Bayesian methods for cortical surface neuroimaging data

项目摘要 功能磁共振成像（fMRI）分析的高级贝叶斯统计方法， 在父母资助R 01 EB 027119中开发的，产生大脑功能组织的准确测量， 个别科目。因此，它们特别适合于推进基于fMRI的大脑的开发。 阿尔茨海默病（AD）和轻度认知障碍（MCI）的生物标志物。准确的生物标志物是 需要早期诊断和识别可能表现出足够的临床试验参与者 在整个试验中下降，以充分测试干预措施。基于MRI的功能性生物标志物可以作为 在正电子发射前进行廉价、非侵入性和广泛可用的第一线筛查措施 使用断层摄影（PET）成像。阿尔茨海默病神经影像学倡议（ADNI）于2009年启动。 2004年开发和验证AD临床试验的生物标志物。ADNI-3的一个主要目标是， ADNI，是为了促进诊断模型和精确医学方法的发展，以确定 患者进行治疗性临床试验，包括使用静息态fMRI（rs-fMRI）。在增刊中 项目，我们将适用于开发的方法，在家长补助金，以提取有关的拓扑特征 大脑的功能组织、功能连接和功能网络的结构。这些特征 将用于开发诊断和预后模型，以预测ADNI-3的当前疾病状态。 此外，我们将利用ADNI-2和ADNI-3队列之间的重叠来研究转化为 MCI或AD。我们将使用一个大型的独立的holdout集来验证这些模型，以确保严格性， 在我们的结果中的普遍性。该项目利用现有的长期协作环境， 印第安纳州大学、布卢明顿和犹他州大学。

PROJECT SUMMARY Advanced Bayesian statistical methods for the analysis of functional magnetic resonance imaging (fMRI), developed in parent grant R01EB027119, produce accurate measures of functional brain organization in individual subjects. As a result, they are uniquely suited for advancing the development of fMRI-based brain biomarkers for Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). Accurate biomarkers are needed for early diagnosis and for identification of clinical trial participants who are likely to exhibit sufficient decline across the trial to adequately test the intervention. Functional MRI-based biomarkers may serve as an inexpensive, non-invasive, and widely-available first-line screening measure before positron emission tomography (PET) imaging is used. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) was launched in 2004 to develop and validate biomarkers for AD clinical trials. A principal goal of ADNI-3, the latest iteration of ADNI, is to promote the development of diagnostic models and precision medicine approaches to identify patients for therapeutic clinical trials, including the use of resting-state fMRI (rs-fMRI). In this supplement project, we will apply the methods developed in the parent grant to extract features related to the topological functional organization of the brain, functional connectivity, and texture of functional networks. These features will be used to develop diagnostic and prognostic models to predict current disease status in ADNI-3. Additionally, we will utilize the overlap between the ADNI-2 and ADNI-3 cohorts to investigate conversion to MCI or AD. We will validate these models using a large independent holdout set to ensure rigor and generalizability in our results. This project leverages an existing long-term collaborative environment between Indiana University, Bloomington, and the University of Utah.