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

Nature Biomedical Engineering (2025)Cite this article

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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.

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Fig. 1: Movement-responsive DBS theoretical framework.
Fig. 2: Optimizing the embedded movement classifier.
Fig. 3: Personalizing power bands improves algorithm performance.
Fig. 4: Final offline model performance.
Fig. 5: Performance of neural classifiers is maintained during online adaptive stimulation.
Fig. 6: Movement-responsive stimulation impacts self-perceived therapeutic quality, movement speed and dyskinesia in a performance-dependent manner.
Fig. 7: Movement adaptive stimulation increases keypress responsiveness during naturalistic typing.

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Data availability

All processed data supporting the analyses of this study or displayed in figures are available via the Dryad repository at https://doi.org/10.5061/dryad.4xgxd25hw (ref. 69). Any additional requests for raw data may be directed to the corresponding author and will be provided under reasonable request and with additional consent of the study participant.

Code availability

All machine learning and data analysis code is available via GitHub at https://github.com/Weill-Neurohub-OPTiMaL/dixon-2025-move-adbs (ref. 70). The rcssim package is available via GitHub at https://github.com/Weill-Neurohub-OPTiMaL/rcs-simulation (ref. 71).

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Acknowledgements

This work was supported by a Weill Neurohub Investigators Grant (J.L.G., J.A.H. and S.J.L.) and NIH UG3NS140730 award. The Weill Neurohub funding agency and NIH played no role in the study design, data collection, analysis, interpretation of the data or the writing of this paper. We would like to thank our participant, A. Perttula, for their involvement in the study and for supporting the research with their time for the experiments.

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Authors and Affiliations

  1. Department of Neurology, University of California San Francisco, San Francisco, CA, USA

    Tanner C. Dixon, Shravanan Ravi & Simon J. Little

  2. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Gabrielle Strandquist & Jeffrey A. Herron

  3. Biophysics Graduate Group, University of California-Berkeley, Berkeley, CA, USA

    Alicia Zeng & Jack L. Gallant

  4. Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA

    Tomasz Frączek & Jeffrey A. Herron

  5. Graduate Program in Bioengineering, University of Washington, Seattle, WA, USA

    Raphael Bechtold & Jeffrey A. Herron

  6. University of California, Berkeley – University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA

    Daryl Lawrence

  7. Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA

    Philip A. Starr

  8. Department of Electrical Engineering and Computer Sciences, University of California-Berkeley, Berkeley, CA, USA

    Jack L. Gallant

  9. Department of Psychology, University of California-Berkeley, Berkeley, CA, USA

    Jack L. Gallant

  10. Department of Neurological Surgery, University of Washington, Seattle, WA, USA

    Jeffrey A. Herron

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  1. Tanner C. Dixon
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Contributions

T.C.D., J.A.H. and S.J.L. conceived the study and designed the research. T.C.D. and S.R. scheduled and oversaw the experiments. T.C.D. and D.L. designed and programmed the optimization pipeline and related tools. G.S., A.Z., T.F., R.B. and S.R. designed the data collection infrastructure. T.C.D., G.S., A.Z. and R.B. analysed the data. P.A.S. performed the implantation procedures. T.C.D. wrote the paper, and all other authors performed reviews. J.L.G., J.A.H. and S.J.L. acquired funding and supervised the study.

Corresponding author

Correspondence to Simon J. Little.

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Competing interests

T.C.D. is a current employee of iota Biosciences, Inc., but was not an employee when the study was designed and initiated. S.R. is a current employee of Medtronic, Inc., but was not an employee when the study was designed and initiated. S.J.L. is a consultant for iota Biosciences, Inc. P.A.S. received fellowship programme financial support from Medtronic, Inc., and Boston Scientific, Inc., is a consultant for InBrain Neuroelectronics, Inc., and serves on the DSMB for Neuralink, Inc. All other authors declare no competing interests. No commercial entity played any role in experimental design or analysis/interpretation of study results, provided financial support for this study, or has any obvious means of gaining or losing financially through this publication. A patent application has been submitted for the movement-responsive aDBS and optimization approach (applicant, UCSF; application number, 65/531,506; inventors, T.C.D., S.J.L., J.A.H., D.L. and P.A.S.).

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Real-time movement-responsive aDBS.

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Dixon, T.C., Strandquist, G., Zeng, A. et al. Movement-responsive deep brain stimulation for Parkinson’s disease using a remotely optimized neural decoder. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01438-0

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