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

基本信息

批准号：
10212068
负责人：
MYRIAM FORNAGE
金额：
$ 140.63万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
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/10212068
关键词：
3-Dimensional Affect Aging Algorithms Alzheimer&apos 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 Graph 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 bioimaging bioinformatics tool 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 high dimensionality human old age (65+)imaging informatics imaging modality interest learning strategy loss of function multidisciplinary multimodality neural network neuroimaging novel radiologist therapeutic development trait vector whole genome

项目摘要

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) 的特点是认知和功能进行性损害记忆功能障碍，是老年人痴呆症最常见的形式。影响560万人 65岁以上的美国人对患者提出了巨大且不断增加的要求，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，我们对生物学的理解和 AD 的病理生理学仍然有限，阻碍了治疗和药物开发的进展预防策略。 AD 的遗传学研究已成功鉴定出 40 个新基因座，但这些基因座仅解释了总体疾病风险的一小部分，这表明有机会进行额外的研究发现。先进的神经影像学是当前 AD 临床和研究的重要组成部分研究，通常集中于由神经系统开发的相对较少的成像表型放射科医生。然而，人们对利用高内容信息越来越感兴趣。大规模、高维多模态神经影像数据来识别新型 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，它使用原始图像导出成像内容的最佳向量表示，可用作信息 AD内表型。为了克服传统上归因于深度学习的低可解释性，整个基因组序列数据提供了识别 DL- 背后的新基因的机会衍生成像内表型并测试它们与 AD 和 AD 相关特征的关联大队列样本。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、阿尔茨海默病测序项目 (ADSP)、阿尔茨海默病神经影像计划 (ADNI) 以及心脏和衰老队列基因组流行病学研究 (CHARGE) 联盟将由多学科研究小组。我们将从神经影像学中得出 AD 内表型英国生物银行中使用深度学习 (DL) 的数据。我们将确定相关的新遗传位点具有DL衍生的成像内表型并优化这些的共同遗传性使用英国生物银行遗传数据分析内表型与 AD 相关表型。我们将利用与 AD Consortia 的资源和合作以及深度学习衍生的神经影像的力量内表型来识别阿尔茨海默病和 AD 相关特征的新基因。另外，我们将开发基于深度学习的神经影像协调和插补方法并分发向研究界提供实施软件。我们期望发现相关的新基因 AD 的研究可能有助于了解 AD 的分子基础和潜在的新治疗方法。