Shi Yingchao, Zeng Weiming, Wang Nizhuan, Wang Shujiang, Huang Zhijian
Lab of Digital Image and Intelligent Computation, Shanghai Maritime University Shanghai, China.
Front Psychol. 2015 Jul 23;6:1030. doi: 10.3389/fpsyg.2015.01030. eCollection 2015.
Effective mental sub-health early warning mechanism is of great significance in the protection of individual mental health. The traditional mental health assessment method is mainly based on questionnaire surveys, which may have some uncertainties. In this study, based on the relationship between the default mode network (DMN) and the mental health status, we proposed a human mental sub-health early warning method by utilizing two-fold support vector machine (SVM) model, where seafarers' fMRI data analysis was utilized as an example. The method firstly constructed a structural-functional DMN template by combining the anatomical automatic labeling template with the functional DMN extracted by independent component analysis. Then, it put forward a two-fold SVM-based classifier, with one-class SVM utilized for the training of the initial classifier and two-class SVM utilized to refine the classification performance, to identify seafarers' mental health status by utilizing the correlation coefficients (CCs) among the areas of structural-functional DMN as the features. The experimental results showed that the proposed model could discriminate the seafarers with DMN function alteration from the healthy control (HC) effectively, and further the results demonstrated that when compared with the HC group, the brain functional disorders of the mental sub-healthy seafarers mainly manifested as follows: the functional connectivity of DMN had obvious alteration; the CCs among the different DMN regions were significant lower; the regional homogeneity decreased in parts of the prefrontal cortex and increased in multi-regions of the parietal, temporal and occipital cortices; the fractional amplitude of low-frequency fluctuation decreased in parts of the prefrontal cortex and increased in parts of the parietal cortex. All of the results showed that fMRI-based analysis of brain functional activities could be effectively used to distinguish the mental health and sub-health status.
有效的心理亚健康预警机制对保护个体心理健康具有重要意义。传统的心理健康评估方法主要基于问卷调查，可能存在一些不确定性。在本研究中，基于默认模式网络（DMN）与心理健康状况之间的关系，我们提出了一种利用双重支持向量机（SVM）模型的人类心理亚健康预警方法，其中以海员的功能磁共振成像（fMRI）数据分析为例。该方法首先通过将解剖自动标记模板与独立成分分析提取的功能DMN相结合，构建了一个结构 - 功能DMN模板。然后，提出了一种基于双重SVM的分类器，其中一类SVM用于训练初始分类器，二类SVM用于优化分类性能，通过利用结构 - 功能DMN区域之间的相关系数（CCs）作为特征来识别海员的心理健康状况。实验结果表明，所提出的模型能够有效地区分具有DMN功能改变的海员与健康对照组（HC），并且进一步的结果表明，与HC组相比，心理亚健康海员的脑功能障碍主要表现如下：DMN的功能连接有明显改变；不同DMN区域之间的CCs显著降低；前额叶皮层部分区域的局部一致性降低，顶叶、颞叶和枕叶皮层的多个区域增加；前额叶皮层部分区域的低频波动分数振幅降低，顶叶皮层部分区域增加。所有结果表明，基于fMRI的脑功能活动分析可有效地用于区分心理健康和亚健康状态。
