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Related Concept Videos

Parkinson's Disease: Treatment01:24

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Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
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Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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Combining Multimodal Biomarkers to Guide Deep Brain Stimulation Programming in Parkinson Disease.

Ashesh Shah1, Thuy-Anh Khoa Nguyen2, Katrin Peterman1

  • 1Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.

Neuromodulation : Journal of the International Neuromodulation Society
|February 27, 2022
PubMed
Summary

An algorithm using subthalamic nucleus local field potentials (LFPs) and imaging markers can efficiently identify optimal deep brain stimulation (DBS) contacts. This approach significantly speeds up DBS programming for Parkinson disease patients.

Keywords:
DBS programmingParkinson diseasedeep brain stimulationlocal field potentialssubthalamic nucleus

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Clinical Neurology

Background:

  • Deep brain stimulation (DBS) programming is a manual, time-intensive process.
  • Optimizing DBS lead contact selection is crucial for effective Parkinson disease treatment.
  • Subthalamic nucleus (STN) local field potentials (LFPs) show potential for guiding DBS programming.

Purpose of the Study:

  • To evaluate an algorithmic approach for selecting optimal DBS stimulation contacts.
  • To investigate the predictive value of combined STN LFP features and imaging markers.
  • To predict key clinical DBS parameters: efficacy, therapeutic window, and side-effect threshold.

Main Methods:

  • Recorded STN LFPs from multicontact DBS leads in 27 hemispheres at rest and during movement.
  • Analyzed features across multiple frequency bands (alpha, beta, gamma, HFO).
  • Incorporated anatomical stimulation sweet spots and combined electrophysiological and imaging markers.

Main Results:

  • Both resting and movement-state LFP features improved prediction accuracy.
  • Resting gamma, modulated beta, and movement-modulated HFO were highly predictive.
  • The algorithm achieved nearly 90% accuracy in identifying optimal contacts, outperforming single-marker approaches.
  • Combining electrophysiological and imaging markers further enhanced prediction.

Conclusions:

  • Algorithmic selection of LFP and imaging markers offers an efficient DBS programming strategy.
  • This approach can improve clinical efficiency and patient outcomes in DBS therapy.
  • LFP-guided DBS programming represents a significant advancement in Parkinson disease management.