Technology development for closed-loop deep brain stimulation to treat refractory neuropathic pain

项目摘要 众所周知，许多疼痛综合征几乎对所有治疗都是难治的，并给患者带来巨大的成本 和社会脑深部电刺激（DBS）治疗难治性疼痛障碍显示了早期的希望， 缺乏长期疗效的证明。当前DBS设备提供“开环”连续刺激 因此由于神经系统的适应和不能适应自然的 慢性疼痛状态的波动。如果相关的神经生物标志物能够显著改善DBS， 疾病状态可以用作“闭环”DBS算法中的反馈信号， 在需要时提供刺激。这种方法可能有助于避免随着时间的推移产生耐受性 并且使得慢性疼痛的动态特征能够以个性化的方式作为目标。 优化生物标志物检测和刺激递送的脑靶点也可能显著影响 功效最近对人类的成像研究指出，额叶皮层区域在支持大脑活动中起着关键作用。 疼痛的情感和认知维度，这可能是比先前目标更有效的DBS目标 参与基本的躯体感觉处理。前扣带（ACC）和 眶额皮质（OFC）与疼痛的高级处理相关，最近的临床试验表明， 将ACC确定为用于疼痛的神经调节的有希望的刺激靶点。在这项研究中，我们将目标 ACC和OFC用于生物标志物发现和闭环刺激。我们将开发数据驱动的刺激 使用新型神经接口设备（Medtronic Activa PC+S）治疗慢性疼痛的控制算法， 允许在非卧床环境中记录纵向颅内信号。通过构建和验证这个 技术能力的植入设备，我们将授权DBS慢性疼痛适应症和进步 更普遍的DBS的个性化，精确的方法。 我们将招募10名中风后疼痛、幻肢综合征和脊髓损伤疼痛的患者， 三期临床试验。我们将首先确定低和高疼痛状态的生物标志物，以确定最佳的神经 用于个体疼痛预测的信号（目标1）。然后我们将使用这些疼痛生物标志物来开发闭环 DBS的算法，并测试在慢性疼痛中进行闭环DBS的可行性和有效性。 单盲、假手术对照临床试验（目的2）。我们的主要结果指标将是疼痛， 情绪和功能评分以及定量感觉测试。在最后阶段，我们将评估 闭环DBS算法相对于传统开环DBS的有效性（目标3），并评估 DBS对慢性刺激的耐受性。这项研究的成功完成将导致第一个 用于预测递送镇痛刺激的慢性疼痛状态的实时波动的算法，以及 将通过推进植入式设备技术证明闭环DBS用于缓解疼痛的可行性。

PROJECT SUMMARY Many pain syndromes are notoriously refractory to almost all treatment and pose significant costs to patients and society. Deep brain stimulation (DBS) for refractory pain disorders showed early promise but demonstration of long-term efficacy is lacking. Current DBS devices provide “open-loop” continuous stimulation and thus are prone to loss of effect owing to nervous system adaptation and a failure to accommodate natural fluctuations in chronic pain states. DBS could be significantly improved if neural biomarkers for relevant disease states could be used as feedback signals in “closed-loop” DBS algorithms that would selectively provide stimulation when it is needed. This approach may help avert the development of tolerance over time and enable the dynamic features of chronic pain to be targeted in a personalized fashion. Optimizing the brain targets for both biomarker detection and stimulation delivery may also markedly impact efficacy. Recent imaging studies in humans point to the key role of frontal cortical regions in supporting the affective and cognitive dimensions of pain, which may be more effective DBS targets than previous targets involved in basic somatosensory processing. Pathological activity in the anterior cingulate (ACC) and orbitofrontal cortex (OFC) is correlated with the higher-order processing of pain, and recent clinical trials have identified ACC as a promising stimulation target for the neuromodulation of pain. In this study we will target ACC and OFC for biomarker discovery and closed-loop stimulation. We will develop data-driven stimulation control algorithms to treat chronic pain using a novel neural interface device (Medtronic Activa PC+S) that allows longitudinal intracranial signal recording in an ambulatory setting. By building and validating this technological capacity in an implanted device, we will empower DBS for chronic pain indications and advance personalized, precision methods for DBS more generally. We will enroll ten patients with post-stroke pain, phantom limb syndrome and spinal cord injury pain in our three-phase clinical trial. We will first identify biomarkers of low and high pain states to define optimal neural signals for pain prediction in individuals (Aim 1). We will then use these pain biomarkers to develop closed-loop algorithms for DBS and test the feasibility and efficacy of performing closed-loop DBS for chronic pain in a single-blinded, sham controlled clinical trial (Aim 2). Our main outcome measures will be a combination of pain, mood and functional scores together with quantitative sensory testing. In the last phase, we will assess the efficacy of closed-loop DBS algorithms against traditional open-loop DBS (Aim 3) and assess mechanisms of DBS tolerance in response to chronic stimulation. Successful completion of this study would result in the first algorithms to predict real-time fluctuations in chronic pain states for the delivery of analgesic stimulation and would prove the feasibility of closed-loop DBS for pain-relief by advancing implantable device technology.