基于子图相似性的多动症患者脑网络分析

Brain Network Analysis of Patients with ADHD Based on Subnetwork Similarity

多动症会严重影响儿童发育,对多动症患者的有效诊断受到广泛关注。该文结合脑网络的拓扑结构信息和图上的信号,提出一种基于稀疏表示的图相似性计算方法,从微观到宏观分析脑区之间的差异。该方法使用Pearson相关系数构建全连通脑网络,基于稀疏表示从底层结构中提取节点子网络,根据图核函数计算子网络相似性,最后给出了脑网络相似性的全局指标。以受试者间的相似性作为特征在公共数据集ADHD-200上的分类实验结果表明,该方法能够以93.1%的准确度区分多动症患者和健康对照者,分类性能明显优于其他已有算法。此外,结果表明多动症患者在中央前回、丘脑、海马和脑岛等脑区之间有更强的连接。

Attention deficit hyperactivity disorder(ADHD)seriously affects children’s development,so extensive attention has been paid to its effective diagnosis.A new method for calculating graph similarity is proposed,which combines the topological information of brain networks with signals on the network.The Pearson correlation coefficient is used to construct the fully connected brain network.Based on the sparse representation,the node subnetwork is extracted from the underlying structure,and the similarity of the subnetwork is calculated according to the graph kernel function.Finally,the global index of brain network similarity is given.Experimental results of classifying ADHD-200 in the public dataset characterized by similarity between subjects show that the proposed method can distinguish ADHD patients and healthy people with 93.1%accuracy,and the classification performance is significantly superior than other existing methods.In addition,it is found that ADHD patients have stronger connections in brain regions,such as anterior central gyrus,thalamus,hippocampus and insula.