Association of Tau and Neuronal Hypometabolism with Positron Emission Tomography in Alzheimer's Disease

项目总结 国家老龄和阿尔茨海默氏症协会最近提出了ATN框架，以 在研究中系统地对阿尔茨海默病(AD)进行分类，并将标准化生物标志物与 临床评估。该框架基于淀粉样蛋白(A)的存在或不存在的二元标识， Tau(T)和神经退行性(N)病理，其中AD定义为A/T/N±。这种生物标志物离散化 可能会简化AD的诊断和管理，但不能更定量地分离T和N 超越T/N±的表述。 我们有兴趣研究神经元对T病理的反应，包括易感性(T&lt；N)和弹性 (T&gt；N)。N已被定义为包括磁共振成像(MRI)上的结构性体积损失(NS)和 18F-脱氧葡萄糖(FDG)正电子发射断层扫描(PET)显示神经元代谢(NM)降低。 还没有实质性的调查比较T和NM，这可能需要更细微的差异， 超越二进制描述的比较。 在这里，我们将利用AD神经成像倡议(ADNI)来调查伴随的匹配和不匹配 T和NM用18F-氟他西平和FDG PET。我们的目标是(1)定义源自以下各项的“T/NM不匹配”度量 包括感兴趣区域聚类、体素阈值和深度学习等几种方法；(2)评价 T/NM不匹配与临床因素(包括横断面和纵向)的关系 T病理的认知、预后和进展)和(3)评估T/NM不匹配之间的关系 N的附加措施(包括结构N)。 无论有没有疾病改良型AD治疗，我们体内的分子神经成像 策略可能会使人们更全面地了解病变的定位和顺序 导致阿尔茨海默病的事件和对T的独特生物反应，包括功能恢复。

PROJECT SUMMARY The National Institute on Aging and Alzheimer’s Association recently proposed the ATN framework to systematically classify Alzheimer Disease (AD) in research studies and integrate standardized biomarkers with clinical assessment. The framework is based on binary designations of the presence or absence of amyloid (A), tau (T) and neurodegenerative (N) pathology, wherein AD is defined as A+/T+/N±. This biomarker discretization may simplify AD diagnosis and management but does not enable a more quantitative dissociation of T and N beyond the statement of T+/N±. We are interested in studying neuronal responses to T pathology, including susceptibility (T<N) and resilience (T>N). N has been defined to include structural volume loss (NS) on magnetic resonance imaging (MRI) and lower neuronal metabolism (NM) on positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG). There has not been substantial investigation comparing T with NM, which may necessitate a more nuanced, comparison beyond the binary descriptions. Here, we will utilize the AD Neuroimaging Initiative (ADNI) to investigate the match and mismatch of concomitant T and NM with 18F-flortaucipir and FDG PET. We aim to (1) define “T/NM mismatch” measures derived from several methods including region-of-interest clustering, voxelwise thresholding and deep learning, (2) evaluate the relationships between T/NM mismatch with clinical factors (including cross-sectional and longitudinal cognition, prognosis and progression of T pathology) and (3) assess relationships between T/NM mismatch additional measures of N (including structural NS). Whether in the presence or absence of disease-modifying AD therapy, our in vivo molecular neuroimaging strategy may empower a more comprehensive understanding of the localization and sequence of pathological events leading to AD and unique biological responses to T, including functional resilience.