In a recent series of experiments, we demonstrated that a visuomotor adaptation task, 12 hours of left arm immobilization, and rapid transcranial magnetic stimulation (rTMS) during waking can each induce local changes in the topography of electroencephalographic (EEG) slow wave activity (SWA) during subsequent non-rapid eye movement (NREM) sleep. However, the poor spatial resolution of EEG and the difficulty of relating scalp potentials to the activity of the underlying cortex limited the interpretation of these results. In order to better understand local cortical regulation of sleep, we used source modeling to show that plastic changes in specific cortical areas during waking produce correlated changes in SWA during sleep in those same areas. We found that implicit learning of a visuomotor adaptation task induced an increase in SWA in right premotor and sensorimotor cortices when compared to a motor control. These same areas have previously been shown to be selectively involved in the performance of this task. We also found that arm immobilization resulted in a decrease in SWA in sensorimotor cortex. Inducing cortical potentiation with repetitive transcranial magnetic stimulation (rTMS) caused an increase in SWA in the targeted area and a decrease in SWA in the contralateral cortex. Finally, we report the first evidence that these modulations in SWA may be related to the dynamics of individual slow waves. We conclude that there is a local, plasticity dependent component to sleep regulation and confirm previous inferences made from the scalp data.

Objective

It has been hypothesized that slow wave activity, a well established measure of sleep homeostasis that increases after waking and decreases after sleep, may reflect changes in cortical synaptic strength. If so, the amplitude of sensory evoked responses should also vary as a function of time awake and asleep in a way that reflects sleep homeostasis.

Methods

Using 256-channel, high-density electroencephalography (EEG) in 12 subjects, auditory evoked potentials (AEP) and spontaneous waking data were collected during wakefulness before and after sleep.

Results

The amplitudes of the N1 and P2 waves of the AEP were reduced after a night of sleep. In addition, the decline in N1 amplitude correlated with low-frequency EEG power during non-rapid eye movement sleep and spontaneous wakefulness, both homeostatically regulated measures of sleep need.

Conclusion

The decline in AEP amplitude after a night of sleep may reflect a homeostatic reduction in synaptic strength.

Significance

These findings provide further evidence for a connection between synaptic plasticity and sleep homeostasis.

Sleep spindles are an electroencephalographic (EEG) hallmark of non-rapid eye movement (NREM) sleep and are believed to mediate many sleep-related functions, from memory consolidation to cortical development. Spindles differ in location, frequency, and association with slow waves, but whether this heterogeneity may reflect different physiological processes and potentially serve different functional roles remains unclear. Here we utilized a unique opportunity to record intracranial depth EEG and single-unit activity in multiple brain regions of neurosurgical patients to better characterize spindle activity in human sleep. We find that spindles occur across multiple neocortical regions, and less frequently also in the parahippocampal gyrus and hippocampus. Most spindles are spatially restricted to specific brain regions. In addition, spindle frequency is topographically organized with a sharp transition around the supplementary motor area between fast (13-15Hz) centroparietal spindles often occurring with slow wave up-states, and slow (9-12Hz) frontal spindles occurring 200ms later on average. Spindle variability across regions may reflect the underlying thalamocortical projections. We also find that during individual spindles, frequency decreases within and between regions. In addition, deeper sleep is associated with a reduction in spindle occurrence and spindle frequency. Frequency changes between regions, during individual spindles, and across sleep may reflect the same phenomenon, the underlying level of thalamocortical hyperpolarization. Finally, during spindles neuronal firing rates are not consistently modulated, although some neurons exhibit phase-locked discharges. Overall, anatomical considerations can account well for regional spindle characteristics, while variable hyperpolarization levels can explain differences in spindle frequency.

