Local and Widespread Slow Waves in Stable NREM Sleep: Evidence for Distinct Regulation Mechanisms

Novel murine closed-loop auditory stimulation paradigm elicits macrostructural sleep benefits in neurodegeneration

Boosting slow-wave activity (SWA) by modulating slow waves through closed-loop auditory stimulation (CLAS) might provide a powerful non-pharmacological tool to investigate the link between sleep and neurodegeneration. Here, we established mouse CLAS (mCLAS)-mediated SWA enhancement and explored its effects on sleep deficits in neurodegeneration, by targeting the up-phase of slow waves in mouse models of Alzheimer's disease (AD, Tg2576) and Parkinson's disease (PD, M83). We found that tracking a 2 Hz component of slow waves leads to highest precision of non-rapid eye movement (NREM) sleep detection in mice, and that its combination with a 30° up-phase target produces a significant 15-30% SWA increase from baseline in wild-type (WTAD) and transgenic (TGAD) mice versus a mock stimulation group. Conversely, combining 2 Hz with a 40° phase target yields a significant increase ranging 30-35% in WTPD and TGPD mice. Interestingly, these phase-target-triggered SWA increases are not genotype dependent but strain specific. Sleep alterations that may contribute to disease progression and burden were described in AD and PD lines. Notably, pathological sleep traits were rescued by mCLAS, which elicited a 14% decrease of pathologically heightened NREM sleep fragmentation in TGAD mice, accompanied by a steep decrease in microarousal events during both light and dark periods. Overall, our results indicate that model-tailored phase targeting is key to modulate SWA through mCLAS, prompting the acute alleviation of key neurodegeneration-associated sleep phenotypes and potentiating sleep regulation and consolidation. Further experiments assessing the long-term effect of mCLAS in neurodegeneration may majorly impact the establishment of sleep-based therapies.

Sleep and cardiac autonomic modulation in older adults: Insights from an at-home study with auditory deep sleep stimulation

The autonomic nervous system regulates cardiovascular activity during sleep, likely impacting cardiovascular health. Aging, a primary cardiovascular risk factor, is associated with cardiac autonomic disbalance and diminished sleep slow waves. Therefore, slow waves may be linked to aging, autonomic activity and cardiovascular health. However, it is unclear how sleep and slow waves are linked to cardiac autonomic profiles across multiple nights in older adults. We conducted a randomized, crossover trial involving healthy adults aged 62-78 years. Across 2 weeks, we applied auditory stimulation to enhance slow waves and compared it with a SHAM period. We measured sleep parameters using polysomnography and derived heart rate, heart rate variability approximating parasympathetic activity, and blood pulse wave approximating sympathetic activity from a wearable. Here, we report the results of 14 out of 33 enrolled participants, and show that heart rate, heart rate variability and blood pulse wave within sleep stages differ between the first and second half of sleep. Furthermore, baseline slow-wave activity was related to cardiac autonomic activity profiles during sleep. Moreover, we found auditory stimulation to reduce heart rate variability, while heart rate and blood pulse wave remained unchanged. Lastly, within subjects, higher heart rate coincided with increased slow-wave activity, indicating enhanced autonomic activation when slow waves are pronounced. Our study shows the potential of cardiac autonomic markers to offer insights into participants' baseline slow-wave activity when recorded over multiple nights. Furthermore, we highlight that averaging cardiac autonomic parameters across a night may potentially mask dynamic effects of auditory stimulation, potentially playing a role in maintaining a healthy cardiovascular system.

Thalamic Stroke and Sleep Study: Sleep-Wake, Autonomic Regulation, and Cognition

BACKGROUND

Thalamic stroke (TS) often presents with complex clinical manifestations, including sleep-wake disturbances, cognitive deficits, and autonomic dysregulation, yet the interaction between these functional alterations remains poorly understood. We aimed to investigate these interactions in a case-control lesion study.

METHODS

Patients with acute TS and no-stroke controls were included prospectively in this study. The data were collected from June 2020 to September 2022 at the stroke unit or sleep laboratory of the Inselspital (Bern). Sleep-wake variables (questionnaires, actigraphy, polysomnography including electroencephalography-based sleep macroarchitecture and microarchitecture, and analysis of electroencephalography spectral power), nocturnal heart rate variability, and cognition (5 tests: processing speed, attention, working memory, visual memory, and verbal memory) were assessed at study inclusion (within 5 days poststroke for patients with stroke).

