基于机器学习的脑网络分析方法及应用

脑网络学习旨在从整体上研究大脑各功能区的交互,对于人类深入了解大脑功能和结构以及对一些脑疾病的诊断都具有非常重要的作用。作为脑网络分析的重要工具,机器学习由于能够从数据中学习规律并对未知数据进行预测,已成为近年来脑网络分析领域一个新的研究热点。本文综述了近年来基于机器学习技术在脑网络分析中的典型研究方法和应用,主要从网络的构建、特征学习和分类预测等3个方面加以介绍。最后,总结全文并展望未来研究方向。

Machine-Learning-Based Brain Network Analysis: Method and Application

Brain network aims to study the interaction of brain functional regions as a whole system, which plays a very important role for the understanding of brain function and structure, as well as the diagnosis of some brain diseases. As an important tool to analyze brain networks, machine learning has become a new focus of research since, and it can obtain the rules via automatically analyzing data and apply these rules to predict the unknown data. This paper reviews the concepts, methods and applications of brain network analysis, and mainly discusses some related works based on machine learning techniques from the following three aspects, i.e., construction of bran network, feature learning and classification and prediction. Finally, The conclution is drawed, and some new directions for future research is forecasted.