The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue

Abstract

The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroen-cephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (Cp) for beta, alpha, and delta bands and the character path length (Lp) for all EEG bands. The normalized measures γ showed significant increases in beta, alpha, and delta bands, and λ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

A novel time-frequency multilayer network for multivariate time series analysis

“…P-values of graph energy E (weighted global efficiency E g w ) between alert state and fatigue state are all much smaller than 0.001 (a benchmark for determining the significant difference from the p-value), indicating that our MTFM network method allows characterizing difference between alert state and fatigue state during driving. For each subject, weighted global efficiency presents growth trends, suggesting that a greater synchronization of neural assemblies is achieved as the brain state changes from alert to mental fatigue, which agree with the findings in [51,57]. We combine weighted global efficiency and graph energy to construct a two-dimensional feature vectors.…”

“…For each subject, the continuous EEG signals are segmented into 5 s non-overlapped epochs, and then we obtain 60 alert epochs and 60 fatigue epochs. The raw signals are down- [51,53]. We then overlap all 13 layers into a weighted projection network P i j , via equation (14).…”

“…Fu et al [50] proposed a dynamic model based on Hidden Markov Model to estimate driving fatigue from EEG, electromyogram, and respiration signals. Zhao et al [51] investigated reconfiguration changes in functional networks of different EEG bands during fatigue driving. Min et al [52] detected the driving fatigue through multiple entropy fusion and got high accuracy.…”

Dynamic Default Mode Network across Different Brain States

“…The brain is a complex system in which the dynamic adjustment of network organization over multiple time scales is crucial for mediating perception and cognition2223242526272829303132. Characterizing the dynamics of brain FC or properties of network topology is thought to be important for gaining a better understanding of brain function and behavioural performance13333435.…”

Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

“…Kaminski and Blinowska noted that the spread of electrical activity becomes smeared by volume conduction, and they obtained clear and reproducible results in their study51. Similarly to many recent reports using EEG to assess brain functional connectivity183038455253545556, we also assessed functional connectivity patterns in scalp regions instead of in the source domain. We emphasize that both the experimental results and the analyses in the present study were restricted to brain-scalp regions.…”