Hybrid Functional Brain Network With First-Order and Second-Order Information for Computer-Aided Diagnosis of Schizophrenia
Hybrid Functional Brain Network With First-Order and Second-Order Information for Computer-Aided Diagnosis of Schizophrenia
复制标题
具有一阶和二阶信息的混合功能脑网络用于精神分裂症的计算机辅助诊断
DOI：
10.3389/fnins.2019.00603
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发表时间：
2019
影响因子：
4.3
通讯作者：
Daoqiang Zhang
中科院分区：
文献类型：
--
作者：
Qi Zhu;Huijie Li;Jiashuang Huang;Xijia Xu;Donghai Guan;Daoqiang Zhang
脑功能连接网络（BFCN）分析已被广泛应用于精神疾病的诊断，如精神分裂症。在BFCN方法中，脑网络的构建是核心任务之一，因为它对诊断结果有很大的影响。现有的BFCN构造方法大多只考虑了每对脑区之间存在的一阶关系，而忽略了有用的高阶信息，包括全脑的多区域相关性。一些早期精神分裂症患者的脑功能网络有细微的变化，这是传统的BFCN构建方法无法检测到的。众所周知，高阶方法通常比低阶方法对信号的细微变化更敏感。为了利用大脑区域之间的高阶信息，我们定义了三个大脑区域之间的三元组相关性，并基于每个三元组中的连接差异和顺序信息推导出二阶大脑网络。为了充分利用不同脑网络之间的互补信息，提出了一种混合融合一阶和二阶脑网络的方法。该方法用于识别精神分裂症的生物标志物。在6个精神分裂症数据集（共包括439名患者和426名对照）上的实验结果表明，该方法在精神分裂症的诊断中优于现有的脑网络方法。
Brain functional connectivity network (BFCN) analysis has been widely used in the diagnosis of mental disorders, such as schizophrenia. In BFCN methods, brain network construction is one of the core tasks due to its great influence on the diagnosis result. Most of the existing BFCN construction methods only consider the first-order relationship existing in each pair of brain regions and ignore the useful high-order information, including multi-region correlation in the whole brain. Some early schizophrenia patients have subtle changes in brain function networks, which cannot be detected in conventional BFCN construction methods. It is well-known that the high-order method is usually more sensitive to the subtle changes in signal than the low-order method. To exploit high-order information among brain regions, we define the triplet correlation among three brain regions, and derive the second-order brain network based on the connectivity difference and ordinal information in each triplet. For making full use of the complementary information in different brain networks, we proposed a hybrid approach to fuse the first- and second-order brain networks. The proposed method is applied to identify the biomarkers of schizophrenia. The experimental results on six schizophrenia datasets (totally including 439 patients and 426 controls) show that the proposed method outperforms the existing brain network methods in the diagnosis of schizophrenia.