Functional Magnetic Resonance Imaging and Deep Learning to Improve Deep Brain Stimulation Therapy

功能磁共振成像和深度学习改善脑深部刺激疗法

• 批准号: 10717563
• 负责人: Afis Ajala
• 金额: --
• 依托单位: GENERAL ELECTRIC GLOBAL RESEARCH CTR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10717563
• 关键词: Adoption; Adverse effects; Alzheimer' s Disease; Artificial Intelligence; Bilateral; Brain; Classification; Clinical; Complication; Consumption; Data; Data Collection; Deep Brain Stimulation; Dystonia; Electrodes; Engineering; Ensure; Essential Tremor; Financial Hardship; Frequencies; Functional Magnetic Resonance Imaging; Goals; Health; Hour; Implant; Implanted Electrodes; Improve Access; Location; Magnetic Resonance Imaging; Major Depressive Disorder; Manuals; Maps; Methods; Modeling; Neurologist; Neurosurgeon; Operative Surgical Procedures; Outcome; Parkinson Disease; Pathologic; Patient-Focused Outcomes; Patients; Physicians; Physiologic pulse; Pilot Projects; Protocols documentation; Publishing; Qualifying; Quality of life; Reporting; Research; STN stimulation; Sampling; Signal Transduction; System; Testing; Therapeutic; Time; Tissues; Training; Universities; Visit; Width; brain volume; clinical care; comparative efficacy; data acquisition; deep learning; deep learning model; energy efficiency; feature extraction; improved; interest; mild cognitive impairment; multidisciplinary; novel; predictive modeling; programs; radiologist; rapid technique; response; side effect; standard of care; success; treatment center; validation studies; voltage

PROJECT SUMMARY/ABSTRACT Successful treatment of Parkinson's disease (PD) using deep brain stimulation (DBS) therapy requires an optimal setting of stimulation parameters to correct brain function anomalies. The commonly employed DBS 1.0 electrodes have only four contact locations (with no stimulation directionality) that are used to electric pulses to a target volume of the brain. DBS 1.0 electrodes require the optimization of four stimulation parameters: signal frequency, voltage, pulse width, and contact location. In current standard-of-care optimization protocol, the DBS parameters are adjusted (via trial and error) until the physician determines an optimal set of parameters. This empirical optimization protocol requires numerous clinical visits (~6 weeks interval) that substantially increases the time to optimization (TTO) per patient (~1 year), financial burden, and ultimately limits the number of patients that can have access to DBS therapy. Even though there are more effective electrodes, DBS 1.0 electrodes are mostly used by clinicians because their smaller parameter space pose less difficulty during manual clinical optimization. However, DBS 1.0 electrodes cannot be directed to stimulate a smaller volume of tissue, which can lead to extraneous stimulations that can reduce patient clinical benefits and increase side effects. By contrast, the newer DBS electrodes (dubbed DBS 2.0) have a greater number of contact locations and can be programmed to stimulate a smaller volume of tissue at multiple levels and directions. Several published reports have shown that DBS 2.0 electrodes (compared to DBS 1.0) are more energy-efficient and improve patient outcomes with lesser side-effects and a wider therapeutic window. However, the expanded DBS 2.0 parameter space has made empirical programming of the electrodes difficult as the TTO per patient is beyond acceptable clinical timeframes. This increased difficulty has hindered adoption of DBS 2.0 electrodes by clinicians. To significantly shorten and simplify DBS 2.0 parameter optimization—thus enabling its wider adoption for more precise therapy—a uniquely qualified multi- disciplinary team of magnetic resonance imaging (MRI) physicists, artificial intelligence (AI) engineers, and clinicians from GE Research and the University Health Network propose to: 1) develop a semi-automated functional MRI (fMRI) and deep learning (DL)-based system for rapid optimization of DBS 2.0 parameters; 2) demonstrate its clinical benefit in the treatment of PD patients using bilateral stimulation of the sub-thalamic nucleus with DBS 2.0 electrodes in a pilot study. Success of this program will decrease the TTO per patient for PD patients with DBS 2.0 implants to ~1 hour, and will improve patient throughput and outcomes in the treatment of PD. The proposed fMRI-DL-based optimization method may also improve access by making it possible for non-expert centers (without highly specialized clinicians) to carry out stimulation parameters optimization in patients after the electrode insertion surgery have been completed in expert centers.

项目摘要/摘要 使用脑深部电刺激（DBS）疗法成功治疗帕金森病（PD）需要 刺激参数的最佳设置以校正脑功能异常。常用的DBS 1.0电极仅具有四个接触位置（没有刺激方向性）， 脉冲到大脑的目标体积。DBS 1.0电极需要优化四种刺激 参数：信号频率、电压、脉冲宽度和触点位置。在当前的标准治疗中 在优化方案中，调整DBS参数（通过试错法），直到医生确定 最佳参数集。该经验优化方案需要多次临床访视（约6周 间隔），这大大增加了每例患者的优化时间（TTO）（约1年），经济负担， 并最终限制了能够获得DBS治疗的患者的数量。即使有更多的 有效电极，DBS 1.0电极主要由临床医生使用，因为其较小的参数空间 在手动临床优化过程中造成的困难较小。但是，DBS 1.0电极不能直接用于 刺激较小体积的组织，这可能导致可能降低患者临床 增加副作用。相比之下，较新的DBS电极（称为DBS 2.0）具有更大的 接触位置的数量，并且可以编程以在多个水平刺激较小体积的组织 和方向。几份已发表的报告表明，DBS 2.0电极（与DBS 1.0相比） 节能和改善患者的结果，副作用更少，治疗窗口更宽。 然而，扩展的DBS 2.0参数空间使得电极的经验编程变得困难 因为每例患者的TTO超出了可接受的临床时间范围。这种增加的难度阻碍了 临床医生采用DBS 2.0电极。显著缩短和简化DBS 2.0参数 优化-从而使其更广泛地采用更精确的治疗-一个独特的合格的多， 由磁共振成像（MRI）物理学家、人工智能（AI）工程师和 来自GE研究和大学健康网络的临床医生建议：1）开发一种半自动化的 基于功能性MRI（fMRI）和深度学习（DL）的系统，用于快速优化DBS 2.0参数; 2） 证明其在使用丘脑下双侧刺激治疗PD患者中的临床受益 DBS 2.0电极在一项初步研究中。该计划的成功将减少每例患者的TTO， 使用DBS 2.0植入的PD患者可缩短至约1小时，并将改善患者的吞吐量和结局。 治疗PD。所提出的基于fMRI-DL的优化方法还可以通过使其 非专家中心（没有高度专业的临床医生）可以执行刺激参数 电极插入手术后的患者优化已在专家中心完成。