IMPROVED MONITORING OF PREMANIFEST AND EARLY HUNTINGTONS USING 7T MULTIMODAL MRI

项目摘要 亨廷顿病(HD)是一种遗传性神经退行性疾病，潜伏期长，通常持续 进入成年早期，直到运动、认知和精神症状公开影响功能能力和 然后随着时间的推移而逐渐前进。发病年龄、症状严重程度和进展速度各不相同。 在受影响的个人中显著增加，迫切需要制定客观指标来表征 疾病状况，以便适当地咨询患者，设计临床试验，并评估 假定的治疗干预。现有的基于年龄和基因检测缺失的预后标志物 疾病进展的预测能力和指标使用临床和神经心理学评估 敏感度和重复性较差。纹状体体积的纵向变化是既定的标志 用于疾病进展，但未能捕捉到患者在疾病过程中看到的异质性。 本研究的目的是提高对脑局部变化的敏感度。 基于定量磁化率图(QSM)的症状性HD多对比7T磁共振成像 结构磁共振的形态计量学，以及脑连通性的扩散张量成像分析 空间变化的、时间相关的、能够预测HD病程的多模式模型。 我们建议使用解剖学、敏感性和扩散加权技术对患者进行纵向研究。 核磁共振检查。使用纹状体体积和形状的测量，定量敏感性的区域值，以及 通过7T MRI检查，我们的目标是检测和表征脑白质弥散。 神经元丢失、疾病相关铁沉积和白质的区域分布和时间进程 在这种混乱中的伤害。将使用高场7T磁共振来加强对微观组织的测量 易感性，以及最近开发的纹状体形状分析和白质扩散技术 应用改变来增加对疾病进展的敏感度。数据将在45个人中收集 45例有早期症状的HD患者，以及30例健康对照。 AIM 1将评估多对比7T MRI在监测亚临床和早期HD进展中的作用 评估成像参数的区域横断面差异和患者内部的纵向变化。 目标2将确定在疾病负担的多变量模型中包括QSM是否会改善 通过生成患者水平的多变量模型和 区分每个疾病阶段的受影响大脑区域的体素级别空间图以及相关成像 衡量疾病负担、认知功能和临床损害的指标。 拟议的研究将最终导致对铁与铁之间的复杂关系的理解 HD中的沉积和萎缩，增强了预测接近临床症状发作的能力，并创造 开发HD多变量风险预测模型的框架。

PROJECT ABSTRACT Huntington's disease (HD) is a genetic neurodegenerative disorder with a long latent period that usually lasts into early adulthood, until motor, cognitive and psychiatric symptoms overtly impact functional capacity and then gradually advance with time. The age of onset, severity of symptoms, and rate of progression vary significantly across affected individuals, and there is a strong need to develop objective metrics to characterize disease status in order to appropriately counsel patients, design clinical trials, and evaluate the efficacy of putative therapeutic interventions. Existing markers for prognosis based on age and genetic testing lack predictive power, and indicators of disease progression use clinical and neuropsychological evaluation that suffer from poor sensitivity and reproducibility. Longitudinal changes in striatal volume are established markers for disease progression, but fail to capture the heterogeneity seen during the disease course across patients. The goal of this study is to increase the sensitivity to regional brain changes in premanifest and early symptomatic HD multi-contrast 7T MRI based on quantitative susceptibility mapping (QSM), quantitative morphometry of structural MRI, and brain connectivity analysis with diffusion tensor imaging in order to develop spatially varying, time-dependent, multi-modal models capable of predicting disease course in HD. We propose to longitudinally study patients using anatomic, susceptibility-sensitive, and diffusion-weighted MRI. Using measurements of striatal volume and shape, regional values of quantitative susceptibility, and tract-specific white matter diffusion derived from 7T MRI examinations, we aim to detect and characterize the regional distribution and temporal course of neuronal loss, disease-related iron deposition, and white matter injury in this disorder. High-field 7T MRI will be used to enhance measurements of microscopic tissue susceptibility, and recently developed techniques for striatal shape analysis and white matter diffusion alterations are applied to increase the sensitivity to disease progression. Data will be collected in 45 individuals with premanifest HD and 45 individuals with early symptomatic HD, as well as 30 healthy controls. Aim 1 will evaluate multi-contrast 7T MRI for monitoring progression of subclinical and early HD by estimating regional cross-sectional differences and within-patient longitudinal changes in imaging parameters. Aim 2 will determine whether including QSM in a multivariate model of disease burden improves predictive accuracy of the manifestation of symptoms by generating a patient-level multivariate model and voxel-level spatial map of affected brain regions that discriminates each disease stage, and relating imaging metrics to measures of disease burden, cognitive function, and clinical impairment. The proposed study will ultimately result in an understanding of the complicated relationship between iron deposition and atrophy in HD, enhance the ability to predict proximity to clinical onset of symptoms, and create a framework for developing multivariate risk prediction models in HD.