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

项目概要 众所周知，许多疼痛综合症几乎对所有治疗都难以治愈，并且给患者带来了巨大的费用 和社会。深部脑刺激（DBS）治疗难治性疼痛疾病显示出早期前景，但 缺乏长期疗效的证明。当前的 DBS 设备提供“开环”连续刺激 因此，由于神经系统的适应和未能适应自然，很容易失去效果 慢性疼痛状态的波动。如果相关的神经生物标志物可以显着改善 DBS 疾病状态可以用作“闭环”DBS 算法中的反馈信号，选择性地 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 并能够以个性化的方式针对慢性疼痛的动态特征。 优化生物标志物检测和刺激传递的大脑目标也可能显着影响 功效。最近的人类成像研究指出额叶皮质区域在支持 疼痛的情感和认知维度，这可能是比以前的目标更有效的 DBS 目标 参与基本体感处理。前扣带回 (ACC) 的病理活动和 眶额皮质（OFC）与疼痛的高阶处理相关，最近的临床试验表明 确定 ACC 是一个有前途的疼痛神经调节刺激靶点。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) 控制算法来治疗慢性疼痛 允许在动态环境中记录纵向颅内信号。通过构建和验证这个 植入设备的技术能力，我们将授权 DBS 治疗慢性疼痛适应症并推进 更普遍的 DBS 个性化、精确方法。 我们将招募 10 名患有中风后疼痛、幻肢综合征和脊髓损伤疼痛的患者 三期临床试验。我们将首先确定低痛和高痛状态的生物标志物，以确定最佳神经 个体疼痛预测信号（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 算法，并测试对慢性疼痛进行闭环 DBS 的可行性和有效性 单盲、假对照临床试验（目标 2）。我们的主要结果指标将是痛苦的结合， 情绪和功能评分以及定量感官测试。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）并评估闭环 DBS 算法的机制 DBS 对慢性刺激的耐受性。成功完成这项研究将获得第一个 预测慢性疼痛状态实时波动以提供镇痛刺激的算法 将通过先进的植入设备技术证明闭环 DBS 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.