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）是一项大型多中心研究，其中从200名健康对照、400名轻度认知障碍（MCI）患者和200名轻度阿尔茨海默病（AD）患者中收集了系列临床、生物学、神经心理学和神经影像学数据。这项拟议的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的形态测量和代谢变化，目的是确定最佳预测变量，用于识别有发生进行性AD相关神经退行性变风险的个体。由此确定的变量可作为II期和III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数据库中下载。将使用基于FreeSurfer软件的方法对所有基线结构MRI进行自动体积分割、皮质表面重建和大脑分区，以获得局部皮质厚度和皮质下体积的测量值。将对系列MRI进行自动、纵向、受试者内变化分析，以确定与疾病进展相关的区域特异性结构变化轨迹。将用于量化MRI衍生的解剖学定义的感兴趣区域内代谢活动的半自动化程序应用于基线期的PET数据，以确定校正形态学差异前后所有皮质和皮质下结构中代谢活动的疾病相关差异的效应量。将计算校正形态学变化前后受试者内代谢活性随时间的变化，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，以确定区分对照与MCI受试者以及区分转化为AD的MCI受试者与诊断保持稳定的受试者的灵敏度和特异性。将增加PET和认知测量，以确定它们是否提高分类准确性。将对在第一个研究年的测试阶段期间从对照和MCI受试者获得的结构性指标进行多变量分析，以确定预测转化为AD风险的最佳指标集。将评估代谢和认知指标，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后的图像体积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中发生的大脑变化的理解;确定用于临床试验的候选神经影像学生物标志物;促进其他AD研究者的研究;并提供用于其他衰老相关疾病研究的规范性数据。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）最早阶段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发展为AD的能力，并将为研究人员提供客观的措施，可用于评估新治疗方法预防或延迟与AD相关的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用于未来的研究，用于检测和监测其他神经系统疾病中发生的大脑变化。

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.