树结构组套索人脑超网络构建与分类

已有研究证明，基于连接多个节点的静息态脑功能超网络已成功应用于脑部精神疾病的诊断和分类研究。然而大多数超网络通过套索方法进行建模，但该方法不能解决脑部功能协同合作的问题。因而提出基于分组的超网络构建方法，但仍存在局限性：一方面它们多是基于自动分组，忽视了人脑中复杂的先验组结构；另一方面它们方法虽然组级上实现了预设组的考虑，但组间仍然是自动选择，导致缺乏先验知识的解释。考虑到该问题，本文提出树结构组套索方法进一步改善超网络的构建。首先使用树结构组套索方法来构建脑功能超网络。其次，对构建成的超网络进行局部聚类属性计算。然后对从超网络提取的特征中选取最具判别性的特征。最后，对选择出来的差异特征进行分类。实验结果表明，树结构组套索方法得到较好的分类性能，其分类准确率达到87.25%。

Construction and Classification of Human Hypernetwork with Tree Structured Group Lasso

Existing studies have proved that the resting state brain function hypernetwork based on connecting multiple nodes has been successfully applied to the diagnosis and classification of brain diseases. However, most of the hypernetworks are modeled by the Lasso method, but this method cannot solve the problem of brain function collaboration. Hence, the construction method of the hypernetwork based on group is proposed, but there are still limitations: on the one hand, they are mostly based on automatic grouping, ignoring the complex prior group structure in human brain; On the other hand, although they realize the consideration of preset groups at group level, they still choose automatically among groups, which leads to the lack of prior knowledge interpretation. Considering this problem, this paper proposes a 4-step construction of an improved the hypernetwork with a tree structured group lasso method: First, constructing the hypernetwork with the tree structured group lasso method; Secondly, calculating the local clustering attributes of the constructed hypernetwork. Then the most discriminative features are selected from the features extracted from the hypernetwork. At last, classifying the selected differences. The experimental results have shown that the tree structured group lasso method has good classification performance with an accuracy of 87.25%.