A tradeoff between efficiency and robustness in the hippocampal-neocortical memory network during human and rodent sleep.

Sleep constitutes a brain state of disengagement from the external world that supports memory consolidation and restores cognitive resources. The precise mechanisms how sleep and its varied stages support information processing remain largely unknown. Synaptic scaling models imply that daytime learning accumulates neural information, which is then consolidated and downregulated during sleep. Currently, there is a lack of in-vivo data from humans and rodents that elucidate if, and how, sleep renormalizes information processing capacities. From an information-theoretical perspective, a consolidation process should entail a reduction in neural pattern variability over the course of a night. Here, in a cross-species intracranial study, we identify a tradeoff in the neural population code during sleep where information coding efficiency is higher in the neocortex than in hippocampal archicortex in humans than in rodents as well as during wakefulness compared to sleep. Critically, non-REM sleep selectively reduces information coding efficiency through pattern repetition in the neocortex in both species, indicating a transition to a more robust information coding regime. Conversely, the coding regime in the hippocampus remained consistent from wakefulness to non-REM sleep. These findings suggest that new information could be imprinted to the long-term mnemonic storage in the neocortex through pattern repetition during sleep. Lastly, our results show that task engagement increased coding efficiency, while medically-induced unconsciousness disrupted the population code. In sum, these findings suggest that neural pattern variability could constitute a fundamental principle underlying cognitive engagement and memory formation, while pattern repetition reflects robust coding, possibly underlying the consolidation process.

Defining slow wave sleep without slow waves.

Recent research by Parks, Schneider, and colleagues demonstrates that brain states during rodent sleep can be predicted from neural activity on millisecond and micrometer scales. These findings contradict the traditional view that defines sleep by brain-wide oscillations. Instead, this work posits that nonoscillatory activity governs different brain states.

Multiple Intrinsic Timescales Govern Distinct Brain States in Human Sleep.

Human sleep exhibits multiple, recurrent temporal regularities, ranging from circadian rhythms to sleep stage cycles and neuronal oscillations during nonrapid eye movement sleep. Moreover, recent evidence revealed a functional role of aperiodic activity, which reliably discriminates different sleep stages. Aperiodic activity is commonly defined as the spectral slope χ of the 1/frequency (1/fχ) decay function of the electrophysiological power spectrum. However, several lines of inquiry now indicate that the aperiodic component of the power spectrum might be better characterized by a superposition of several decay processes with associated timescales. Here, we determined multiple timescales, which jointly shape aperiodic activity using human intracranial electroencephalography. Across three independent studies (47 participants, 23 female), our results reveal that aperiodic activity reliably dissociated sleep stage-dependent dynamics in a regionally specific manner. A principled approach to parametrize aperiodic activity delineated several, spatially and state-specific timescales. Lastly, we employed pharmacological modulation by means of propofol anesthesia to disentangle state-invariant timescales that may reflect physical properties of the underlying neural population from state-specific timescales that likely constitute functional interactions. Collectively, these results establish the presence of multiple intrinsic timescales that define the electrophysiological power spectrum during distinct brain states.

Spectral Slope and Lempel-Ziv Complexity as Robust Markers of Brain States during Sleep and Wakefulness.

Nonoscillatory measures of brain activity such as the spectral slope and Lempel-Ziv complexity are affected by many neurological disorders and modulated by sleep. A multitude of frequency ranges, particularly a broadband (encompassing the full spectrum) and a narrowband approach, have been used especially for estimating the spectral slope. However, the effects of choosing different frequency ranges have not yet been explored in detail. Here, we evaluated the impact of sleep stage and task engagement (resting, attention, and memory) on slope and complexity in a narrowband (30-45 Hz) and broadband (1-45 Hz) frequency range in 28 healthy male human subjects (21.54 ± 1.90 years) using a within-subject design over 2 weeks with three recording nights and days per subject. We strived to determine how different brain states and frequency ranges affect slope and complexity and how the two measures perform in comparison. In the broadband range, the slope steepened, and complexity decreased continuously from wakefulness to N3 sleep. REM sleep, however, was best discriminated by the narrowband slope. Importantly, slope and complexity also differed between tasks during wakefulness. While narrowband complexity decreased with task engagement, the slope flattened in both frequency ranges. Interestingly, only the narrowband slope was positively correlated with task performance. Our results show that slope and complexity are sensitive indices of brain state variations during wakefulness and sleep. However, the spectral slope yields more information and could be used for a greater variety of research questions than Lempel-Ziv complexity, especially when a narrowband frequency range is used.

