Sustaining Attention for a Prolonged Duration Affects Dynamic Organizations of Frequency-Specific Functional Connectivity

Abstract

Sustained attention encompasses a cascade of fundamental functions. The human ability to implement a sustained attention task is supported by brain networks that dynamically formed and dissolved through oscillatory synchronization. The decrement of vigilance induced by prolonged task engagement affects sustained attention. However, little is known about which stage or combinations are affected by vigilance decrement. Here, we applied an analysis framework composed of weighted phase lag index (wPLI) and tensor component analysis (TCA) to an EEG dataset collected during 80 min sustained attention task to examine the electrophysiological basis of such effect. We aimed to characterize the phase-coupling networks to untangle different phases involved in sustained attention and study how they are modulated by vigilance decrement. We computed the time–frequency domain wPLI from each block and subject and constructed a fourth-order tensor, containing the time, frequency, functional connectivity (FC), and blocks × subjects. This tensor was subjected to the TCA to identify the interacted and low-dimensional components representing the frequency-specific dynamic FC (fdFC). We extracted four types of neuromakers during a sustained attention task, namely the pre-stimulus alpha right-lateralized parieto-occipital FC, the post-stimulus theta fronto-parieto-occipital FC, delta fronto-parieto-occipital FC, and beta right/left sensorimotor FCs. All these fdFCs were impaired by vigilance decrement. These fdFCs, except for the beta left sensorimotor network, were restored by rewards, although the restoration by reward in the beta right sensorimotor network was transient. These findings provide implications for dissociable effects of vigilance decrement on sustained attention by utilizing the tensor-based framework.

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

“…Our results showed that dynamical complexity measures were related to the changing process of response, e.g., dynamical complexity measure MFEI (r = −0.35, p < 0.001) were significantly correlated to the task performance during sustained attention task. Previous studies also demonstrated that attention dynamically modulates brain rhythms (Liu et al, 2020;McCusker et al, 2020;Pagnotta et al, 2020). McCusker et al (2020) observed multi-spectral oscillatory robust effected by attention dynamically for both the directed and divided attention experiments in a MEG study.…”

“…McCusker et al (2020) observed multi-spectral oscillatory robust effected by attention dynamically for both the directed and divided attention experiments in a MEG study. Liu et al (2020) applied an complex analysis framework composed of weighted phase lag index and tensor component analysis and they found dynamic organizations of frequency-specific function connectivity can track the decrement and motivation of attention in sustained task. In a simplified perspective, dynamical complexity analysis conducted in this study may offer additional predictive value for attention.…”

Dynamic Community Detection for Brain Functional Networks During Music Listening With Block Component Analysis

“…We thus consider the tensor decomposition (or tensor component analysis) based methods for such dynamic community detection [18], [31], [34], [35], [36], [37] since the tensor decomposition enables multi-timescale dimensionality reduction both within and across temporal evolution for multiple subjects in a purely data-driven method. Tensor decomposition has recently been regarded as an extension of PCA for dynamic brain network analysis across subjects, where time-frequency vectorized adjacency matrices were formed into a tensor and decomposed into components characterizing brain network patterns with spectral-temporal features [9], [38], [39], [40] or temporal features [41], [42]. However, in this case, the topological organization of brain networks is unable to be directly revealed in the resulting components.…”