Bayesian methods for cortical surface neuroimaging data

用于皮质表面神经影像数据的贝叶斯方法

• 批准号: 10289056
• 负责人: Amanda Mejia
• 金额: $ 36.38万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10289056
• 关键词: Advanced Development; Aging; Alzheimer' s Disease; Alzheimer’s disease biomarker; Anus; Base of the Brain; Bayesian Analysis; Bayesian Method; Biological Markers; Brain; Clinical; Clinical Trials; Data; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Ensure; Exhibits; Functional Magnetic Resonance Imaging; Goals; Image; Indiana; Individual; Intervention; Magnetic Resonance Imaging; Measures; Methods; Modeling; National Institute of Biomedical Imaging and Bioengineering; Patients; Positron-Emission Tomography; Process; Rest; Statistical Models; Surface; Testing; Texture; Therapeutic Clinical Trial; Universities; Utah; base; clinical trial participant; cohort; collaborative environment; man; mild cognitive impairment; neuroimaging; parent grant; precision medicine; prognostic model; screening

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.

项目摘要 用于分析功能磁共振成像（fMRI）的晚期贝叶斯统计方法， 在父级授予R01EB027119中开发，在 个人主题。结果，它们非常适合促进基于fMRI的大脑的发展 阿尔茨海默氏病（AD）和轻度认知障碍（MCI）的生物标志物。准确的生物标志物是 早期诊断和识别可能表现足够的临床试验参与者所需 在整个试验中的下降以充分测试干预措施。基于功能性MRI的生物标志物可以作为 廉价，无创和广泛的一线筛选措施在正电子发射之前 使用断层扫描（PET）成像。阿尔茨海默氏病神经影像学计划（ADNI）发起了 2004年开发和验证生物标志物进行AD临床试验。 ADNI-3的主要目标，最新的迭代 ADNI是为了促进诊断模型和精确医学方法的开发以识别 进行热临床试验的患者，包括使用静止状态fMRI（RS-FMRI）。在此补品中 项目，我们将应用父母赠款中开发的方法来提取与拓扑相关的功能 大脑的功能组织，功能连接性和功能网络的纹理。这些功能 将用于开发诊断和预后模型，以预测ADNI-3中当前疾病状态。 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或广告。我们将使用大型独立保留集验证这些模型，以确保严格和 我们的结果的概括性。该项目利用了现有的长期协作环境 印第安纳大学，布卢明顿和犹他大学。