Wang Ying-Fang, Huang Yuan, Chang Xiao-Yi, Guo Si-Ying, Chen Yu-Qi, Wang Ming-Zhu, Liu Kai-Le, Huang Fang-Fang
Department of Preventive Medicine, College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang, China.
Front Neurol. 2025 May 22;16:1581105. doi: 10.3389/fneur.2025.1581105. eCollection 2025.
Effective connectivity (EC) refers to the directional influences or causal relationships between brain regions. In the field of artificial intelligence, machine learning has demonstrated remarkable proficiency in image recognition and the complex dataset analysis. In recent years, machine learning models leveraging EC have been increasingly used to classify neurodegenerative diseases and differentiate them from healthy controls. This review aims to comprehensively examine research employing EC-derived from techniques such as functional magnetic resonance imaging, electroencephalography, and magnetoencephalography-in conjunction with machine learning methods to classify neurodegenerative diseases.
We conducted a literature search in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, collecting articles published prior to June 13, 2024, from the PubMed and Embase databases.
We selected 16 relevant studies based on predefined inclusion criteria: six focused on Alzheimer's disease (AD), six on mild cognitive impairment (MCI), one on Parkinson's disease (PD), two on both AD and MCI, and one on both AD and PD. We summarized the methods for EC feature extraction and selection, the application of classifiers, validation techniques, and the accuracy of the classification models.
The integration of EC with machine learning techniques has demonstrated promising potential in the classification of neurodegenerative diseases. Studies have shown that combining EC with multimodal features such as functional connectivity offers novel approaches to enhancing the performance of classification models.
有效连接性（EC）是指脑区之间的定向影响或因果关系。在人工智能领域，机器学习在图像识别和复杂数据集分析方面已展现出卓越的能力。近年来，利用有效连接性的机器学习模型越来越多地被用于对神经退行性疾病进行分类，并将其与健康对照区分开来。本综述旨在全面考察运用源自功能磁共振成像、脑电图和脑磁图等技术的有效连接性，并结合机器学习方法对神经退行性疾病进行分类的研究。
我们按照系统评价和荟萃分析的首选报告项目（PRISMA）指南进行文献检索，从PubMed和Embase数据库收集2024年6月13日前发表的文章。
我们根据预定义的纳入标准选择了16项相关研究：6项聚焦于阿尔茨海默病（AD），6项聚焦于轻度认知障碍（MCI），1项聚焦于帕金森病（PD），2项同时涉及AD和MCI，1项同时涉及AD和PD。我们总结了有效连接性特征提取和选择的方法、分类器的应用、验证技术以及分类模型的准确性。
有效连接性与机器学习技术的整合在神经退行性疾病分类方面已显示出有前景的潜力。研究表明，将有效连接性与功能连接性等多模态特征相结合，为提高分类模型的性能提供了新方法。
