Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease

阿尔茨海默病深度学习神经影像内表型的遗传学

• 批准号: 10675679
• 负责人: MYRIAM FORNAGE
• 金额: $ 114.07万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10675679
• 关键词: 3-Dimensional; Affect; Aging; Algorithms; Alzheimer' s Disease; Alzheimer’s disease biomarker; American; Area; Back; Bioinformatics; Biology; Brain imaging; Caregivers; Clinical; Collaborations; Communities; Computer software; Data; Dementia; Elderly; Functional disorder; Genes; Genetic; Genetic study; Healthcare; Heart; Heritability; Image; Impaired cognition; Investigation; Label; Machine Learning; Maps; Memory; Mendelian randomization; Methods; Modeling; Molecular; Molecular Epidemiology; Neurodegenerative Disorders; Participant; Patients; Pattern; Phenotype; Prevention strategy; Public Health; Research; Research Personnel; Resources; Sampling; Source Code; Structure; Testing; Time; Training; Visualization; aging brain; biobank; bioinformatics tool; biomedical imaging; cognitive function; cohort; connectome; data analysis pipeline; data resource; data standards; deep learning; disorder risk; endophenotype; genetic architecture; genome wide association study; genome-wide; genomic epidemiology; genomic locus; graph neural network; high dimensionality; human old age (65+); imaging informatics; imaging modality; interest; learning strategy; loss of function; multidisciplinary; multimodal neuroimaging; neuroimaging; novel; therapeutic development; trait; vector; whole genome

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. To overcome the low interpretability traditionally attributed to DL, whole genome sequence data provide an opportunity to identify novel genes underlying the DL- derived imaging endophenotypes and test their association with AD and AD-related traits in large cohort samples. The proposed project will leverage existing neuroimaging and genetic data resources from the UK Biobank, the Alzheimer's Disease Sequencing Project (ADSP), the Alzheimer's Disease Neuroimaging Initiative (ADNI), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and will be conducted by a multidisciplinary team of investigators. We will derive AD endophenotypes from neuroimaging data in the UK Biobank using deep learning (DL). We will identify novel genetic loci associated with DL-derived imaging endophenotypes and optimize the co-heritability of these endophenotypes with AD-related phenotypes using UK Biobank genetic data. We will leverage resources and collaborations with AD Consortia and the power of DL-derived neuroimaging endophenotypes to identify novel genes for Alzheimer's Disease and AD-related traits. Also, we will develop DL-based neuroimaging harmonization and imputation methods and distribute implementation software to the research community. We expect to discover new genes relevant to AD which may leads to understanding of molecular basis of AD and potential new treatment.

阿尔茨海默病（AD）的特征在于认知和行为的进行性损害， 记忆功能减退，是老年痴呆症最常见的形式。影响560万人 65岁以上的美国人对病人的要求越来越高， 护理人员和医疗资源，使这种情况成为最重要的公众 我们这个时代的健康问题。尽管进行了广泛的研究，但我们对生物学和 AD的病理生理学仍然是有限的，阻碍了治疗和预防AD的发展。 预防战略。AD的遗传学研究已经成功地鉴定了40个新的基因座，但这些基因座 仅解释了整体疾病风险的一小部分，这表明有机会增加 发现。先进的神经影像学是目前AD临床和研究的重要组成部分 研究，这通常集中在相对较少的成像表型开发的神经， 放射科医生然而，人们越来越有兴趣利用高内容信息， 大规模、高维多模态神经成像数据来鉴定新的AD生物标志物。 深度学习（DL）方法是机器学习研究的一个新兴领域，使用原始图像 导出成像内容的最佳矢量表示，其可以用作信息 AD内表型。为了克服传统上归因于DL的低可解释性， 基因组序列数据提供了一个机会，以确定新的基因基础的DL- 衍生成像内表型，并测试它们与AD和AD相关性状的关联， 大队列样本。拟议的项目将利用现有的神经成像和遗传学 数据资源来自英国生物银行，阿尔茨海默病测序项目（ADSP）， 阿尔茨海默病神经影像学倡议（ADNI），以及心脏和衰老队列 基因组流行病学研究（CHARGE）财团，并将由 多学科的研究团队。我们将从神经影像学中推导出AD的内在表型 英国生物银行的数据使用深度学习（DL）。我们将鉴定出新的基因位点， 与DL衍生的成像内表型，并优化这些的协同遗传性 使用UK Biobank遗传数据的AD相关表型的内表型。我们将利用 与AD联盟的资源和合作以及DL衍生神经成像的力量 内表型，以确定阿尔茨海默病和AD相关性状的新基因。另外我们 将开发基于DL的神经成像协调和估算方法，并分发 软件开发到研究社区。我们希望能发现新的相关基因 这可能导致对AD的分子基础的理解和潜在的新治疗。