Santaniello Sabato, Gale John T, Sarma Sridevi V
Biomedical Engineering Department and CT Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, Connecticut.
Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia.
Wiley Interdiscip Rev Syst Biol Med. 2018 Sep;10(5):e1421. doi: 10.1002/wsbm.1421. Epub 2018 Mar 20.
Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive-compulsive disorders, essential tremor, Parkinson's disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients' responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson's disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease.
在过去30年里，深部脑刺激（DBS）已被用于治疗多种慢性神经疾病，如肌张力障碍、强迫症、特发性震颤、帕金森病，以及最近用于治疗痴呆症、抑郁症、认知障碍和癫痫。尽管DBS应用广泛，但它给临床医生和工程师都带来了诸多挑战。一个挑战是设计新颖、更有效的DBS疗法，这受到对治疗性DBS细胞机制缺乏全面了解的阻碍。另一个挑战是临床结果存在冗余性，即不同的DBS方案可能产生相似的临床益处，但可供选择一个方案而非另一个方案的信息（如预测模型、纵向数据、指标等）却很少。最后，患者对DBS的反应存在很大差异，这迫使临床医生通过冗长的编程过程为每个患者仔细调整刺激设置。过去几年，神经工程和系统生物学领域的研究人员一直在应对这些挑战，具体目标是开发新颖的DBS疗法、设计方法和计算工具，以优化DBS对每个患者的治疗效果。此外，正在努力使DBS治疗能自动适应疾病症状的波动。本文对目前可用于治疗帕金森病的定量方法进行了综述，重点介绍了系统理论方法在理解DBS作用下大脑复杂神经元回路的全局动态方面所做的贡献。本文分类如下：转化医学、基因组学与系统医学>治疗方法；分析与计算方法>计算方法；分析与计算方法>动力学方法；生理学>健康与疾病中的哺乳动物生理学。