Human REM sleep recalibrates neural activity in support of memory formation.

The proposed mechanisms of sleep-dependent memory consolidation involve the overnight regulation of neural activity at both synaptic and whole-network levels. Now, there is a lack of in vivo data in humans elucidating if, and how, sleep and its varied stages balance neural activity, and if such recalibration benefits memory. We combined electrophysiology with in vivo two-photon calcium imaging in rodents as well as intracranial and scalp electroencephalography (EEG) in humans to reveal a key role for non-oscillatory brain activity during rapid eye movement (REM) sleep to mediate sleep-dependent recalibration of neural population dynamics. The extent of this REM sleep recalibration predicted the success of overnight memory consolidation, expressly the modulation of hippocampal-neocortical activity, favoring remembering rather than forgetting. The findings describe a non-oscillatory mechanism how human REM sleep modulates neural population activity to enhance long-term memory.

An evolutionary conserved division-of-labor between archicortical and neocortical ripples organizes information transfer during sleep.

Systems-level memory consolidation during sleep depends on the temporally precise interplay between cardinal sleep oscillations. Specifically, hippocampal ripples constitute a key substrate of the hippocampal-neocortical dialog underlying memory formation. Recently, it became evident that ripples are not unique to archicortex, but constitute a wide-spread neocortical phenomenon. To date, little is known about the morphological similarities between archi- and neocortical ripples. Moreover, it remains undetermined if neocortical ripples fulfill distinct functional roles. Leveraging intracranial recordings from the human medial temporal lobe (MTL) and neocortex during sleep, our results reveal region-specific functional specializations, albeit a near-uniform morphology. While MTL ripples synchronize the memory network to trigger directional MTL-to-neocortical information flow, neocortical ripples reduce information flow to minimize interference. At the population level, MTL ripples confined population dynamics to a low-dimensional subspace, while neocortical ripples diversified the population response; thus, constituting an effective mechanism to functionally uncouple the MTL-neocortical network. Critically, we replicated the key findings in rodents, where the same division-of-labor between archi- and neocortical ripples was evident. In sum, these results uncover an evolutionary preserved mechanism where the precisely coordinated interplay between MTL and neocortical ripples temporally segregates MTL information transfer from subsequent neocortical processing during sleep.

Oscillatory and aperiodic neuronal activity in working memory following anesthesia.

OBJECTIVE: Anesthesia and surgery are associated with cognitive impairment, particularly memory deficits. So far, electroencephalography markers of perioperative memory function remain scarce. METHODS: We included male patients >60 years scheduled for prostatectomy under general anesthesia. We obtained neuropsychological assessments and a visual match-to-sample working memory task with simultaneous 62-channel scalp electroencephalography 1 day before and 2 to 3 days after surgery. RESULTS: Twenty-six patients completed both pre- and postoperative sessions. Compared with preoperative performance, verbal learning deteriorated after anesthesia (California Verbal Learning Test total recall; t25 = -3.25, p = 0.015, d = -0.902), while visual working memory performance showed a dissociation between match and mismatch accuracy (match*session F1,25 = 3.866, p = 0.060). Better verbal learning was associated with an increase of aperiodic brain activity (total recall r = 0.66, p = 0.029, learning slope r = 0.66, p = 0.015), whereas visual working memory accuracy was tracked by oscillatory theta/alpha (7 - 9 Hz), low beta (14 - 18 Hz) and high beta/gamma (34 - 38 Hz) activity (matches: p < 0.001, mismatches: p = 0.022). CONCLUSIONS: Oscillatory and aperiodic brain activity in scalp electroencephalography track distinct features of perioperative memory function. SIGNIFICANCE: Aperiodic activity provides a potential electroencephalographic biomarker to identify patients at risk for postoperative cognitive impairments.

