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

闭环脑深部刺激治疗难治性神经病理性疼痛的技术开发

• 批准号: 10223445
• 负责人: Edward Chang
• 金额: $ 51.11万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10223445
• 关键词: Absence of pain sensation; Accelerometer; Affective; Algorithms; Analgesics; Anterior; Area; Biological Markers; Blinded; Brain; Brain region; Categories; Characteristics; Chronic; Clinic; Clinical Trials; Cognitive; Computer software; Controlled Clinical Trials; Data; Deep Brain Stimulation; Detection; Development; Devices; Dimensions; Disease; Electrophysiology (science); Enrollment; Failure; Feedback; Frequencies; Goals; Healthcare; Home; Human; Individual; Institutional Review Boards; Intractable Pain; Measures; Methods; Moods; Movement Disorders; Nervous system structure; Outcome Measure; Pain; Pain Disorder; Pain Measurement; Pathologic; Patients; Phantom Limb; Phantom Limb Pain; Phase; Phase I Clinical Trials; Physiological; Placebos; Positioning Attribute; Randomized; Refractory; Resistance; Sensory; Signal Transduction; Single-Blind Study; Site; Societies; Spinal cord injury; Syndrome; Technology; Testing; Time; United States Centers for Medicare and Medicaid Services; animal imaging; base; biomarker discovery; chronic pain; chronic pain patient; comparative efficacy; cost; effectiveness evaluation; experience; falls; feasibility testing; human imaging; imaging study; implantable device; improved; improved outcome; individual patient; neurophysiology; neuroregulation; neurotransmission; novel; opioid epidemic; pain processing; pain reduction; pain relief; pain score; pain signal; pain symptom; painful neuropathy; post stroke; post stroke pain; reduce symptoms; relating to nervous system; response; side effect; somatosensory; spinal cord injury pain; technology development; trial comparing

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.

项目摘要 众所周知，许多疼痛综合症对几乎所有治疗都具有难以忍受，并为患者造成了巨大的成本 和社会。对难治性疼痛障碍的深脑刺激（DBS）表现出早期的承诺，但 缺乏长期效率的演示。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无法适应自然 慢性疼痛状态的波动。如果神经生物标志物用于相关，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 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的发展发展 并使慢性疼痛的动态特征以个性化的方式成为目标。 优化生物标志物检测和刺激输送的大脑目标也可能显着影响 功效。人类的最新成像研究表明，额叶皮质区域在支持该研究中的关键作用 疼痛的情感和认知维度，这可能比以前的目标更有效DBS目标 参与基本的体感处理。前扣带回（ACC）和 眶额皮质（OFC）与疼痛的高阶处理相关，最近的临床试验已有 确定ACC是疼痛神经调节的有前途的刺激靶标。在这项研究中，我们将针对 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）来治疗慢性疼痛的控制算法 允许在卧床环境中记录纵向颅内信号。通过构建和验证 我们将在植入设备中的技术能力，我们将赋予DBS的慢性疼痛适应症和进步 DBS的个性化，精确的方法。 我们将在我们的十名患有势后疼痛，幻影肢体综合征和脊髓损伤疼痛的患者中 三相临床试验。我们将首先确定低疼痛状态和高疼痛状态的生物标志物，以定义最佳中性 个体疼痛预测的信号（AIM 1）。然后，我们将使用这些疼痛生物标志物发展闭环 DBS的算法并测试执行闭环DB的可行性和效率 单盲，假对照临床试验（AIM 2）。我们的主要结果措施将是疼痛的结合， 情绪和功能分数以及定量感觉测试。在最后阶段，我们将评估 闭环DBS算法对传统开环DB（AIM 3）和评估机制的功效 响应慢性刺激的DBS耐受性。成功完成这项研究将导致第一个 预测慢性疼痛状态中实时波动的算法，以递送镇痛刺激和 通过推进可植入的设备技术，可以证明闭环DBS可用于疼痛的可行性。