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.

项目概要 用于分析功能磁共振成像 (fMRI) 的先进贝叶斯统计方法， 在母基金 R01EB027119 中开发，对功能性大脑组织进行准确测量 因此，它们非常适合促进基于功能磁共振成像的大脑的发展。 阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 的生物标志物是准确的生物标志物。 早期诊断和临床试验鉴定谁可能表现出足够的 在整个试验中下降，以充分测试基于功能性 MRI 的生物标志物可以作为一种干预措施。 正电子发射前廉价、非侵入性且广泛使用的一线筛查措施 使用断层扫描 (PET) 成像 阿尔茨海默病神经影像计划 (ADNI) 于 2017 年启动。 2004 年开发和验证 AD 临床试验的生物标志物 ADNI-3 的主要目标是最新版本。 ADNI，旨在促进诊断模型和精准医学方法的发展，以识别 患者进行治疗性临床试验，包括使用静息态功能磁共振成像 (rs-fMRI)。 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结构。 将用于开发诊断和预后模型来预测 ADNI-3 当前的疾病状态。 此外，我们将利用 ADNI-2 和 ADNI-3 队列之间的重叠来调查向 我们将使用大型独立保留集来验证这些模型，以确保严谨性和有效性。 我们的结果具有普遍性，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分校和犹他大学。