摘要
为了提高静息态功能脑网络可信度,引用基于随机分块模型的网络重构方法对脑网络进行重构。通过网络指标的分析,验证该方法在脑网络中的适用性,找到网络中的虚假边;采用网络最大联通子集的方法来分析虚假边对网络连通性的影响。实验结果表明,该方法可用于脑网络的重构,通过重构可以找到影响脑网络连通性的虚假边,提高了连通性的可信度。
In order to improve the reliability of functional brain network in resting state,a method based on stochastic block model is introduced to reconstruct functional brain network in resting state.Network indexes are analyzed to verify whether the method is suitable for use in brain network.Spurious interactions of brain network are found,and the method of the biggest linking subset of brain network is used on analyze the influence of spurious interactions on network connectivity.The experimental results show that the method of construction can be used in functional brain network in resting state,and the reconstructed network improves the reliability of functional brain network in resting state.With the reconstruction spurious interactions influencing functional brain network can be found,and the removal of these spurious interactions can improve the reliability of network connectivity.
出处
《太原理工大学学报》
CAS
北大核心
2016年第2期218-222,共5页
Journal of Taiyuan University of Technology
基金
国家自然科学基金资助项目:抑郁症fMRI数据分析方法及辅助诊断治疗模型研究(61170136)
多模态脑功能复杂网络分析方法及应用研究(61373101)
抑郁症EEG功能脑网络构建及异常特征分析研究(61472270)
基于解剖距离及节点相似度的多尺度脑功能网络建模方法研究(61402318)
教育部高等学校博士学科点专项科研基金课题资助项目(20131402110006)
太原理工大学青年基金资助项目:抑郁症静息态功能脑网络拓扑属性差异分析研究(2012L014
2013T047)
关键词
重构
可信度
功能脑网络
随机分块模型
复杂网络
reconstruction
reliability
functional brain network
stochastic block model
complex network
