心理不良已成为海员常见职业病。本项目基于功能磁共振成像(fMRI)和格兰杰因果模型(GCM)，研究海员脑功能有效连接检测模型及海员心理健康状况评估方法。主要内容包括：（1）开展fMRI信号去卷积运算研究，并针对静息态fMRI数据缺乏任务刺激先验信息的特点，发展基于信号波峰数据驱动的静息态fMRI信号盲去卷积模型；（2）开展GCM模型的非线性研究，分别引进多项式系数回归模型和神经网络模型，发展非线性GCM脑功能有效网络检测模型；（3）研究GCM动态有效连接检测方法，发展一种自适应滑动时间窗的GCM动态有效连接检测模型，更好地揭示大脑有效连接动态性模式；（4）研究一种基于fMRI-GCM分析框架下的海员心理健康状况精细分级评估系统，并在此基础上，进一步探索不同层级水平海员心理健康状况的变化发展趋势。本项目研究将为完善和发展脑功能有效连接检测方法、促进海员身心健康和航运作业安全产生积极意义。

Mental sub-health status has become a common occupational disease of seafarers. Based on functional Magnetic Resonance Imaging (fMRI) and Granger Causality Model (GCM), this project is to study effective connectivity detection models of seafarers' brain regions and assessment methods for the mental health status of seafarers. Main contents include: (1) studying on fMRI signal deconvolution algorithm. In light of the characteristics of lacking a priori information of stimulation in resting-state fMRI data, developing a data-driven model of blind deconvolution in resting-state fMRI signal based on the fMRI signal wave crest; (2) carrying out the nonlinear research of the GCM model, and developing nonlinear GCM models for brain effective connectivity network detection by introducing the polynomial coefficient regression model and the neural network model respectively ; (3) studying dynamic effective connectivity detection methods of GCM, and developing a method of adaptive sliding time window which better reveals dynamic models of brain effective connectivity; (4) Doing research on a fine grading evaluation system of seafarer psychological health under the framework of fMRI-GCM analysis, and on this basis, further exploring the change trend of seafarers mental health status among different hierarchical levels. This project will bring positive significance to improve and develop brain effective connectivity detection methods, and to promote the seafarers' physical and mental health and the safety of shipping operation.