A brief nap is beneficial for human route-learning: The role of navigation experience and EEG spectral power |
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Authors: | Erin J. Wamsley Matthew A. Tucker Jessica D. Payne Robert Stickgold |
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Affiliation: | Center for Sleep and Cognition, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215, USA |
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Abstract: | Here, we examined the effect of a daytime nap on changes in virtual maze performance across a single day. Participants either took a short nap or remained awake following training on a virtual maze task. Post-training sleep provided a clear performance benefit at later retest, but only for those participants with prior experience navigating in a three-dimensional (3D) environment. Performance improvements in experienced players were correlated with delta-rich stage 2 sleep. Complementing observations that learning-related brain activity is reiterated during post-navigation NREM sleep in rodents, the present data demonstrate that NREM sleep confers a performance advantage for spatial memory in humans.A growing body of animal and human literature suggests that the consolidation of memories occurs optimally during periods of post-learning sleep. Nonrapid eye movement sleep (NREM), in particular, may be beneficial for the offline consolidation of hippocampus-dependent learning. The neurophysiological basis for this hypothesis is derived largely from electrophysiological studies in rodents, demonstrating that patterns of hippocampal place cell activity first seen during waking exploration are later reexpressed during post-learning sleep (Wilson and McNaughton 1994; Kudrimoti et al. 1999; Nadasdy et al. 1999; Ji and Wilson 2007). Behavioral studies in humans meanwhile demonstrate that NREM sleep is beneficial for declarative memory performance, relative to equivalent periods of wakefulness (Plihal and Born 1997; Tucker et al. 2006). However, the memory tasks typically employed in human research are quite different from those used in rodents, with human studies most often focusing on the memorization of verbal or visual stimuli (Plihal and Born 1997; Schabus et al. 2004; Clemens et al. 2005; Ellenbogen et al. 2006; Tucker et al. 2006; Daurat et al. 2008). Thus far, sleep-dependent memory reactivation has not been established to be directly beneficial for memory performance in an animal model, as the protocols employed in this research typically involve well-learned simple tasks which do not easily lend themselves to measurement of learning across time (Wilson and McNaughton 1994; Kudrimoti et al. 1999). Although the hippocampal memory reactivation described in rodents is a possible explanation for the effect of NREM sleep on human declarative memory, widely divergent methodologies employed across species prohibit confidence in this conclusion.Bridging this conceptual gap, a small handful of studies have begun to explore the relationship between spatial navigation and NREM sleep in humans. Notably, a PET study by Peigneux et al. (2004) demonstrated that learning-related hippocampal activity seen while training on a virtual maze task is again expressed during post-learning human sleep. Furthermore, this hippocampal reactivation strongly predicted overnight improvement on the task (Peigneux et al. 2004). Additional studies have suggested a link between sleep and other types of spatial-related learning, including mental rotation performance (Plihal and Born 1999), the ability to reproduce a complex figure (Clemens et al. 2006; Tucker and Fishbein 2008), performance on a computerized version of Milner''s (1965) “bolt head” maze (Tucker and Fishbein 2008), and memory for the location of verbal information on a screen (Daurat et al. 2008).Yet it remains unclear whether sleep, relative to wakefulness, provides a performance benefit for human route-learning in the context of a realistic spatial environment. Navigation through virtual environments is a strongly hippocampus-dependent task (Peigneux et al. 2004; Astur et al. 2005) and provides an experimental model closely paralleling the spatial exploration tasks employed in the rodent literature. However, the few studies reporting effects of sleep on human navigation performance have been contradictory. Using a navigation task similar to that of Peingeux et al. (2004), Orban et al. (2006) failed to detect any effect of post-learning sleep deprivation on maze performance but did find evidence of altered task-related brain activity, concluding that sleep supports “covert” memory reorganization (Orban et al. 2006). In direct contrast, Ferrara et al. found