RESUMEN

Sleep loss has detrimental effects on cognitive and emotional functioning. These impairments have been associated with alterations in EEG measures of power spectrum and event-related potentials, however the impact of sleep loss on inter trial phase coherence (ITPC), a measure of phase consistency over experimental trials, remains mostly unknown. ITPC is thought to reflect the ability of the neural response to temporally synchronize with relevant events, thus optimizing information processing. In the current study we investigated the effects of sleep deprivation on information processing by evaluating the phase consistency of steady-state visual evoked potentials (ssVEPs) as well as amplitude-based measures of ssVEPs, obtained from a group of 18 healthy individuals following 24 h of total sleep deprivation and after a night of habitual sleep. An ssVEP task was utilized, which included the presentation of dots flickering at 7.5 Hz, along with a cognitive-emotional task. Our results show that ITPC is significantly reduced under sleep deprivation relative to habitual sleep. Interestingly, decreased ITPC under sleep deprivation was associated with decreased behavioral performance in the psychomotor vigilance task (PVT), a validated measure of reduced vigilance following a lack of sleep. The results suggest that the capability of the brain to synchronize with rhythmic stimuli is disrupted without sleep. Thus, decreased ITPC may represent an objective and mechanistic measure of sleep loss, allowing future work to study the relation between brain-world synchrony and the specific functional impairments associated with sleep deprivation.

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RESUMEN

A fundamental issue in the dynamics of complex systems is the resilience of the network in response to targeted attacks. This paper explores the local dynamics of the network attack process by investigating the order of removal of the nodes that have maximal degree, and shows that this dynamic network response can be predicted from the graph's initial connectivity. We demonstrate numerically that the maximal degree M(τ) of the network at time step τ decays exponentially with τ via a topology-dependent exponent. Moreover, the order in which sites are removed can be approximated by considering the network's "hierarchy" function h, which measures for each node V_{i} how many of its initial nearest neighbors have lower degree versus those that have a higher one. Finally, we show that the exponents we identified for the attack dynamics are related to the exponential behavior of spreading activation dynamics. The results suggest that the function h, which has both local and global properties, is a novel nodal measurement for network dynamics and structure.