论文标题
计算模型推进了帕金森氏病的深度大脑刺激
Computational model advance deep brain stimulation for Parkinson's disease
论文作者
论文摘要
深脑刺激（DBS）已成为晚期帕金森氏病的有效干预措施，但DBS的确切机制尚不清楚。在这篇综述中，我们讨论了DBS的历史，基底神经节（BG）的解剖结构和内部结构，BG在帕金森氏病中的异常病理变化以及计算模型如何帮助理解和推进DBS。我们还描述了两种类型的模型：数学理论模型和临床预测模型。数学理论模型模拟了BG的神经元或神经网络，以阐明DBS的机械原理，而临床预测模型则更多地集中在患者的结果上，有助于适应每个患者的治疗计划并提高新型电极设计。最后，我们提供有关未来技术的见解和展望。
Deep brain stimulation(DBS)has become an effective intervention for advanced Parkinson's disease, but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia(BG), the abnormal pathological changes of the BG in Parkinson's disease, and how computational models can help understand and advance DBS. We also describe two types of models:mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients' outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies.
