Early detection of Huntington's Disease: Longitudinal analysis of basal ganglia and cortical thickness

项目总结/摘要 亨廷顿氏病（HD）是一种神经退行性疾病，通过MRI可以检测到大脑异常 在临床诊断前一至二十年进行研究。需要采取敏感的成果措施， 预显HD期间的临床试验，目的是尽可能早地进行干预和治疗。 PREDICT-HD研究（NS 040068）确定了基底神经节和皮质灰质的纵向改变 脑实质萎缩是表现前HD患者的主要神经影像学表现。然而，测量 噪音是一个严重的问题，因为它会影响在疾病进展早期检测异常的能力。 该提案的目的是开发量化大脑皮层和大脑皮层的创新方法。 基底神经节在时间和空间上一致的方式，并利用这些技术，以改善 现有PREDICT-HD数据库中患者的HD进展量化。我们假设 更准确的量化将提供更灵敏的HD进展测量， 在临床诊断之前对纵向变化的敏感性。量化预计将大大 由于我们新颖的时间和空间上下文感知分割， 策略，其利用纵向MRI数据的固有冗余。将实现三个具体目标： 目标1.开发并验证一种新的时间和空间一致的分割方法， HD纵向研究中基底神经节的定量。预计影响特别大 对于具有弱边界的结构，例如核壳，这很难用现有的方法量化。 接近。将通过与专家手动分割进行比较来完成验证。 目标二。开发并验证一种新的纵向皮质表面重建方法，用于颞叶 HD纵向研究中一致的皮质厚度定量。我们的方法将利用时间 图像到图像上下文，同时避免过度正则化。验证将基于以下方面的重现性： 使用公共数据集进行的人口研究中的重测扫描和统计鉴别力。 目标3。评估从我们的新分割中获得的成像测量的统计灵敏度的增加 方法在纵向预显HD队列中，并通过记录 它们与已知的临床结果评估（COA）和遗传变量的关联。我们将使用1246 从Predict-HD扫描，以评估开发的方法的灵敏度和验证临床变量。 我们预计所提出的分割方法将大大提高现有成像的灵敏度- 基于HD的测量。这将为开发和评估早期治疗干预提供一种手段 因此，我们需要采取有效的预防和治疗策略，以预防疾病发作和减缓疾病进展。同样重要的是， 预计本提案中开发的方法对于提高灵敏度至关重要， 其他神经退行性疾病，如阿尔茨海默病或帕金森病。

PROJECT SUMMARY/ABSTRACT Huntington's disease (HD) is a neurodegenerative disease where brain abnormalities can be detected via MRI studies one to two decades prior to clinical diagnosis. Sensitive outcome measures are needed for enabling clinical trials during pre-manifest HD with the goal of intervention and treatment at the earliest stage possible. The PREDICT-HD study (NS040068) identified longitudinal alterations in the basal ganglia and cortical gray matter atrophy as the primary neuroimaging findings in pre-manifest HD patients. However, measurement noise is a serious concern as it can affect the ability to detect abnormalities early in the disease progression. The objective of this proposal is to develop innovative methods for quantifying the cerebral cortex and the basal ganglia in a temporally and spatially consistent manner and to leverage these techniques to improve the quantification of HD progression in patients from the existing PREDICT-HD database. We hypothesize that more accurate quantification will provide more sensitive measures of HD progression, leading to increased sensitivity to longitudinal changes prior to clinical diagnosis. The quantification is expected to be substantially more accurate than currently possible due to our novel temporal- and spatial- context-aware segmentation strategy, which leverages the inherent redundancy of longitudinal MRI data. Three specific aims will be fulfilled: Aim 1. Develop and validate a novel temporally and spatially consistent segmentation method for quantification of the basal ganglia in longitudinal studies of HD. The impact is expected to be especially large for structures with weak boundaries, such as the nucleus accumbens, which are hard to quantify with existing approaches. Validation will be accomplished via comparison with expert manual segmentations. Aim 2. Develop and validate a novel longitudinal cortical surface reconstruction method for temporally consistent cortical thickness quantification in longitudinal studies of HD. Our approach will utilize temporal image-to-image context while avoiding over-regularization. The validation will be based on reproducibility in test-retest scans and statistical discrimination power in population studies, using public datasets. Aim 3. Assess the increase in statistical sensitivity of imaging measures derived from our new segmentation approaches in a longitudinal pre-manifest HD cohort, and validate these imaging measures by documenting their association with known clinical outcome assessments (COA's) and genetic variables. We will use 1246 scans from Predict-HD to evaluate the sensitivity of developed methods and validate against clinical variables. We anticipate the proposed segmentation methods to substantially increase the sensitivity of existing imaging- based measures in HD. This will provide a means of developing and evaluating early therapeutic intervention strategies in order to prevent disease onset and slow disease progression. Equally significant, the innovative methods to be developed in this proposal are expected to be crucially important for increased sensitivity for other neurodegenerative disorders such as Alzheimer's disease or Parkinson's disease.