Two types of slow waves in anesthetized and sleeping brains (original) (raw)

Large-Scale Cortical Dynamics of Sleep Slow Waves

PLoS ONE, 2012

Slow waves constitute the main signature of sleep in the electroencephalogram (EEG). They reflect alternating periods of neuronal hyperpolarization and depolarization in cortical networks. While recent findings have demonstrated their functional role in shaping and strengthening neuronal networks, a large-scale characterization of these two processes remains elusive in the human brain. In this study, by using simultaneous scalp EEG and intracranial recordings in 10 epileptic subjects, we examined the dynamics of hyperpolarization and depolarization waves over a large extent of the human cortex. We report that both hyperpolarization and depolarization processes can occur with two different characteristic time durations which are consistent across all subjects. For both hyperpolarization and depolarization waves, their average speed over the cortex was estimated to be approximately 1 m/s. Finally, we characterized their propagation pathways by studying the preferential trajectories be...

A continuum model for the dynamics of the phase transition from slow-wave sleep to REM sleep

Modeling Phase Transitions in the Brain, 2009

The cortical transition from the slow-wave pattern of sleep (SWS) to the rapideye-movement (REM) pattern is a dramatic feature of the somnogram. Indeed, the change in the electrocorticogram (ECoG) is so abrupt that the moment of transition usually can be identified with a time-resolution of about one second . Although the neuromodulatory environment and electroencephalographic patterns recorded during the steady states of SWS and REM have been well described , the dynamics of the transition itself has been described only in a qualitative observational fashion [12], and has not been the focus of detailed quantitative modeling.

Sound Asleep: Processing and Retention of Slow Oscillation Phase-Targeted Stimuli

PLoS ONE, 2014

The sleeping brain retains some residual information processing capacity. Although direct evidence is scarce, a substantial literature suggests the phase of slow oscillations during deep sleep to be an important determinant for stimulus processing. Here, we introduce an algorithm for predicting slow oscillations in real-time. Using this approach to present stimuli directed at both oscillatory up and down states, we show neural stimulus processing depends importantly on the slow oscillation phase. During ensuing wakefulness, however, we did not observe differential brain or behavioral responses to these stimulus categories, suggesting no enduring memories were formed. We speculate that while simpler forms of learning may occur during sleep, neocortically based memories are not readily established during deep sleep. Citation: Cox R, Korjoukov I, de Boer M, Talamini LM (2014) Sound Asleep: Processing and Retention of Slow Oscillation Phase-Targeted Stimuli. PLoS ONE 9(7): e101567.

Sleep, neuroengineering and dynamics

Cognitive Neurodynamics, 2012

Modeling of consciousness-related phenomena and neuroengineering are fields that are rapidly growing together. We review recent approaches and developments and point out some promising directions of future research: Understanding the dynamics of consciousness states and associated oscillations, pathological oscillations as well as their treatment by stimulation, neuroprosthetics and braincomputer-interface approaches, and stimulation approaches that probe, influence and strengthen memory consolidation. In all these fields, computational models connect theory, neurophysiology and neuroengineering research and pave a way towards medical applications.

Oscillating circuitries in the sleeping brain

Nature Reviews Neuroscience, 2019

Since the first recordings of electrical activity in the brain 1,2 , scalp electroencephalography (EEG) has been commonly used to measure the differences in generalized brain activity between wakefulness and sleep. The classical descriptions of sleep-related brain activity, which were derived from low spatial resolution EEG, led to the distinction between rapid eye movement (REM, also termed paradoxical) sleep and non-REM (NREM) sleep (Fig. 1a). However, the multifaceted organization of sleep-related brain activity in space and time has only been appreciated in the past decade. Theta and gamma rhythms are the hallmarks of REM sleep as recorded by scalp EEG or intracranial local field potentials (LFPs, Fig. 1a), whereas the predominant oscillations during NREM sleep are slow oscillations, delta waves, spindles and sharp wave-ripples (SWRs). These typical sleeprelated oscillations result from the synchronous activity of neural circuits restricted to the thalamus, neocortex or hippocampus. Their amplitudes correlate with the level of synchronization of underlying neuronal firing, and strongly depend on the intrinsic properties of ion channels, transporters and receptors expressed at the cell membrane, cell morphology, and extrinsic influences from synaptic inputs and background neural activity (that is, noise). At the network level, neural circuit oscillations m i g ht a l s o r e s ult f r o m m o n os y n aptic i n t er a c tions b e t we en fast excitatory and inhibitory neurons, feedback loops (for example, recurrent thalamocortical resonance) and slower forms of neuromodulation. Advances in multichannel surface and intracranial electrophysiological recordings in humans and rodents, together with functional imaging of brain activity across sleep states, have revealed a complex landscape of regionspecific activity. For example, 21-channel EEG recordings in humans showed a marked increase of oscillatory activity in the theta band concomitant with a decrease in the alpha band at the onset of NREM sleep; this increase

Spontaneous neural activity during human slow wave sleep

Proceedings of The National Academy of Sciences, 2008

Slow wave sleep (SWS) is associated with spontaneous brain oscillations that are thought to participate in sleep homeostasis and to support the processing of information related to the experiences of the previous awake period. At the cellular level, during SWS, a slow oscillation (<1 Hz) synchronizes firing patterns in large neuronal populations and is reflected on electroencephalography (EEG) recordings as large-amplitude, low-frequency waves. By using simultaneous EEG and event-related functional magnetic resonance imaging (fMRI), we characterized the transient changes in brain activity consistently associated with slow waves (>140 V) and delta waves (75-140 V) during SWS in 14 non-sleep-deprived normal human volunteers. Significant increases in activity were associated with these waves in several cortical areas, including the inferior frontal, medial prefrontal, precuneus, and posterior cingulate areas. Compared with baseline activity, slow waves are associated with significant activity in the parahippocampal gyrus, cerebellum, and brainstem, whereas delta waves are related to frontal responses. No decrease in activity was observed. This study demonstrates that SWS is not a state of brain quiescence, but rather is an active state during which brain activity is consistently synchronized to the slow oscillation in specific cerebral regions. The partial overlap between the response pattern related to SWS waves and the waking default mode network is consistent with the fascinating hypothesis that brain responses synchronized by the slow oscillation restore microwake-like activity patterns that facilitate neuronal interactions.

A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

bioRxiv (Cold Spring Harbor Laboratory), 2021

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scaleintegrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirical spontaneous and stimulus-evoked dynamics in the space, time, phase, and frequency domains. Remarkably, the model also reproduces brain-wide enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.