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Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients.

Sebastián Castaño-Candamil1, Benjamin I Ferleger2, Andrew Haddock2

  • 1Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany.

Frontiers in Human Neuroscience
|November 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel adaptive deep brain stimulation (aDBS) system for essential tremor (ET). The new system uses patient-specific neural markers and adapts to changing tremor dynamics, reducing stimulation while maintaining symptom control.

Keywords:
adaptive deep brain stimulationclosed-loop deep brain stimulationdeep brain stimulationessential tremormachine learningneural decoding

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Deep brain stimulation (DBS) is a key therapy for Parkinson's disease (PD) and essential tremor (ET).
  • Adaptive DBS (aDBS) aims to enhance efficacy and reduce side effects by adjusting stimulation based on neural signals.
  • Current aDBS systems rely on generalized neural markers and assume stable symptom dynamics, limiting their adaptability.

Purpose of the Study:

  • To develop and evaluate an improved aDBS system for essential tremor (ET).
  • To incorporate patient- and session-specific neural markers (NMs) identified through a data-driven approach.
  • To implement a control strategy that accommodates short-term non-stationary dynamics in tremor.

Main Methods:

  • Developed a machine learning model to estimate tremor intensity from electrocorticographic (ECoG) signals for patient-specific NMs.
  • Designed a control strategy that accounts for local variations in tremor statistics.
  • Conducted online testing with three chronically implanted ET patients across five sessions.

Main Results:

  • The novel aDBS system demonstrated symptom suppression at least equivalent to continuous DBS in 3 out of 4 tests.
  • Significant reduction in overall stimulation (at least 24%) was achieved compared to continuous DBS.
  • In one test, symptom suppression was comparable to both continuous DBS and no treatment, indicating system adaptability.

Conclusions:

  • Introduced a novel aDBS system for ET, featuring session-specific NMs via machine learning and a non-stationary dynamics-aware control strategy.
  • The system proved suitable for ET, showing potential for improved therapeutic outcomes and reduced stimulation.
  • This work paves the way for further investigation in larger patient cohorts.