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

项目摘要 国家老龄化研究所和阿尔茨海默氏症协会最近提出了ATN框架， 在研究中对阿尔茨海默病（AD）进行系统分类，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命名， tau（T）和神经变性（N）病理学，其中AD定义为A+/T+/N±。这种生物标志物离散化 可以简化AD的诊断和管理，但不能实现T和N的更定量分离 超越了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<N）和弹性 （T>N）。N已被定义为包括磁共振成像（MRI）上的结构体积损失（NS）， 用18F-氟脱氧葡萄糖（FDG）在正电子发射断层扫描（PET）上显示低神经元代谢（NM）。 还没有大量的调查比较T与NM，这可能需要一个更微妙的， 超越二进制描述的比较。 在这里，我们将利用AD神经影像学倡议（ADNI）来调查伴随的匹配和不匹配。 用18F-flortaucipir和FDG PET进行T和NM。我们的目标是（1）定义“T/NM不匹配”的措施， 几种方法，包括感兴趣区域聚类，体素阈值和深度学习，（2）评估 T/NM不匹配与临床因素（包括横断面和纵向）的关系 认知、预后和T病理学进展）和（3）评估T/NM错配之间的关系 N的附加措施（包括结构NS）。 无论是否存在改善疾病的AD治疗，我们的体内分子神经成像 策略可以使更全面地了解病理性的定位和序列， 导致AD的事件和对T的独特生物学反应，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.