SCH: EAGER: New Approach: Early Diagnosis of Alzheimer's Disease Based on Magnetic Resonance Imaging (MRI) via High-Dimensional Image Feature Identification

阿尔茨海默病（AD）是一种进行性神经退行性痴呆，是老年人群中最常见的疾病之一。由于几个原因，强烈建议对阿尔茨海默病进行早期诊断。首先，它可以帮助显著减少AD造成的社会和经济影响，并使人们能够更好地管理和提前计划。其次，它可能为寻求早期治疗和干预的新科学方法的研究人员提供更多信息。然而，在目前的临床实践中，早期诊断往往是一个挑战。虽然医院定期收集神经影像学数据，但放射科医生很难手动读取高维图像数据进行分析和解释。该项目提出了未经测试但具有潜在变革性的研究方法，以识别基于磁共振成像（MRI）的AD早期诊断的高维图像特征。该项目将推动机器学习、优化、统计学、图像科学和生物信息学的研究，并有可能用于解决除脑图像之外的其他高维图像。该项目还通过跨学科研究、培训和教育产生更广泛的影响。本项目有以下两个目标：1)开发基于稀疏编码的算法来识别结构MRI图像的特征，用于对AD患者和其他诊断组进行分类。这将使得区分AD患者、轻度认知障碍（MCI）个体或健康个体的图像的关键结构特征得以识别。2)开发基于张量Tucker核心分解的优化和机器学习算法，用于纵向功能MRI图像的高维图像标记检测。这种方法可以降低从AD患者的纵向MRI图像中检测标记物的计算复杂度。预计所开发的算法将增强高维神经影像学标记物的检测和诊断分类。该研究项目如果成功，将为临床医生提供来自更大人群的信息，并显著减轻放射科医生的负担，从而极大地影响目前的AD诊断实践。

Alzheimer's disease (AD) is a form of progressive neurodegenerative dementia and one of the most common diseases in the aging population. Early diagnosis of AD is strongly recommended for several reasons. First, it can helps to significantly reduce the social and economic impacts caused by AD and allow people to better manage and plan ahead. Second, it may provide more information for researchers seeking new scientific approaches for early treatment and intervention. However, in current clinical practice early diagnosis is often a challenge. While neuroimaging is routinely collected in hospitals, it is very hard for radiologists to manually read the high-dimensional image data for analysis and interpretation. This project proposes untested but potentially transformative research approaches to identify high-dimensional image features for AD early diagnosis based on Magnetic Resonance Imaging (MRI). This project will advance the research in machine learning, optimization, statistics, image science and bioinformatics, and potentially be used to address other high-dimensional images besides brain images. The project also has broader impacts through cross-disciplinary research, training and education. This project has the following two aims: 1) Develop sparse coding based algorithms to identify features of structural MRI images for classifying AD patients and other diagnostic groups. This will allow the key structural features of images that separate AD patients, individuals with mild cognitive impairment (MCI) or healthy individuals to be identified. 2) Develop optimization and machine learning algorithms based on tensor Tucker core decomposition for high-dimensional image-marker detection from longitudinal functional MRI images. This approach should reduce the high computational complexity of marker detection from the longitudinal MRI images of AD patients. It is anticipated that the developed algorithms will enhance high-dimensional neuroimaging marker detection and diagnostic classification. This research project, if successful, will greatly impact the current practice of AD diagnosis by providing clinical doctors with the information from a larger population and also significantly easing the burden of radiologists.