Sleeping on the motor engram: The multifaceted nature of sleep-related motor memory consolidation

Sleep Strengthens Predictive Sequence Coding

Predictive-coding theories assume that perception and action are based on internal models derived from previous experience. Such internal models require selection and consolidation to be stored over time. Sleep is known to support memory consolidation. We hypothesized that sleep supports both consolidation and abstraction of an internal task model that is subsequently used to predict upcoming stimuli. Human subjects (of either sex) were trained on deterministic visual sequences and tested with interleaved deviant stimuli after retention intervals of sleep or wakefulness. Adopting a predictive-coding approach, we found increased prediction strength after sleep, as expressed by increased error rates to deviant stimuli, but fewer errors for the immediately following standard stimuli. Sleep likewise enhanced the formation of an abstract sequence model, independent of the temporal context during training. Moreover, sleep increased confidence for sequence knowledge, reflecting enhanced metacognitive access to the model. Our results suggest that sleep supports the formation of internal models which can be used to predict upcoming events in different contexts.SIGNIFICANCE STATEMENT To efficiently interact with the ever-changing world, we predict upcoming events based on similar previous experiences. Sleep is known to benefit memory consolidation. However, it is not clear whether sleep specifically supports the transformation of past experience into predictions of future events. Here, we find that, when human subjects sleep after learning a sequence of predictable visual events, they make better predictions about upcoming events compared with subjects who stayed awake for an equivalent period of time. In addition, sleep supports the transfer of such knowledge between different temporal contexts (i.e., when sequences unfold at different speeds). Thus, sleep supports perception and action by enhancing the predictive utility of previous experiences.

Somatosensory electrical stimulation improves skill acquisition, consolidation, and transfer by increasing sensorimotor activity and connectivity

The interaction between the somatosensory and motor systems is important for normal human motor function and learning. Enhancing somatosensory input using somatosensory electrical stimulation (SES) can increase motor performance, but the neuronal mechanisms underlying these effects are largely unknown. With EEG, we examined whether skill acquisition, consolidation, and interlimb transfer after SES was related to increased activity in sensorimotor regions, as assessed by the N30 somatosensory evoked potential or rather increased connectivity between these regions, as assessed by the phase slope index (PSI). Right- and left-hand motor performance and EEG measures were taken before, immediately after, and 24 h ( day 2) after either SES ( n = 12; 5 men) or Control ( n = 12; 5 men). The results showed skill acquisition and consolidation in the stimulated right hand immediately after SES (6%) and on day 2 (9%) and interlimb transfer to the nonstimulated left hand on day 2 relative to Control (8%, all P < 0.05). Increases in N30 amplitudes correlated with skill acquisition while PSI from electrodes that represent the posterior parietal and primary somatosensory cortex to the electrode representing the primary motor cortex correlated with skill consolidation. In contrast, interlimb transfer did not correlate with the EEG-derived neurophysiological estimates obtained in the present study, which may indicate the involvement of subcortical structures in interlimb transfer after SES. In conclusion, weak peripheral somatosensory inputs in the form of SES improve skill acquisition, consolidation, and interlimb transfer that coincide with different cortical adaptations, including enhanced N30 amplitudes and PSI. NEW & NOTEWORTHY The relationship between adaptations in synaptic plasticity and motor learning following somatosensory electrical stimulation (SES) is incompletely understood. Here, we used for the first time a multifactorial approach that examined skill acquisition, consolidation, and interlimb transfer following 20 min of SES. In addition, we quantified sensorimotor integration and the magnitude and direction of connectivity with EEG. Following artificial electrical stimulation, increases in sensorimotor integration and connectivity were found to correlate with skill acquisition and consolidation, respectively.