Consciousness is supported by near-critical slow cortical electrodynamics.

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory dynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magnetoencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of low-frequency cortical electric oscillations away from this critical point; conversely, we show that psychedelics may increase the information richness of cortical activity by tuning low-frequency cortical oscillations closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC) and show that assessing the proximity of slow cortical oscillatory electrodynamics to the edge-of-chaos critical point may be useful as an index of consciousness in the clinical setting.

Aperiodic sleep networks promote memory consolidation.

Hierarchical synchronization of sleep oscillations establishes communication pathways to support memory reactivation, transfer, and consolidation. From an information-theoretical perspective, oscillations constitute highly structured network states that provide limited information-coding capacity. Recent findings indicate that sleep oscillations occur in transient bursts that are interleaved with aperiodic network states, which were previously considered to be random noise. We argue that aperiodic activity exhibits unique and variable spatiotemporal patterns, providing an ideal information-rich neurophysiological substrate for imprinting new mnemonic patterns onto existing circuits. We discuss novel avenues in conceptualizing and quantifying aperiodic network states during sleep to further understand their relevance and interplay with sleep oscillations in support of memory consolidation.

An electrophysiological marker of arousal level in humans.

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, 'wake-like' EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.

Electroencephalogram (EEG for short) is a widespread technique that helps to monitor the electrical activity of the brain. In particular, it can be used to examine, recognize and compare different states of brain consciousness such as sleep, wakefulness or general anesthesia. Yet, during rapid eye movement sleep (the sleep phase in which dreaming occurs), the electrical activity of the brain is similar to the one recorded during wakefulness, making it difficult to distinguish these states based on EEG alone. EEG records brain activity in the shape of rhythmic waves whose frequency, shape and amplitude vary depending on the state of consciousness. In the EEG signal from the human brain, the higher frequency waves are weaker than the low-frequency waves: a measure known as spectral slope reflects the degree of this difference in the signal strength. Previous research suggests that spectral slope can be used to distinguish wakefulness from anesthesia and non-REM sleep. Here, Lendner et al. explored whether certain elements of the spectral slope could also discern wakefulness from all states of reduced arousal. EEG readings were taken from patients and volunteers who were awake, asleep or under anesthesia, using electrodes placed either on the scalp or into the brain. Lendner et al. found that the spectral slope could distinguish wakefulness from anesthesia, deep non-REM and REM sleep. The changes in the spectral slope during sleep could accurately track the degree of arousal with great temporal precision and across a wide range of time scales. This method means that states of consciousness can be spotted just from a scalp EEG. In the future, this approach could be embedded into the techniques used for monitoring sleep or anesthesia during operations; it could also be harnessed to monitor other low-response states, such as comas.

Bidirectional prefrontal-hippocampal dynamics organize information transfer during sleep in humans.

How are memories transferred from short-term to long-term storage? Systems-level memory consolidation is thought to be dependent on the coordinated interplay of cortical slow waves, thalamo-cortical sleep spindles and hippocampal ripple oscillations. However, it is currently unclear how the selective interaction of these cardinal sleep oscillations is organized to support information reactivation and transfer. Here, using human intracranial recordings, we demonstrate that the prefrontal cortex plays a key role in organizing the ripple-mediated information transfer during non-rapid eye movement (NREM) sleep. We reveal a temporally precise form of coupling between prefrontal slow-wave and spindle oscillations, which actively dictates the hippocampal-neocortical dialogue and information transfer. Our results suggest a model of the human sleeping brain in which rapid bidirectional interactions, triggered by the prefrontal cortex, mediate hippocampal activation to optimally time subsequent information transfer to the neocortex during NREM sleep.