Literature reviews Sleep is central to consolidation and learning

NREM sleep contributes to and nonrapid eye movement (NREM) sleep. For example, total sleep time was 448 and 472 min- memory performance utes on nights 1 and 2 for the gifted individual compared with a mean of 455 minutes in controls vidence for a relationship between sleep, learn- with normal memory function. However, cyclic Eing and memory has been provided by behav- alternating pattern (CAP) analysis of the gifted ioral and neurocognitive studies, and it is thought individuals sleep revealed interesting features that that sleep influences the plastic cerebral changes differed from the norm (table I). CAP is thought that underlie learning and memory. How sleep to be responsible for NREM sleep stability, as it is relates to the exceptionally good memory of gifted involved in the structural organization of NREM individuals – the focus of a recent case study by sleep. It corresponds to prolonged oscillation of Ferini-Strambi and colleagues – has, however, the cortical electrical activity evaluated through received little . electroencephalogram (EEG) patterns between two reciprocal functional states termed phase A and Ferini-Strambi et al. assessed the sleep patterns of phase B. In humans, phase A of the CAP is classi- a male subject with an outstanding memory per- fied into three subtypes: phase A , characterized by formance to understand the specific structural 1 synchronized EEG patterns; phase A , character- components of sleep that were associated with 2 ized by a balanced mixture of synchronized and superior memory. The exceptional memory per- desynchronized EEG patterns; and phase A , char- formance of this individual was confirmed by a 3 acterized mostly by desynchronized EEG patterns. variety of standard psychometric tests, including The investigators recorded a CAP that was typified short-term memory (span), logical memory and by an increased number of periodic fluctuations verbal episodic learning tasks. during NREM sleep (7–8 SD units above the The patient’s sleep structure was assessed by number observed in age-matched controls). polysomnography on two consecutive nights and Indeed, evaluation of the phase A sleep subtypes

was found to be similar to that of control individ- demonstrated that phase A1, in particular, was uals, in terms of sleep induction and maintenance, increased (68.2% and 69.1% on nights 1 and 2, as well as percentage rapid eye movement (REM) respectively) compared with controls (61.4%). As

Table I. Cyclic alternating pattern (CAP) rate by sleep stage on two nights in an individual with superior memory and in individuals with normal memory function (n=10).1

Sleep stage CAP rate (%)

Individual with superior memory Normal individuals (mean ± standard Night 1 Night 2 deviation)

Non-rapid eye movement 63.2 64.5 31.9 ± 7.0

Stage 1 39.3 38.2 38.3 ± 16.4

Stage 2 66.5 70.7 32.6 ± 6.4

Stage 3 78.3 79.1 44.0 ± 16.3

Stage 4 68.7 69.8 24.4 ± 11.5

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phase A1 is related to the achievement and main- sleep, REM sleep, and pre- and postsleep wakeful- tenance of slow-wave sleep (SWS), this individual ness) following an extended period of training in had particularly efficient SWS. This is noteworthy, the topographical memory task; and Group 3 was as SWS itself is believed to generate conditions monitored during all stages of nocturnal sleep favorable for the consolidation of memory traces without prior training. Sleep was monitored by acquired during wakefulness. polysomnography and changes in rCBF were measured by positron-emission tomography. The authors conclude that their observations are in line with the hypothesis for a link between NREM To determine the specificity of rCBF patterns for sleep and declarative memory performance, as has spatial declarative, rather than procedural impli- been put forward in previous publications that cit, learning, the investigators included rCBF data implicate the early period of sleep, dominated by previously obtained from another study, in which SWS, in the consolidation of declarative memory. subjects had been extensively trained for a proce- Although confirmatory studies in larger cohorts are dural serial reaction time (SRT) task (Group 4). needed, the results of the present case study pro- The investigators found that, during wakefulness, vide an insight into the relationship between sleep the rCBF pattern in subjects who were scanned and memory performance in gifted individuals. ■ while practicing the topographical memory task Ferini-Strambi L, Ortelli P, Castronovo V, Cappa S. differed significantly from the pattern for the SRT Increased periodic arousal fluctuations during task, confirming that the navigation network was non-REM sleep are associated with superior activated. The specific regions of the brain collec- memory. Brain Res Bull 2004;63(6):439–42. tively known as the navigation network are pre- dominantly responsible for spatial learning and include the right , the right caudate Slow-wave sleep and nucleus, and the right inferior parietal and bilat- eral medial parietal regions.2 The authors also found a correlation between performance in the consolidation topographical task and increases of rCBF in specific areas of the hippocampal region, indicating that nformation acquired during wakefulness is hippocampal activity positively relates to the accur- Ibelieved to be actively restructured and strength- acy of route finding. ened during sleep, facilitating memory formation Compared with wakefulness, rCBF was markedly and consolidation. Observations from rodent stud- increased during SWS, stage 2 sleep and REM ies support this hypothesis, as they suggest that the sleep across all groups, regardless of the presleep same sets of neurons that are activated in the hip- experience (trained in the topographical memory pocampal region of the brain during spatial learn- task, not trained in this task or trained in the SRT ing are reactivated during subsequent NREM task). However, although the broad pattern of rCBF sleep. This ‘off-line’ replay of hippocampal activ- observed during sleep was similar between groups, ity might be involved in the consolidation of newly significant differences in the amplitude of rCBF acquired spatial information and its gradual trans- reactivation were identified through a series of lation into long-term memory stores. interaction analyses. These analyses showed that In their study, Peigneux and colleagues focused on topographical training vs no training was associated spatial memory formation in humans. They moni- with greater activity in hippocampal and parahip- tored regional cerebral blood flow (rCBF), a pocampal regions during posttraining NREM sleep marker of local synaptic activity, in healthy male (especially SWS sleep) when compared either with subjects (aged 18.3–29.9 years) under three circum- REM sleep or wakefulness. Interestingly, interac- stances: Group 1 was monitored during training tion analyses comparing topographic task-trained for a topographical memory task (the subjects had subjects with SRT-trained subjects generated simi- to find their way inside a complex three- lar results, indicating that the observed neuronal dimensional virtual town); Group 2 was monitored activation depends on the type of learning and not during all stages of nocturnal sleep (SWS, stage 2 simply on intensive stimulation prior to sleep.

