Structural & Metabolic Neuroimaging Biomarkers in Early Alzheimer's Disease

描述(申请人提供)：阿尔茨海默病神经成像计划(ADNI)是一项大型的多地点研究，收集了200名健康对照组、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、神经心理学和神经成像数据。这项拟议的辅助研究旨在分析所有ADNI结构和代谢神经成像数据，以表征早期AD的形态和代谢变化，目的是确定最佳预测变量，以确定发生进行性AD相关神经变性的风险个体。由此确定的变量可以作为第二阶段和第三阶段临床治疗试验的替代终点。为了实现这些目标，将从可公开访问的ADNI数据库下载磁共振成像、正电子发射计算机断层扫描和认知数据。方法基于Freesurfer软件，对所有基线结构磁共振成像进行自动体积分割、皮质表面重建和大脑切片，获得局部皮质厚度和皮质下体积的测量结果。将在系列磁共振成像上进行自动的、纵向的、受试者内的变化分析，以确定与疾病进展相关的区域特定结构变化轨迹。半自动的程序，用于量化MRI衍生的解剖定义的感兴趣区域内的代谢活动，将应用于基线会议的PET数据，以确定所有皮质和皮质下结构中与疾病相关的代谢活动差异的影响大小，校正形态差异前后。将计算校正形态变化前后受试者体内代谢活动随时间的变化，以确定与疾病相关的代谢变化的区域特定轨迹。多变量分类分析将被应用于MRI测量，以确定区分对照和MCI受试者的敏感性和特异性，以及区分转换为AD的MCI受试者和那些诊断稳定的MCI受试者。将增加宠物和认知测量，以确定它们是否提高了分类准确率。在第一个研究年的测试期间，将对从对照和MCI受试者获得的结构性措施进行多变量分析，以确定预测转换为AD风险的最佳措施集。将对代谢和认知指标进行评估，以确定它们是否提高了预测能力。所有从这项研究得出的数据值和处理后的图像卷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发生的脑变化的了解；确定用于临床试验的候选神经影像生物标志物；促进其他AD研究人员的研究；并为其他与衰老相关的疾病的研究提供标准化数据。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)早期阶段大脑结构和新陈代谢变化的重要新信息，并将这些测量与认知表现的变化联系起来。这一知识可能会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衡量标准，用于评估预防或延缓与阿尔茨海默病相关的神经退行性变的新疗法的能力。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.