title: Customizing deep brain stimulation to the patient using computational models.
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.
authors: Cleveland Clinic Foundation, Cleveland, OH 44195, USA. mcintyc@ccf.org
authors: Cameron C
authors: CC
authors: McIntyre
authors: Anneke M
authors: AM
authors: Frankenmolle
authors: Jennifer
authors: J
authors: Wu
authors: Angela M
authors: AM
authors: Noecker
authors: Jay L
authors: JL
authors: Alberts
mesh_terms: D000465:Algorithms; D003071:Cognition; D003198:Computer Simulation; D003201:Computers; D046690:Deep Brain Stimulation; D004566:Electrodes; D004594:Electrophysiology / Q000379:methods; D004867:Equipment Design; D006801:Humans; D009046:Motor Neurons; D009048:Motor Skills; D010300:Parkinson Disease / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D012984:Software