Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (Parent grant)

项目摘要 阿尔茨海默病（Alzheimer's disease，AD）是一种以认知和记忆功能进行性损害为特征的疾病 并且是老年痴呆症中最常见的形式。它影响了560万65岁以上的美国人， 对患者、护理人员和医疗资源提出了巨大且不断增长的要求， 这是我们这个时代最重要的公共卫生问题之一。尽管进行了广泛的研究， 对AD的生物学和病理生理学的理解仍然有限，阻碍了开发AD的进展。 治疗和预防策略。AD的遗传学研究已经成功地确定了40个新的基因座， 这些只能解释总体疾病风险的一小部分，这意味着有机会进行更多的发现。 先进的神经影像学是目前AD临床和研究调查的重要组成部分， 集中在相对较少的影像表型开发的神经放射科医生。然而，越来越多的 在大规模、高维多模态神经成像中利用高内容信息的兴趣 数据来鉴定新的AD生物标志物。深度学习（DL）方法，机器学习的新兴领域 研究，使用原始图像来导出成像内容的最佳矢量表示，其可用作 信息AD内表型。拟议补充的总体目标是对人工智能进行基准测试 我们正在开发一个标准化的神经成像数据集的算法。我们将研究两个主题：预测 与基线T1加权脑MRI相比的临床下降（预后）， 与脑MRI衍生的内表型相关的基因组序列数据。这是一个合作与 其他两个U01奖项，以提高严谨性和可重复性。我们将使软件工具和结果 公开可用。这将对更大的研究社区产生积极影响。

Project Summary Alzheimer’s disease (AD) is characterized by the progressive impairment of cognitive and memory functions and is the most common form of dementia in the elderly. It affects 5.6 million Americans over the age of 65 and exacts tremendous and increasing demands on patients, caregivers, and healthcare resources, making this condition among the most significant public health problems of our time. Despite extensive studies, our understanding of the biology and pathophysiology of AD is still limited, hindering advances in the development of therapeutic and preventive strategies. Genetic studies of AD have successfully identified 40 novel loci but these explain only a fraction of the overall disease risk, suggesting opportunities for additional discoveries. Advanced neuroimaging is an essential part of current AD clinical and research investigations, which generally focus on relatively few imaging phenotypes developed by neuro- radiologists. However, there is a growing interest in exploiting the high-content information in large-scale, high dimensional multimodal neuroimaging data to identify novel AD biomarkers. Deep learning (DL) methods, an emerging area of machine learning research, uses raw images to derive optimal vector representations of imaging contents, which can be used as informative AD endophenotypes. The overall goal of the proposed supplement is to benchmark the AI algorithms we are developing on a standardized neuroimaging dataset. We will work on two topics: Predicting clinical decline (prognosis) from baseline T1-weighted brain MRI, and Discovery of genetic loci in whole- genome sequence data associated with brain MRI-derived endophenotypes. This is a collaboration with the other two U01 awards to improve the rigor and reproducibility. We will make the software tools and results publicly available. This will positively impact the larger research community.