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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Customizing deep brain stimulation to the patient using computational models.

Cameron C McIntyre1, Anneke M Frankenmolle, Jennifer Wu

  • 1Cleveland Clinic Foundation, Cleveland, OH 44195, USA. mcintyc@ccf.org

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary
This summary is machine-generated.

Optimizing deep brain stimulation (DBS) for Parkinson's disease (PD) using a computational model improved cognitive-motor function and reduced power needs. This approach mitigates cognitive declines without sacrificing motor benefits in PD patients.

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Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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Area of Science:

  • Neuroscience
  • Neurological Surgery
  • Computational Biology

Background:

  • Bilateral subthalamic nucleus (STN) deep brain stimulation (DBS) effectively treats advanced Parkinson's disease (PD) motor symptoms.
  • Cognitive function decline is a known adverse effect associated with STN DBS.
  • Optimizing stimulation parameters is crucial for maximizing therapeutic benefits while minimizing side effects.

Purpose of the Study:

  • To evaluate the cognitive-motor performance of PD patients using clinically determined versus computationally modeled STN DBS settings.
  • To determine if a computational model can minimize current spread to non-motor areas within the STN.
  • To assess the impact of optimized DBS parameters on motor function, cognitive-motor performance, and power consumption.

Main Methods:

  • A computational model (Cicerone DBS software) was used to define STN DBS parameters minimizing current spread to non-motor regions.
  • Ten PD patients with STN DBS systems were assessed using both clinically determined and model-derived stimulation parameters.
  • Cognitive-motor performance was evaluated under dual-task conditions, alongside motor improvements measured by the UPDRS-III scale.

Main Results:

  • Both clinically determined and model-derived parameters equally improved motor scores (UPDRS-III).
  • Cognitive-motor performance was significantly better with model-derived parameters compared to clinically determined ones under dual-task conditions.
  • The model-derived parameters resulted in a 66% reduction in power consumption.

Conclusions:

  • Computational modeling of STN DBS parameters can mitigate cognitive-motor declines in PD patients.
  • Optimized stimulation parameters improve cognitive-motor function without compromising motor symptom relief.
  • This modeling approach offers a more efficient and potentially safer method for STN DBS in Parkinson's disease, reducing power usage.