基于动态脑功能网络的心理疲劳神经机理研究

With the accelerating pace of life and gradually violent competition, mental fatigue has become an inevitable problem lingering in the modern society, seriously affecting the efficiency and safety of work as well as physical and mental health. Due to these undesirable consequences, great attempts have been made to unmask the underlying neural mechanism of mental fatigue. It is noteworthy that various fatigue-inducing paradigms have been introduced in the pioneering fatigue studies and the findings across these studies are far from conclusive. Moreover, most of the fatigue-related studies applied univariate approach on the obtained brain signals, neglecting the important connection information. Recent conceptualizations suggest that the human brain forms a large-scale network of interconnected regions within the human connectome that provides the substrate for neural communication and functional processing, thereby leading new insights for fatigue studies. In the present proposal, the principle investigator would introduce a novel analysis framework for quantitative assessment of dynamic brain networks and applied it for investigating the neural mechanism of mental fatigue. Particularly, the principle investigator would extend previous work to optimize the construction method of EEG-based dynamic functional connectivity and dynamic brain network; as well as to obtain the evolution model of dynamic brain network with the time-on-task effect. Once the dynamic brain networks were obtained, we would then define new temporal domain parameters and develop advance quantitative assessment metrics for better representing the spatio-temporal architecture of the dynamic brain networks. Finally, through applying this novel analysis framework in mental fatigue experiments with different fatigue-inducing paradigms, we would provide some of the first quantitative and comprehensive insights into the neural mechanism of mental fatigue. We believe the findings of the present proposal would significantly increase our understanding of the complex nature of the neural mechanism of mental fatigue, facilitate the following research and development of portable/wearable fatigue monitoring equipment, reinforce the idea of proper arrangement of work and rest, improve the efficiency and safety of work, and maintain physical and mental health.

当今社会，心理疲劳已经成为现代人挥之不去的共性问题，严重影响工作效率，危害生产生活安全和身心健康。然而受限于疲劳实验的多样性和数据分析方法的滞后性，传统基于单一疲劳诱发范式的心理疲劳研究对于揭示心理疲劳复杂的神经机理显得越来越力不从心。在前期工作的基础上，本项目提出采用先进的动态脑功能连接模型模拟心理疲劳的演化过程，通过定量网络分析方法对心理疲劳的神经机理进行研究。首先，优化现有的基于脑电的动态功能连接和动态脑网络的构建方法，获取心理疲劳状态下动态脑功能网络演化模型；其次，针对动态脑功能网络所增加的额外时间维度信息定义新的网络参数，开发新的定量分析方法；最后，通过先进的动态脑功能网络分析方法寻找不同的心理疲劳范式的作用规律，进一步揭示心理疲劳的复杂神经机理。该项目的研究成果将极大的增加我们对于心理疲劳神经机理的了解，对于后续开展疲劳监测设备开发、保证安全高效生产和维持身心健康具有重要意义。

当今社会，心理疲劳已经成为现代人挥之不去的共性问题，严重影响工作效率，危害生产生活安全。如何通过创新的神经工程手段揭示心理疲劳的神经机制对于探索心理疲劳快速恢复方案以及开发自动检测系统有着重要意义。本项目通过优化现有的动态脑功能连接和动态脑网络的构建方法获取心理疲劳状态下脑功能网络演化模型；并针对其三维时空特性开发新的定量分析方法，通过定量分析不同心理疲劳范式的神经活动规律从而揭示心理疲劳复杂的神经机制；在此基础上结合穿戴式系统以及虚拟现实技术开发疲劳自动检测技术。相关技术的成熟与应用将会极大地提升生产效率，降低因为心理疲劳导致的安全生产事故。.本研究重要结果包括：1）设计了多种疲劳诱发范式，通过行为学表征验证了持续注意认知任务与低负荷模拟驾驶任务诱发心理疲劳的有效性；2）结合短时滑动窗与频带功能连接技术构建了动态脑功能网络，通过重新定义时域路径定量分析清醒状态与疲劳状态下动态脑功能网络的三维时空结构特征，发现随着疲劳程度的加深，网络的三维时空信息传输效率呈现显著降低趋势，从而将心理疲劳的脑功能连接异常假说拓展到动态脑功能网络连接；3）通过脑功能连接构建的心理疲劳特征与机器学习算法结合，构建了疲劳自动检测模型并成功地在多种心理疲劳范式下验证了其有效性；4）开发了一款融合穿戴式脑电采集设备与数据驱动疲劳检测模型的驾驶疲劳检测系统，实现了系统的便携性与有效性。.综上，本项目资助期间完成了申请书种提出的研究内容，解决了心理疲劳复杂神经机理解析、心理疲劳动态脑功能网络演化模型以及动态脑功能网络连接和脑网络定量分析算法开发关键科学问题，达到了预期研究目标。本研究深入探索了多种疲劳诱发范式下心理疲劳的复杂神经机制，构建了一系列用于定量分析动态脑功能网络的分析算法，实现了心理疲劳的精准识别，研究成果发表于领域权威期刊并申请专利多项。相关技术正在积极尝试进行产业化推广。