多节点规模下的抑郁症功能脑网络分类研究

Classification of Multi-scale Functional Brain Network in Depression

脑网络作为复杂网络分析方法在神经影像领域的应用已得到广泛的认可。研究发现脑网络中的节点规模对网络的拓扑属性会产生重要的影响。利用静息态功能影像数据,在5种不同的节点规模下分别完成抑郁症患者和正常对照的脑网络构建,比较了网络拓扑属性的变化,并选择了4种不同的分类算法进行脑疾病分类研究。结果表明,网络节点数量不仅对拓扑属性产生了影响,而且对分类模型的构建也有直接作用。支持向量机(RBF核函数)模型在节点规模为250时,分类效果最好,平均正确率为83.18%。该研究结果在抑郁症的临床诊断中具有重要的应用价值,为基于脑网络的机器学习分类研究在网络节点规模的选择上提供了重要的参考依据。

As a complex network analysis method, brain network has been widely accepted in the field of neuroimaging. According to the research, the scale of nodes in the brain has a major impact on the network topological properties. This paper used the resting state functional imaging data to construct brain networks for patients and normal controls respectively under five different node scales and compared variances of the network topological properties, and then selected four different algorithms to do the classification. The results show that the node scale can not only affect the topological properties, but also has a direct effect on the construction of classification model. Support vector machine （RBF kernel function） model shows the best classification results when the node scale is 250, the average accuracy is 83.18%. The research results have an important application value in the clinical diagnosis of depression, and provide a significant reference basis on the network nodes＇ selection based on machine learning of brain network.