利用动态功能连接对健康危险性行为特征的预测

Prediction of the Health-Risk Behavior by Using Dynamic Functional Connectivity

为了研究健康危险性行为的脑网络特征,该文采集了49个被试的静息态功能磁共振数据。使用每一个对象动态功能连接网络的低频振荡振幅作为特征,利用支持向量回归对个体的健康危险行为进行预测。结果表明动态功能连接能较好地预测健康危险性行为特征,并提取了与之相关的功能连接模式,对预测有重要作用的连接绝大部分位于网络之间,且主要呈现为带状盖网络和额顶网络之间的连接,以及感觉运动网络与它们之间的连接相关。

In order to investigate the brain network characteristics of the health-risk behavior,we collected fMRI data of 49 subjects under rest state.The fluctuation amplitude of dynamic functional connectivity is used as the features of support vector regression(SVR)to predict the health-risk behavior.The results show a good correlation between spontaneous fluctuation of rest state and the health-risk behavior.Some informational functional connectivities could be used to predict the health-risk behavior and they mainly locate among the connections of networks:mainly cingulo-opercular network,frontoparietal network,sensorimotor network,etc..