Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson’s disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

中文翻译：

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深部脑刺激中激活的组织体积建模及其临床影响：综述

深部脑刺激（DBS）是一种神经调节疗法，已被 FDA 批准用于治疗各种疾病，包括但不限于运动障碍（例如帕金森病和特发性震颤）、癫痫和强迫症。用于估计激活组织体积 (VTA) 的计算方法与脑成像技术相结合，构成了从回顾性临床研究中生成的模型的基础，用于预测 DBS 患者的结果。例如，VTA 模型用于生成基于目标和网络的概率刺激图，这在预测 DBS 治疗结果方面发挥着至关重要的作用。这篇综述定义了组织激活（或调节）的计算方法，包括使用启发式和临床推导的估计的方法，以及依赖有限元方法和生物物理轴突模型的更多计算涉及的方法。我们定义模型参数，并提供可用于集成神经成像和神经激活预测的商业、开源和学术模拟平台的比较。此外，我们回顾了使用这些建模方法作为疾病函数的临床研究。通过描述组织激活建模方法并强调其在临床研究中的应用，我们为神经工程和临床神经调节界提供了可能影响未来 DBS 研究建模方法采用的观点。