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Customizing Deep Brain Stimulation to the Patient Using Computational Models

机译:使用计算模型定制对患者的深脑刺激

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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.
机译:双侧次粒子(STN)深脑刺激(DBS)在改善晚期帕金森病(PD)的基本电机迹象方面是有效的;然而,认知函数下降已经与此程序有关。本研究的目的是评估在临床确定的刺激设置或从计算模型的临床确定的刺激设置或设置期间评估植入STN DBS系统的10个PD患者的认知电机性能。 Cicerone DBS软件用于定义模型参数,使得最小化电流扩展到STN的非电机区域。临床确定的和模型定义参数在改善传统临床评级规模(UPDRS-III)上的电动机分数同样有效。在适度的双任务条件下,与模型导出参数相比,认知电机性能更差。此外,模型参数提供了功耗降低了66%。这些结果表明,可以减轻与双边STN相关的认知电动机下降,而不会影响电动机效益,利用最小化电流蔓延到STN的非电动区域的刺激参数。

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