Assessing ASL CBF as a biomarker for early Alzheimer's disease detection and disease progression

抽象的。阿尔茨海默病(AD)是一种致命的神经退行性疾病，影响数千万人 人民。AD的首要研究重点是寻找对早期疾病和疾病进展敏感的生物标记物 因为这可能会为寻找和评估有效的治疗方法提供最好的机会 目前无法治愈的脑部疾病。人脑的能量供应依赖于脑血流(CBF)。 清除废物。先前的研究表明，CBF减少会导致神经元不活动和神经毒性 废物堆积和随后的神经元死亡，最终可能导致AD痴呆。量测 CBF及其纵向走势可能为早期AD及其发病机制提供一个极具潜力的生物标志物 进步。动脉自旋标记(ASL)灌注MRI是一种无需使用 外源示踪剂。因为它相对便宜，而且可以重复多次，所以非常适合 纵向AD研究。ASL MRI对AD和先兆AD的敏感性已显示在许多 其他组和我们进行的横断面研究(将AD与对照组进行比较)。但仍有几个重要问题 仍然没有答案，包括ASL CBF对早期AD和AD进展的预测能力， 阿尔茨海默病患者脑血流量纵向变化规律及性别差异。解决这些问题需要大量的资金 由于信噪比(SNR)低，纵向数据的大小和分析ASL MRI数据的专业知识。这个 这个项目的目的是通过利用我们在ASL MRI方面的广泛专业知识来解决这些悬而未决的问题 以及ADNI(一项正在进行的大型多部位AD神经成像研究)积累的ASL纵向数据。这个 该项目的新信息或结果将包括ASL CBF对早期AD、CBF的预测能力 疾病进展或逆转时的变化率，AD及其进展中的性别影响，以及 基于深度机器学习的下一代ASL MRI处理算法及基于深度机器学习的AD 预测模型。我们将首先使用更大的样本和更新的样本来确认我们之前的ADNI ASL CBF结果 方法(之前不可用)。然后我们将检查ASL CBF对追踪和预测疾病的敏感性 进行性或认知性衰退。将明确检查性别对AD患者CBF的影响，这可能揭示 女性AD患病率较高的线索。我们将使用基于DL的ASL去噪来重新审视这些研究 算法。追求这些目标将有助于将ASL CBF建立为AD生物标志物，并提供多功能的AD 使用ASL CBF以及ADNI提供的其他有价值的生物标志物的预测模型。正在开发和 共享DL ASL MRI去噪方法，不仅有利于AD的研究，也有利于各种科学研究 基于ASL MRI的项目。这一创新但具有临床重要性的项目的可行性由我们的 数十年的经验和在研究的每个方面进行的大量试点调查，包括ASL MRI， AD ASL学习和机器学习。

ABSTRACT. Alzheimer’s Disease (AD) is a fatal neurodegenerative disease affecting tens of millions of people. A top research priority in AD is to find a biomarker sensitive to early disease and disease progression because that will likely provide the best opportunities for searching and evaluating effective treatments for this currently incurable brain disease. Human brain relies on cerebral blood flow (CBF) for its energy supply and waste removal. Previous research has indicated that CBF reductions cause neuron inactivity and neurotoxic waste accumulation and subsequently neuron death, which may eventually lead to AD dementia. Measuring CBF and following its longitudinal course may then provide a highly potential biomarker for early AD and its progression. Arterial spin labeling (ASL) perfusion MRI is a technique for quantifying CBF without using exogenous tracers. Because it is relatively cheaper and can be repeated many times, it is well suited for longitudinal AD research. Sensitivity of ASL MRI to AD and prodromal AD has been shown in many cross-sectional studies (comparing AD to controls) by other groups and us. But several important questions still remain unanswered including the prediction power of ASL CBF for early AD and AD progression, the longitudinal CBF change patterns, and sex difference of CBF in AD. Addressing those questions needs large size longitudinal data and expertise for analyzing ASL MRI data due to the low signal-to-noise-ratio (SNR). The purpose of this project is to address those open questions by leveraging our extensive expertise on ASL MRI and the accumulating longitudinal ASL data from ADNI (a large ongoing multi-site AD neuroimaging study). The novel information or outcome from this project will include the prediction power of ASL CBF for early AD, CBF change rate when disease progresses or reverts, gender effects in AD and its progression, and a next-generation ASL MRI processing algorithm based on deep-machine learning (DL), and a DL-based AD prediction model. We will first confirm our previous ADNI ASL CBF findings using larger sample and updated methods (not available before). We will then check sensitivity of ASL CBF for tracking and predicting disease progression or cognitive declines. Gender effects on CBF in AD will be explicitly examined, which may reveal a clue for the higher prevalence of AD in females. We will revisit those studies using the DL-based ASL denoising algorithm. Pursuing those aims will help establishing ASL CBF as an AD biomarker and provide a versatile AD prediction model using ASL CBF as well as other valuable biomarkers provided in ADNI. Developing and sharing the DL ASL MRI denoising method will benefit not only AD research but also the various scientific projects based on ASL MRI. The feasibility of this innovative but clinically important project is ensured by our decades of experience and the substantial pilot investigations in each aspect of the study, including ASL MRI, AD ASL study, and machine learning.