Sleep forms memory for finger skills

Results

Total sleep time was closely comparable during both the nocturnal and daytime retention intervals (Table 1). Expected circadian influences on sleep expressed themselves in a decreased time spent in stage 2 sleep (P < 0.01) and a tendency toward increased time in stage 1 sleep and rapid eye movement (REM) sleep (P < 0.1) during daytime sleep in comparison with nocturnal sleep. Initial learning of the sequence before the 8-hour retention intervals was comparable for all four conditions. Performance rate, i.e., the number of correctly completed sequences per 30 s, at learning was 12.96 ± 1.06 for the nocturnal sleep condition, 14.17 ± 0.8 for the nocturnal wake condition, 13.13 ± 0.72 for the daytime sleep condition, and 13.26 ± 0.58 for the daytime wake condition (P > 0.4, for pairwise comparisons). Also, error rates at learning did not differ among the conditions (nocturnal sleep: 7.68 ± 0.8; nocturnal wake: 6.51 ± 0.98; daytime sleep: 6.46 ± 0.65; daytime wake: 6.59 ± 0.98; P > 0.3 for pairwise comparisons).

Performance rates were generally improved at retesting after the 8-h retention interval, with this improvement strongly depending on sleep versus wakefulness during the retention interval [F(1,36) = 167.0, P < 0.001 for main effect before/after and F(1,36) = 31.81, P < 0.001 for before/after × sleep/wake ANOVA interaction; Fig. 2A]. Indeed, the benefit for performance rates was considerably stronger after retention intervals of sleep than that revealed for the corresponding nocturnal or daytime retention intervals of wakefulness [F(1,18) = 19.40, P < 0.001 and F(1,18) = 12.93, P < 0.002, for before/after × sleep/wake interaction, respectively, for the nocturnal and daytime retention intervals]. Performance rate improved on average from 13.04 ± 0.63 to 17.12 ± 0.64, i.e., 33.47% ± 3.73% across the sleep intervals, and from 13.72 ± 0.49 to 15.32 ± 0.52, i.e., 12.49% ± 2.79% across the wake retention intervals (Fig. 3). The performance gain for retention intervals of sleep did not differ between nighttime (29.68% ± 3.72%) and daytime sleep (37.26% ± 6.45%; P > 0.3). However, improvements across wake retention intervals were stronger when placed during the day (17.56% ± 2.62%) than at night (7.41% ± 4.49%; P < 0.05, for separate pairwise comparisons). In fact, these analyses revealed that only performance gains across the daytime wake interval were significant (P < 0.001), whereas changes across the nocturnal wake interval were not (P > 0.2).

• Download figure
• Open in new tab
• Download powerpoint

Figure 2

Performance gains on the finger-to-thumb opposition task are indicated by the difference between training and retrieval testing (A) for performance rate (mean number of correctly completed sequences per 30 s) and (B) error count (mean number of errors per 30 s). (Left) Mean (± SEM) differences are indicated for 8-h retention intervals of sleep (black bars) and wakefulness (gray bars) placed during daytime and at night. (Right) In additional experiments, effects of a 48-h retention interval were tested which was filled either with two nights of regular sleep (black bars) or a first night of sleep deprivation followed by a night of recovery sleep (gray bars). *, P < 0.05. **, P < 0.001 for tests against zero and for differences between the effects of the retention intervals.

• Download figure
• Open in new tab
• Download powerpoint

Figure 3

Progression of performance on the finger-to-thumb opposition task as indicated by the number of correctly completed sequences sampled at 30-s intervals. Three blocks each of 5 min duration were run before (Training) and after (Retrieval) 8-h retention intervals during which subjects slept (open circles), or remained awake (filled circles). Mean (± SEM; adjusted to first block of training) are shown collapsed across both daytime and nighttime condition (refer to text).

Performance rates also improved within the learning sessions, on average by 0.73 sequences per block (P < 0.001, for a comparison between first and third block). Thus, it could be argued that performance gains at retesting after the retention interval reflect a mere repetition effect. This was excluded in supplementary analyses taking into account the estimated repetition effect by linearly extrapolating the individual performance gains within the learning session. This analysis confirmed a significant improvement in performance rates after the retention intervals [F(1,36) = 57.31, P < 0.001 for main effect before/after], in particular when filled with sleep [F(1,18) = 16.54, P < 0.001 and F(1,18) = 8.68, P < 0.01, for before/after × sleep/wake ANOVA interaction for nocturnal and daytime retention intervals, respectively]. Also, separate analysis of the wake retention interval confirmed significance selectively for the daytime retention condition (P < 0.01).