RESULTS

Data from 16 patients with TS and 32 control volunteers were analyzed. All patients with stroke had lesions of the ventral nuclei, while 9 of 16 patients with stroke also had lesions in the mediodorsal nucleus (1 bilateral). TS was characterized by long sleep duration and high nocturnal heart rate variability with parasympathetic dominance. The alterations in sleep electroencephalography included a decrease in cyclic alternating pattern index, slow spindle density, the quantity of isolated sawtooth wave segments, and electroencephalography spectral power predominantly affecting the alpha band. The mediodorsal lesions were associated with a decrease in sleep spindle amplitude and slow wave amplitude and with an increase in phasic rapid eye movement sleep. Furthermore, patients with TS had deficits in processing speed, working memory, and verbal memory, mostly pronounced in patients with mediodorsal lesions. In a combined data set, multiple correlations were observed between sleep-wake, autonomic, and cognitive parameters, many of which depended on the presence of a TS.

CONCLUSIONS

These findings emphasize the role of the thalamus in the regulation of sleep-wake, autonomic, and cognitive functions and their interactions and provide the theoretical basis for the therapies targeting the thalamus.

Pupil size reveals arousal level fluctuations in human sleep

Recent animal research has revealed the intricate dynamics of arousal levels that are important for maintaining proper sleep resilience and memory consolidation. In humans, changes in arousal level are believed to be a determining characteristic of healthy and pathological sleep but tracking arousal level fluctuations has been methodologically challenging. Here we measured pupil size, an established indicator of arousal levels, by safely taping the right eye open during overnight sleep and tested whether pupil size affects cortical response to auditory stimulation. We show that pupil size dynamics change as a function of important sleep events across different temporal scales. In particular, our results show pupil size to be inversely related to the occurrence of sleep spindle clusters, a marker of sleep resilience. Additionally, we found pupil size prior to auditory stimulation to influence the evoked response, most notably in delta power, a marker of several restorative and regenerative functions of sleep. Recording pupil size dynamics provides insights into the interplay between arousal levels and sleep oscillations.

Sleep and circadian rhythms modeling: From hypothalamic regulatory networks to cortical dynamics and behavior

Sleep and circadian rhythms are regulated by dynamic physiologic processes that operate across multiple spatial and temporal scales. These include, but are not limited to, genetic oscillators, clearance of waste products from the brain, dynamic interplay among brain regions, and propagation of local dynamics across the cortex. The combination of these processes, modulated by environmental cues, such as light-dark cycles and work schedules, represents a complex multiscale system that regulates sleep-wake cycles and brain dynamics. Physiology-based mathematical models have successfully explained the mechanisms underpinning dynamics at specific scales and are a useful tool to investigate interactions across multiple scales. They can help answer questions such as how do electroencephalographic (EEG) features relate to subthalamic neuron activity? Or how are local cortical dynamics regulated by the homeostatic and circadian mechanisms? In this chapter, we review two types of models that are well-positioned to consider such interactions. Part I of the chapter focuses on the subthalamic sleep regulatory networks and a model of arousal dynamics capable of predicting sleep, circadian rhythms, and cognitive outputs. Part II presents a model of corticothalamic circuits, capable of predicting spatial and temporal EEG features. We then discuss existing approaches and unsolved challenges in developing unified multiscale models.

Does slow oscillation-spindle coupling contribute to sleep-dependent memory consolidation? A Bayesian meta-analysis

The active system consolidation theory suggests that information transfer between the hippocampus and cortex during sleep underlies memory consolidation. Neural oscillations during sleep, including the temporal coupling between slow oscillations (SO) and sleep spindles (SP), may play a mechanistic role in memory consolidation. However, differences in analytical approaches and the presence of physiological and behavioral moderators have led to inconsistent conclusions. This meta-analysis, comprising 23 studies and 297 effect sizes, focused on four standard phase-amplitude coupling measures including coupling phase, strength, percentage, and SP amplitude, and their relationship with memory retention. We developed a standardized approach to incorporate non-normal circular-linear correlations. We found strong evidence supporting that precise and strong SO-fast SP coupling in the frontal lobe predicts memory consolidation. The strength of this association is mediated by memory type, aging, and dynamic spatio-temporal features, including SP frequency and cortical topography. In conclusion, SO-SP coupling should be considered as a general physiological mechanism for memory consolidation.

