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: 10397226
• 负责人: HOWARD J ROSEN
• 金额: $ 53.94万
• 依托单位: OHIO STATE UNIVERSITY
• 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 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10397226
• 关键词: PREDICT; ADFTD; Multimodal; Imaging; Prediction

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（AD）是65岁以上成年人最常见的痴呆形式， 额颞痴呆（FTD）是中年痴呆症的主要原因，行为变体 亚型（BVFTD）是最普遍的形式。临床综合征与 病理原因很复杂，这使得准确的诊断困难。例如，多个研究具有 表明大量的AD样性痴呆病例显示了非AD病理学的证据， 作为交易反应DNA结合蛋白43（TDP-43）的夹杂物，一种与临床相关的蛋白 ftd。此外，在15-30％的患者中发现了AD神经病理学 额颞痴呆（FTD）。随着具有潜在疾病改良作用的治疗剂， 需要敏感和特定的生物标记物，以便可以测试它们，然后最终在 适当的患者人群。在这个项目中，我们将专注于临床诊断的BVFTD和AD患者， 并将机器学习应用于从大型多站点汇总的多模式神经影像学（T1，FDG-PET） AD和FTD的研究。我们的目标是开发可以区分BVFTD，AD和控制的新型生物标志物。 我们的假设是，每种神经病理学都与独特的生物标志物签名有关，这些 可以通过良好的临床，神经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脑脊液（CSF）测量β-淀粉样蛋白和TAU评估 我们对AD神经病理学的预测的鲁棒性。在AIM 1中，我们将使用横截面和纵向 结构成像开发用于区分BVFTD与AD的预测生物标志物模型。在目标2中，我们将 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中我们 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，个性化的精密医学的举措， 大数据科学。我们的预测生物标志物模型可以在临床试验中为参与者的选择提供信息，以便我们 可以用更大的能力确定改良疾病的治疗方法。我们的系统生物学方法可以使我们能够 产生有关神经病理起源和进展的基础机制的新问题 流程，创建新的数据和计算工具，这些工具又可以生成新的见解和新的见解 假设。