Mapping the Causal Genetic-Imaging-Clinical Pathway for Alzheimer's Disease

绘制阿尔茨海默病的因果基因-成像-临床路径

• 批准号: 10719571
• 负责人: Bingxin Zhao
• 金额: $ 215.19万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10719571
• 关键词: Adolescent; Affect; Aging; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s Disease Pathway; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Alzheimer' s disease risk; American; Architecture; Behavioral; Biometry; Brain; Brain Diseases; Clinical; Clinical Data; Clinical Pathways; Cognitive; Communities; Companions; Complex; Computer software; Data; Data Analyses; Data Collection; Data Set; Development; Diagnosis; Dimensions; Disease; Disease Progression; Early Diagnosis; Environmental Risk Factor; Experimental Designs; Genes; Genetic; Genetic Risk; Genomics; Goals; Human; Image; Investigation; Joints; Knowledge Portal; Maps; Measures; Methodology; Methods; Modeling; Monte Carlo Method; Morbidity - disease rate; Neurodegenerative Disorders; Neurology; Neurosciences; Outcome; Participant; Pathway interactions; Pattern; Prevention approach; Problem Solving; Procedures; Public Health; Records; Registries; Reproducibility; Research; Risk Factors; Series; Severities; Statistical Methods; Substance Use Disorder; Targeted Research; Training; Variant; Wisconsin; aging brain; analytical method; analytical tool; automated analysis; biobank; clinical predictors; clinically significant; cognitive development; connectome; connectome data; cost; data integration; deep learning; design verification; disability; experience; genetic analysis; genetic architecture; genetic information; genomic biomarker; genomic data; health record; healthy aging; human old age (65+); imaging biomarker; imaging genetics; imaging study; improved; innovation; insight; interest; interoperability; mortality; multidisciplinary; neuropsychiatric disorder; novel; predictive modeling; prevent; response; risk prediction; secondary analysis; statistical learning; success; symptom treatment; targeted treatment; tool; trait; user friendly software; web site

Project Summary/Abstract Alzheimer's disease (AD) presents a massive public health burden, while resulting in significant morbidity and mortality. Recently, tremendous efforts have been taken to collect and analyze data from various pathophysio- logical levels and in different experimental paradigms, but it has been difficult to move from risk factors to causal mechanisms, which may lead to new treatments for slowing or stopping AD. In response to NOT-MH-21-175 on the use of human connectome data for secondary analysis, we will map the Causal Genetic-Imaging-Clinical (CGIC) pathway for AD through jointly harmonizing and analyzing genetic, imaging, and clinical data across multiple biomedical studies. To achieve this goal, we will develop Aim 1: a Robust Functional Connec- tome Analysis (RFCA) framework to uncover the genetic architecture of human brain function by extracting robust functional connectivity metrics at different levels; Aim 2: a CGIC framework to discover AD's causal pathways starting from genes to AD progression-sensitive image features to AD cognitive measures and diagnosis; and Aim 3: a CGIC prediction (CGIC-P) framework for AD risk prediction by using imaging genetic data integra- tion. Furthermore, in Aim 4, we will verify the efficacy of the newly developed statistical tools through extensive Monte Carlo simulations and solve problems with clinical significance by analyzing eight large-scale biomedi- cal studies. These studies include the UK Biobank (UKB) study, the Adolescent Brain Cognitive Development (ABCD) study, the Human Connectome Project (HCP), the IMAGEN study, the Alzheimer's Disease Neuroimag- ing Initiative (ADNI) study, the A4 study, the Open Access Series of Imaging Studies (OASIS) and the Wisconsin Registry for Alzheimer's Prevention (WRAP) study. The joint analysis of multi-type data, including imaging data, genetics/genomics data, health records, and cognitive information, from these studies will provide insight into the pathology of AD progression and healthy aging. The companion software (which will provide many needed analytic tools for the analysis of imaging and genetic data) and the knowledge portal will be disseminated to scientific community through our group BIG-S2's websites and NITRC. A variety of imaging genomic studies of neuropsychiatric disorders, neurodegenerative diseases, and substance use disorders, as well as normal brain development, will benefit from our novel analytical methods and framework. In addition to AD, a better under- standing of brain function and its genomic mechanisms, as well as the therapies used for AD, may inspire new and urgently required approaches to prevention, diagnosis, and treatment of other brain disorders as well.

项目总结/文摘