An Integrated Biomarker Approach to Personalized, Adaptive Deep Brain Stimulation in Parkinson Disease

帕金森病个性化、适应性深部脑刺激的综合生物标志物方法

• 批准号: 10571952
• 负责人: DENNIS Alan TURNER
• 金额: $ 97.61万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10571952
• 关键词: Accelerometer; Accounting; Affect; Algorithms; Architecture; Bilateral; Biological Markers; Bradykinesia; Brain; Clinic; Clinical; Clinical Research; Clinical Trials; Complex; Coupling; Data; Deep Brain Stimulation; Devices; Disease Outcome; Dose; Dyskinetic syndrome; Electrodes; FDA approved; Feedback; Frequencies; Funding; Globus Pallidus; Goals; Guidelines; Home; Human; Implant; Institutional Review Boards; Measurement; Measures; Medical Device; Noise; Operative Surgical Procedures; Outcome; Parkinson Disease; Patient-Focused Outcomes; Patients; Pattern; Persons; Pharmaceutical Preparations; Phase; Physicians; Physiologic pulse; Physiological; Policies; Postoperative Period; Procedures; Prospective cohort; Public Health; Research; Signal Transduction; Site; Sleep; Structure of subthalamic nucleus; Surrogate Markers; Symptoms; System; Technology; Testing; Time; Tremor; Update; Walking; biomarker selection; clinical outcome measures; cohort; comparative; comparative efficacy; energy efficiency; experimental study; flexibility; implantation; improved; innovation; internal control; machine learning model; motor symptom; neural; novel; novel strategies; programs; reduce symptoms; response; sensor; smart watch; success; symposium; symptom management; symptom treatment; synergism; wearable device; wearable sensor technology; Accelerometer; Accounting; Affect; Algorithms; Architecture; Bilateral; Biological Markers; Bradykinesia; Brain; Clinic; Clinical; Clinical Research; Clinical Trials; Complex; Coupling; Data; Deep Brain Stimulation; Devices; Disease Outcome; Dose; Dyskinetic syndrome; Electrodes; FDA approved; Feedback; Frequencies; Funding; Globus Pallidus; Goals; Guidelines; Home; Human; Implant; Institutional Review Boards; Measurement; Measures; Medical Device; Noise; Operative Surgical Procedures; Outcome; Parkinson Disease; Patient-Focused Outcomes; Patients; Pattern; Persons; Pharmaceutical Preparations; Phase; Physicians; Physiologic pulse; Physiological; Policies; Postoperative Period; Procedures; Prospective cohort; Public Health; Research; Signal Transduction; Site; Sleep; Structure of subthalamic nucleus; Surrogate Markers; Symptoms; System; Technology; Testing; Time; Tremor; Update; Walking; biomarker selection; clinical outcome measures; cohort; comparative; comparative efficacy; energy efficiency; experimental study; flexibility; implantation; improved; innovation; internal control; machine learning model; motor symptom; neural; novel; novel strategies; programs; reduce symptoms; response; sensor; smart watch; success; symposium; symptom management; symptom treatment; synergism; wearable device; wearable sensor technology

DBS therapy for Parkinson Disease [PD], the primary, FDA-approved surgical approach, has proven efficacious in clinical trials. However, this continuous stimulation therapy is limited to treatment of a subset of motor symptoms (i.e., tremor, rigidity, bradykinesia and dyskinesias) and requires considerable postoperative clinical adjustment to treat symptoms. Improvements to DBS for PD are being tested, including changes in patterns of stimulation, additional targets, and multiple electrodes. However, a critical new approach involves autonomous parameter adjustment [adaptive DBS] using surrogate physiological biomarkers relevant to clinical symptoms. These biomarkers and autonomous control may be useful for dynamic adjustment, subsequent programming, and long-term optimization of parameters. Adaptive DBS involves recording surrogate signals and developing a control policy that relates these signals to activity through parameter adjustments. This approach could improve DBS therapy across multiple time scales, including short-term dynamics (i.e., over minutes), initial programming (over weeks to months), and long-term, depending on the time course of response to DBS. However, which biomarkers are useful at these various time scales and appropriate, multi- layered control policy, will require testing in comparison to continuous DBS for relative efficacy and efficiency. We hypothesize that integrating multiple biomarkers (in addition to beta band oscillations) across multiple time scales will provide more efficacious adaptive DBS control. To test this hypothesis we will perform long-term recordings of multiple, relevant biomarkers from humans with implanted, advanced implantable pulse generators [IPGs], comparing internal control modes to highly complex external control modes. These clinical experiments will focus on a small, pilot clinical study (n = 6 PD patients) who have undergone implantation of bilateral subthalamic nucleus [STN] and globus pallidus [GP] DBS electrodes, with all 4 electrodes connected to a single Medtronic Summit RC+S recording and stimulating IPG. We have formally analyzed this cohort for efficacy at 1 year, showing that combined STN + GP stimulation is both preferred and better compared to either site alone. We will analyze the comparative efficacy of internal (embedded, available within the IPG) simple adaptive DBS to external (distributed) adaptive DBS, which allows both integrating multiple biomarkers and using a complex, multiple time scale control policy. We will further develop a proportional control feedback program, which specifically integrates the time multiple time constants of PD symptoms, to optimally control PD symptom. These clinical experiments in a unique cohort of research patients will lead to multiple novel outcomes, continuing a direct, within-person comparison of STN, GP, and combined DBS efficacy, analyzing an optimal mix of surrogate biomarkers for enhancing DBS efficacy, and defining an optimal, scalar feedback, proportional control system for treatment on various time scales.

帕金森病的DBS治疗[PD]是FDA批准的手术方法，在临床试验中已证明有效。然而，这种连续的刺激疗法仅限于治疗运动症状的一部分（即震颤，僵硬，胸肌和运动障碍），并且需要大量的术后临床调整才能治疗症状。正在测试对PD的DBS的改进，包括刺激模式的变化，其他靶标和多个电极。但是，一种关键的新方法涉及使用与临床症状相关的替代生理生物标志物的自主参数调整[自适应DBS]。这些生物标志物和自主控制可能可用于动态调整，随后的编程和参数的长期优化。自适应DBS涉及记录替代信号并制定通过参数调整将这些信号与活动相关联的控制策略。这种方法可以改善多个时间尺度的DBS疗法，包括短期动态（即在几分钟之内），初始编程（超过数周到数月）以及长期，具体取决于对DBS的响应时间。但是，哪些生物标志物在这些不同的时间尺度上很有用，以及适当的多层控制策略，将需要与连续DBS相比的相对功效和效率相比进行测试。我们假设在多个时间尺度上整合多个生物标志物（除了β带振荡之外）将提供更有效的自适应DBS控制。为了检验这一假设，我们将对具有植入，高级植入脉冲发生器[IPGS]的人类的多种相关生物标志物进行长期记录，将内部控制模式与高度复杂的外部控制模式进行比较。这些临床实验将集中在一项小型的，试验性的临床研究（n = 6个PD患者）上，这些研究接受了双边丘脑下核[STN]和Globus Pallidus [GP] DBS电极的植入，所有4个电极都与单个Medtronic RC+S RC+S记录和刺激性ipg相连。我们已经正式分析了该队列在1年时的疗效，这表明与单独使用任何一个站点相比，组合的STN + GP刺激既优选又更好。我们将分析对外部（分布式）自适应DBS的内部（IPG内嵌入式，可用）的比较疗效，该自适应DBS既允许整合多个生物标志物，又可以使用复杂的，多个时间尺度控制策略。我们将进一步制定比例控制反馈计划，该计划专门整合了PD症状的时间多个时间常数，以最佳地控制PD症状。在独特的研究患者中，这些临床实验将导致多种新型结果，继续对STN，GP和组合DBS功效进行直接的，个人的比较，分析替代生物标志物的最佳混合物，以增强DBS效率，并确定最佳的，标量的反馈，以对各种时间尺度进行处理。