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

项目摘要/摘要 使用脑深部刺激(DBS)疗法成功治疗帕金森病(PD)需要 最佳刺激参数设置，以纠正脑功能异常。常用的星展银行 1.0电极只有四个触点位置(无刺激方向性)，用于电 脉冲到大脑的目标体积。DBS 1.0电极需要四种刺激的优化 参数：信号频率、电压、脉冲宽度、触点位置。在当前的护理标准中 优化方案，调整DBS参数(通过试错法)，直到医生确定 最优参数集。这种经验性的优化方案需要多次临床访问(~6周 间隔)，大大增加了每个患者的优化时间(TTO)(~1年)、经济负担、 最终限制了可以获得DBS治疗的患者数量。即使有更多 有效电极，DBS 1.0电极因其参数空间较小而被临床医生多采用 在手动临床优化过程中摆出的难度较小。但是，DBS 1.0电极不能定向到 刺激较小体积的组织，这可能会导致外部刺激，从而减少患者的临床 好处和增加副作用。相比之下，较新的DBS电极(称为DBS 2.0)具有更大的 多个接触位置，并可编程以在多个水平刺激较小体积的组织 和方向。一些已发表的报告表明，DBS 2.0电极(与DBS 1.0相比)更多 以更少的副作用和更宽的治疗窗口改善患者的预后。 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 因为每个患者的TTO超出了可接受的临床时间范围。这一增加的困难阻碍了 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-从而使其能够更广泛地采用，以实现更精确的治疗-唯一合格的多 由磁共振成像(MRI)物理学家、人工智能(AI)工程师和 GE Research和大学健康网络的临床医生建议：1)开发半自动 基于功能磁共振(FMRI)和深度学习(DL)的DBS 2.0参数快速优化系统；2) 双侧丘脑下部电刺激治疗帕金森病的临床疗效 在一项先导性研究中，使用DBS 2.0电极。这项计划的成功将降低每个患者的TTO 使用DBS 2.0的PD患者植入到~1小时，将提高患者的吞吐量和预后 帕金森病的治疗。所提出的基于fMRI-DL的优化方法也可以通过使其 非专家中心(没有高度专业化的临床医生)可以进行刺激参数 优化后的患者电极插入手术已在专家中心完成。

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.