Multisite adaptive brain stimulation for multidimensional treatment of refractory chronic pain

项目总结 一系列不同的慢性疼痛综合征几乎对所有的治疗都难以治愈，但涉及到病理活动 在相似的大脑区域。这暗示了脑深部刺激(DBS)治疗顽固性疼痛的潜力。 疾病，但尽管早期有希望，长期效果是缺乏的。目前的DBS装置在解剖学上是有限的 REACH，只针对已知重要的不同大脑区域的子集。此外，星展银行的治疗是直截了当的 以“开环”、连续的方式应用，不考虑潜在的生理。由于这些原因， 缺点是，DBS治疗疼痛往往无效或随着时间的推移显示出效果减弱。治疗效果丧失 可能是由于神经系统适应或刺激未能适应患者特定的动态 疼痛处理的过程。通过寻找单独优化的大脑目标或通过以下方式，DBS可以显著改善 使用疼痛的神经生物标志物，在需要的时候有选择地控制刺激(“闭环式”DBS)。更好 大脑靶点还将处理疼痛的不同维度，如躯体感觉(位置、强度和 情感(情绪和动机)和认知(注意力和记忆)。这项研究的主要目的是 为了测试个性化靶向支持多个疼痛维度的大脑区域的可行性，并 为止痛“闭环式”DBS开发新技术。我们将开发数据驱动的刺激控制 使用新型设备(美敦力顶峰RC+S)治疗慢性疼痛的算法 在非卧床环境中进行的颅内信号记录。通过在植入设备中构建这项技术，我们将 为每个患者量身定做慢性疼痛DBS，并更普遍地改进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 在双侧丘脑、前扣带回、前额叶皮质植入临时电极， 脑岛和杏仁核。这些区域牵涉到疼痛的多个维度。的目标是 试验期是为每个人确定疼痛的候选生物标记物和最佳刺激参数，以及 选择有可能从试验中受益的受试者。一个由6名这样的患者组成的小组将继续进行 到慢性植入最多3个最佳脑区，用于长期记录和刺激。我们将首先 验证低疼痛状态和高疼痛状态的生物标志物，以定义用于个人疼痛预测的神经信号(AIM 1)。然后，我们将使用这些疼痛生物标记物为星展银行开发个性化的闭环算法，并测试 每周一次封闭的DBS治疗慢性疼痛的可行性(目标2)。然后，我们将评估 闭环DBS算法对传统开环DBS或Sham算法在双盲交叉检测中的有效性 过度试验(目标3)和DBS耐受性的测量机制。我们的主要成果指标将是一个组合 疼痛、情绪和功能评分，以及定量感觉测试。成功完成这项工作 研究将导致第一个预测慢性疼痛状态和发展的实时波动的算法 一种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.