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.

项目概要/摘要 使用深部脑刺激 (DBS) 疗法成功治疗帕金森病 (PD) 需要 刺激参数的最佳设置以纠正大脑功能异常常用的 DBS。 1.0 电极只有四个接触位置（无刺激方向性），用于电刺激 DBS 1.0 电极需要对四个刺激进行优化。 参数：信号频率、电压、脉冲宽度和接触位置。 优化方案时，调整 DBS 参数（通过反复试验），直到医生确定 最佳参数集。此经验优化方案需要多次临床访问（约 6 周）。 间隔），这大大增加了每个患者的优化时间（TTO）（〜1年），经济负担， 最终限制了接受 DBS 治疗的患者数量，尽管有更多患者。 有效电极，DBS 1.0电极主要被主教使用，因为它们的参数空间较小 在手动临床优化过程中难度较小，但是 DBS 1.0 电极无法定向到。 刺激较小体积的组织，这可能会导致额外的刺激，从而减少患者的临床症状 相比之下，较新的 DBS 电极（称为 DBS 2.0）具有更大的益处并增加副作用。 多个接触位置，并且可以编程以在多个级别刺激较小体积的组织 几份已发表的报告表明，DBS 2.0 电极（与 DBS 1.0 相比）更多 节能并以更少的副作用和更宽的治疗窗口改善患者的治疗效果。 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，这种增加的难度阻碍了。 殖民者采用 DBS 2.0 电极 显着缩短和简化 DBS 2.0 参数。 优化 - 从而使其能够更广泛地采用更精确的治疗 - 一种独特的合格的多 由磁共振成像 (MRI) 物理学家、人工智能 (AI) 工程师和 来自 GE 研究中心和大学健康网络的支持者提议：1）开发一种半自动化的 基于功能 MRI (fMRI) 和深度学习 (DL) 的系统，用于快速优化 DBS 2.0 参数 2) 证明其通过双侧丘脑下刺激治疗 PD 患者的临床益处 试点研究中使用 DBS 2.0 电极的 Nucleus 将会降低每位患者的 TTO。 PD 患者使用 DBS 2.0 植入大约需要 1 小时，并将提高患者的治疗量和治疗结果 所提出的基于 fMRI-DL 的优化方法还可以通过使其更容易获得。 非专家中心（没有高度专业化的专家）可以执行刺激参数 电极插入手术后患者的优化已在专家中心完成。