Optimizing Patient-Specific Deep Brain Stimulation Models Using Electrophysiology

利用电生理学优化患者特异性深部脑刺激模型

• 批准号: 10543471
• 负责人: Svjetlana Miocinovic
• 金额: $ 51.05万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10543471
• 关键词: 3-Dimensional; Anatomy; Applications Grants; Area; Atlases; Axon; Biomedical Engineering; Biophysics; Brain; Chronic; Clinic; Clinical; Clinical Research; Computer Models; Contralateral; Data; Deep Brain Stimulation; Development; Distal; Electric Stimulation; Electrical Stimulation of the Brain; Electrodes; Electrophysiology (science); Encapsulated; Evoked Potentials; Face; Goals; Human; Image; Implant; Limb structure; Magnetic Resonance Imaging; Measurement; Measures; Methodology; Methods; Modeling; Motor; Motor Evoked Potentials; Muscle; Neural Pathways; Neurons; Operative Surgical Procedures; Outcome; Parkinson Disease; Pathway interactions; Patients; Pattern; Peripheral; Physiologic pulse; Positioning Attribute; Postoperative Period; Research; Research Personnel; Research Project Grants; Source; Stimulus; Structure; Surveys; System; Testing; Tissues; Variant; Visual; Width; biophysical model; clinical development; clinical practice; clinical predictors; cohort; driving force; electric field; experimental study; improved; in vivo; individual patient; insight; model development; models and simulation; multidisciplinary; neural; neural stimulation; neuropsychiatric disorder; predictive modeling; recruit; response; side effect; simulation

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.

项目摘要/摘要 深脑刺激（DB）的患者特异性计算模型已用于了解影响 帕金森氏病（PD）和其他神经精神病学的大脑结构和途径的电刺激 疾病。这些3维（3-D）的基于图像的生物物理模型提供了neu-的视觉表示 DBS电极周围和连接的远端区域周围的Ronal激活模式。结果，它们是 在临床实践（用于靶向和术后编程）和研究中都具有吸引力 机械洞察力和假设发展）。但是，尽管它们在研究中广泛使用，并且 最近介绍了临床实践，缺乏对模型准确性的直接评估。目前，是 未知是否由患者特异性计算DBS模型预测的刺激效应的空间范围是否 反映人脑中真正的神经元激活。因此，最近的临床研究表明 从简单的组织激活（VTA）DBS模型和一般PD的预测之间的相关性 临床结果。驱动力（DF）预测器DBS模型将更现实的轴突轨迹纳入 局部解剖学；但是，尚不清楚DF模型的其他复杂性是否提高了 模拟。为了测试这一点，我们开发了一个实验范式来量化轴突的程度 PD患者中丘脑DBS的途径激活。颅内皮质诱发电位（CEP）和Periph- ER诱发电位（MEP）可以通过DBS区分几种相邻的神经途径的激活。 DBS设置的调制（主动触点，振幅，脉冲宽度）改变了CEP和MEP的幅度。 因此，通过更改DBS设置，可以量化途径募集和激活预测 对于不同的建模方法，可以比较。这项生物工程研究补助金的目标是 与体内电生理学相比，确定患者特异性DBS模型（VTA和DF）的准确性 PD患者的测量。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值（AIM1中的CEP； AIM2中的MEP）进行测试，我们 将确定哪些模型组件对模型模拟最重要。在AIM 3中，我们将比较 这些模型预测到临床DBS副作用的神经途径激活（由皮质 - 造成 可以通过模型测量和预测的NAL/BULBAR道激活）。这将使我们 确定单个患者临床使用所需的模型准确性水平。最佳 在本提案中系统地表征的建模方法将为临床提供第一个经过验证的标准 DBS模型的ICAL和研究应用。