Slow-wave sleep drives sleep-dependent renormalization of synaptic AMPA receptor levels in the hypothalamus

According to the synaptic homeostasis hypothesis (SHY), sleep serves to renormalize synaptic connections that have been potentiated during the prior wake phase due to ongoing encoding of information. SHY focuses on glutamatergic synaptic strength and has been supported by numerous studies examining synaptic structure and function in neocortical and hippocampal networks. However, it is unknown whether synaptic down-regulation during sleep occurs in the hypothalamus, i.e., a pivotal center of homeostatic regulation of bodily functions including sleep itself. We show that sleep, in parallel with the synaptic down-regulation in neocortical networks, down-regulates the levels of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) in the hypothalamus of rats. Most robust decreases after sleep were observed at both sites for AMPARs containing the GluA1 subunit. Comparing the effects of selective rapid eye movement (REM) sleep and total sleep deprivation, we moreover provide experimental evidence that slow-wave sleep (SWS) is the driving force of the down-regulation of AMPARs in hypothalamus and neocortex, with no additional contributions of REM sleep or the circadian rhythm. SWS-dependent synaptic down-regulation was not linked to EEG slow-wave activity. However, spindle density during SWS predicted relatively increased GluA1 subunit levels in hypothalamic synapses, which is consistent with the role of spindles in the consolidation of memory. Our findings identify SWS as the main driver of the renormalization of synaptic strength during sleep and suggest that SWS-dependent synaptic renormalization is also implicated in homeostatic control processes in the hypothalamus.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Exploring the local field potential signal from the subthalamic nucleus for phase-targeted auditory stimulation in Parkinson's disease

BACKGROUND

Enhancing slow waves, the electrophysiological (EEG) manifestation of non-rapid eye movement (NREM) sleep, could potentially benefit patients with Parkinson's disease (PD) by improving sleep quality and slowing disease progression. Phase-targeted auditory stimulation (PTAS) is an approach to enhance slow waves, which are detected in real-time in the surface EEG signal.

OBJECTIVE

We aimed to test whether the local-field potential of the subthalamic nucleus (STN-LFP) can be used to detect frontal slow waves and assess the electrophysiological changes related to PTAS.

METHODS

We recruited patients diagnosed with PD and undergoing Percept™ PC neurostimulator (Medtronic) implantation for deep brain stimulation of STN (STN-DBS) in a two-step surgery. Patients underwent three full-night recordings, including one between-surgeries recording and two during rehabilitation, one with DBS+ (on) and one with DBS- (off). Surface EEG and STN-LFP signals from Percept PC were recorded simultaneously, and PTAS was applied during sleep in all three recording sessions.

RESULTS

Our results show that during NREM sleep, slow waves of the cortex and STN are time-locked. PTAS application resulted in power and coherence changes, which can be detected in STN-LFP.

CONCLUSION

Our findings suggest the feasibility of implementing PTAS using solely STN-LFP signal for slow wave detection, thus without a need for an external EEG device alongside the implanted neurostimulator. Moreover, we propose options for more efficient STN-LFP signal preprocessing, including different referencing and filtering to enhance the reliability of cortical slow wave detection in STN-LFP recordings.

Change in Striatal Functional Connectivity Networks Across Two Years Due to Stimulant Exposure in Childhood ADHD: Results from the ABCD Sample

Widely prescribed for Attention-Deficit/Hyperactivity Disorder (ADHD), stimulants (e.g., methylphenidate) have been studied for their chronic effects on the brain in prospective designs controlling dosage and adherence. While controlled approaches are essential, they do not approximate real-world stimulant exposure contexts where medication interruptions, dosage non-compliance, and polypharmacy are common. Brain changes in real-world conditions are largely unexplored. To fill this gap, we capitalized on the observational design of the Adolescent Brain Cognitive Development (ABCD) study to examine effects of stimulants on large-scale bilateral cortical networks' resting-state functional connectivity (rs-FC) with 6 striatal regions (left and right caudate, putamen, and nucleus accumbens) across two years in children with ADHD. Bayesian hierarchical regressions revealed associations between stimulant exposure and change in rs-FC of multiple striatal-cortical networks, affiliated with executive and visuo-motor control, which were not driven by general psychotropic medication. Of these connections, three were selective to stimulants versus stimulant naive: reduced rs-FC between caudate and frontoparietal network, and between putamen and frontoparietal and visual networks. Comparison with typically developing children in the ABCD sample revealed stronger rs-FC reduction in stimulant-exposed children for putamen and frontoparietal and visual networks, suggesting a normalizing effect of stimulants. 14% of stimulant-exposed children demonstrated reliable reduction in ADHD symptoms, and were distinguished by stronger rs-FC reduction between right putamen and visual network. Thus, stimulant exposure for a two-year period under real-world conditions modulated striatal-cortical functional networks broadly, had a normalizing effect on a subset of networks, and was associated with potential therapeutic effects involving visual attentional control.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Interrelations and functional roles of key oscillatory activities during daytime sleep in older adults

