基于神经影像的复杂脑网络技术用于阿尔兹海默症的研究进展

近年来，人工智能技术被广泛地应用于阿尔兹海默症（AD）的计算机辅助诊断和疾病机理研究，基于图论的复杂网络分析技术是其中一种常见的数据挖掘方法，通过结构磁共振、功能磁共振和PET等神经成像手段获取多模态脑影像信息，结合复杂网络分析方法发现，AD患者大脑结构/功能网络存在拓扑异常改变。这一发现为AD的早期诊断和机理研究提供了新思路。笔者综述了复杂网络分析技术在AD脑结构和功能影像中的临床应用现状，并对其发展趋势进行了展望。

Advances in the study of complex brain network based on neuroimaging in Alzheimer's disease

Artificial intelligence techniques have been widely applied in computer-aided diagnosis and disease mechanism studies for Alzheimer's disease (AD). Graph-based complex network analysis is one of the common data mining methods. A combination of complex network analysis technology and multimodal brain imaging information from neuroimaging methods, such as structural magnetic resonance imaging, functional magnetic resonance imaging, and positron emission computer imaging, could identify the abnormal changes of topological properties in brain structure and functional networks. This result provided new ideas on achieving early diagnosis and mechanism research in patients with AD. In this paper, the clinical application of complex network analysis method in structure and functional AD brain imaging was discussed, and its development trend was prospected.