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

项目摘要 阿尔茨海默病(AD)的特征是认知和记忆功能的进行性损害 是老年人中最常见的痴呆症形式。它影响着560万65岁以上的美国人 对患者、护理人员和医疗保健资源提出了巨大且日益增长的要求，使得 这种情况是我们这个时代最严重的公共卫生问题之一。尽管进行了广泛的研究，我们的 对阿尔茨海默病的生物学和病理生理学的了解仍然有限，阻碍了发展的进展 治疗和预防策略。阿尔茨海默病的遗传学研究已成功识别出40个新的基因座，但 这些只解释了总体疾病风险的一小部分，表明有机会进行更多的发现。 高级神经成像是当前AD临床和研究研究的重要组成部分，通常情况下 专注于神经放射科医生开发的相对较少的成像表型。然而，有一个不断增长的 在大规模、高维多模式神经成像中开发高内容信息的兴趣 识别新的AD生物标记物的数据。深度学习方法--机器学习的一个新兴领域 研究，使用原始图像来获得成像内容的最佳矢量表示，这可以用作 信息丰富的AD内表型。拟议补充方案的总体目标是对人工智能进行基准测试 我们正在标准化的神经成像数据集上开发算法。我们将致力于两个主题：预测 临床(预后)较基线T1加权脑MRI下降，并发现整个 与脑MRI衍生的内表型相关的基因组序列数据。这是与 另外两个U01奖项，以提高严格性和重复性。我们会把软件的工具和结果 向公众开放。这将对更大的研究界产生积极影响。

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.