Structural & Metabolic Neuroimaging Biomarkers in Early Alzheimer's Disease

项目摘要 阿尔茨海默病神经影像学倡议（ADNI）是一项大型多中心研究， 生物学、神经心理学和神经影像学数据收集自200名健康对照者、400名 轻度认知障碍（MCI）患者和200例轻度阿尔茨海默病（AD）患者。这 拟议的辅助研究旨在分析所有ADNI结构和代谢神经影像学数据，以表征 早期AD的形态和代谢变化，目的是确定最佳的预测变量， 识别有发展进行性AD相关神经变性风险的个体。因此，变量 确定的终点可以作为II期和III期临床治疗试验的替代终点。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。 将使用基于FreeSurfer软件的方法进行自动体积分割，皮质 在所有基线结构MRI上进行表面重建和大脑分区，以获得以下测量值： 区域皮质厚度和皮质下体积。自动化、纵向、受试者内变化分析 将在系列MRI上进行，以确定与疾病相关的区域特异性结构变化轨迹 进展用于量化MRI衍生解剖学内代谢活动的半自动化程序- 定义的感兴趣区域将应用于基线会话的PET数据，以确定 治疗前后，所有皮质和皮质下结构中代谢活性的疾病相关差异 校正形态差异。受试者内代谢活性随时间的变化， 在校正形态学变化后，将计算以确定 疾病相关的代谢变化。多变量分类分析将应用于MRI测量， 确定区分对照与MCI受试者以及区分对照与MCI受试者的灵敏度和特异性， MCI受试者从诊断保持稳定的受试者转化为AD。PET和认知测量将 以确定它们是否提高了分类准确性。将进行多变量分析 在第一个研究年的测试期间，从对照组和MCI受试者中获得的结构测量结果 以确定用于预测转化为AD的风险的最佳措施集。代谢和认知 将对这些措施进行评估，以确定它们是否提高了预测能力。所有衍生数据值和 本研究的处理图像卷将通过生物医学信息学公开提供 中国互联网.这项研究将大大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影像学生物标志物;促进其他AD的研究 研究者;并提供用于其他衰老相关疾病研究的规范性数据。相关性 这一项目的结果将提供关于 大脑结构和新陈代谢发生在阿尔茨海默病的最早阶段 (AD)，并将这些措施与认知表现的变化联系起来。这些知识 可能会提高我们预测谁最有可能患上AD的能力， 研究人员的客观措施，可用于评估新的能力， 预防或延迟与AD相关的神经变性的治疗。此外，本发明还 这里开发的高通量神经成像分析方法可用于 未来的研究，用于检测和监测发生在其他神经系统的大脑变化， 紊乱

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.