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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：
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
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/10436262
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 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

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.

阿尔茨海默病（Alzheimer's disease， AD）的特点是认知和神经功能的进行性损害