Certain neurophysiological characteristics of sleep, in particular slow oscillations (SOs), sleep spindles, and their temporal coupling, have been well characterised and associated with human memory abilities. Delta waves, which are somewhat higher in frequency and lower in amplitude compared to SOs, and their interaction with spindles have only recently been found to play a critical role in memory processing of rodents, through a competitive interaction between SO-spindle and delta-spindle coupling. However, human studies that comprehensively address delta wave interactions with spindles and SOs, as well as their functional role for memory are still lacking. Electroencephalographic data were acquired across three naps of 33 healthy older human participants (17 female) to investigate delta-spindle coupling and the interplay between delta- and SO-related activity. Additionally, we determined intra-individual stability of coupling measures and their potential link to the ability to form novel memories in a verbal memory task. Our results revealed weaker delta-spindle compared to SO-spindle coupling. Contrary to our initial hypothesis, we found no evidence for an opposing dependency between SO- and delta-related activities during non-rapid eye movement sleep. Moreover, the ratio between SO- and delta-nested spindles rather than SO-spindle and delta-spindle coupling measures by themselves predicted the ability to form novel memories best. In conclusion, our results do not confirm previous findings in rodents on competitive interactions between delta activity and SO-spindle coupling in older adults. However, they support the hypothesis that SO, delta wave, and spindle activity should be jointly considered when aiming to link sleep physiology and memory formation.

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under-explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High-density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre- and post-auditory stimulation. Results showed that the complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20-40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre-stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.

Sleep and wake in a model of the thalamocortical system with Martinotti cells

The mechanisms leading to the alternation between active (UP) and silent (DOWN) states during sleep slow waves (SWs) remain poorly understood. Previous models have explained the transition to the DOWN state by a progressive failure of excitation because of the build-up of adaptation currents or synaptic depression. However, these models are at odds with recent studies suggesting a role for presynaptic inhibition by Martinotti cells (MaCs) in generating SWs. Here, we update a classical large-scale model of sleep SWs to include MaCs and propose a different mechanism for the generation of SWs. In the wake mode, the network exhibits irregular and selective activity with low firing rates (FRs). Following an increase in the strength of background inputs and a modulation of synaptic strength and potassium leak potential mimicking the reduced effect of acetylcholine during sleep, the network enters a sleep-like regime in which local increases of network activity trigger bursts of MaC activity, resulting in strong disfacilitation of the local network via presynaptic GABAB1a -type inhibition. This model replicates findings on slow wave activity (SWA) during sleep that challenge previous models, including low and skewed FRs that are comparable between the wake and sleep modes, higher synchrony of transitions to DOWN states than to UP states, the possibility of triggering SWs by optogenetic stimulation of MaCs, and the local dependence of SWA on synaptic strength. Overall, this work points to a role for presynaptic inhibition by MaCs in the generation of DOWN states during sleep.

Timing and location of speech errors induced by direct cortical stimulation

Cortical regions supporting speech production are commonly established using neuroimaging techniques in both research and clinical settings. However, for neurosurgical purposes, structural function is routinely mapped peri-operatively using direct electrocortical stimulation. While this method is the gold standard for identification of eloquent cortical regions to preserve in neurosurgical patients, there is lack of specificity of the actual underlying cognitive processes being interrupted. To address this, we propose mapping the temporal dynamics of speech arrest across peri-sylvian cortices by quantifying the latency between stimulation and speech deficits. In doing so, we are able to substantiate hypotheses about distinct region-specific functional roles (e.g., planning versus motor execution). In this retrospective observational study, we analyzed 20 patients (12 female; age range 14-43) with refractory epilepsy who underwent continuous extra-operative intracranial EEG monitoring of an automatic speech task during clinical bedside language mapping. Latency to speech arrest was calculated as time from stimulation onset to speech arrest onset, controlling for individual speech rate. Most instances of motor-based arrest (87.5% of 96 instances) were in sensorimotor cortex with mid-range latencies to speech arrest with a distributional peak at 0.47 seconds. Speech arrest occurred in numerous regions, with relatively short latencies in supramarginal gyrus (0.46 seconds), superior temporal gyrus (0.51 seconds), and middle temporal gyrus (0.54 seconds), followed by relatively long latencies in sensorimotor cortex (0.72 seconds) and especially long latencies in inferior frontal gyrus (0.95 seconds). Nonparametric testing for speech arrest revealed that region predicted latency; latencies in supramarginal gyrus and in superior temporal gyrus were shorter than in sensorimotor cortex and in inferior frontal gyrus. Sensorimotor cortex is primarily responsible for motor-based arrest. Latencies to speech arrest in supramarginal gyrus and superior temporal gyrus (and to a lesser extent middle temporal gyrus) align with latencies to motor-based arrest in sensorimotor cortex. This pattern of relatively quick cessation of speech suggests that stimulating these regions interferes with the outgoing motor execution. In contrast, the latencies to speech arrest in inferior frontal gyrus and in ventral regions of sensorimotor cortex were significantly longer than those in temporoparietal regions. Longer latencies in the more frontal areas (including inferior frontal gyrus and ventral areas of precentral gyrus and postcentral gyrus) suggest that stimulating these areas interrupts a higher-level speech production process involved in planning. These results implicate the ventral specialization of sensorimotor cortex (including both precentral and postcentral gyri) for speech planning above and beyond motor execution.

