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Author Notes:

To whom correspondence should be addressed: warren.grill@duke.edu

D.T.B. and W.M.G. designed research; D.T.B., B.D.S., R.Q.S., D.A.T., R.E.G. and W.M.G. performed research; D.T.B. and W.M.G. analyzed data and wrote the paper.

We thank G. Mills for laboratory support and A. August for assistance with histology.

Competing interests: D.T.B. and W.M.G. are inventors on patent applications related to non-regular patterns of DBS and hold equity in Deep Brain Innovations, LLC, which has licensed intellectual property from Duke University.

R.E.G. receives research funding from Medtronic; he also serves as a consultant to Medtronic and receives compensation for these services. The terms of this arrangement have been reviewed and approved by Emory University in accordance with its conflict of interest policies.

Subjects:

Research Funding:

This work was supported by NIH grants R01 NS040894, R37 NS040894, and R01 NS079312.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Cell Biology
  • Medicine, Research & Experimental
  • Research & Experimental Medicine
  • SUBTHALAMIC NUCLEUS STIMULATION
  • DISEASE RATING-SCALE
  • PARKINSONS-DISEASE
  • OSCILLATORY ACTIVITY
  • NEURONAL-ACTIVITY
  • GENERATOR REPLACEMENT
  • PALLIDAL STIMULATION
  • TREMOR AMPLITUDE
  • BASAL GANGLIA
  • BRADYKINESIA

Optimized temporal pattern of brain stimulation designed by computational evolution

Tools:

Journal Title:

Science Translational Medicine

Volume:

Volume 9, Number 371

Publisher:

, Pages eaah3532-eaah3532

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Brain stimulation is a promising therapy for several neurological disorders, including Parkinson's disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson's disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson's disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.

Copyright information:

© 2017, American Association for the Advancement of Science

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