Efficacy and Mechanisms of Virtual Reality Treatment of Phantom Leg Pain

项目摘要 截肢导致的肢体丧失是一个常见的问题，在中国有近200万人 我们。约90%的截肢患者有持续性感觉 缺失的肢体，称为幻肢，高达85%的人经历持续和 丧失肢体的衰弱疼痛，称为幻肢疼痛(以下简称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) 显著减少膝关节以下截肢受试者的幻肢疼痛。这项工作 这里提出的建议有几个目标。在具体目标1中，我们将随机选择40名患有PLP的受试者 使用我们的主动VR或商用VR疼痛治疗(酷！)。以特定的目标 2我们建议使用干预(主动VR或Cool！)开发针对PLP的家庭干预。那 在特定的目标中，1被证明是最有效的。在具体目标3中，我们将获得多模式超高 PLP患者治疗前后及正常人的7T分辨率磁共振成像 在没有截肢的情况下，我们还将尝试开发预测截肢疗效的成像生物标志物 治疗。影像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记物的算法以预测家庭治疗反应的因素 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。到年底的时候 在授权期内，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联，并探索了行为和 神经影像生物标记物预测治疗反应。这些数据将提供关键的一步 向临床实施PLP的VR治疗方案迈进，并将推动理论 了解PLP的发病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.