Maturation-dependent changes in cortical and thalamic activity during sleep slow waves: Insights from a combined EEG-fMRI study

INTRODUCTION

Studies using scalp EEG have shown that slow waves (0.5-4 Hz), the most prominent hallmark of NREM sleep, undergo relevant changes from childhood to adulthood, mirroring brain structural modifications and the acquisition of cognitive skills. Here we used simultaneous EEG-fMRI to investigate the cortical and subcortical correlates of slow waves in school-age children and determine their relative developmental changes.

METHODS

We analyzed data from 14 school-age children with self-limited focal epilepsy of childhood who fell asleep during EEG-fMRI recordings. Brain regions associated with slow-wave occurrence were identified using a voxel-wise regression that also modelled interictal epileptic discharges and sleep spindles. At the group level, a mixed-effects linear model was used. The results were qualitatively compared with those obtained from 2 adolescents with epilepsy and 17 healthy adults.

RESULTS

Slow waves were associated with hemodynamic-signal decreases in bilateral somatomotor areas. Such changes extended more posteriorly relative to those in adults. Moreover, the involvement of areas belonging to the default mode network changes as a function of age. No significant hemodynamic responses were observed in subcortical structures. However, we identified a significant correlation between age and thalamic hemodynamic changes.

CONCLUSIONS

Present findings indicate that the somatomotor cortex may have a key role in slow-wave expression throughout the lifespan. At the same time, they are consistent with a posterior-to-anterior shift in slow-wave distribution mirroring brain maturational changes. Finally, our results suggest that slow-wave changes may not reflect only neocortical modifications but also the maturation of subcortical structures, including the thalamus.

Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Auditory inputs modulate intrinsic neuronal timescales during sleep

Functional magnetic resonance imaging (fMRI) studies have demonstrated that intrinsic neuronal timescales (INT) undergo modulation by external stimulation during consciousness. It remains unclear if INT keep the ability for significant stimulus-induced modulation during primary unconscious states, such as sleep. This fMRI analysis addresses this question via a dataset that comprises an awake resting-state plus rest and stimulus states during sleep. We analyzed INT measured via temporal autocorrelation supported by median frequency (MF) in the frequency-domain. Our results were replicated using a biophysical model. There were two main findings: (1) INT prolonged while MF decreased from the awake resting-state to the N2 resting-state, and (2) INT shortened while MF increased during the auditory stimulus in sleep. The biophysical model supported these results by demonstrating prolonged INT in slowed neuronal populations that simulate the sleep resting-state compared to an awake state. Conversely, under sine wave input simulating the stimulus state during sleep, the model's regions yielded shortened INT that returned to the awake resting-state level. Our results highlight that INT preserve reactivity to stimuli in states of unconsciousness like sleep, enhancing our understanding of unconscious brain dynamics and their reactivity to stimuli.

The Impact of Transcutaneous Spinal Cord Stimulation on Autonomic Regulation after Spinal Cord Injury: A randomized crossover trial

Importance

Individuals with spinal cord injury (SCI) have significant autonomic nervous system dysfunction. However, despite recent findings postulated to support that spinal cord stimulation improves dynamic autonomic regulation, limited scope of previous testing means the true effects remain unknown.

Objective

To determine whether transcutaneous spinal cord stimulation improves dynamic autonomic regulation after SCI.

Design

Single-blinded, randomized crossover trial with matched cohorts.

Setting

Academic autonomic physiology laboratory.

Participants

Two pairs of well-matched individuals with and without high-thoracic, complete SCI.

Interventions

Sub-motor threshold transcutaneous spinal cord stimulation delivered at T10-T11 using 120Hz, 30Hz, and 30Hz with 5kHz carrier frequency at separate autonomic testing sessions.

Main Outcomes and Measures

Baseline autonomic regulation was characterized with tests of above injury level sympathoexcitation (Valsalva's maneuver), sympathoinhibition (progressive doses of bolus intravenous phenylephrine), and below level sympathoexcitation (foot cold pressor test). At three subsequent visits, this testing battery was repeated with the addition of spinal cord stimulation at each frequency. Changes in autonomic regulation for each frequency were then analyzed relative to baseline testing for each individual and within matched cohorts.

