 2021
PubMed
Summary

Connectomic models accurately predict neural pathway activation during subthalamic deep brain stimulation (DBS) for Parkinson's disease. These findings aid in standardizing DBS modeling for surgical targeting and programming.

Keywords:
Biophysical modelingDeep brain stimulationElectrocorticographyEvoked potentialsParkinson’s diseaseSubthalamic nucleus

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Modeling

Background:

  • Subthalamic deep brain stimulation (DBS) is a key treatment for Parkinson's disease, featuring complex technological parameters.
  • Current in-silico DBS modeling lacks standardized and validated methodologies, hindering research and development.

Purpose of the Study:

  • To assess the precision of patient-specific neural pathway activation estimates in the subthalamic region.
  • To validate these estimates against intracranial evoked potential (EP) recordings.

Main Methods:

  • Utilized advanced connectomic modeling for subthalamic DBS in eleven patients.
  • Focused on hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) activations.
  • Quantified correlations between modeled pathway activations and EP amplitudes.

Main Results:

  • Accurate lead localization and image fusion were crucial for model performance.
  • A global parameter set explained 60% (CSBT) and 73% (HDP) of electrophysiologic activations.
  • Models could extrapolate EP amplitude variations across different electrode configurations and stimulation parameters.

Conclusions:

  • Connectomic DBS models with detailed anatomical and electrical data can predict white matter recruitment.
  • Established findings support the development of connectomic modeling standards for DBS surgery and programming.