神经影像技术为轻度认知损害(MCI)和阿尔茨海默病(AD)早期诊断提供了新手段，对AD防治窗口前移具有重要意义。临床实践中需根据个体信息尽早确定其所处疾病状态(正常对照NC/稳定型MCI/进展型MCI/AD)，统计学上属于多分类诊断问题。当前基于机器学习的AD多分类诊断模型性能均较低，无法达到准确多分类的目的，纵向神经影像特征提取、特征选择与特征融合需要优化。在项目组前期采用机器学习进行NC/AD二分类诊断和纵向认知测评进行个体化AD风险预测基础上，本项目拟利用公共数据库ADNI，探讨通过卷积神经网络进行纵向神经影像特征提取与选择，多核学习进行不同模态特征融合；分别采用数据级、分类器级和组合级集成学习构建AD多分类诊断模型，并进行跨库验证考察其泛化能力；在广泛利用的、低成本的、无创性的认知测评基础上，融合神经影像数据，建立老年人认知衰退动态筛检策略，为AD超早期个性化监测提供统计决策支持。

The development of neuroimaging provides a new technology for the early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD), which is of great significance for the early prevention and treatment of AD. In real clinical applications, given neuroimaging data of an subject, he or she can be categorized into one of the following status, such as normal control (NC), stable MCI (sMCI), progressive MCI (pMCI) and AD. This obviously belongs to a multi-class classification problem in statistics. The AD multi-classification diagnosis models using machine learning have low performance and longitudinal neuroimaging features selection and fusion need to be improved. Based on binary diagnosis model between NC and AD using the single classifier and individual dynamic risk prediction of AD using longitudinal cognitive measures, this study aims: (1) to explore feature extraction, feature selection and feature fusion for longitudinal MRI and PET neuroimaging data by convolutional neural networks and multiple kernel learning respectively using the public database ADNI; (2) to build the personalized AD multi-classification diagnosis model by data level, classifier level, and combination level ensemble learning techniques and to compare classification performance of different classifiers by external data validation; (3) to establish a dynamic screening framework for cognitive decline among the community-dwelling elderly by integrating neuroimaging data with widely available, cost-effective and non-invasive cognitive measures. This study will provide statistical decision support for early dementia monitoring and individualized medicine.