Results

Uninjured controls demonstrated no autonomic deficits at baseline and had no changes with any frequency of stimulation. Contrasting this, and as expected, individuals with SCI had baseline autonomic dysfunction. In a frequency-dependent manner, spinal cord stimulation enhanced sympathoexcitatory responses, normalizing previously impaired Valsalva's maneuvers. However, stimulation exacerbated already impaired sympathoinhibitory responses, resulting in significantly greater mean arterial pressure increases with the same phenylephrine doses compared to baseline. Impaired sympathoexcitatory response below the level of injury were also further exacerbated with spinal cord stimulation. At baseline, neither individual with SCI demonstrated autonomic dysreflexia with the noxious foot cold pressor test; the addition of stimulation led to a dysreflexic response in every trial, with greater relative hypertension and bradycardia indicating no improvement in autonomic regulation.

Conclusions and Relevance

Transcutaneous spinal cord stimulation does not improve autonomic regulation after SCI, and instead likely generates tonic, frequency-dependent sympathoexcitation which may lower the threshold for autonomic dysreflexia.

Sleep and vigilance states: Embracing spatiotemporal dynamics

The classic view of sleep and vigilance states is a global stationary perspective driven by the interaction between neuromodulators and thalamocortical systems. However, recent data are challenging this view by demonstrating that vigilance states are highly dynamic and regionally complex. Spatially, sleep- and wake-like states often co-occur across distinct brain regions, as in unihemispheric sleep, local sleep in wakefulness, and during development. Temporally, dynamic switching prevails around state transitions, during extended wakefulness, and in fragmented sleep. This knowledge, together with methods monitoring brain activity across multiple regions simultaneously at millisecond resolution with cell-type specificity, is rapidly shifting how we consider vigilance states. A new perspective incorporating multiple spatial and temporal scales may have important implications for considering the governing neuromodulatory mechanisms, the functional roles of vigilance states, and their behavioral manifestations. A modular and dynamic view highlights novel avenues for finer spatiotemporal interventions to improve sleep function.

Origin, synchronization, and propagation of sleep slow waves in children

STUDY OBJECTIVES

Sleep slow wave activity, as measured using EEG delta power (<4 Hz), undergoes significant changes throughout development, mirroring changes in brain function and anatomy. Yet, age-dependent variations in the characteristics of individual slow waves have not been thoroughly investigated. Here we aimed at characterizing individual slow wave properties such as origin, synchronization, and cortical propagation at the transition between childhood and adulthood.

METHODS

We analyzed overnight high-density (256 electrodes) EEG recordings of healthy typically developing children (N=21, 10.3±1.5 years old) and young healthy adults (N=18, 31.1±4.4 years old). All recordings were preprocessed to reduce artifacts, and NREM slow waves were detected and characterized using validated algorithms. The threshold for statistical significance was set at p=0.05.

RESULTS

The slow waves of children were larger and steeper, but less widespread than those of adults. Moreover, they tended to mainly originate from and spread over more posterior brain areas. Relative to those of adults, the slow waves of children also displayed a tendency to more strongly involve and originate from the right than the left hemisphere. The separate analysis of slow waves characterized by high and low synchronization efficiency showed that these waves undergo partially distinct maturation patterns, consistent with their possible dependence on different generation and synchronization mechanisms.

CONCLUSIONS

Changes in slow wave origin, synchronization, and propagation at the transition between childhood and adulthood are consistent with known modifications in cortico-cortical and subcortico-cortical brain connectivity. In this light, changes in slow-wave properties may provide a valuable yardstick to assess, track, and interpret physiological and pathological development.

Sleep-A brain-state serving systems memory consolidation

Although long-term memory consolidation is supported by sleep, it is unclear how it differs from that during wakefulness. Our review, focusing on recent advances in the field, identifies the repeated replay of neuronal firing patterns as a basic mechanism triggering consolidation during sleep and wakefulness. During sleep, memory replay occurs during slow-wave sleep (SWS) in hippocampal assemblies together with ripples, thalamic spindles, neocortical slow oscillations, and noradrenergic activity. Here, hippocampal replay likely favors the transformation of hippocampus-dependent episodic memory into schema-like neocortical memory. REM sleep following SWS might balance local synaptic rescaling accompanying memory transformation with a sleep-dependent homeostatic process of global synaptic renormalization. Sleep-dependent memory transformation is intensified during early development despite the immaturity of the hippocampus. Overall, beyond its greater efficacy, sleep consolidation differs from wake consolidation mainly in that it is supported, rather than impaired, by spontaneous hippocampal replay activity possibly gating memory formation in neocortex.

Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.