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Importantly, performance in the topographic task mean IQ scores as determined by a general intel- improved following a night’s sleep. Trained sub- ligence test based on verbal and performance jects in Group 3 came closer to their target desti- potential (HiQ=125.8, MedQ=114.7, LoQ=102.5, nation in the virtual town within an allotted time Control=108.3). Following acclimatization to the limit than on the previous day. This overnight gain sleep laboratory (two nights), baseline values in performance occurred in all subjects and was were recorded for EEG, electrooculograms and strongly correlated with the amplitude of rCBF in electromyograms. Prior to the post-training distinct hippocampal and the parahippocampal polysomnographic evaluation, subjects in the regions during SWS (r=0.94). active group performed two learning tasks (‘mirror trace’ task and ‘tower of Hanoi’ task), while con- The authors conclude that, in humans, hippocam- trol subjects watched movies or read. The follow- pal activity during SWS is modulated by recent ing day, subjects in the active group were retested waking spatial experience and, furthermore, that on the learning tasks. the greater the amplitude of hippocampal reactiva- tion during SWS sleep the greater the overnight The authors found that the time taken to finish the improvement in performance of a spatial memory learning tasks dropped significantly from training task. According to the authors, these results sup- to retest in all learning groups (P<0.0000), regard- port the hypothesis that enhanced hippocampal less of IQ. Unsurprisingly, they also found that IQ activity during posttraining SWS reflects off-line affected performance, as the HiQ group made processing of memory traces, which eventually fewer errors during training than the MedQ leads to the improvement in performance the next group, who in turn made fewer errors than the day. ■ LoQ group on the ‘tower of Hanoi’ task. For the ‘mirror trace’ task, there was no difference during Peigneux P, Laureys S, Fuchs S, et al. training between HiQ and MedQ groups, although Are spatial strengthened in the human more errors were registered for the LoQ group. hippocampus during slow wave sleep? Neuron Upon retesting the following day, these intergroup 2004;44(3):535–45. differences disappeared.

IQ also affected REM sleep. The trained and con- REM sleep intensity trol groups were similar at baseline, but differed – an indicator of learning substantially in two parameters of REM sleep on the posttraining night. There were a significantly potential? greater number of REM periods in all trained groups compared with the control group EM sleep plays a key role in memory consol- (P<0.0003 for HiQ group, P<0.0006 for MedQ Ridation and learning, and there is evidence to group, P<0.02 for LoQ group). Furthermore, suggest that the duration and intensity of REM REM density (total number of REM periods/total sleep increases following learning of a procedural duration of REM sleep) was increased vs baseline task. Conversely, enforced REM sleep deprivation for all trained subjects combined. Importantly, after acquisition of a procedural task has been compared with the control group, REM density reported to lead to subsequent memory deficits. was significantly greater in the HiQ and MedQ However, little is known about how REM sleep, groups (P<0.01 and P<0.04, respectively), but not intelligence quotient (IQ) and our learning ability the LoQ group. interrelate. Correlation analyses, performed to examine the Smith and coworkers addressed this controversial relationship between sleep states and task per- question in a recent study that investigated post- formance, revealed that REM density on the post- training sleep in 24 male and female subjects aged training night was indeed correlated with 19–25 years, following cognitive procedural improved performance in the ‘tower of Hanoi’ learning tasks known to require REM sleep for task during four of four REM periods (r=–0.56, optimal . All subjects were P<0.05) and on the ‘mirror trace’ task during assigned to one of four groups on the basis of their one of four REM periods (r=–0.53, P<0.05).