that spatial memory is improved when a retention interval falls across a night of sleep, relative to when route memory must be retained during daytime wakefulness, or across a night of sleep deprivation (Ferrara et al. 2006, 2008).The present study clarifies these issues by examining the effect of a post-learning nap on complex route-learning in a three-dimensional (3D) virtual environment. When controls are tested at a different time of day than sleep participants, circadian confounds may present a substantial problem. Alternatively, overnight protocols employing sleep-deprived subjects necessarily suffer from confounds related to this sleep deprivation during the retention interval. The use of a daytime nap as a sleep intervention avoids these pitfalls by allowing all subjects to be trained and tested at the same circadian time, and in the absence of sleep deprivation. A series of recent studies confirm that a daytime nap is sufficient to induce performance improvements on declarative and procedural memory tasks, relative to wake subjects (Mednick et al. 2003; Backhaus and Junghanns 2006; Nishida and Walker 2006; Tucker et al. 2006; Lahl et al. 2008; Tucker and Fishbein 2008).Participants (n = 53, 34 female) were trained on a virtual maze-learning task at 12:30 pm. Following training, nap participants lay down for a 1.5-h sleep opportunity. These subjects were allowed to obtain as much NREM sleep as possible but were awoken at the first signs of REM (see Table | Open in a separate windowaThere were no significant differences between groups on any measure, but there was a trend for total sleep time (TST) to be greater in experienced players (P = 0.052).Means ± SD. SWS, slow wave sleep stages 3 and 4. %, Percent of TST. Of the nap participants, n = 12 did not enter SWS during the sleep period, and n = 3 were awoken from REM sleep. Due to artifact, the sleep recording for one novice player was unusable.The virtual maze task was a simple 3D environment designed for this research (; see also Supplemental Methods). In brief, subjects initially spent 5 min exploring a complex maze and were instructed to remember the layout of the maze environment as well as possible. Subsequently, subjects navigated through the same maze during three test trials, in which they were instructed to reach a specified goal point as quickly as possible. Performance was assessed as time required to reach the goal on each trial, and improvement was calculated as the change in performance from the last training trial (trial 3), to mean performance on the three retest trials (trials 4–6, administered at 5:30 pm). All subjects rated their prior experience with 3D-style game environments on a five-point scale, on which they assessed their typical frequency of play ranging from “every day” to “less than once per year.”Open in a separate windowA sample screen from one location within the maze, as seen by the subject, displayed alongside a bird''s-eye view layout of difficulty level 3.We hypothesized that post-learning sleep would lead to enhanced retest performance on this hippocampus-dependent spatial task. Furthermore, we expected that sleep-dependent performance improvements would correlate with spectral power in low-frequency EEG bands during the nap (<1 Hz slow oscillation and/or 1–4 Hz delta power).Maze performance improved significantly across the six training and retest trials (F(5,230) = 2.35, P = 0.04, η2p = 0.05). Overall, performance changes across the retention interval did not differ significantly between nap and wake subjects (for raw improvement: t(46) = 1.22, P > 0.2; percentage improvement: t(46) = 1.5, P > 0.1). We observed, however, that baseline performance on the final training trial was strongly dependent on prior experience with 3D games, as self-assessed on a five-point scale (F(4,43) = 4.92, P = 0.002; see Supplemental Methods). Prior research suggests that individuals who perform poorly on learning tasks prior to sleep fail to exhibit sleep-dependent performance improvements (Tucker and Fishbein 2008). We therefore investigated whether the effect of sleep on maze performance might be mediated by subjects’ virtual navigation experience. Post-hoc tests (Tukey''s HSD) revealed that only subjects at the bottom of the experience scale (no prior game experience or less than once per year) differed at baseline from subjects at other experience levels (Supplemental Fig. S1). The sample was therefore split into novice (n = 16, experience less than once per year; mean time to complete last training trial = 421 sec ± 209 SD) and experienced players (n = 32, experience equal to or greater than once per year; mean = 184 sec ± 150; t(46) = 4.5, P < 0.001, d = 1.3; see Table |