在长时间的认知任务中保持注意力集中通常会引起高水平的心理疲劳。心理疲劳可以描述为一种主观疲惫的感觉，通常表现为对眼前任务参与感的降低，客观上体现为与任务相关的认知、行为执行能力和表现降低。为了有效地减少实际工作中由心理疲劳引起的不良后果，众多来自不同领域的研究者付出了持续不断的努力以进一步理解其潜在的神经机制。其中神经工程和人因工程领域的相关研究者提出了通过采用先进的脑影像技术手段，对心理疲劳产生过程中的神经活动变化进行定量分析，从而揭示其作用机理的研究思路。本文首先对有关心理疲劳的神经影像学研究成果进行了介绍，并结合脑连接图谱等新的影像学证据对这些研究中广泛使用的单变量分析方法的缺点进行了讨论。近10年来，越来越多的研究认为心理疲劳与大脑各区域之间功能连接的重组有关，而图论分析方法的提出也为定量分析功能连接重组提供了新的视角。针对这一新的研究趋势，本文较为全面地概述了心理疲劳的脑连接相关研究成果，归纳总结了多变量脑功能连接分析方法在心理疲劳神经机制研究中的意义。目前这一新兴研究领域的相关研究成果还相对较少，但脑连接组的应用不仅有助于阐明神经工效学这一新生领域中心理疲劳的潜在神经机制，而且在不久的将来有望实现心理疲劳的自动检测及分类，从而避免疲劳相关的不良后果。

Maintaining sustained attention during a prolonged cognitive task often comes at a cost: high levels of mental fatigue. Heuristically, mental fatigue refers to a feeling of tiredness or exhaustion, and a disengagement from the task at hand; it manifests as impaired cognitive and behavioral performance. In order to effectively reduce the undesirable yet preventable consequences of mental fatigue in many real-world workspaces, a better understanding of the underlying neural mechanisms is needed, and continuous efforts have been devoted to this topic. In comparison with conventional univariate approaches, which are widely utilized in fatigue studies, convergent evidence has shown that multivariate functional connectivity analysis may lead to richer information about mental fatigue. In fact, mental fatigue is increasingly thought to be related to the deviated reorganization of functional connectivity among brain regions in recent studies. In addition, graph theoretical analysis has shed new light on quantitatively assessing the reorganization of the brain functional networks that are modulated by mental fatigue. This review article begins with a brief introduction to neuroimaging studies on mental fatigue and the brain connectome, followed by a thorough overview of connectome studies on mental fatigue. Although only a limited number of studies have been published
thus far, it is believed that the brain connectome can be a useful approach not only for the elucidation of underlying neural mechanisms in the nascent field of neuroergonomics, but also for the automatic detection and classification of mental fatigue in order to address the prevention of fatigue-related human error in the near future.