slow-wave sleep
Research Papers
Automatized online prediction of slow-wave peaks during non-rapid eye movement sleep in young and old individuals: Why we should not always rely on amplitude thresholds
Brain-state-dependent stimulation during slow-wave sleep is a promising tool for the treatment of psychiatric and neurodegenerative diseases. A widely used slow-wave prediction algorithm required for brain-state-dependent stimulation is based on a specific amplitude threshold in the electroencephalogram. However, due to decreased slow-wave amplitudes in aging and psychiatric conditions, this approach might miss many slow-waves because they do not fulfill the amplitude criterion. Here, we compared slow-wave peaks predicted via an amplitude-based versus a multidimensional approach using a topographical template of slow-wave peaks in 21 young and 21 older healthy adults. We validate predictions against the gold-standard of offline detected peaks. Multidimensionally predicted peaks resemble the gold-standard regarding spatiotemporal dynamics but exhibit lower peak amplitudes. Amplitude-based prediction, by contrast, is less sensitive, less precise and - especially in the older group - predicts peaks that differ from the gold-standard regarding spatiotemporal dynamics. Our results suggest that amplitude-based slow-wave peak prediction might not always be the ideal choice. This is particularly the case in populations with reduced slow-wave amplitudes, like older adults or psychiatric patients. We recommend the use of multidimensional prediction, especially in studies targeted at populations other than young and healthy individuals.
View Full Paper →About Sleep's Role in Memory
Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of “sleep and memory” research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.
View Full Paper →The Significance of Sigma Neurofeedback Training on Sleep Spindles and Aspects of Declarative Memory
The functional significance of sleep spindles for overnight memory consolidation and general learning aptitude as well as the effect of four 10-minute sessions of spindle frequency (11.6–16 Hz, sigma) neurofeedback-training on subsequent sleep spindle activity and overnight performance change was investigated. Before sleep, subjects were trained on a paired-associate word list task after having received either neurofeedback training (NFT) or pseudofeedback training (PFT). Although NFT had no significant impact on subsequent spindle activity and behavioral outcomes, there was a trend for enhanced sigma band-power during NREM (stage 2 to 4) sleep after NFT as compared to PFT. Furthermore, a significant positive correlation between spindle activity during slow wave sleep (in the first night half) and overall memory performance was revealed. The results support the view that the considerable inter-individual variance in sleep spindle activity can at least be partly explained by differences in the ability to acquire new declarative information. We conclude that the short NFT before sleep was not sufficient to efficiently enhance phasic spindle activity and/or to influence memory processing. NFT was, however, successful in increasing sigma power, presumably because sigma NFT effects become more easily evident in actually trained frequency bands than in associated phasic spindle activity.
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