ENIGMA Parkinson’s Initiative: A Global Initiative for Parkinson’s Disease

摘要 帕金森氏病(PD)是一种毁灭性的、进行性的神经退行性脑部疾病，目前尚无治愈方法。 这种疾病困扰着全球1000万人-仅在美国就有约150万人，约5万-6万新病例 每年诊断一次。风险因素或干预措施极难评估，因为我们缺乏客观的衡量标准 帕金森病如何影响大脑。脑成像在全球范围内的广泛应用已经导致了几个有希望的指标 测量大脑中帕金森病的进展--基底节和运动皮质的结构变化， 弥散磁共振成像(DMRI)显示的神经连接，以及大脑功能同步性的中断 静息状态功能磁共振成像(FMRI)所见。尽管有这些发现，影响疾病严重性的因素 很难发现，因为大多数PD扫描的成像研究&lt；100名患者。大多数PD研究是在 来自美国和欧洲的孤立队列，限制了全球推广。影响帕金森病进展的因素 都很难核实，导致了可重复性危机。响应美国国立卫生研究院对更多可重复性研究的呼吁， 在这里，我们启动了Enigma的全球帕金森氏症倡议。谜团最近出版了最大的 精神分裂症、双相情感障碍、抑郁症、癫痫和自闭症的神经影像研究 无序。凭借Enigma的全球协调、强大的财团方式，我们计划克服 小规模研究的危机，动力和重复性差。拼合解剖泛函、扩散泛函和静息泛函 来自21个深度评估的国际队列的MRI指标-来自美国、巴西、台湾、新西兰、 荷兰、意大利、瑞士、南非、中国和俄罗斯--我们问：这种疾病如何影响大脑 结构、神经连接和功能同步性？哪些成像生物标记物跟踪疾病进展 并持续预测临床结果？帕金森病的遗传风险基因是否有助于预测脑功能衰退？什么PD 成像可以识别亚型或簇吗？结合来自2,307名患者和1,264名对照的多模式数据， 我们将彻底评估帕金森病的预测因素和脑生物标记物。我们的目标是：(1)评估和排名 全球帕金森病的结构、扩散和静息状态功能磁共振生物标志物；(2)评估增加的 多基因风险评分在预测帕金森病脑生物标志物中的价值；(3)预测未来功能 随着机器学习、多模式成像和基因组学的发展，帕金森病的发病率下降。我们将使用基因数据和 我们队列中来自PD患者和健康对照组的基线临床变量构建了一个集合 预测运动障碍协会综合分数年变化率的模型--统一 帕金森氏病评定量表第二部分和第三部分。我们将对下降的最佳预测因素进行排名，并评估 它们在国际上表现强劲。通过更好地对患者结果的方差进行建模，我们的多模式预测模型 将通过对疾病负担的生物标记物进行排名并确定它们所处的环境来支持PD临床试验 使用可靠、准确、可行。

ABSTRACT Parkinson’s disease (PD) is a devastating, progressive neurodegenerative brain disease, with no known cure. The disease afflicts 10 million people worldwide - ~1.5 million in the U.S. alone, and ~50k-60k new cases are diagnosed annually. Risk factors or interventions are extremely hard to evaluate as we lack objective metrics of how PD affects the brain. The vast global availability of brain imaging has led to several promising metrics to gauge PD progression in the brain - structural changes in the basal ganglia and motor cortex, abnormalities in neural connectivity seen with diffusion MRI (dMRI), and disruptions of the brain’s functional synchrony across regions, seen with resting state functional MRI (fMRI). Despite these findings, factors that affect disease severity are difficult to discover, as most imaging studies of PD scan <100 patients. Most PD research is conducted in isolated cohorts from the US and Europe, limiting worldwide generalizability. Factors that affect PD progression are hard to verify, leading to a crisis of reproducibility. Responding to NIH’s call for more reproducible studies, here we launch ENIGMA’s Worldwide Parkinson’s Initiative. ENIGMA recently published the largest neuroimaging studies of schizophrenia, bipolar disorder, major depression, epilepsy, and autism spectrum disorder. With ENIGMA’s globally coordinated, highly powered consortium approach, we plan to overcome the crisis of small studies with poor power and reproducibility. Pooling anatomic, diffusion and resting state functional MRI metrics from 21 deeply assessed international cohorts - from the US, Brazil, Taiwan, New Zealand, the Netherlands, Italy, Switzerland, South Africa, China, and Russia - we ask: How does the illness affect the brain's structure, neural connectivity, and functional synchrony? What imaging biomarkers track disease progression and consistently predict clinical outcomes? Do genetic risk loci for PD help predict brain decline? What PD subtypes, or clusters, can imaging identify? Combining multimodal data from 2,307 patients and 1,264 controls, we will thoroughly evaluate predictors and brain biomarkers in PD. Our aims are to: (1) Evaluate and rank structural, diffusion, and resting state functional MRI biomarkers of PD worldwide; (2) Evaluate the added value of polygenic risk scores (PRS) in predicting PD brain biomarkers; (3) Predict future functional decline in PD with machine learning, multi-modal imaging and genomics. We will use genetic data and baseline clinical variables from PD patients and healthy controls across our cohorts to construct an ensemble of models to predict the annual rate of change in combined scores from the Movement Disorder Society—Unified Parkinson's Disease Rating Scale parts II and III. We will rank the best predictors of decline, and assess how robust they are internationally. By better modeling variance in patient outcomes, our multimodal predictive model will empower PD clinical trials by ranking biomarkers of disease burden and determining the contexts where they are reliable, accurate, and feasible to use.