Electrophysiological Biomarkers to Optimize DBS for Depression

项目摘要 胼胝体下扣带回（SCC）白色物质的脑深部刺激（DBS）是一种新兴的治疗方法 治疗难治性抑郁症（TRD）的策略，已发表的研究表明持续的长期 40-60%的植入患者具有抗抑郁作用。正电子发射的汇聚证据 断层扫描（PET）、脑电图（EEG）和弥散纤维束成像（DTI）强烈表明， DBS通过直接调节SCC（异常神经回路中的关键枢纽）来调节其临床益处。 尽管在这个众所周知的难以治疗的患者群体中有令人鼓舞的持续长期效果， SCC DBS和其他DBS靶点治疗TRD的随机对照试验由于初始结果失败而暂停 以满足预定义的临床终点。虽然这一建议不能直接解决这些问题，但一个明确的 为了将来进行有效的测试并最终推广这种治疗，下一步必须 开发基于大脑的生物标志物，以指导电极导线放置，并在进行中滴定刺激参数 在乎在缺乏此类生物标志物指导DBS使用的情况下， 在试验过程中实施临床程序，导致不明确和可能误导的试验 结果，以及随后放弃潜在有用的治疗。为了克服这些局限性，我们 建议开发和测试客观的方法，通过优化个人的可靠设备配置， DBS-SCC治疗与人体功能解剖和关键电生理变量有关。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，以定义新的控制策略来指导编程 DBS输送的决策。SCC局部场电位（LFP）的持续测量将与 脑电图（EEG）和事件相关电位研究（ERP）作为实验性 用于TRD的胼胝体下扣带回DBS的临床试验，以识别（1）对变化敏感的振荡信号 在频率和电流参数在纤维束成像定义的最佳目标和（2）轨道与抑郁症 国家随着时间。基于连接组和机器学习的方法将用于定义最强大的 网络生物标志物及其响应特征。一旦定义，控制策略将在一秒钟内进行测试 阶段可行性研究，其中将根据抑郁症大脑选择初始刺激的参数 状态生物标志物和为校正从预定目标信号的漂移而进行的调整。如果成功，数据- 驱动模型和控制策略将实现DBS刺激的客观、合理的临床编程， 为靶点识别、刺激启动和长期刺激提供了新的模型和方法 监测和管理接受这种治疗的患者。 .

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. .