Movement-responsive deep brain stimulation for Parkinson's disease using a remotely optimized neural decoder.

Journal: Nature biomedical engineering
Published Date: Jun 27, 2025

Abstract

Deep brain stimulation (DBS) has garnered widespread use as an effective treatment for advanced Parkinson's disease. Conventional DBS (cDBS) provides electrical stimulation to the basal ganglia at fixed amplitude and frequency, yet patients' therapeutic needs are often dynamic with residual symptom fluctuations or side effects. Adaptive DBS (aDBS) is an emerging technology that modulates stimulation with respect to real-time clinical, physiological or behavioural states, enabling therapy to dynamically align with patient-specific symptoms. Here we report an aDBS algorithm intended to mitigate movement slowness by delivering targeted stimulation increases during movement using decoded motor signals from the brain. Our approach demonstrated improvements in dominant hand movement speeds and study participant-reported therapeutic efficacy compared with an inverted control, as well as increased typing speed and reduced dyskinesia compared with cDBS. Furthermore, we demonstrate proof of principle of a machine learning pipeline capable of remotely optimizing aDBS parameters in a home setting. This work illustrates the potential of movement-responsive aDBS as a promising therapeutic approach and highlights how machine learning-assisted programming can simplify complex optimization to facilitate translational scalability.

Authors

  • Tanner C Dixon
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Gabrielle Strandquist
    Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
  • Alicia Zeng
    Biophysics Graduate Group, University of California-Berkeley, Berkeley, CA, USA.
  • Tomasz Frączek
    Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA.
  • Raphael Bechtold
    Graduate Program in Bioengineering, University of Washington, Seattle, WA, USA.
  • Daryl Lawrence
    University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA.
  • Shravanan Ravi
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Philip A Starr
    Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
  • Jack L Gallant
    Biophysics Graduate Group, University of California-Berkeley, Berkeley, CA, USA.
  • Jeffrey A Herron
    Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
  • Simon J Little
    Department of Neurology, University of California San Francisco, San Francisco, CA, USA. Simon.Little@ucsf.edu.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals