Electrophysiological Biomarkers to Optimize DBS for Depression

电生理生物标志物优化 DBS 治疗抑郁症

• 批准号: 9869948
• 负责人: Helen S Mayberg
• 金额: $ 136.8万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9869948
• 关键词: Acute; Address; Algorithms; American; Anatomy; Antidepressive Agents; Base of the Brain; Biological Markers; Biometry; Brain; Caring; Characteristics; Chronic; Clinical; Clinical Trials; Clinical Trials Design; Data; Deep Brain Stimulation; Devices; Diffusion; Electrodes; Electroencephalography; Electrophysiology (science); Event-Related Potentials; Failure; Feasibility Studies; Frequencies; Future; Goals; Human; Implant; Individual; Lead; Location; Long-Term Effects; Machine Learning; Major Depressive Disorder; Measurement; Measures; Mediating; Mental Depression; Methods; Modeling; Monitor; Operative Surgical Procedures; Outcome; Parkinson Disease; Patients; Performance; Phase; Policies; Positron-Emission Tomography; Procedures; Protocols documentation; Publishing; Randomized; Resistance; Scalp structure; Severities; Signal Transduction; Site; Standardization; Stimulus; Symptoms; System; Testing; Time; algorithm development; antidepressant effect; base; clinical decision support; clinical implementation; connectome; control trial; evidence base; follow-up; implantation; improved; indexing; live stream; neural circuit; neuroregulation; novel; novel strategies; open label; patient population; response; support tools; targeted biomarker; tractography; treatment strategy; treatment-resistant depression; white matter

PROJECT SUMMARY Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) white matter is an emerging new treatment strategy for treatment resistant depression (TRD) with published studies demonstrating sustained long-term antidepressant effects in 40-60% of implanted patients. Converging evidence from positron emission tomography (PET), electroencephalography (EEG) and diffusion tractography (DTI) strongly suggests that DBS mediates its clinical benefits by direct modulation of the SCC--a key hub in an aberrant neural circuit. Despite encouraging sustained long-term effects in this notoriously difficult to treat patient population, randomized controls trials of SCC DBS and other DBS targets for TRD are now on hold as initial results failed to meet predefined clinical endpoints. While this proposal cannot address those failures directly, a clear necessary next step for effective future testing and eventual dissemination of this treatment is the need to develop brain-based biomarkers to guide lead placement and to titrate stimulation parameters during ongoing care. In the absence of such biomarkers to guide DBS use, there will continue to be variability in the implementation of clinical procedures during testing, leading to ambiguous and possibly misleading trial outcomes, and subsequent abandonment of a potentially useful treatment. To overcome these limitations, we propose to develop and test objective methods for reliable device configuration in individuals by optimizing DBS-SCC treatment with respect to human functional anatomy and key electrophysiological variables. We will leverage the capabilities of a novel bi-directional neuromodulation system (Medtronic RC+S) that allows live streaming of oscillatory activity at the site of stimulation to define novel control strategies to guide programming decisions for DBS delivery. Ongoing measurements of SCC local field potentials (LFPs) will be combined with electroencephalography (EEG) and event related potential studies (ERP) performed as part of an experimental clinical trial of subcallosal cingulate DBS for TRD to identify an oscillatory signal that (1) is sensitive to changes in frequency and current parameters at the tractography defined optimal target and (2) tracks with depression state over time. Connectome-based and machine learning approaches will be used to define the most robust network biomarker and its response characteristics. Once defined, the control policy will be tested in a second phase feasibility study where parameters for initial stimulation will be selected based on the depression brain state biomarker and adjustments made to correct drift from the predefined target signal. If successful, the data- driven model and control strategy will enable objective, rational clinical programming of DBS stimulation for depression and provide a new model and approach for target identification, stimulation initiation and long-term monitoring and management of patients receiving this treatment. .

项目摘要 潜能扣带（SCC）白质的深脑刺激（DB）是一种新的治疗方法 抗治疗抑郁（TRD）的策略，已发表的研究证明了持续的长期 40-60％的植入患者的抗抑郁作用。正电子发射的融合证据 断层扫描（PET），脑电图（EEG）和扩散拖拉术（DTI）强烈表明 DBS通过直接调节SCC（一个异常神经回路中的关键枢纽）来介导其临床益处。 尽管在这种臭名昭著的治疗患者人群中产生了长期持续的长期影响，但 随着初始结果失败，SCC DBS和其他DBS目标的随机对照试验现已持有 满足预定义的临床终点。虽然该提议无法直接解决这些失败，但很明显 有效的未来测试和最终传播此治疗的必要下一步是需要 开发基于大脑的生物标志物，以指导铅放置和滴定刺激参数 关心。在没有这样的生物标志物来指导DBS使用的情况下，将继续存在可变性 在测试过程中实施临床程序，导致模棱两可且可能具有误导性试验 结果，随后放弃了潜在的有用治疗方法。为了克服这些限制，我们 建议通过优化为个人配置的可靠设备配置开发和测试目标方法 DBS-SCC对人类功能解剖结构和关键电生理变量的处理。我们将 利用新型双向神经调节系统（Medtronic RC+S）的功能 在刺激部位的振荡活动流动以定义新的控制策略以指导编程 DBS交付的决定。 SCC局部现场电位（LFP）的持续测量将与 脑电图（EEG）和事件相关潜在研究（ERP）作为实验的一部分进行 TRD的亚易毛扣带DBS的临床试验，以识别（1）对变化敏感的振荡信号 在拖拉术定义的最佳目标和（2）凹陷的频率和电流参数上 随着时间的流逝。基于连接组和机器学习方法将用于定义最强大的 网络生物标志物及其响应特征。定义后，控制策略将在一秒钟内进行测试 相可行性研究，其中将根据抑郁脑选择初始刺激的参数 状态生物标志物和调整以纠正预定义的目标信号的漂移。如果成功，数据 - 驱动的模型和控制策略将使DBS刺激的目标，合理的临床编程 抑郁症并为目标识别，刺激启动和长期提供新的模型和方法 监测和管理接受此治疗的患者。 。