Accuracy of performance, as indicated by a decrease in the number of errors per 30 s, also improved only after retention intervals of sleep [on average by 30.07%, F(1,19) = 14.41, P < 0.001], but remained unchanged across retention intervals of wakefulness regardless of whether placed during the night or daytime [F(1,19) = 0.09, P > 0.8, Fig. 2B]. The improving effects of the sleep intervals on performance accuracy were closely comparable for the nocturnal and daytime sleep conditions (P > 0.9).

For the retention sleep conditions, we also assessed the relationship between time spent in the different sleep stages and performance gains (across daytime and nighttime conditions) by using Pearson's correlation. Improvement in performance rate was proportional to the time spent in REM sleep (r = 0.61, P < 0.004). Correlation with time in slow wave sleep (SWS; r = 0.01, P > 0.9), stage 2 sleep (r = −0.37, P > 0.1) and stage 1 sleep (r = 0.14, P > 0.5) remained nonsignificant. Also, dividing sleep time into four quarters did not reveal evidence that performance gains were correlated with the amount of early SWS or late REM sleep, or with stage 2 sleep during any of these intervals.

Comparing effects of sleep and wake retention intervals could be in principle confounded by unspecific effects of sleep loss on motor performance. Self-reports of mood and feelings of activation revealed that in comparison to normal nocturnal sleep, subjects after nights of sleep deprivation, felt more tired (P < 0.001), less activated (P < 0.01) and less concentrated (P < 0.05) as revealed by an adjective check-list (24). Nevertheless, a substantial contamination of motor performance by these subjective feelings seems unlikely in light of the fact that initial performance at learning was comparable for all retention conditions, regardless of whether this period was preceded by sleep or wakefulness. To further rule out unspecific motor effects of tiredness, particularly on retrieval tested after nocturnal wakefulness, two additional groups of eight naïve subjects each practiced the finger motor sequences in the morning at 7:30 a.m. after a night of regular sleep and sleep deprivation. Performance on the two conditions was indeed closely comparable with regard to both performance rate (regular sleep: 13.34 ± 0.45; sleep deprivation: 13.38 ± 0.34; P > 0.9) and errors (regular sleep: 7.61 ± 0.96; sleep deprivation: 5.63 ± 0.6; P > 0.1).

We also investigated whether the selective gain in performance after nocturnal sleep as compared with a retention period of nocturnal wakefulness is preserved after an additional night, in which sleep deprived subjects had recovery sleep. For this purpose, six additional subjects were trained, as in the main experiment, on one of the finger sequences, and retested after a 48-h retention interval that was filled either with two consecutive nights of regular sleep or with a first night of sleep deprivation followed by a second night of recovery sleep. Initial learning before the retention intervals did not differ between the sleep and sleep deprivation condition with respect to both, performance rates (P > 0.3), and error rates (P > 0.8). At retrieval testing 48 h later, the improvement in performance was distinctly more pronounced when subjects had slept the night after training (from 14.92 ± 1.28 to 18.94 ± 0.96, i.e., 28.99% ± 5.41%) than when they stayed awake in this first night [from 16.20 ± 1.89 to 17.92 ± 1.86, i.e., 11.19% ± 3.03%; F(1,5) = 45.98, P < 0.001, for before/after × sleep/wake interaction; Fig. 2]. Errors also decreased only when subjects were retested after two regular nights of sleep (from 7.97 ± 2.28 to 7.0 ± 2.44, i.e., −15.78% ± 13.07%), and increased when subjects had stayed awake the night after training (from 7.61 ± 1.53 to 8.23 ± 1.74, i.e., 12.76% ± 18.96%) with these differences, however, not reaching significance.

Finally, we tested the specificity of the improving effect of sleep on motor memories. Before an 8-h interval of nocturnal sleep, 10 other subjects were trained on one of the two finger sequences, as in the main experiment. At retrieval testing after the retention interval subjects were first tested on the untrained sequence and, 1 h later, on the sequence trained before the retention interval. As expected, performance improved across sleep only for the trained sequence (from 13.95 ± 1.1 to 17.52 ± 0.93, P < 0.001), whereas performance on the untrained sequence was similar to that at training before sleep (13.95 ± 1.1 versus 14.48 ± 1.17, P > 0.3).

