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

阿尔茨海默氏病深度学习衍生的神经影像内表型的遗传学（家长资助）

基本信息

批准号：
10827718
负责人：
MYRIAM FORNAGE
金额：
$ 38.06万
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/10827718
关键词：
Administrative Supplement Affect Alzheimer&apos s Disease Alzheimer’s disease biomarker American Architecture Area Award Benchmarking Biology Caregivers Clinical Clinical Trials Collaborations Communities Computer software Coupled Data Data Set Dementia Elderly Functional disorder Funding Genetic Genetic Markers Genetic study Goals Grant Graph Healthcare Heritability Image Impaired cognition Investigation Link Machine Learning Magnetic Resonance Imaging Memory Methods Modeling Outcome Measure Parents Patients Phenotype Prevention strategy Process Prognosis Public Health Reproducibility Research Resources Software Tools Standardization Suggestion Testing Time U-Series Cooperative Agreements United States National Institutes of Health Work artificial intelligence algorithm biobank brain magnetic resonance imaging clinical predictors data standards deep learning disorder risk endophenotype genetic architecture genetic association genome wide association study genomic locus high dimensionality human old age (65+)imaging genetics improved interest learning strategy multimodal neuroimaging neural neuroimaging novel outcome prediction parent grant programs response synergism therapeutic development trait vector whole genome

项目摘要

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.

项目概要阿尔茨海默病（AD）的特点是认知和记忆功能进行性损害它是老年人中最常见的痴呆症，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不断增长的要求，使得尽管进行了广泛的研究，但我们仍然认为这是我们这个时代最重要的公共卫生问题之一。对 AD 的生物学和病理生理学的了解仍然有限，阻碍了发展的进步 AD 的治疗和预防策略的遗传学研究已成功鉴定出 40 个新基因座，但这些仅解释了总体疾病风险的一小部分，表明有更多发现的机会。先进的神经影像学是当前 AD 临床和研究的重要组成部分，通常关注神经放射学家开发的相对较少的成像表型，但是，这种成像表型正在不断增长。对利用大规模、高维多模态神经成像中的高内容信息感兴趣数据来识别新型AD生物标志物深度学习（DL）方法，这是机器学习的一个新兴领域。研究，使用原始图像来导出成像内容的最佳矢量表示，可以用作所提出的补充的总体目标是对人工智能进行基准测试。我们正在标准化神经影像数据集上开发的算法我们将致力于两个主题：预测。与基线 T1 加权脑 MRI 相比，临床衰退（预后），以及发现整体遗传位点与大脑 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奖项旨在提高软件工具和结果的严谨性和可重复性。这将对更大的研究界产生积极影响。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study.

YouTube 上阿拉伯语 COVID-19 疫苗信息的传播：一项网络曝光研究。

DOI：
10.1177/20552076231205714
发表时间：
2023-01
期刊：
DIGITAL HEALTH
影响因子：
3.9
作者：
Zeid, Nour;Tang, Lu;Amith, Muhammad Tuan
通讯作者：
Amith, Muhammad Tuan

Mining the Metabolic Capacity of Clostridium sporogenes Aided by Machine Learning.

机器学习辅助挖掘产孢梭菌的代谢能力。

DOI：
10.1002/anie.202319925
发表时间：
2024
期刊：
Angewandte Chemie (International ed. in English)
影响因子：
0
作者：
Ouyang,Huanrong;Xu,Zhao;Hong,Joshua;Malroy,Jeshua;Qian,Liangyu;Ji,Shuiwang;Zhu,Xuejun
通讯作者：
Zhu,Xuejun

Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts.