Structural & Metabolic Neuroimaging Biomarkers in Early Alzheimer's Disease

项目总结 阿尔茨海默病神经成像倡议(ADNI)是一项大型的多点研究，在该研究中， 生物、神经心理和神经成像数据收集自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。这 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 早期阿尔茨海默病的形态和代谢变化，目的是确定最佳预测变量 确定有发生进行性AD相关神经变性的风险的个体。因此，这些变量 已确定的药物可作为第二阶段和第三阶段临床治疗试验的替代终点。要实现这些目标 Goals、MRI、PET和认知数据将从可公开访问的ADNI数据库下载。 基于Freesurfer软件的方法将被用于执行自动体积分割，皮质 在所有基线结构磁共振成像上进行表面重建和大脑切片以获得测量 区域皮质厚度和皮质下体积。自动的、纵向的、主题内的变更分析 将对系列核磁共振成像进行检查，以确定与疾病相关的特定区域结构变化轨迹 进步。半自动的程序，量化内的代谢活动的核磁共振成像解剖- 定义的感兴趣区域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的代谢活动在疾病相关前后的差异 修正形态测量学差异。受试者内代谢活动随时间的变化，之前和 在对形态变化进行校正后，将计算以确定区域特定轨迹 与疾病相关的代谢变化。多变量分类分析将应用于MRI措施，以 确定区分对照和MCI受试者的敏感性和特异性，以及区分 诊断稳定的患者转为AD的MCI受试者。宠物和认知测量将 以确定它们是否提高了分类精度。将进行多变量分析 在第一个研究年度的测试期间，来自对照和MCI受试者的结构性测量 以确定预测转换为AD风险的最佳措施集。代谢和认知 将对措施进行评估，以确定它们是否提高了预测能力。所有派生数据值和 这项研究的处理后的图像卷将通过生物医学信息学公开提供 研究网络。这项研究将显著提高对早期大脑变化的理解 AD；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的研究 研究人员；并提供标准数据，用于其他与衰老有关的疾病的调查。相关性 该项目的结果将提供关于 阿尔茨海默病早期的脑结构和代谢 (AD)，并将这些措施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AD的能力，并将提供 具有客观测量的研究人员可以用来评估新的 预防或延缓与阿尔茨海默病相关的神经变性的治疗。另外， 本文开发的高通量神经成像分析方法可用于 检测和监测发生在其他神经科的脑变化的未来研究 精神错乱。

PROJECT SUMMARY 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. 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.