Bayesian methods for cortical surface neuroimaging data

项目总结 用于功能磁共振成像(FMRI)分析的高级贝叶斯统计方法， 在Parent Grant R01EB027119中开发，产生准确的脑组织功能测量 个别受试者。因此，它们特别适合于促进基于fmri的大脑的发展。 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 用于早期诊断和确定临床试验参与者可能表现出足够的 在整个试验中不断下降，以充分测试干预措施。基于功能磁共振成像的生物标记物可以作为 正电子发射前的廉价、非侵入性和广泛可用的一线筛查措施 使用断层扫描(PET)成像。阿尔茨海默病神经成像倡议(ADNI)于#年启动 2004年开发和验证AD临床试验的生物标记物。ADNI-3的主要目标是最新版本的 ADNI，是为了促进诊断模型的发展和精准医学的识别方法 患者进行治疗性临床试验，包括使用静息功能磁共振成像(rs-fmri)。在本增刊中 项目中，我们将应用在父资助中开发的方法来提取与拓扑相关的特征 大脑的功能组织、功能连接和功能网络的纹理。这些功能 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换为 MCI或AD。我们将使用大型独立坚持集来验证这些模型，以确保严格性和 在我们的结果中的泛化。该项目利用现有的长期协作环境 印第安纳大学、布鲁明顿大学和犹他大学。

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.