Validation of an automated wireless system for sleep monitoring during daytime naps.

An automated wireless system (WS) for sleep monitoring was recently developed and validated for assessing nighttime sleep. Here, we aimed to evaluate the validity of the WS to correctly monitor daytime sleep during naps compared to polysomnography (PSG). We found that the WS underestimated wake, sleep onset latency, and wake after sleep onset. Meanwhile, it overestimated total sleep time, sleep efficiency, and duration of REM sleep. Sensitivity was moderate for wake (58.51%) and light sleep (66.92%) and strong for deep sleep (83.46%) and REM sleep (82.12%). These results demonstrated that the WS had a low ability to detect wake and systematically overscored REM sleep, implicating the WS as an inadequate substitute for PSG in diagnosing sleep disorders or for research in which sleep staging is essential.

The benefit of offline sleep and wake for novel object recognition.

How do we segment and recognize novel objects? When explicit cues from motion and color are available, object boundary detection is relatively easy. However, under conditions of deep camouflage, in which objects share the same image cues as their background, the visual system must reassign new functional roles to existing image statistics in order to group continuities for detection and segmentation of object boundaries. This bootstrapped learning process is stimulus dependent and requires extensive task-specific training. Using a between-subject design, we tested participants on their ability to segment and recognize novel objects after a consolidation period of sleep or wake. We found a specific role for rapid eye movement (REM, n = 43) sleep in context-invariant novel object learning, and that REM sleep as well as a period of active wake (AW, n = 35) increased segmentation of context-specific object learning compared to a period of quiet wake (QW, n = 38; p = .007 and p = .017, respectively). Performance in the non-REM nap group (n = 32) was not different from the other groups. The REM sleep enhancement effect was especially robust for the top performing quartile of subjects, or "super learners" (p = .037). Together, these results suggest that the construction and generalization of novel representations through bootstrapped learning may benefit from REM sleep, and more specific object learning may also benefit from AW. We discuss these results in the context of shared electrophysiological and neurochemical features of AW and REM sleep, which are distinct from QW and non-REM sleep.