Discussion

Results indicate an improvement in finger motor skills that is distinctly greater and more consistent across retention periods of sleep than of wakefulness. Finger skills after a time of wakefulness were improved only with regard to performance rate, but not to error rate, and only when the retention period took place during daytime. Effects of tiredness do not explain our findings because performance on the task used here was shown to be unaffected by prior sleep deprivation. Moreover, memory for the trained motor sequence was still superior after sleep than after a vigil on the night immediately after training even when retrieval testing was postponed for another 24 h, including a night of sleep for all subjects. Because subjects in these experiments had sufficiently slept before retrieval on both conditions, fatigue and other factors induced by sleep deprivation can be safely ruled out as possible confounds. Importantly, this finding indicates that sleep enhances the formation of memory for the motor skill only within a critical time frame after training. Sleep occurring after training, rather than sleep before recall, appears to be effective. This conclusion complements results of a most recent study employing a similar tapping task (25). Donchin et al. (26) failed to find an influence of sleep versus sleep deprivation on the memory for reaching movements. This outcome could point to an effect of sleep depending on the type of motor memory. Alternatively, sensitivity of different performance measures to the influence of sleep may vary, though this cannot be decided on the basis of the available data. The present result of a sleep-dependent enhancement in motor memories adds to previous evidence indicating a similar essential role of sleep in the formation of perceptual discrimination skills (11, 27, 28).

Performance improvements observed during daytime wakefulness but not during nighttime wakefulness suggest both that the wake state per se is not sufficient to promote memory formation for the trained finger skill, and that circadian factors play a role in consolidation. Further evidence of a circadian influence comes from our observation that, across both the sleep and wake retention conditions, performance gains on average were greater for the retention intervals positioned during daytime than during the night. Moreover, it is conceivable that the circadian influence on the formation of motor memories during wake retention periods differs in quality from that during sleep. After daytime wake periods, the improvement in finger sequence tapping remained restricted to performance rate and was not paralleled by a decrease in errors, suggesting that only sleep leads to changes in internal representations improving motor accuracy. Also, previous studies indicated that performance on motor skills (reaching movements), though becoming more resistant to interference from similar behaviors (2, 5), did not improve in accuracy across wake periods of 6 hours (1). Thus, consolidation during daytime wakefulness appears to spare certain aspects of the internal motor representations which are enhanced only by sleep.

Skill learning is characterized essentially by rather discrete changes in low-level representations within the hierarchical organization of motor systems and, importantly, shows little generalization (6, 29). Here, we found that the enhancing effect of sleep on memory for a motor sequence is highly specific with regard to the trained finger sequence and does not generalize to a control sequence containing the identical finger movements in a mirror-reversed order. This further excludes effects not specifically linked to the task, e.g., on motor fluency, but speaks for a direct influence of sleep on forming the internal model for this particular finger sequence.

At the neuronal level, the consolidation of motor skill memories has been considered to involve a reorganization of motor representations residing predominantly in the primary motor cortex (M1) (7, 16, 30–32). Human studies using functional magnetic resonance imaging and transcranial magnetic stimulation have shown that the amount of motor training in our subjects is sufficient to trigger plastic neuronal changes in M1, whereby the initially fragile motor representations become increasingly stabilized (7, 8). Moreover, findings with positron emission tomography suggested that consolidation of a serial motor task is based on a covert reactivation of brain structures already activated at training, with the signs of reactivation being most obvious during REM sleep (33). In the present study, exploratory calculation of correlation coefficients indicated greater performance gains in subjects with high amounts of REM sleep. Although, in light of the limited size of the subject sample, this result needs to be considered with caution, it would also point to a particular relevance of REM sleep in procedural memory formation.

Nevertheless, the covert reprocessing of newly acquired motor representations during sleep is a concept which could explain the considerable performance gain in the finger motor sequence task seen after sleep. This gain, consisting of increased speed and a reduced number of false reactions, cannot arise from a nonselective strengthening of connectivity within the acquired representations, but implies a reorganization that enhances correct reactions to the exclusion of false ones. This reorganization, at the cellular level, probably involves processes such as synaptic long-term potentiation and depression (34–38), as well as synaptogenesis (39, 40) in the motor cortex, which may particularly benefit from the specific orchestration of neurotransmitters in the different sleep stages (41–43). However, the synaptic factors critically involved in sleep-dependent formation of skill memory remain to be identified. Demonstrating an essential role of sleep in the formation of memory for motor skills, our data extend previous observations of a similar function of sleep with regard to perceptual skills. In generalizing these observations to skills of everyday life (such as learning a musical instrument or sport), we would conclude that sleep is required to achieve optimum performance on any of these skills.