Evidence map›Paper›PMID 40579487›Full record

ArticleNature biomedical engineering2026

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

Tanner C Dixon, Gabrielle Strandquist, Alicia Zeng, Tomasz Frączek, Raphael Bechtold, Daryl Lawrence, Shravanan Ravi, Philip A Starr, Jack L Gallant, Jeffrey A Herron and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Adaptive Deep Brain Stimulation for Parkinson's Disease: Navigating the Roadblocks to Clinical Implementation.Movement disorders : official journal of the Movement Disorder Society · 2026
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Closing the loop in DBS: A data-driven approach.Parkinsonism & related disorders · 2025
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Tanner C DixonDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-3151-3362
Gabrielle StrandquistPaul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-8345-6905
Alicia ZengBiophysics Graduate Group, University of California-Berkeley, Berkeley, CA, USA.
Tomasz FrączekGraduate Program in Neuroscience, University of Washington, Seattle, WA, USA.
Raphael BechtoldGraduate Program in Bioengineering, University of Washington, Seattle, WA, USA.
Daryl LawrenceUniversity of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA.
Shravanan RaviDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0009-0007-8540-5718
Philip A StarrDepartment of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-2733-4003
Jack L GallantBiophysics Graduate Group, University of California-Berkeley, Berkeley, CA, USA.ORCID http://orcid.org/0000-0001-7273-1054
Jeffrey A HerronPaul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-9813-0094
Simon J LittleDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA. Simon.Little@ucsf.edu.ORCID http://orcid.org/0000-0001-6249-6230

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Deep Brain StimulationParkinson DiseaseAgedAlgorithmsFemaleHumansMachine LearningMaleMiddle AgedMovement

Identifiers

PMID40579487

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.