Slow waves and learning: beyond correlations

Sleep smart-optimizing sleep for declarative learning and memory

The last decade has witnessed a spurt of new publications documenting sleep's essential contribution to the brains ability to form lasting memories. For the declarative memory domain, slow wave sleep (the deepest sleep stage) has the greatest beneficial effect on the consolidation of memories acquired during preceding wakefulness. The finding that newly encoded memories become reactivated during subsequent sleep fostered the idea that reactivation leads to the strengthening and transformation of the memory trace. According to the active system consolidation account, trace reactivation leads to the redistribution of the transient memory representations from the hippocampus to the long-lasting knowledge networks of the cortex. Apart from consolidating previously learned information, sleep also facilitates the encoding of new memories after sleep, which probably relies on the renormalization of synaptic weights during sleep as suggested by the synaptic homeostasis theory. During wakefulness overshooting potentiation causes an imbalance in synaptic weights that is countered by synaptic downscaling during subsequent sleep. This review briefly introduces the basic concepts and central findings of the research on sleep and memory, and discusses implications of this lab-based work for everyday applications to make the best possible use of sleep's beneficial effect on learning and memory.

EEG power during waking and NREM sleep in primary insomnia

OBJECTIVE

Pathophysiological models of insomnia invoke the concept of 24-hour hyperarousal, which could lead to symptoms and physiological findings during waking and sleep. We hypothesized that this arousal could be seen in the waking electroencephalogram (EEG) of individuals with primary insomnia (PI), and that waking EEG power would correlate with non-REM (NREM) EEG.

METHODS

Subjects included 50 PI and 32 good sleeper controls (GSC). Five minutes of eyes closed waking EEG were collected at subjects' usual bedtimes, followed by polysomnography (PSG) at habitual sleep times. An automated algorithm and visual editing were used to remove artifacts from waking and sleep EEGs, followed by power spectral analysis to estimate power from 0.5-32 Hz.

RESULTS

We did not find significant differences in waking or NREM EEG spectral power of PI and GSC. Significant correlations between waking and NREM sleep power were observed across all frequency bands in the PI group and in most frequency bands in the GSC group.

CONCLUSIONS

The absence of significant differences between groups in waking or NREM EEG power suggests that our sample was not characterized by a high degree of cortical arousal. The consistent correlations between waking and NREM EEG power suggest that, in samples with elevated NREM EEG beta activity, waking EEG power may show a similar pattern.

A neocortical delta rhythm facilitates reciprocal interlaminar interactions via nested theta rhythms

Delta oscillations (1-4 Hz) associate with deep sleep and are implicated in memory consolidation and replay of cortical responses elicited during wake states. A potent local generator has been characterized in thalamus, and local generators in neocortex have been suggested. Here we demonstrate that isolated rat neocortex generates delta rhythms in conditions mimicking the neuromodulatory state during deep sleep (low cholinergic and dopaminergic tone). The rhythm originated in an NMDA receptor-driven network of intrinsic bursting (IB) neurons in layer 5, activating a source of GABAB receptor-mediated inhibition. In contrast, regular spiking (RS) neurons in layer 5 generated theta-frequency outputs. In layer 2/3 principal cells, outputs from IB cells associated with IPSPs, whereas those from layer 5 RS neurons related to nested bursts of theta-frequency EPSPs. Both interlaminar spike and field correlations revealed a sequence of events whereby sparse spiking in layer 2/3 was partially reflected back from layer 5 on each delta period. We suggest that these reciprocal, interlaminar interactions may represent a "Helmholtz machine"-like process to control synaptic rescaling during deep sleep.