Background: The thalamic reticular nucleus (TRN) is a shell-shaped gamma amino butyric acid (GABA)ergic nucleus, which is uniquely placed between the thalamus and the cortex, because it receives excitatory afferents from both cortical and thalamic neurons and sends inhibitory projections to all nuclei of the dorsal thalamus. Method: A review of the evidence suggesting that the TRN is implicated in the neurobiology of schizophrenia. Results: TRN-thalamus circuits are implicated in bottom-up as well as top-down processing. TRN projections to nonspecific nuclei of the dorsal thalamus mediate top-down processes, including attentional modulation, which are initiated by cortical afferents to the TRN. TRN-thalamus circuits are also involved in bottom-up activities, including sensory gating and the transfer to the cortex of sleep spindles. Intriguingly, deficits in attention and sensory gating have been consistently found in schizophrenics, including first-break and chronic patients. Furthermore, high-density electroencephalographic studies have revealed a marked reduction in sleep spindles in schizophrenics. Conclusion: On the basis of our current knowledge on the molecular and anatomo-functional properties of the TRN, we suggest that this thalamic GABAergic nucleus may be involved in the neurobiology of schizophrenia.

Slow waves are the most prominent electroencephalographic (EEG) feature of non-rapid eye movement (NREM) sleep. During NREM sleep, cortical neurons oscillate approximately once every second between a depolarized upstate, when cortical neurons are actively firing, and a hyperpolarized downstate, when cortical neurons are virtually silent (Steriade et al., 1993a; Destexhe et al., 1999; Steriade et al., 2001). Intracellular recordings indicate that the origins of the slow oscillation are cortical and that cortico-cortical connections are necessary for their synchronization (Steriade et al. 1993b; Amzica and Steriade, 1995; Timofeev and Steriade, 1996; Timofeev et al., 2000). The currents produced by the near-synchronous slow oscillation of large populations of neurons appear on the scalp as EEG slow waves (Amzica and Steriade, 1997).

Despite this cellular understanding, questions remain about the role of specific cortical structures in individual slow waves. Early EEG studies of slow waves in humans were limited by the small number of derivations employed and by the difficulty of relating scalp potentials to underlying brain activity (Brazier 1949; Roth et al 1956). Functional neuroimaging methods offer exceptional spatial resolution but lack the temporal resolution to track individual slow waves (Maquet, 2000; Dang-Vu et al., 2008). Intracranial recordings in patient populations are limited by the availability of medically necessary electrode placements and can be confounded by pathology and medications (Nir et al., 2010; Cash et al., 2009; Wenneberg 2010).

Source modeling of high-density EEG recordings offers a unique opportunity for neuroimaging sleep slow waves. So far, the results have challenged several of the influential topographic observations about slow waves that had persisted since the original EEG recordings of sleep. These recent analyses revealed that individual slow waves are idiosyncratic cortical events and that the negative peak of the EEG slow wave often involves cortical structures not necessarily apparent from the scalp, like the inferior frontal gyrus, anterior cingulate, posterior cingulate and precuneus (Murphy et al., 2009). In addition, not only do slow waves travel (Massimini et al., 2004), but they often do so preferentially through the areas comprising the major connectional backbone of the human cortex (Hagmann et al., 2008). In this chapter we will review the cellular, intracranial recording and neuroimaging results concerning EEG slow waves. We will also confront a long held belief about peripherally evoked slow waves, also known as K-complexes, namely that they are modality-independent and do not involve cortical sensory pathways. The analysis included here is the first to directly compare K-complexes evoked with three different stimulation modalities within the same subject on the same night using high-density EEG.

Slow waves and sleep spindles are the two main oscillations occurring during NREM sleep. While slow oscillations are primarily generated and modulated by the cortex, sleep spindles are initiated by the thalamic reticular nucleus (TRN), and regulated by thalamo-reticular and thalamo-cortical circuits. In a recent high-density electroencephalographic (hd-EEG) study we found that 18 medicated schizophrenics had reduced sleep spindles compared to healthy and depressed subjects during the first NREM episode. Here we investigated whether spindle deficits were: a) present in a larger sample of schizophrenic patients; b) consistent across the night; c) related to antipsychotic medications; d) suggestive of impairments in specific neuronal circuits. Whole night hd-EEG recordings were performed in 49 schizophrenics, 20 non-schizophrenic patients on antipsychotics and 44 healthy subjects. In addition to sleep spindles, several parameters of slow waves were assessed. Schizophrenics had whole-night deficits in spindle power (12–16 Hz) and in slow (12–14 Hz) and fast (14–16 Hz) spindle amplitude, duration, number and integrated spindle activity (ISA) in prefrontal, centroparietal and temporal regions. ISA and spindle number had the largest effect sizes (ES≥2.21). By contrast, no slow wave deficits were found in schizophrenics. These results indicate that spindle deficits i) can be reliably established in schizophrenics, ii) are stable across the night, iii) are unlikely to be due to antipsychotic medications, and iv) point to deficits in TRN and thalamo-reticular circuits.

