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: 10547822
• 负责人: Helen S Mayberg
• 金额: $ 152.72万
• 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 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10547822
• 关键词: Acute; Address; Algorithms; American; Anatomy; Antidepressive Agents; Biological Markers; Biometry; Brain; Caring; Characteristics; Chronic; Clinical; Clinical Trials; Clinical Trials Design; 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; Maps; 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, Controlled Trials; Resistance; Scalp structure; Severities; Signal Transduction; Site; Standardization; Stimulus; Symptoms; System; Testing; Time; Titrations; algorithm development; antidepressant effect; brain based; candidate identification; clinical decision support; clinical implementation; connectome; data-driven model; evidence base; follow-up; implantation; improved; indexing; live stream; neural circuit; neuroregulation; novel; novel strategies; novel therapeutic intervention; 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. .

项目总结