基于最小生成树的MCI脑网络分类

摘要

为更好地将脑网络的拓扑属性应用于轻度认知障碍的分类研究中,提出利用最小生成树构造无偏差脑网络,通过其拓扑属性准确刻画网络之间的差异,避免传统网络中连接强度带来的影响.分别提取早期轻度认知障碍、晚期轻度认知障碍和正常老年人这3组被试的无权网络和最小生成树的拓扑属性作为分类特征,使用支持向量机进行分类研究.实验结果表明,基于最小生成树的分类方法比传统无权网络具有更好地分类效果,表明最小生成树能更准确度量脑网络的结构变化,可以用于阿尔兹海默病的早期辅助诊断.%To improve the classification performance of mild cognitive impairment (MCI) based on brain network topological,the minimum spanning tree (MST) method was proposed to construct unbiased brain network,which accurately described the difference between networks through its topological property,and avoided the influence of connection strength in the traditional net work.The classification characteristics of early MCI,late-MCI and normal control in the un-weighted brain network and MST were extracted and classified using support vector machine (SVM).Experimental results show that the classification method based on the MST has better effects than the traditional un-weighted network,which indicated that the MST can measure the structural changes of the brain network more accurately,which can be used in the early diagnosis of Alzheimer's disease.