Liu Zhian, Zhang Ming, Xu Gongcheng, Huo Congcong, Tan Qitao, Li Zengyong, Yuan Quan
Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, China.
Interdisciplinary Division of Biomedical Engineering, Faculty of Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong.
Front Behav Neurosci. 2017 Oct 31;11:211. doi: 10.3389/fnbeh.2017.00211. eCollection 2017.
Driving a vehicle is a complex activity that requires high-level brain functions. This study aimed to assess the change in effective connectivity (EC) between the prefrontal cortex (PFC), motor-related areas (MA) and vision-related areas (VA) in the brain network among the resting, simple-driving and car-following states. Twelve young male right-handed adults were recruited to participate in an actual driving experiment. The brain delta [HbO] signals were continuously recorded using functional near infrared spectroscopy (fNIRS) instruments. The conditional Granger causality (GC) analysis, which is a data-driven method that can explore the causal interactions among different brain areas, was performed to evaluate the EC. The results demonstrated that the hemodynamic activity level of the brain increased with an increase in the cognitive workload. The connection strength among PFC, MA and VA increased from the resting state to the simple-driving state, whereas the connection strength relatively decreased during the car-following task. The PFC in EC appeared as the causal target, while the MA and VA appeared as the causal sources. However, l-MA turned into causal targets with the subtask of car-following. These findings indicate that the hemodynamic activity level of the cerebral cortex increases linearly with increasing cognitive workload. The EC of the brain network can be strengthened by a cognitive workload, but also can be weakened by a superfluous cognitive workload such as driving with subtasks.
驾驶车辆是一项复杂的活动，需要高级脑功能。本研究旨在评估静息、简单驾驶和跟车状态下大脑网络中前额叶皮层（PFC）、运动相关区域（MA）和视觉相关区域（VA）之间有效连接性（EC）的变化。招募了12名年轻右利手男性成年人参与实际驾驶实验。使用功能近红外光谱（fNIRS）仪器连续记录大脑的δ[HbO]信号。采用条件格兰杰因果关系（GC）分析来评估EC，这是一种数据驱动的方法，可探索不同脑区之间的因果相互作用。结果表明，大脑的血流动力学活动水平随认知负荷的增加而升高。PFC、MA和VA之间的连接强度从静息状态到简单驾驶状态增加，而在跟车任务期间连接强度相对降低。在EC中，PFC表现为因果目标，而MA和VA表现为因果源。然而，在跟车子任务中，左MA转变为因果目标。这些发现表明，大脑皮层的血流动力学活动水平随认知负荷的增加呈线性增加。大脑网络的EC可因认知负荷而增强，但也可因多余的认知负荷（如执行子任务驾驶）而减弱。
