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

项目总结 众所周知，许多疼痛综合征对几乎所有的治疗都难以奏效，并给患者带来了巨大的成本。 和社会。深部脑刺激(DBS)治疗顽固性疼痛障碍显示出早期的希望，但 缺乏长期疗效的证据。目前的DBS设备提供“开环”持续刺激 因此容易由于神经系统适应和不能适应自然环境而失去作用 慢性疼痛状态的波动。如果相关的神经生物标记物能显著改善DBS 疾病状态可以作为反馈信号用于“闭环”DBS算法，该算法将有选择地 在需要的时候提供刺激。这种方法可能有助于避免随着时间的推移而形成耐受性 并使慢性疼痛的动态特征能够以个性化的方式被瞄准。 优化生物标志物检测和刺激传递的大脑靶点也可能显著影响 功效。最近对人类的成像研究表明，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 参与基本的体感处理。前扣带回(ACC)和前扣带回的病理活动 眼眶前额叶皮质(OFC)与疼痛的高级处理相关，最近的临床试验已经 发现ACC是一种很有前途的痛觉神经调节刺激靶点。在这项研究中，我们将针对 ACC和OFC用于生物标记物发现和闭环刺激。我们将发展数据驱动的刺激 使用新型神经接口设备(美敦力Activa PC+S)治疗慢性疼痛的控制算法 允许在移动环境中进行纵向颅内信号记录。通过构建和验证 植入式设备的技术能力，我们将支持DBS用于慢性疼痛指征和先进 适用于星展银行更一般的个性化、精确化方法。 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在我们的 三期临床试验。我们将首先确定低疼痛状态和高疼痛状态的生物标记物，以定义最佳神经 个人疼痛预测信号(目标1)。然后，我们将使用这些疼痛生物标志物来开发闭合环 DBS的算法，并测试对慢性疼痛进行闭环式DBS治疗的可行性和有效性 单盲、假对照临床试验(目标2)。我们的主要结果衡量标准将是痛苦的组合， 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)和评估机制 DBS对慢性刺激的耐受性。这项研究的成功完成将产生第一个 用于提供止痛刺激和止痛的慢性疼痛状态实时波动预测算法 将通过推进植入性装置技术来证明闭环式DBS用于止痛的可行性。

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.