Optimizing Patient-Specific Deep Brain Stimulation Models Using Electrophysiology

项目摘要/摘要 针对患者的脑深部刺激(DBS)的计算模型已经被用来理解这种效果 电刺激对帕金森氏病(PD)和其他神经精神疾病患者脑结构和通路的影响 精神错乱。这些基于图像的3维(3-D)生物物理模型提供了神经网络的可视化表示。 DBS电极周围和连接的远端区域的区域激活模式。结果，他们被骗了- 在临床实践(用于靶向和术后规划)和研究(用于 机械论的见解和假说的发展)。然而，尽管它们在研究中被广泛使用，而且 最近引入临床实践，缺乏对模型准确性的直接评估。目前，它是 未知由患者特定的计算DBS模型预测的刺激效应的空间范围 反映了人脑中真正的神经元激活。因此，最近的临床研究表明， 单纯组织激活体积(VTA)DBS模型预测与一般PD的相关性 临床结果。驱动力(DF)预测器DBS模型将更真实的轴突轨迹纳入到 然而，尚不清楚DF模型的额外复杂性是否提高了临床诊断的准确性。 这些模拟。为了测试这一点，我们开发了一种实验范式来量化轴突的程度。 帕金森病患者丘脑底核DBS激活通路的研究脑皮层诱发电位(CEP)与外周神经功能 外周运动诱发电位(MEP)可以区分DBS对邻近神经通路的激活。 DBS设置(有源触点、幅度、脉冲宽度)的调制会改变CEP和MEP的幅度。 因此，通过改变DBS设置，可以量化路径招募，并且激活预测 对于不同的建模方法可以进行比较。这项生物工程研究资助计划的目标是 确定患者特定的DBS模型(VTA和DF)与体内电生理的准确性 帕金森病患者的测量结果。我们假设DF模型在生物物理上比VTA模型更准确。 ELS。我们将使用两种不同的电生理测量来测试这一点(AIM1中的CEP；AIM2中的MEP)，我们 将确定哪些模型组件对模型模拟最关键。在目标3中，我们将比较 由模型预测的神经通路激活对临床DBS副作用(由皮质脊髓灰质炎- NAL/球束激活，可通过模型进行实验测量和预测)。这将使我们能够 以确定个别患者临床使用所需的模型准确度水平。最优的 该方案中系统描述的建模方法将为临床应用提供首个有效的标准。 DBS模型的理论和研究应用。

PROJECT SUMMARY/ABSTRACT Patient-specific computational models of deep brain stimulation (DBS) have been used to understand the effects of electrical stimulation on brain structures and pathways in Parkinson's disease (PD) and other neuropsychiatric disorders. These 3-dimensional (3-D), image-based, biophysical models provide a visual representation of neu- ronal activation patterns around the DBS electrode and in connected distal regions. As a result, they are con- ceptually attractive in both clinical practice (for targeting and postoperative programming) and in research (for mechanistic insights and hypotheses development). However, despite their widespread use in research, and recent introduction into clinical practice, the direct assessment of model accuracy is lacking. At present, it is unknown if the spatial extent of stimulation effects predicted by the patient-specific computational DBS models reflect genuine neuronal activations in the human brain. Consequently, recent clinical studies have shown poor correlations between predictions from simple volume of tissue activation (VTA) DBS models and general PD clinical outcomes. Driving force (DF) predictor DBS models incorporate more realistic axonal trajectories into the local anatomy; however, it is unclear if the additional complexity of DF models improves the clinical accuracy of the simulations. To test this, we have developed an experimental paradigm to quantify the degree of axonal pathway activation by subthalamic DBS in PD patients. Intracranial cortical evoked potentials (cEP) and periph- eral motor evoked potentials (mEP) can differentiate activation of several neighboring neural pathways by DBS. Modulation of DBS settings (active contacts, amplitude, pulse width) alters the amplitude of cEP and mEP. Therefore, by changing the DBS settings, pathway recruitment can be quantified, and the activation predictions for different modeling methods can be compared. The goal of this Bioengineering Research Grant proposal is to determine the accuracy of patient-specific DBS models (VTA and DF) compared to in-vivo electrophysiologic measurements in PD patients. We hypothesize that DF models are more biophysically accurate than VTA mod- els. We will test this using two different electrophysiological measurements (cEP in Aim1; mEP in Aim2), and we will identify which model components are the most critical to the model simulations. In Aim 3 we will compare neural pathway activations predicted by the models to clinical DBS side effects (resulting from corticospi- nal/bulbar tract activation that can be experimentally measured and predicted by the models). This will allow us to determine the level of model accuracy that is necessary for clinical use in individual patients. The optimal modeling approach systematically characterized in this proposal will provide the first validated standard for clin- ical and research applications of DBS models.