Multisite adaptive brain stimulation for multidimensional treatment of refractory chronic pain

项目摘要 各种各样的慢性疼痛综合征几乎对所有治疗都是难治的，但涉及病理活动 相似的大脑区域。这表明脑深部电刺激（DBS）对顽固性疼痛的治疗潜力 疾病，但尽管早期的承诺，缺乏长期的疗效。目前的DBS器械在解剖学上受到限制， 到达，只针对已知重要的不同大脑区域的子集。此外，DBS疗法直截了当地 以“开环”、连续的方式应用，而不考虑潜在的生理学。由于这些 然而，DBS治疗疼痛通常无效或随着时间的推移效果减弱。治疗效果丧失 可能是由于神经系统适应或刺激失败以适应患者特定的动力学 疼痛处理的过程。DBS可以通过寻求单独优化的大脑靶点或 使用疼痛的神经生物标记物来选择性地控制需要时的刺激（“闭环”DBS）。更好 大脑目标还将解决疼痛的不同维度，如躯体感觉（位置，强度， 持续时间），情感（情绪和动机）和认知（注意力和记忆）。本研究的主要目的是 测试支持多个疼痛维度的大脑区域的个性化靶向的可行性， 开发新的技术，用于治疗疼痛的“闭环”DBS。我们将开发数据驱动的刺激控制 使用一种新型器械（Medtronic Summit RC+S）治疗慢性疼痛的算法 在流动环境中进行颅内信号记录。通过在植入设备中构建这种技术，我们将 为每位患者定制慢性疼痛DBS，并更普遍地推进DBS的精确方法。 从住院试验期开始，患有各种难治性慢性疼痛综合征的受试者将 在丘脑、前扣带回、前额皮质， 杏仁核和杏仁核。这些区域与疼痛的多个维度有关。的目标 试验期旨在确定每个个体的疼痛候选生物标志物和最佳刺激参数，以及 选择有可能从试验中获益的受试者。然后，6名此类患者的亚组将继续进行 到慢性植入多达3个“最佳”大脑区域，以进行长期记录和刺激。我们将首先 验证低和高疼痛状态的生物标志物，以定义用于个体疼痛预测的神经信号（目的 1）。然后，我们将使用这些疼痛生物标志物来开发DBS的个性化闭环算法，并测试 在每周阻滞中进行闭环DBS治疗慢性疼痛的可行性（目标2）。然后我们将评估 在一项双盲交叉试验中，闭环DBS算法相对于传统开环DBS或假手术的有效性 试验（目标3）并测量DBS耐受性的机制。我们的主要成果措施将是 疼痛、情绪和功能评分以及定量感觉测试。成功完成本 这项研究将产生第一个预测慢性疼痛状态和发展的实时波动的算法。 治疗目前无法治愈的疾病的新疗法

PROJECT SUMMARY A diverse array of chronic pain syndromes are refractory to almost all treatment but involve pathological activity in similar brain regions. This suggests therapeutic potential for deep brain stimulation (DBS) for refractory pain disorders, but despite early promise, long-term efficacy is lacking. Current DBS devices are limited in anatomical reach, targeting only a subset of the distinct brain regions known to be important. Further, DBS therapy is bluntly applied in an “open-loop,” continuous fashion without regard to underlying physiology. As a result of these shortcomings, DBS for pain is often ineffective or shows diminished effect over time. Loss of therapeutic effect may be due to nervous system adaptation or a failure of stimulation to accommodate patient- specific dynamics of pain processing. DBS could be significantly improved by seeking individually optimized brain targets or by using neural biomarkers of pain to selectively control stimulation when it is needed (“closed-loop” DBS). Better brain targets would also address the different dimensions of pain such as somatosensory (location, intensity and duration), affective (mood and motivation) and cognitive (attention and memory). The main goal of this study is to test the feasibility of personalized targeting of brain regions that support multiple pain dimensions and to develop new technology for “closed-loop” DBS for pain. We will develop data-driven stimulation control algorithms to treat chronic pain using a novel device (Medtronic Summit RC+S) that allows longitudinal intracranial signal recording in an ambulatory setting. By building this technology in an implanted device, we will tailor chronic pain DBS to each patient and advance precision methods for DBS more generally. Beginning with an inpatient trial period, subjects with various refractory chronic pain syndromes will undergo bilateral surgical implant of temporary electrodes in the thalamus, anterior cingulate, prefrontal cortex, insula and amygdala. These regions have been implicated in the multiple dimensions of pain. The goal of the trial period is to identify candidate biomarkers of pain and optimal stimulation parameters for each individual, and to select subjects who show likelihood to benefit from the trial. A subgroup of 6 such patients will then proceed to chronic implantation of up to 3 “optimal” brain regions for long-term recording and stimulation. We will first validate biomarkers of low- and high-pain states to define neural signals for pain prediction in individuals (Aim 1). We will then use these pain biomarkers to develop personalized closed-loop algorithms for DBS and test the feasibility of performing closed-loop DBS for chronic pain in weekly blocks (Aim 2). We will then assess the efficacy of closed-loop DBS algorithms against traditional open-loop DBS or sham in a double-blinded cross- over trial (Aim 3) and measure mechanisms of DBS tolerance. Our main outcome measures will be a combination of pain, mood and functional scores together with quantitative sensory testing. Successful completion of this study would result in the first algorithms to predict real-time fluctuations in chronic pain states and development of a new therapy for currently untreatable diseases.