Algorithms for programming deep brain stimulation systems

描述(申请人提供)：特发性震颤(ET)是美国最常见的运动障碍，在40岁以上的所有成年人中有4%受到影响。对于以下个人： 运动症状对药物治疗无效，严重影响日常生活，脑深部刺激(DBS)被认为是唯一的治疗选择。尽管DBS技术最近取得了进展，但很大一部分植入DBS的ET患者将因为DBS导联放置不当而获得不充分的震颤控制，而其他患者将在1-2年后失去治疗效果，部分原因是神经刺激器编程选择不灵活。临床上对植入式DBS导联设计的需求越来越大，这种设计能够使临床医生更好地塑造大脑内的电场，特别是在刺激的情况下 通过放置不当的DBS领导会导致低门槛副作用。我们最近对放射节段DBS导联的研究显示了有希望的结果，但了解如何在这种导联上编程刺激设置仍然是使这些导联在临床环境中实用的关键挑战。我们提出的研究将整合高场磁共振成像、计算建模和电生理学，以开发一种经过实验验证的计算编程算法，通过高维DBS电极阵列促进临床确定特定对象的神经刺激器设置。具体地说，我们将：1)开发一种计算算法，可以简化使用径向分段电极阵列的丘脑深部脑刺激导线的编程过程；2)量化计算算法可以在多大程度上准确预测通过非人类灵长类动物丘脑中靶向不良的DBS阵列的电流；以及3)比较刺激小脑丘脑和丘脑皮质通路时在初级运动皮质(M1)诱导的层特异性神经元动力学。

DESCRIPTION (provided by applicant): Essential tremor (ET) is the most common movement disorder in the United States, affecting 4% of all adults over the age of 40. For individuals whose motor symptoms are refractory to medication and significantly impair their daily living, deep brain stimulation (DBS) is considered to be the only therapeutic option. Despite recent advances in DBS technology, a significant portion of ET patients with DBS implants will receive inadequate tremor control because of poorly placed DBS leads, while others will lose efficacy of the therapy after 1-2 years due in part to inflexible neurostimulator programming options. There is a strong and growing clinical need for implantable DBS lead designs that can enable clinicians to better sculpt electric fields within the brain, especially in cases where stimulation through a poorly placed DBS lead results in low-threshold side-effects. Our recent studies with a radially-segmented DBS lead have shown promising results, but knowing how to program the stimulation settings on such a lead remains a critical challenge towards making these leads practical in a clinical setting. Our proposed study will integrate high-field magnetic resonance imaging, computational modeling, and electrophysiology to develop an experimentally-validated computational programming algorithm that facilitates clinical determination of subject-specific neurostimulator settings through high-dimensional DBS electrode arrays. Specifically, we will: 1) develop a computational algorithm that can simplify the programming process of thalamic deep brain stimulation leads with radially-segmented electrode arrays; 2) quantify the degree to which the computational algorithms can accurately predict current steering through poorly targeted DBS arrays in the thalamus in non-human primates; and 3) compare the layer-specific neuronal dynamics induced in primary motor cortex (M1) during stimulation of the cerebellothalamic versus thalamocortical pathway in non-human primates.