Dynamic reconfiguration of functional brain networks after acute sleep deprivation: Implications for spatial working...

Acute sleep deprivation (ASD) impairs cognitive functions, particularly spatial working memory (SWM), which is highly sensitive to fatigue. However, its dynamic neural underpinnings remain unclear. Existing studies on dynamic functional network connectivity (dFNC) in the context of ASD vary considerably in sample size and state extraction parameters, which limits the comparability across studies. Emerging evidence suggests that ASD may induce reorganization of brain network topology, yet its temporal dynamics remain underexplored. This study systematically investigates ASD-induced alterations in the temporal and topological dynamics of dFNC and their association with SWM performance.

Fifty healthy adult males underwent magnetic resonance imaging scanning and cognitive assessment both before and after ASD. To characterize dynamic brain connectivity, a sliding-window-based dFNC approach was employed to identify connectivity states and temporal features. Graph-theoretical metrics were calculated to quantify the coefficient of variation (CV) at both global and nodal levels. Additionally, correlations between the above measures and performance on the 1-back SWM task were examined.

Three dFNC states were identified. Among them, State 1 exhibited high integration within and between the sensorimotor, auditory, and visual networks, with participants showing a greater tendency to dwell in this state after ASD. Dynamic topological analysis revealed that ASD significantly altered the CV in several global and nodal metrics. The CV of the nodal clustering coefficient in independent component 94, which belongs to the salience network, was positively correlated with the false alarm rate in the 1-back SWM task.

ASD induces abnormal state transitions and changes the temporal flexibility of brain network topology. These findings suggest a potential compensatory reorganization mechanism in the brain following ASD and offer new dynamic neural insights into ASD-related SWM impairments.