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疲劳驾驶中的脑网络变化:基于有效连接分析和活动记录仪数据的真实环境纵向研究

Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data.

作者信息

Fonseca André, Kerick Scott, King Jung-Tai, Lin Chin-Teng, Jung Tzyy-Ping

机构信息

Center of Mathematics, Computation and Cognition, Federal University of ABC, São Paulo, Brazil.

Swartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, CA, United States.

出版信息

Front Hum Neurosci. 2018 Nov 12;12:418. doi: 10.3389/fnhum.2018.00418. eCollection 2018.

Abstract

The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.

摘要

对驾驶过程中神经生理变化的分析可以阐明疲劳的机制,疲劳被认为是车辆事故的一个重要原因。警觉性的波动可以作为大脑网络连接的变化来研究,这反映在信息传递的方向和幅度上。这些变化不仅由任务执行时间引起,还由睡眠质量引起。在一项为期5个月的前所未有的纵向研究中,在接近真实世界的环境中,在持续注意力驾驶任务期间收集了每日采样的活动记录仪和脑电图数据。通过使用与受试者反应时间相关的性能指标和与睡眠质量相关的预测分数,我们识别驾驶员的疲劳水平,并通过分析脑区之间的动态耦合,研究他们在不同频段的有效连接性变化。研究结果支持这样的假设,即结合脑电图、行为和活动记录仪数据可以揭示警觉性下降的新特征。此外,使用诸如收敛交叉映射等定向测量方法有助于开发疲劳对策装置。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79dd/6240698/1fba8790e267/fnhum-12-00418-g0001.jpg

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