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: 10436262
• 负责人: MYRIAM FORNAGE
• 金额: $ 113.05万
• 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/10436262
• 关键词: 3-Dimensional; Affect; Aging; Algorithms; Alzheimer' s Disease; Alzheimer’s disease biomarker; American; Area; Back; Bioinformatics; Biology; Brain imaging; Caregivers; Clinical Research; 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; Supervision; Testing; Time; Training; Visualization; aging brain; base; 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; radiologist; 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.

阿尔茨海默氏病（AD）的特征是认知和 记忆功能，是老年痴呆症最常见的形式。它影响560万 65岁以上的美国人，对患者的巨大和需求越来越大， 护理人员和医疗保健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，我们对生物学和 AD的病理生理学仍然有限，阻碍了治疗性发展的进步 预防策略。 AD的遗传研究成功鉴定了40个新的基因座，但是这些 仅解释总体疾病风险的一小部分，这表明机会额外 发现。高级神经影像学是当前AD临床和研究的重要组成部分 研究通常集中于相对较少的成像表型，由神经 - 放射科医生。但是，对利用高含量信息的兴趣越来越 大规模的高维多模式神经影像学数据，以识别新型的AD生物标志物。 深度学习方法（DL）方法是机器学习研究的新兴领域，使用原始图像 得出成像内容的最佳矢量表示，可以用作信息丰富 广告内型。为了克服传统上归因于DL的低解释性 基因组序列数据提供了一个机会，可以鉴定DL-的基因的新基因 衍生成像内表型并测试其与AD和与AD相关性状的关联 大型队列样品。拟议的项目将利用现有的神经影像和遗传 来自英国生物银行的数据资源，阿尔茨海默氏病测序项目（ADSP）， 阿尔茨海默氏病神经影像学倡议（ADNI）和心脏和衰老的同龄人 基因组流行病学（电荷）联盟的研究，将由A进行 调查人员的多学科团队。我们将从神经影像中得出广告型内表型 英国生物库的数据使用深度学习（DL）。我们将确定新颖的遗传基因座相关 使用DL衍生的成像内表型并优化了这些的共同性 使用UK Biobank遗传数据具有与广告相关表型的内型型。我们将利用 与广告联盟的资源和合作以及DL衍生的神经影像的力量 内表型鉴定阿尔茨海默氏病和广告相关性状的新型基因。另外，我们 将开发基于DL的神经影像学协调和插补方法并分发 向研究社区实施软件。我们希望发现新基因相关 可能导致对AD分子基础和潜在新处理的AD。