Structural & Metabolic Neuroimaging Biomarkers in Early Alzheimer's Disease

描述（由申请人提供）：阿尔茨海默病神经影像学倡议（ADNI）是一项大型多站点研究，其中收集了200名健康对照者、400名轻度认知障碍（MCI）患者和200名轻度阿尔茨海默病（AD）患者的一系列临床、生物学、神经心理学和神经影像学数据。这项辅助研究旨在分析所有ADNI结构和代谢神经影像学数据，以表征早期AD的形态学和代谢变化，目的是确定最佳预测变量，以识别有进展性AD相关神经退行性变风险的个体。由此确定的变量可以作为2期和3期临床治疗试验的替代终点。为了实现这些目标，MRI、PET和认知数据将从可公开访问的ADNI数据库下载。基于FreeSurfer软件的方法将用于在所有基线结构mri上执行自动体积分割，皮质表面重建和大脑包裹化，以获得区域皮质厚度和皮质下体积的测量。自动的、纵向的、受试者内部的变化分析将在系列mri上进行，以确定与疾病进展相关的区域特异性结构变化轨迹。在mri衍生的解剖学定义的感兴趣区域内量化代谢活动的半自动程序将应用于基线会议的PET数据，以确定在纠正形态测量差异之前和之后，所有皮质和皮质下结构中代谢活动疾病相关差异的效应大小。将计算受试者体内代谢活动随时间的变化，在校正形态变化之前和之后，以确定疾病相关代谢变化的区域特异性轨迹。多变量分类分析将应用于MRI测量，以确定区分对照和MCI患者的敏感性和特异性，以及区分MCI患者转化为AD的患者和诊断保持稳定的患者。将增加PET和认知测量来确定它们是否提高了分类准确性。在第一个研究年度的测试阶段，将对从对照组和MCI受试者中获得的结构测量值进行多变量分析，以确定预测转化为AD风险的最佳测量集。将评估代谢和认知测量，以确定它们是否能提高预测能力。本研究的所有衍生数据值和处理图像量将通过生物医学信息学研究网络公开提供。这项研究将显著提高对早期AD发生的大脑变化的理解；确定用于临床试验的候选神经成像生物标志物；促进其他AD研究者的研究；并为其他衰老相关疾病的研究提供规范性数据。公共卫生相关性：该项目的结果将提供有关阿尔茨海默病（AD）早期发生的大脑结构和代谢变化的重要新信息，并将这些措施与认知表现的变化联系起来。这些知识可以提高我们预测谁最有可能患上阿尔茨海默病的能力，并将为研究人员提供客观的措施，用于评估新疗法预防或延缓阿尔茨海默病相关神经退行性变的能力。此外，这里开发的高通量神经成像分析方法可以用于未来的研究，以检测和监测发生在其他神经系统疾病中的大脑变化。

DESCRIPTION (provided by applicant): The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a large multi-site study in which serial clinical, biological, neuropsychological and neuroimaging data are being collected from 200 healthy controls, 400 individuals with mild cognitive impairment (MCI) and 200 patients with mild Alzheimer's disease (AD). This proposed ancillary study aims to analyze all ADNI structural and metabolic neuroimaging data to characterize morphometric and metabolic changes in early AD, with the goal of determining optimal predictor variables for identifying individuals at risk for developing progressive AD-related neurodegeneration. The variables thus identified could serve as surrogate endpoints in Phase 2 and 3 clinical treatment trials. To achieve these goals, MRI, PET, and cognitive data will be downloaded from the publicly accessible ADNI database. Methods based on FreeSurfer software will be used to perform automated volumetric segmentation, cortical surface reconstruction and cerebral parcellation on all baseline structural MRIs to obtain measures of regional cortical thickness and subcortical volumes. Automated, longitudinal, within-subject change analyses will be performed on serial MRIs to determine region-specific structural change trajectories related to disease progression. Semi-automated procedures for quantifying metabolic activity within MRI-derived anatomically- defined regions of interest will be applied to PET data from the baseline session to determine effect size of disease-related differences in metabolic activity in all cortical and subcortical structures, before and after correcting for morphometric differences. Within-subject change in metabolic activity over time, before and after correcting for morphometric changes, will be computed to determine region-specific trajectories of disease-related metabolic changes. Multivariate classification analyses will be applied to MRI measures to determine sensitivity and specificity for discriminating controls from subjects with MCI, and for discriminating MCI subjects who convert to AD from those who remain stable in diagnosis. PET and cognitive measures will be added to determine whether they improve classification accuracy. Multivariate analyses will be performed on structural measures obtained from control and MCI subjects during the test sessions of the first study year to determine the optimal set of measures for predicting risk of conversion to AD. Metabolic and cognitive measures will be assessed to determine whether they improve predictive ability. All derived data values and processed image volumes from this study will be made publicly available through the Biomedical Informatics Research Network. This study will significantly enhance understanding of brain changes that occur in early AD; identify candidate neuroimaging biomarkers for use in clinical trials; facilitate the research of other AD investigators; and provide normative data for use in investigation of other aging-related disorders. PUBLIC HEALTH RELEVANCE: The results of this project will provide important new information about the changes in brain structure and metabolism that occur in the earliest stages of Alzheimer's Disease (AD), and relate these measures to change in cognitive performance. This knowledge may improve our ability to predict who is most likely to develop AD and will provide researchers with objective measures that can be used to assess the ability of new treatments to prevent or delay the neurodegeneration associated with AD. Additionally, the high-throughput neuroimaging analysis methods developed here could be used in future studies for detecting and monitoring brain changes that occur in other neurological disorders.