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Author Notes:

Sabato Santaniello, Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269. Email: sabato.santaniello@uconn.edu

The authors would like to thank E. B. Montgomery Jr. and E. N. Eskandar for providing valuable data and discussions on the topic of DBS over the years.

The authors declare no conflict of interest to be disclosed at the time of publication.

Subjects:

Research Funding:

Work of authors in this field was supported by Burroughs Wellcome Fund CASI Award 1007274; NSF PECASE Award 1055560; and NIH Grant R01NS073118-02.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Medicine, Research & Experimental
  • Research & Experimental Medicine
  • deep brain stimulation
  • neural systems and control
  • neuroengineering
  • neuroprosthetics
  • Parkinson's disease
  • NEURAL SPIKING ACTIVITY
  • SUBTHALAMIC NUCLEUS STIMULATION
  • HIGH-FREQUENCY STIMULATION
  • BASAL GANGLIA
  • EVOKED-POTENTIALS
  • NEURONAL-ACTIVITY
  • PALLIDAL NEURONS
  • VERBAL FLUENCY
  • MODEL
  • MOTOR

Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease

Tools:

Journal Title:

Wiley Interdisciplinary Reviews: Systems Biology and Medicine

Volume:

Volume 10, Number 5

Publisher:

, Pages e1421-e1421

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive–compulsive disorders, essential tremor, Parkinson’s disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients’ responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson’s disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease.

Copyright information:

© 2018 Wiley Periodicals, Inc.

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