Movement-responsive deep brain stimulation for Parkinson’s Disease using a remotely optimized neural decoder Article Swipe
Tanner C. Dixon
,
Gabrielle Strandquist
,
Alicia Zeng
,
Tomek Fraczek
,
Raphael Bechtold
,
Daryl Lawrence
,
Shravanan Ravi
,
Philip A. Starr
,
Jack L. Gallant
,
Jeffrey A. Herron
,
Simon Little
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.08.14.24312002
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.08.14.24312002
Deep brain stimulation (DBS) has garnered widespread use as an effective treatment for advanced Parkinson’s Disease (PD). 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 behavioral states, enabling therapy to dynamically align with patient-specific symptoms. Here, we report a novel aDBS algorithm intended to mitigate movement slowness by delivering targeted stimulation increases during movement using decoded motor signals from the brain. Compared to cDBS and a control algorithm that decreases stimulation during movement, our approach demonstrates enhanced clinical efficacy, with improvements in movement speed, reduced dyskinesia, and better patient-reported therapeutic quality. 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 new therapeutic approach and highlights how machine learning assisted programming can simplify optimization to facilitate translational scalability.
Related Topics
Concepts
Deep brain stimulation
Dyskinesia
Computer science
Movement disorders
Scalability
Parkinson's disease
Stimulation
Brain stimulation
Motor symptoms
Pipeline (software)
Essential tremor
Physical medicine and rehabilitation
Neuroscience
Artificial intelligence
Medicine
Disease
Psychology
Database
Pathology
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.08.14.24312002
- https://www.medrxiv.org/content/medrxiv/early/2024/08/20/2024.08.14.24312002.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401834144
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401834144Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2024.08.14.24312002Digital Object Identifier
- Title
-
Movement-responsive deep brain stimulation for Parkinson’s Disease using a remotely optimized neural decoderWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-20Full publication date if available
- Authors
-
Tanner C. Dixon, Gabrielle Strandquist, Alicia Zeng, Tomek Fraczek, Raphael Bechtold, Daryl Lawrence, Shravanan Ravi, Philip A. Starr, Jack L. Gallant, Jeffrey A. Herron, Simon LittleList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.08.14.24312002Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2024/08/20/2024.08.14.24312002.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2024/08/20/2024.08.14.24312002.full.pdfDirect OA link when available
- Concepts
-
Deep brain stimulation, Dyskinesia, Computer science, Movement disorders, Scalability, Parkinson's disease, Stimulation, Brain stimulation, Motor symptoms, Pipeline (software), Essential tremor, Physical medicine and rehabilitation, Neuroscience, Artificial intelligence, Medicine, Disease, Psychology, Database, Pathology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3Per-year citation counts (last 5 years)
- References (count)
-
57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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