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

  • 摘要: 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。

     

    Abstract: 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..

     

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