Targeting targeted memory reactivation: Characteristics of cued reactivation in sleep

Targeted memory reactivation (TMR) is a technique in which sensory cues associated with memories during wake are used to trigger memory reactivation during subsequent sleep. The characteristics of such cued reactivation, and the optimal placement of TMR cues, remain to be determined. We built an EEG classification pipeline that discriminated reactivation of right- and left-handed movements and found that cues which fall on the up-going transition of the slow oscillation (SO) are more likely to elicit a classifiable reactivation. We also used a novel machine learning pipeline to predict the likelihood of eliciting a classifiable reactivation after each TMR cue using the presence of spindles and features of SOs. Finally, we found that reactivations occurred either immediately after the cue or one second later. These findings greatly extend our understanding of memory reactivation and pave the way for development of wearable technologies to efficiently enhance memory through cueing in sleep.

Information maximization explains state-dependent synaptic plasticity and memory reorganization during non-rapid eye movement sleep

Slow waves during the non-rapid eye movement (NREM) sleep reflect the alternating up and down states of cortical neurons; global and local slow waves promote memory consolidation and forgetting, respectively. Furthermore, distinct spike-timing-dependent plasticity (STDP) operates in these up and down states. The contribution of different plasticity rules to neural information coding and memory reorganization remains unknown. Here, we show that optimal synaptic plasticity for information maximization in a cortical neuron model provides a unified explanation for these phenomena. The model indicates that the optimal synaptic plasticity is biased toward depression as the baseline firing rate increases. This property explains the distinct STDP observed in the up and down states. Furthermore, it explains how global and local slow waves predominantly potentiate and depress synapses, respectively, if the background firing rate of excitatory neurons declines with the spatial scale of waves as the model predicts. The model provides a unifying account of the role of NREM sleep, bridging neural information coding, synaptic plasticity, and memory reorganization.

Closed-loop modulation of local slow oscillations in human NREM sleep

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations.

Individual slow wave events give rise to macroscopic fMRI signatures and drive the strength of the BOLD signal in human resting-state EEG-fMRI recordings

The slow wave state is a general state of quiescence interrupted by sudden bursts of activity or so-called slow wave events (SWEs). Recently, the relationship between SWEs and blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals was assessed in rodent models which revealed cortex-wide BOLD activation. However, it remains unclear which macroscopic signature corresponds to these specific neurophysiological events in the human brain. Therefore, we analyzed simultaneous electroencephalographic (EEG)-fMRI data during human non-REM sleep. SWEs individually detected in the EEG data were used as predictors in event-related fMRI analyses to examine the relationship between SWEs and fMRI signals. For all 10 subjects we identified significant changes in BOLD activity associated with SWEs covering substantial parts of the gray matter. As demonstrated in rodents, we observed a direct relation of a neurophysiological event to specific BOLD activation patterns. We found a correlation between the number of SWEs and the spatial extent of these BOLD activation patterns and discovered that the amplitude of the BOLD response strongly depends on the SWE amplitude. As altered SWE propagation has recently been found in neuropsychiatric diseases, it is critical to reveal the brain's physiological slow wave state networks to potentially establish early imaging biomarkers for various diseases long before disease onset.

Effects of auditory sleep modulation approaches on brain oscillatory and cardiovascular dynamics

Slow waves, the hallmark feature of deep nonrapid eye movement sleep, do potentially drive restorative effects of sleep on brain and body functions. Sleep modulation techniques to elucidate the functional role of slow waves thus have gained large interest. Auditory slow wave stimulation is a promising tool; however, directly comparing auditory stimulation approaches within a night and analyzing induced dynamic brain and cardiovascular effects are yet missing. Here, we tested various auditory stimulation approaches in a windowed, 10 s ON (stimulations) followed by 10 s OFF (no stimulations), within-night stimulation design and compared them to a SHAM control condition. We report the results of three studies and a total of 51 included nights and found a large and global increase in slow-wave activity (SWA) in the stimulation window compared to SHAM. Furthermore, slow-wave dynamics were most pronouncedly increased at the start of the stimulation and declined across the stimulation window. Beyond the changes in brain oscillations, we observed, for some conditions, a significant increase in the mean interval between two heartbeats within a stimulation window, indicating a slowing of the heart rate, and increased heart rate variability derived parasympathetic activity. Those cardiovascular changes were positively correlated with the change in SWA, and thus, our findings provide insight into the potential of auditory slow wave enhancement to modulate cardiovascular restorative conditions during sleep. However, future studies need to investigate whether the potentially increased restorative capacity through slow-wave enhancements translates into a more rested cardiovascular system on a subsequent day.

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.

