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

项目总结 阿尔茨海默氏症(AD)是65岁以上成年人最常见的痴呆症形式， 额颞叶痴呆(FTD)是中年痴呆的主要原因，其行为变异 亚型(BvFTD)是最常见的形式。临床证候与中医辨证分型的关系 病理原因复杂，难以准确诊断。例如，多项研究已经 表明相当大比例的AD样痴呆症病例显示出非AD病理的证据，如 作为反式反应DNA结合蛋白43(TDP-43)的包涵体，TDP-43是一种与临床相关的蛋白质 FTD。此外，临床诊断为阿尔茨海默病的患者中，有15%-30%的人有AD神经病理。 额颞性痴呆(FTD)。随着具有潜在疾病修改效果的治疗试剂的开发， 将需要敏感和特定的生物标记物，以便它们可以进行测试，然后最终用于 适当的患者群体。在这个项目中，我们将重点关注临床诊断为bvFTD和AD的患者， 并将机器学习应用于从大型多站点收集的多模式神经成像(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，每种神经病理都与一个不同的生物标记物信号有关，而这些 特征可以通过良好的临床、神经学和神经解剖学特征来发现。 我们将使用可用的淀粉样蛋白成像和脑脊液中β-淀粉样蛋白和tau的测量来评估 我们对阿尔茨海默病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和AD的预测性生物标志物模型。在《目标2》中我们将 使用横断面和纵向FDG-PET成像来开发预测性生物标记物模型。在《目标3》中我们 将评估结构成像和FDG-PET成像作为预测生物标记物模型的组合。 相关性：这项研究支持美国国立卫生研究院关于长期、个性化的精确医学和 大数据科学。我们的预测性生物标记物模型可以为临床试验中的参与者选择提供信息，以便我们 能够以更大的力量识别疾病修改治疗方法。我们的系统生物学方法可以使我们 对神经病理的起源和发展的潜在机制提出新的问题 流程，创建新的数据和计算工具，进而产生新的见解和新的 假设。

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.