Customizing deep brain stimulation to the patient using computational models

Annu Int Conf IEEE Eng Med Biol Soc. 2009:2009:4228-9. doi: 10.1109/IEMBS.2009.5334592.

Abstract

Bilateral subthalamic (STN) deep brain stimulation (DBS) is effective in improving the cardinal motor signs of advanced Parkinson's disease (PD); however declines in cognitive function have been associated with this procedure. The aim of this study was to assess cognitive-motor performance of 10 PD patients implanted with STN DBS systems during either clinically determined stimulation settings or settings derived from a computational model. Cicerone DBS software was used to define the model parameters such that current spread to non-motor areas of the STN was minimized. Clinically determined and model defined parameters were equally effective in improving motor scores on the traditional clinical rating scale (UPDRS-III). Under modest dual-task conditions, cognitive-motor performance was worse with clinically determined compared to model derived parameters. In addition, the model parameters provided a 66% reduction in power consumption. These results indicate that the cognitive-motor declines associated with bilateral STN can be mitigated, without compromising motor benefits, utilizing stimulation parameters that minimize current spread into non-motor regions of the STN.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cognition
  • Computer Simulation
  • Computers
  • Deep Brain Stimulation*
  • Electrodes
  • Electrophysiology / methods
  • Equipment Design
  • Humans
  • Motor Neurons
  • Motor Skills
  • Parkinson Disease / physiopathology*
  • Signal Processing, Computer-Assisted
  • Software