Go to JCI Insight
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Alerts
  • Advertising
  • Job board
  • Subscribe
  • Contact
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Gastroenterology
    • Immunology
    • Metabolism
    • Nephrology
    • Neuroscience
    • Oncology
    • Pulmonology
    • Vascular biology
    • All ...
  • Videos
    • Conversations with Giants in Medicine
    • Author's Takes
  • Reviews
    • View all reviews ...
    • Immune Environment in Glioblastoma (Feb 2023)
    • Korsmeyer Award 25th Anniversary Collection (Jan 2023)
    • Aging (Jul 2022)
    • Next-Generation Sequencing in Medicine (Jun 2022)
    • New Therapeutic Targets in Cardiovascular Diseases (Mar 2022)
    • Immunometabolism (Jan 2022)
    • Circadian Rhythm (Oct 2021)
    • View all review series ...
  • Viewpoint
  • Collections
    • In-Press Preview
    • Commentaries
    • Research letters
    • Letters to the editor
    • Editorials
    • Viewpoint
    • Top read articles
  • Clinical Medicine
  • JCI This Month
    • Current issue
    • Past issues

  • Current issue
  • Past issues
  • Specialties
  • Reviews
  • Review series
  • Conversations with Giants in Medicine
  • Author's Takes
  • In-Press Preview
  • Commentaries
  • Research letters
  • Letters to the editor
  • Editorials
  • Viewpoint
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Alerts
  • Advertising
  • Job board
  • Subscribe
  • Contact

Usage Information

Multiple stimulation parameters influence efficacy of deep brain stimulation in parkinsonian mice
Jonathan S. Schor, Alexandra B. Nelson
Jonathan S. Schor, Alexandra B. Nelson
Published June 13, 2019
Citation Information: J Clin Invest. 2019;129(9):3833-3838. https://doi.org/10.1172/JCI122390.
View: Text | PDF
Concise Communication Neuroscience

Multiple stimulation parameters influence efficacy of deep brain stimulation in parkinsonian mice

  • Text
  • PDF
Abstract

Deep brain stimulation (DBS) is used to treat multiple neuropsychiatric disorders, including Parkinson’s disease (PD). Despite widespread clinical use, its therapeutic mechanisms are unknown. Here, we developed a mouse model of subthalamic nucleus (STN) DBS for PD, to permit investigation using cell type–specific tools available in mice. We found that electrical STN DBS relieved bradykinesia, as measured by movement velocity. In addition, our model recapitulated several hallmarks of human STN DBS, including rapid onset and offset, frequency dependence, dyskinesia at higher stimulation intensity, and associations among electrode location, therapeutic benefit, and side effects. We used this model to assess whether high-frequency stimulation is necessary for effective STN DBS and whether low-frequency stimulation can be effective when paired with compensatory adjustments in other parameters. We found that low-frequency stimulation, paired with greater pulse width and amplitude, relieved bradykinesia. Moreover, a composite metric incorporating pulse width, amplitude, and frequency predicted therapeutic efficacy better than frequency alone. We found a similar relationship between this composite metric and movement speed in a retrospective analysis of human data, suggesting that correlations observed in the mouse model may extend to human patients. Together, these data establish a mouse model for elucidating mechanisms of DBS.

Authors

Jonathan S. Schor, Alexandra B. Nelson

×

Usage data is cumulative from March 2022 through March 2023.

Usage JCI PMC
Text version 2,302 178
PDF 436 71
Figure 402 0
Supplemental data 457 39
Citation downloads 58 0
Totals 3,655 288
Total Views 3,943

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

Advertisement

Copyright © 2023 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

Sign up for email alerts