PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

PREDICT-ADFTD：AD/FTD 的多模态影像预测和鉴别诊断

• 批准号: 9240349
• 负责人: HOWARD J ROSEN
• 金额: $ 72.32万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9240349
• 关键词: Address; Adult; Affect; Age; Aging; Alzheimer' s Disease; American; Amyloid beta-Protein; Autopsy; Behavioral; Big Data; Biological Markers; Brain; Cerebrospinal Fluid; Characteristics; Classification; Clinical; Clinical Trials; Cognitive; Complex; DNA-Binding Proteins; Data; Data Science; Data Set; Databases; Dementia; Diagnosis; Differential Diagnosis; Disease; Elderly; Face; Frontotemporal Dementia; Frontotemporal Lobar Degenerations; Funding; Genetic; Genetic Research; Goals; Image; Intervention; Learning; Machine Learning; Measures; Meta-Analysis; Methods; Modeling; Multimodal Imaging; National Institute of Neurological Disorders and Stroke; Neurologic; Participant; Pathologic; Pathologic Processes; Pathology; Patients; Pattern; Positioning Attribute; Positron-Emission Tomography; Prevalence; Process; Proteins; Research; Research Personnel; Research Support; Series; Source; Spatial Distribution; Syndrome; Systems Biology; Technology; Testing; Training; Transact; United States National Institutes of Health; Validation; Variant; accurate diagnosis; amyloid imaging; behavioral variant frontotemporal dementia; biomarker development; clinical Diagnosis; computerized tools; design; experience; fluorodeoxyglucose positron emission tomography; improved; insight; learning strategy; middle age; multidisciplinary; multimodality; mutation carrier; neuroimaging; neuropathology; non-alzheimer dementia; novel marker; patient population; precision medicine; predictive marker; predictive modeling; programs; response; search engine; specific biomarkers; success; tau Proteins

PROJECT SUMMARY Alzheimer's dementia (AD) is the most common form of dementia in adults over the age of 65, and Frontotemporal dementia (FTD) is the leading cause of dementia in middle age, with the behavioral variant subtype (bvFTD) being the most prevalent form. The relationships between clinical syndromes and pathological causes are complex, which makes accurate diagnosis difficult. For example, multiple studies have indicated that a significant proportion of cases of AD-like dementia show evidence of non-AD pathology, such as inclusions of the transactive response DNA-binding protein 43 (TDP-43), a protein associated with clinical FTD. Also, AD neuropathology has been found in 15–30% of patient with the clinical diagnosis of frontotemporal dementia (FTD). As treatment agents with potential disease-modifying effects are developed, sensitive and specific biomarkers will be needed, so that they can be tested and then eventually used in the appropriate patient populations. In this project, we will focus on clinically diagnosed bvFTD and AD patients, and apply machine learning to multimodal neuroimaging (T1, FDG-PET) data pooled from large, multisite studies of AD and FTD. Our goal is to develop novel biomarkers that can differentiate bvFTD, AD and controls. Our hypothesis is that each neuropathology is associated with a distinct biomarker signature, and these signatures can be discovered through well-characterized clinical, neurological and neuroanatomical profiles. We will use available amyloid imaging and cerebrospinal fluid (CSF) measures of β-amyloid and tau to assess the robustness of our predictions of AD neuropathologies. In Aim 1 we will use cross-sectional and longitudinal structural imaging to develop predictive biomarker models for differentiating bvFTD vs. AD. In Aim 2 we will use cross-sectional and longitudinal FDG-PET imaging to develop predictive biomarker models. In Aim 3 we will evaluate the combination of structural and FDG-PET imaging as predictive biomarker models. Relevance: This research supports NIH initiatives on long-term, personalized precision medicine and big data science. Our predictive biomarker models can inform participant selection in clinical trials so that we can identify disease-modifying treatments with greater power. Our system-biology approach can enable us to generate new questions on mechanisms underlying the origin and progression of neuro-pathological processes, create new data and computational tools that can in turn generate new insights and new hypotheses.

项目摘要 阿尔茨海默氏症痴呆症（AD）是65岁以上成年人最常见的痴呆症形式，并且 额颞叶痴呆（FTD）是中年痴呆的主要原因， 亚型（bvFTD）是最常见的形式。临床证候与 病因复杂，诊断困难。例如，多项研究 表明AD样痴呆的病例中有很大一部分显示出非AD病理学的证据， 作为反式反应DNA结合蛋白43（TDP-43）的内含物，TDP-43是一种与临床相关的蛋白质， FTD。此外，在15-30%的临床诊断为AD的患者中发现了AD神经病理学。 额颞叶痴呆（FTD）。随着具有潜在疾病改善作用的治疗剂的开发， 将需要敏感和特异的生物标志物，以便它们可以被测试，然后最终用于 合适的患者群体。在本项目中，我们将重点关注临床诊断的bvFTD和AD患者， 并将机器学习应用于从大型多站点汇集的多模态神经成像（T1，FDG-PET）数据， AD和FTD的研究。我们的目标是开发能够区分bvFTD、AD和对照的新型生物标志物。 我们的假设是，每种神经病理学都与不同的生物标志物特征相关，这些生物标志物特征与神经病理学特征相关。 可以通过良好表征的临床、神经学和神经解剖学特征来发现特征。 我们将使用可用的淀粉样蛋白成像和β-淀粉样蛋白和tau蛋白的脑脊液（CSF）测量来评估 我们对AD神经病理学预测的稳健性。在目标1中，我们将使用横截面和纵向 结构成像，以开发用于区分bvFTD与AD的预测性生物标志物模型。在目标2中， 使用横截面和纵向FDG-PET成像来开发预测性生物标志物模型。在目标3中， 将评估结构和FDG-PET成像作为预测生物标志物模型的组合。 相关性：这项研究支持NIH关于长期、个性化精准医学的倡议， 大数据科学我们的预测生物标志物模型可以为临床试验中的参与者选择提供信息， 可以找到更有效的治疗方法。我们的系统生物学方法可以使我们 对神经病理学的起源和发展机制提出了新的问题， 过程，创造新的数据和计算工具，从而产生新的见解和新的 假设