Multimodal Brain Network Analysis Reveals Divergent Dysconnectivity Patterns During Mental Fatigue: A Concurrent EEG-fMRI Study

For the apparent importance of mental fatigue in neuroergonomics, continuous efforts have been made to reveal the underlying neural mechanisms. Using concurrent EEG-fMRI network analysis, this work aims to reveal fatigue-related brain network reorganization. Specifically, multimodal neuroimaging data were acquired from 35 healthy participants during a 15-min sustained attention task (i.e., psychomotor vigilance task). A monotonically decreasing pattern of behavioral performance was revealed where the first and last 3-min windows were determined as the most vigilant and fatigued states. Multimodal brain network architectures within these two states were then quantitatively compared. We found that EEG and fMRI networks exhibited divergent yet interrelated reorganizations. Specifically, MF-related deficiency in parallel information transmission was revealed in multiple EEG frequency bands, yet only local efficiency was altered in fMRI network. Moreover, a convergent decrease of nodal efficiency mainly resided in the default mode network was found in both EEG and fMRI networks, indicating a decline in cognitive control capacity during mental fatigue. Overall, by integrating multimodal EEG-fMRI network analyses, this work provides novel insights into the dynamic neural adaptations to mental fatigue, enhancing our understanding of the underlying neural mechanisms.