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：
10660742
负责人：
Laila Rasmy Gindy Bekhet
金额：
$ 11.7万
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/10660742
关键词：
2019-nCoV Acute Admission activity All of Us Research Program Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Award Big Data Brain Brain imaging COVID-19 COVID-19 complications COVID-19 patient COVID-19 survivors Categories Cerebrospinal Fluid Classification Clinical Clinical Data Cognitive Communities Data Electronic Health Record Encephalopathies Event Genetic Genetic study Headache Health Knowledge Label Long COVID Medical Genetics Modeling Neural Network Simulation Neurologic Neurologic Symptoms Outcome Parents Participant Pathogenesis Patients Pattern Phase Phenotype Physicians Post-Acute Sequelae of SARS-CoV-2 Infection Prevention Reporting Research Research Personnel Resources Risk Structure Time Training United States United States National Institutes of Health Virus Work base biobank brain tissue clinical phenotype cohort common symptom coronavirus disease cytokine deep learning deep learning model endophenotype experience genetic association genetic information genome wide association study health data indexing insight neuroimaging parent grant post COVID-19 complications post-COVID-19 predict clinical outcome predictive modeling prevent public health relevance recurrent neural network supervised learning

项目摘要

Abstract There is still a lack of knowledge on the key genetic factors associated with post acute syndrome for COVID-19 patients, especially those related to neurological complications. In this study, we will utilize both the genetic and clinical data available through the All of Us researchers platform to study the genetic association with COVID-19 complications. In order to more accurately phenotype the patients based on their clinical trajectory mostly recorded in their electronic health records, we will utilize a pretrained deep learning model trained on more than four million patients from the N3C cohort. The pretrained model will be further fine-tuned on the All of US data, and will be used to phenotype the patients with genetic data. Further GWAS study will be performed to correlate between the deep learning based phenotype and the genetic information. Successful completion of this project will bring new insights to guide COVID-19 patients treatment to better prevent or manage further complications.

抽象的对于与 COVID-19 急性后综合症相关的关键遗传因素仍然缺乏了解患者，特别是那些与神经系统并发症相关的患者。在这项研究中，我们将利用遗传以及通过 All of Us 研究人员平台提供的临床数据，以研究与 COVID-19 并发症。为了根据患者的临床轨迹更准确地对患者进行表型分析大部分记录在他们的电子健康记录中，我们将利用经过预训练的深度学习模型 N3C 队列中超过 400 万患者。预训练模型将在 All 上进一步微调美国数据，并将用于通过遗传数据对患者进行表型分析。进一步的 GWAS 研究将执行以关联基于深度学习的表型和遗传信息。成功的该项目的完成将为指导 COVID-19 患者的治疗提供新的见解，以更好地预防或管理进一步的并发症。