基于独立成分的加权高阶脑网络的分类方法

Weighted High-order Brain Network Classification Method Based on Independent Component

为了解决传统的磁共振成像数据分类方法正确率较低的问题,提出了一种基于独立成分分析的高阶功能连接网络,使用加权图的频繁子图挖掘和判别性特征选择进行分类研究的方法。该方法不需要依赖先验的脑图谱模板,充分考虑了扫描时间内的时变特性,且将频繁子图挖掘应用到了加权图上。同时为了更准确地找到具有判别性的子图特征,也提出了几种新的判别性特征选择方法。结果发现基于独立成分的加权高阶脑网络的静息态功能磁共振成像分类方法有效地提高了阿尔兹海默症诊断的正确率。

In order to solve the problem that the traditional fMRI classification method has a low accuracy,this paper proposes a method that uses frequent subgraph mining and discriminative feature selection of weighted graphs for classification research based on independent component analysis and high-order functional connection network.This method does not need to rely on a priori brain tap template,but fully considers the time-varying characteristics in the scan time and applies frequent subgraph mining to the weighted map.At the same time,in order to more accurately find discriminative subgraph features,this paper also proposes several new discriminative feature selection methods.The results show that resting-state functional magnetic resonance imaging classification based on independent component and weighted high-order brain networks effectively improved the diagnostic accuracy of Alzheimer's disease.