载脂蛋白Eε4与ε2等位基因相反调控晚发性阿尔茨海默病发病风险的神经网络机制研究

项目名称： 载脂蛋白Eε4与ε2等位基因相反调控晚发性阿尔茨海默病发病风险的神经网络机制研究

项目编号： No.81500919

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 医药、卫生

项目作者： 束昊

作者单位： 东南大学

项目金额： 17.5万元

中文摘要： 载脂蛋白E（APOE）ε4等位基因是目前唯一确证的晚发性阿尔茨海默病（LOAD）遗传风险因子，APOEε2等位基因是减少LOAD发病风险的保护因子。神经网络损害是联系LOAD病理改变与临床症状的关键环节。为充分研究APOEε4与ε2等位基因相反调控LOAD发病风险的神经机制，明确LOAD超早期诊断的神经影像特征，本项目将基于研究组前期LOAD遗传影像学研究，参考APOE等位基因检测结果进一步扩充遗忘型轻度认知损害（aMCI）临床数据库；采用多模态MRI影像扫描分析技术联合既有研究样本，在正常对照与aMCI组内或组间分析各APOE等位基因脑功能与结构网络特征；紧密结合多维度认知神经心理评估，解析各APOE等位基因认知功能特征及其神经网络基础；同时分析年龄对各APOE等位基因神经网络效应的调控作用，为后续基于发病风险程度分层探寻LOAD影像学预测标记与干预靶点提供依据。

中文关键词： 阿尔茨海默病；载脂蛋白E；遗传影像学；功能磁共振成像；脑网络

英文摘要： The apolipoprotein E (APOE) ε4 allele is the only established late-onset Alzheimer's disease (LOAD) genetic risk factor, while the APOE ε2 allele is a protective factor against LOAD onset. The neural network disruption is the key mediator linking LOAD pathologies and clinical symptom. This project will recruit amnestic mild cognitive impairment (aMCI) patients with APOE genotype reference to enlarge our research group’s clinical dataset of aMCI, in order to investigate the mechanism of opposite modulations between APOE ε4 and ε2 alleles on LOAD onset at neural network level thoroughly and facilitate specifying LOAD neuroimaging characteristic at ultra-early stage. First, we will employ multi-modal MRI scan and analysis approach to investigate each APOE allele group’s structural and functional network characteristic in normal control and aMCI groups respectively, combining with current dataset. Second, we will analyze each APOE allele group’s cognitive function profile and its neural network substrate, by integrating neural network analysis with multi-dimension neuropsychological assessment. Third, we will investigate age modulation on each APOE allele’s neural network profile. In all, this project will provide evidence on exploring LOAD imaging marker and intervention target for different LOAD risk subjects in future.

英文关键词： Alzheimer's disease;apolipoprotein E;imaging genetics;functional MRI;brain network