The consolidation of memories in a variety of learning processes benefits from post-training sleep, and recent work has suggested a role for sleep slow wave activity (SWA). Previous studies using a visuomotor learning task showed a local increase in sleep SWA in right parietal cortex, which was correlated with post-sleep performance enhancement. In these as in most similar studies, learning took place in the evening, shortly before sleep. Thus, it is currently unknown whether learning a task in the morning, followed by the usual daily activities, would also result in a local increase in sleep SWA during the night, and in a correlated enhancement in performance the next day. To answer this question, a group of subjects performed a visuomotor learning task in the morning and was retested the following morning. Whole night sleep was recorded with high-density EEG. We found an increase of SWA over the right posterior parietal areas that was most evident during the second sleep cycle. Performance improved significantly the following morning, and the improvement was positively correlated with the SWA increase in the second sleep cycle. These results suggest that training-induced changes in sleep SWA and post-sleep improvements do not depend upon the time interval between original training and sleep.

Sleep slow wave activity (SWA) is thought to reflect sleep need, increasing in proportion to the prior time awake and decreasing during sleep, though the underlying mechanisms are unclear. Recent studies have shown that procedures presumably leading to local plastic changes in the cerebral cortex can lead to local changes in SWA during subsequent sleep. To further investigate the connection between cortical plasticity and sleep SWA, in this study we employed a paired associative stimulation (PAS) protocol, in which median nerve stimuli were followed at different intervals (25 or 10 ms) by transcranial magnetic stimulation (TMS) pulses to the contralateral cortical hand area. As expected, such a protocol lead to a sustained increase (LTP-like) or decrease (LTD-like) of cortical excitability as measured by motor evoked potentials. By employing a TMS-compatible high-density electroencephalographic (EEG) system, we also found that, in individual subjects, TMS-evoked cortical responses over sensorimotor cortex changed with different interstimulus intervals. Moreover, during subsequent sleep, SWA increased locally in subjects whose TMS-evoked cortical responses had increased after PAS, and decreased in subjects whose cortical responses had decreased. Changes in TMS-evoked cortical EEG response and change in sleep SWA were localized to similar cortical regions and were positively correlated. Together, these results suggest that changes in cortical excitability in opposite directions lead to corresponding changes in local sleep regulation, as reflected by SWA, providing evidence for a tight relationship between cortical plasticity and sleep intensity.

Background

Sleep slow wave activity (SWA) is thought to reflect sleep need, increasing in proportion to the length of prior wakefulness and decreasing during sleep. However, the process responsible for SWA regulation is not known. We showed recently that SWA increases locally after a learning task involving a circumscribed brain region, suggesting that SWA may reflect plastic changes triggered by learning.

Methodology/Principal Findings

To test this hypothesis directly, we used transcranial magnetic stimulation (TMS) in conjunction with high-density EEG in humans. We show that 5-Hz TMS applied to motor cortex induces a localized potentiation of TMS-evoked cortical EEG responses. We then show that, in the sleep episode following 5-Hz TMS, SWA increases markedly (+39.1±17.4%, p<0.01, n = 10). Electrode coregistration with magnetic resonance images localized the increase in SWA to the same premotor site as the maximum TMS-induced potentiation during wakefulness. Moreover, the magnitude of potentiation during wakefulness predicts the local increase in SWA during sleep.

Conclusions/Significance

These results provide direct evidence for a link between plastic changes and the local regulation of sleep need.