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

摘要 帕金森病（PD）是一种破坏性的，渐进性神经退行性脑疾病，目前尚无治愈方法。 这种疾病在全世界范围内折磨着1000万人-仅在美国就有150万人， 每年诊断。风险因素或干预措施很难评估，因为我们缺乏客观的指标， 帕金森病如何影响大脑脑成像的广泛全球可用性导致了几个有前途的指标， 测量大脑中的PD进展-基底神经节和运动皮层的结构变化， 扩散MRI（dMRI）观察到的神经连接，以及大脑功能同步性的破坏。 静息状态功能性磁共振成像（fMRI）。尽管有这些发现，影响疾病严重程度的因素 很难发现，因为大多数PD成像研究扫描<100例患者。大多数PD研究都是在 来自美国和欧洲的孤立队列，限制了全球的普遍性。影响PD进展的因素 很难验证，导致再现性危机。为了响应NIH对更多可重复研究的呼吁， 在这里我们启动了谜的全球帕金森病倡议。谜最近发布了最大的 精神分裂症、双相情感障碍、重性抑郁症、癫痫和自闭症谱系的神经影像学研究 disorder.通过ENIGMA的全球协调，强大的联盟方法，我们计划克服 小规模研究的危机，效力和可重复性差。汇集解剖、扩散和静息状态功能 来自21个深入评估的国际队列的MRI指标-来自美国、巴西、中国台湾、新西兰、 荷兰、意大利、瑞士、南非、中国和俄罗斯--我们问：这种疾病是如何影响大脑的 结构，神经连接，和功能同步？哪些成像生物标志物可以追踪疾病进展 并持续预测临床结果PD的遗传风险位点有助于预测大脑衰退吗？什么警察 亚型或集群，成像可以识别吗？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子和脑生物标志物。我们的目标是：（1）评估和排名 全球PD的结构、弥散和静息状态功能性MRI生物标志物;（2）评价增加的 多基因风险评分（PRS）在预测PD脑生物标志物中的价值;（3）预测未来功能 通过机器学习、多模式成像和基因组学降低PD。我们将使用基因数据， 我们的队列中来自PD患者和健康对照的基线临床变量，以构建一个整体， 模型来预测运动障碍协会的综合评分的年变化率 帕金森病分级量表第二部分和第三部分。我们将对衰退的最佳预测因素进行排名，并评估 他们在国际上很强大。通过更好地模拟患者结局的方差，我们的多模式预测模型 将通过对疾病负担的生物标志物进行排名，并确定它们 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.