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Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation.

Carmen Camara1,2,3, Narayan P Subramaniyam4, Kevin Warwick5

  • 1Department of Computer Science, Carlos III University of Madrid, 28903 Madrid, Spain. macamara@pa.uc3m.es.

Sensors (Basel, Switzerland)
|June 5, 2019
PubMed
Summary

Parkinson's Disease resting tremor is linked to increased non-linear brain activity in the Sub-Thalamic Nucleus (STN). Recurrence network analysis can detect these changes, potentially improving deep brain stimulation for tremor control.

Keywords:
Deep Brain Stimulation (DBS)Local Field Potentials (LFPs)Parkinson’s Disease (PD)Recurrence Networks (RNs)Support Vector Machine (SVM)nonlinear dynamics

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Area of Science:

  • Neuroscience
  • Complex Systems
  • Biomedical Engineering

Background:

  • Parkinson's Disease (PD) is a leading neurodegenerative disorder characterized by resting tremor.
  • Sub-Thalamic Nucleus (STN) Local Field Potentials (LFPs) are crucial for understanding PD motor symptoms.
  • The complex dynamics of STN-LFPs during tremor remain poorly understood.

Purpose of the Study:

  • To investigate the non-linear dynamics of STN-LFPs in Parkinson's patients.
  • To characterize the spatiotemporal dynamics of STN-LFPs during tremor episodes.
  • To evaluate recurrence network analysis for analyzing STN-LFP data.

Main Methods:

  • Utilized ε-recurrence networks (RNs) to analyze STN-LFPs from Parkinsonian patients.
  • Applied graph theoretical measures to characterize the geometric properties of the LFP attractor.
  • Examined non-linear dynamical behavior during different movement conditions and tremor states.

Main Results:

  • STN-LFP activity exhibited increased non-linearity during Parkinson's tremor episodes.
  • ε-recurrence network analysis effectively distinguished transitions between movement conditions.
  • The method demonstrated potential for anticipating tremor onset.

Conclusions:

  • STN-LFP dynamics become more non-linear with Parkinson's tremor.
  • Recurrence network analysis is a viable tool for characterizing STN-LFP complexity.
  • This approach may enable advanced, demand-driven deep brain stimulation systems for tremor management.