Role of normal sleep and sleep apnea in human memory processing

A fundamental problem in the field of obstructive sleep apnea (OSA) and memory is that it has historically minimized the basic neurobiology of sleep's role in memory. Memory formation has been classically divided into phases of encoding, processing/consolidation, and retrieval. An abundance of evidence suggests that sleep plays a critical role specifically in the processing/consolidation phase, but may do so differentially for memories that were encoded using particular brain circuits. In this review, we discuss some of the more established evidence for sleep's function in the processing of declarative, spatial navigational, emotional, and motor/procedural memories and more emerging evidence highlighting sleep's importance in higher order functions such as probabilistic learning, transitive inference, and category/gist learning. Furthermore, we discuss sleep's capacity for memory augmentation through targeted/cued memory reactivation. OSA - by virtue of its associated sleep fragmentation, intermittent hypoxia, and potential brain structural effects - is well positioned to specifically impact the processing/consolidation phase, but testing this possibility requires experimental paradigms in which memory encoding and retrieval are separated by a period of sleep with and without the presence of OSA. We argue that such paradigms should focus on the specific types of memory tasks for which sleep has been shown to have a significant effect. We discuss the small number of studies in which this has been done, in which OSA nearly uniformly negatively impacts offline memory processing. When periods of offline processing are minimal or absent and do not contain sleep, as is the case in the broad literature on OSA and memory, the effects of OSA on memory are far less consistent.

Transient synchronization of hippocampo-striato-thalamo-cortical networks during sleep spindle oscillations induces motor memory consolidation

Sleep benefits motor memory consolidation. This mnemonic process is thought to be mediated by thalamo-cortical spindle activity during NREM-stage2 sleep episodes as well as changes in striatal and hippocampal activity. However, direct experimental evidence supporting the contribution of such sleep-dependent physiological mechanisms to motor memory consolidation in humans is lacking. In the present study, we combined EEG and fMRI sleep recordings following practice of a motor sequence learning (MSL) task to determine whether spindle oscillations support sleep-dependent motor memory consolidation by transiently synchronizing and coordinating specialized cortical and subcortical networks. To that end, we conducted EEG source reconstruction on spindle epochs in both cortical and subcortical regions using novel deep-source localization techniques. Coherence-based metrics were adopted to estimate functional connectivity between cortical and subcortical structures over specific frequency bands. Our findings not only confirm the critical and functional role of NREM-stage2 sleep spindles in motor skill consolidation, but provide first-time evidence that spindle oscillations [11-17 Hz] may be involved in sleep-dependent motor memory consolidation by locally reactivating and functionally binding specific task-relevant cortical and subcortical regions within networks including the hippocampus, putamen, thalamus and motor-related cortical regions.

Linking Network Activity to Synaptic Plasticity during Sleep: Hypotheses and Recent Data

Research findings over the past two decades have supported a link between sleep states and synaptic plasticity. Numerous mechanistic hypotheses have been put forth to explain this relationship. For example, multiple studies have shown structural alterations to synapses (including changes in synaptic volume, spine density, and receptor composition) indicative of synaptic weakening after a period of sleep. Direct measures of neuronal activity and synaptic strength support the idea that a period of sleep can reduce synaptic strength. This has led to the synaptic homeostasis hypothesis (SHY), which asserts that during slow wave sleep, synapses are downscaled throughout the brain to counteract net strengthening of network synapses during waking experience (e.g., during learning). However, neither the cellular mechanisms mediating these synaptic changes, nor the sleep-dependent activity changes driving those cellular events are well-defined. Here we discuss potential cellular and network dynamic mechanisms which could underlie reductions in synaptic strength during sleep. We also discuss recent findings demonstrating circuit-specific synaptic strengthening (rather than weakening) during sleep. Based on these data, we explore the hypothetical role of sleep-associated network activity patterns in driving synaptic strengthening. We propose an alternative to SHY—namely that depending on experience during prior wake, a variety of plasticity mechanisms may operate in the brain during sleep. We conclude that either synaptic strengthening or synaptic weakening can occur across sleep, depending on changes to specific neural circuits (such as gene expression and protein translation) induced by experiences in wake. Clarifying the mechanisms underlying these different forms of sleep-dependent plasticity will significantly advance our understanding of how sleep benefits various cognitive functions.