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Furthermore, the authors identified a strong corre- training increases in REM sleep intensity might lation between IQ and retest performance in one of provide a biological marker of learning potential. the learning tasks (r=–0.72, P<0.05 for improve- They further speculate that this might partly be a ment in speed; r=–0.56, P<0.05 for improvement consequence of genetic differences, which lead to a in error rate), and a substantial but not significant more efficient REM sleep system in subjects with a correlation for retest performance in the other task. high IQ. ■

Taken together, these correlations imply a link Smith CT, Nixon MR, Nader RS. between the intensity of REM sleep (number of Post-training increases in REM sleep intensity events and density) and learning ability. Such a implicate REM sleep in memory processing and link may indeed be borne out in IQ measures, and provide a biological marker of learning potential. the authors speculate that the magnitude of post- Learn Mem 2004;11(6):714–9.

Comment

Enormous efforts have been made in trying to understand how memories are formed, maintained and even improved. Current theory sug- gests that memories evolve in unique stages over time so that, following initial ‘’, a second step termed ‘consolidation’ is required, resulting in more permanent and improved memory traces. A plethora of research now indicates that sleep (both REM and NREM) is criti- cal for memory consolidation,3 and all three of the above reports offer new insights into this relationship. The study by Smith et al. demonstrated how superior learning ability is associated with post-training increases in the rapid eye movements of REM sleep (REMs). What is intriguing, however, is that while subjects with a lower IQ performed worse on the memory tests prior to sleep, when retested postsleep, all participants, regardless of IQ, improved to similar performance levels. Thus, the sleep-dependent learning process offers delayed improvements for all, effectively normalizing next-day memory, with those of lower IQ benefiting most from inter- vening sleep. In what has been called ‘pseudo IQ’ or abnormal testing-evaluation procedure, the correlation analyses further revealed that increased posttraining REMs were proportional to next-day (although not IQ). This finding is pertinent considering data from rodent studies illustrating that REM-sleep pontogeniculooccipital (PGO) waves increase following daytime learning and are posi- tively correlated with the degree of next-day memory retention.4 REMs may potentially be the expression of human PGO waves, and the findings of Smith et al. provide compelling support for the idea that, as in animals, unique physiological events of REM sleep initiate plastic brain changes, the consequence of which is improved memory. Using positron emission tomography neuroimaging, Peigneux et al. offer complementary evidence that another sleep phenomenon instigates memory improvement. Several animal and human studies have shown that daytime learning is associated with a signature pattern of neural activity and that, during subsequent sleep, the same neural signature re-emerges – a form of off-line memory replay. However, these previous studies have not investigated whether the amount of replay during sleep is associated with next-day memory enhancement. This is exactly what the elegant study of Peigneux and colleagues has shown. It should be cautioned that the study suffers from low subject power, with only six participants in each experimental group. Nevertheless, these data provide, for the first time, a potential functional purpose of REM sleep neural replay: a trigger of memory improvement, not simply a consequence of prior daytime experience. Finally, data from Ferini-Strambi and colleagues suggest that better SWS (fewer NREM periodic arousals), reflected in the CAP rate, is found in an individual with remarkable capacity. Such case studies are always restricted in their inference, but the result cultivates sev-

eral exciting questions. First, is the difference in phase A1 CAP rate a response to greater daytime learning or, conversely, is the increased capacity to learn a consequence of this altered sleep structure? Secondly, if the former is true, is the increased phase A1 CAP rate the result of superior daytime acquisition/encoding, or a reflection of subsequent overnight consolidation? Finally, in normal subjects, is there also a

correlation between phase A1 CAP rate and memory capability, suggesting a potential mechanistic route to trigger improved memory func- tioning? Luckily, all these questions are testable experimentally. Perhaps the most striking finding across all three studies is that memory processing was not associated with changes in basic sleep-stage meas-

ures, but with distinct neurophysiological sleep events (REMs, neural replay and phase A1 of CAP, respectively). This highlights the need for more sophisticated approaches to investigating sleep-dependent memory processing, far beyond simple sleep stages, which are unlikely to rep- resent an accurate index of underlying neural plasticity. With continued research, the importance of sleep in modifying our memories will only increase and, as a consequence, we can look forward to new advances in treating disorders of memory, and even improving the cap- acity of our own! Matthew Walker, Assistant Professor of Psychiatry, Director of the Sleep and Neuroimaging Laboratory, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA References 1. Ferini-Strambi L, Ortelli P, Castronovo V, et al. Increased periodic arousal fluctuations during non-REM sleep are associated to superior memory. Brain Res Bull 2004;63(6):439–42. 2. Maguire EA, Burgess N, Donnett JG, et al. Knowing where and getting there: a human navigation network. Science 1998;280(5365):921–4. 3. Walker MP, Stickgold R. Sleep-dependent learning and memory consolidation. Neuron 2004;44:121–33. 4. Datta S. Avoidance task training potentiates phasic pontine-wave density in the rat: a mechanism for sleep-dependent plasticity. J Neurosci 2000;20:8607–13.

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