![]() ![]() The prevalence of mobile phone addiction (MPA) has increased rapidly in recent years, and it has had a certain negative impact on emotions (e.g., anxiety and depression) and cognitive capacities (e.g., executive control and working memory). Findings may help explain the mechanistic relationships between dream and memory reactivations and may contribute to the development of sleep-based methods to optimize complex skill learning. TMR did not itself influence dream content but its effects on performance were greater when coexisting with task-dream reactivations in REM sleep. REM dreams that reactivated kinesthetic elements of the VR task (e.g., flying, driving) were also associated with higher improvement on the task than were dreams that reactivated visual elements (e.g., landscapes) or that had no reactivations. Findings indicate that learning benefits most from TMR when applied in REM sleep compared to a Control-sleep group. Healthy participants completed a virtual reality (VR) flying task prior to and following a morning nap or rest period during which task-associated tones were readministered in either SWS, REM sleep, wake or not at all. Specifically, we asked if TMR-induced or task-dream reactivations in either slow-wave (SWS) or rapid eye movement (REM) sleep benefit whole-body procedural learning. To experimentally address this question, we used targeted memory reactivation (TMR), i.e., application, during sleep, of a stimulus that was previously associated with learning, to assess whether it influences task-related dream imagery. Dreaming may play a role in memory improvement and may reflect these memory reactivations. Sleep facilitates memory consolidation through offline reactivations of memory traces. Note activation in the superior parietal lobule. ![]() Standardized current density (calculated using sLORETA) for the M TRC microstate, thresholded at 0.003). Global explained variance (GEV) during baseline rest, post-training rest and N2 sleep is plotted for each training microstate (error bars: ± SEM *SnPM p < 0.01). In order to assess the presence of VMT-training microstates in rest and sleep, the six training microstates derived from the Nap group were fit to baseline rest, post-training rest, and subsequent N2 sleep in both the Nap (top) and Control (bottom) groups. This set of microstates contains the canonical four microstates 58, labeled here as A-D. In the analysis pooling all subjects who performed the VMT, five microstates were identified during VMT training. In each analysis, one microstate (gold box) showed increased GEV in post-training rest compared to pre-training rest. Six microstates were present in the EEG during VMT training for both Nap and Wake groups. ![]() Topographic maps (red positive, blue negative) of the microstates derived from VMT training data in the Nap group, Wake group, and both groups combined. ![]()
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