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Updated: Aug 17, 2025

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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Automated deep brain stimulation programming based on electrode location: a randomised, crossover trial using a

Jan Roediger1, Till A Dembek2, Johannes Achtzehn3

  • 1Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

The Lancet. Digital Health
|December 17, 2022
PubMed
Summary
This summary is machine-generated.

An algorithm using neuroimaging data, StimFit, achieved deep brain stimulation results comparable to standard care for Parkinson's disease motor symptoms. This approach could significantly reduce programming time for effective treatment.

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Area of Science:

  • Neurology
  • Neurosurgery
  • Biomedical Engineering

Background:

  • Deep brain stimulation (DBS) of the subthalamic nucleus (STN) effectively manages Parkinson's disease (PD) motor symptoms.
  • Optimizing DBS stimulation parameters is crucial but traditionally involves a lengthy trial-and-error process.

Purpose of the Study:

  • To evaluate the efficacy of stimulation parameters generated by the StimFit algorithm, which utilizes neuroimaging data.
  • To compare StimFit-guided DBS programming with the standard of care (SoC) in PD patients.

Main Methods:

  • A double-blind, randomized, crossover, non-inferiority trial involving 35 Parkinson's disease patients with STN DBS.
  • Patients received DBS programming via StimFit and SoC methods in a randomized sequence.
  • Motor function was assessed using the MDS-UPDRS part III scale under both programming conditions.

Main Results:

  • StimFit demonstrated non-inferiority to SoC, with a mean difference of -1.6 points in MDS-UPDRS-III scores (p=0.0038).
  • Both methods significantly improved motor scores from baseline (OFF-stimulation) to ON-stimulation.
  • Acute side effects occurred in 17% of patients with initial StimFit programming, requiring amplitude reduction.

Conclusions:

  • Data-driven algorithms like StimFit can predict DBS parameters yielding motor symptom control comparable to standard care.
  • This automated approach holds the potential to substantially reduce the time required for achieving optimal Parkinson's disease treatment parameters.