Efficacy and Mechanisms of Virtual Reality Treatment of Phantom Leg Pain

项目摘要 截肢是一个常见的问题，发生在近200万人， 我们大约90%的截肢患者会有持续的感觉 的缺失肢体，被称为幻肢，高达85%的经验持续和 在缺失的肢体中的使人衰弱的疼痛，称为幻肢痛（以下称为PLP）。我们之前 展示了具有主动腿部运动和虚拟肢体视觉的虚拟现实（VR） 显著减少膝下截肢受试者的幻肢疼痛。工作 这里提出的有几个目标。在特定目标1中，我们将随机分配40例PLP受试者， 使用我们的Active VR或市售的VR疼痛治疗（Cool！）进行治疗。具体目标 2我们建议使用干预（Active VR或Cool！）的 在具体目标1中被证明是最有效的。在具体目标3中，我们将获得多峰超高 治疗前后PLP受试者和正常受试者的分辨率（7 T）MRI成像 我们还将尝试开发成像生物标志物来预测 治疗影像学研究将解决一些关于PLP神经基础的争议 更广泛地探索人类的神经可塑性。最后，在具体目标4中，我们建议确定 可用于基于临床生物标志物的算法中以预测对基于家庭的 VR治疗使用分类和回归树（CART）分析来自目标2的数据，我们 将确定预测治疗反应的行为和神经解剖学因素。结束时 在补助期内，我们将确定两种VR治疗PLP的相对疗效，评估 低成本家庭治疗的可行性和有效性，决定了神经解剖学的变化 使用先进的方法与治疗反应相关，并探讨了行为和 预测治疗反应的神经影像学生物标志物。这些数据将提供关键的一步 对PLP的VR治疗方案的临床实施，并将推进理论 了解PLP的机制和功能神经解剖学。

Project Summary Limb loss due to amputation is a common problem, occurring in nearly 2 million people in the US. Approximately 90% of individuals with limb amputation experience the persistent sensation of the missing extremity, known as a phantom limb, and up to 85% experience persistent and debilitating pain in the missing limb, termed phantom limb pain (hereafter PLP). We previously demonstrated that Virtual Reality (VR) with active leg movements and vision of a virtual limb significantly reduce phantom limb pain in subjects with below the knee amputations. The work proposed here has several objectives. In Specific Aim 1 we will randomize 40 subjects with PLP to treatment with our Active VR or a commercially available VR pain treatment (Cool!). In Specific Aim 2 we propose to develop a home intervention for PLP using the intervention (Active VR or Cool!) that in Specific Aim 1 proved to be most efficacious. In specific Aim 3 we will obtain multimodal ultra-high resolution (7T) MRI imaging in subjects with PLP before and after treatment, and normal subjects without amputation; we will also attempt to develop imaging biomarkers that predict efficacy of treatment. Imaging studies will address a number of controversies regarding the neural basis of PLP and explore human neuroplasticity more generally. Finally, in Specific Aim 4 we propose to determine factors that could be used in a clinical biomarker-based algorithm to predict response to home-based VR treatment. Using classification and regression tree (CART) analysis with the data from Aim 2, we will identify behavioral and neuroanatomic factors that predict treatment response. By the end of the grant period, we will have determined the relative efficacy of two VR treatments for PLP, assessed the feasibility and efficacy of a low-cost home-based treatment, determined the neuroanatomic changes associated with treatment response using advanced methods, and explored the behavioral and neuroimaging biomarkers predicting treatment response. These data will provide a critical step toward clinical implementation of a VR treatment protocol for PLP and will advance theoretical understanding of the mechanisms and functional neuroanatomy of PLP.