Alterations of sleep oscillations in Alzheimer's disease: A potential role for GABAergic neurons in the cortex, hippocampus, and thalamus

Sleep abnormalities are widely reported in patients with Alzheimer's disease (AD) and are linked to cognitive impairments. Sleep abnormalities could be potential biomarkers to detect AD since they are often observed at the preclinical stage. Moreover, sleep could be a target for early intervention to prevent or slow AD progression. Thus, here we review changes in brain oscillations observed during sleep, their connection to AD pathophysiology and the role of specific brain circuits. Slow oscillations (0.1-1 Hz), sleep spindles (8-15 Hz) and their coupling during non-REM sleep are consistently reduced in studies of patients and in AD mouse models although the timing and magnitude of these alterations depends on the pathophysiological changes and the animal model studied. Changes in delta (1-4 Hz) activity are more variable. Animal studies suggest that hippocampal sharp-wave ripples (100-250 Hz) are also affected. Reductions in REM sleep amount and slower oscillations during REM are seen in patients but less consistently in animal models. Thus, changes in a variety of sleep oscillations could impact sleep-dependent memory consolidation or restorative functions of sleep. Recent mechanistic studies suggest that alterations in the activity of GABAergic neurons in the cortex, hippocampus and thalamic reticular nucleus mediate sleep oscillatory changes in AD and represent a potential target for intervention. Longitudinal studies of the timing of AD-related sleep abnormalities with respect to pathology and dysfunction of specific neural networks are needed to identify translationally relevant biomarkers and guide early intervention strategies to prevent or delay AD progression.

The Rise and Fall of Slow Wave Tides: Vacillations in Coupled Slow Wave/Spindle Pairing Shift the Composition of Slow Wave Activity in Accordance With Depth of Sleep

Slow wave activity (SWA) during sleep is associated with synaptic regulation and memory processing functions. Each cycle of non-rapid-eye-movement (NREM) sleep demonstrates a waxing and waning amount of SWA during the transitions between stages N2 and N3 sleep, and the deeper N3 sleep is associated with an increased density of SWA. Further, SWA is an amalgam of different types of slow waves, each identifiable by their temporal coupling to spindle subtypes with distinct physiological features. The objectives of this study were to better understand the neurobiological properties that distinguish different slow wave and spindle subtypes, and to examine the composition of SWA across cycles of NREM sleep. We further sought to explore changes in the composition of NREM cycles that occur among aging adults. To address these goals, we analyzed subsets of data from two well-characterized cohorts of healthy adults: (1) The DREAMS Subjects Database (n = 20), and (2) The Cleveland Family Study (n = 60). Our analyses indicate that slow wave/spindle coupled events can be characterized as frontal vs. central in their relative distribution between electroencephalography (EEG) channels. The frontal predominant slow waves are identifiable by their coupling to late-fast spindles and occur more frequently during stage N3 sleep. Conversely, the central-associated slow waves are identified by coupling to early-fast spindles and favor occurrence during stage N2 sleep. Together, both types of slow wave/spindle coupled events form the composite of SWA, and their relative contribution to the SWA rises and falls across cycles of NREM sleep in accordance with depth of sleep. Exploratory analyses indicated that older adults produce a different composition of SWA, with a shift toward the N3, frontal subtype, which becomes increasingly predominant during cycles of NREM sleep. Overall, these data demonstrate that subtypes of slow wave/spindle events have distinct cortical propagation patterns and differ in their distribution across lighter vs. deeper NREM sleep. Future efforts to understand how slow wave sleep and slow wave/spindle coupling impact memory performance and neurological disease may benefit from examining the composition of SWA to avoid potential confounds that may occur when comparing dissimilar neurophysiological events.

Ongoing neural oscillations predict the post-stimulus outcome of closed loop auditory stimulation during slow-wave sleep

Large slow oscillations (SO, 0.5-2 Hz) characterise slow-wave sleep and are crucial to memory consolidation and other physiological functions. Manipulating slow oscillations may enhance sleep and memory, as well as benefitting the immune system. Closed-loop auditory stimulation (CLAS) has been demonstrated to increase the SO amplitude and to boost fast sleep spindle activity (11-16 Hz). Nevertheless, not all such stimuli are effective in evoking SOs, even when they are precisely phase locked. Here, we studied what factors of the ongoing activity patterns may help to determine what oscillations to stimulate to effectively enhance SOs or SO-locked spindle activity. Hence, we trained classifiers using the morphological characteristics of the ongoing SO, as measured by electroencephalography (EEG), to predict whether stimulation would lead to a benefit in terms of the resulting SO and spindle amplitude. Separate classifiers were trained using trials from spontaneous control and stimulated datasets, and we evaluated their performance by applying them to held-out data both within and across conditions. We were able to predict both when large SOs occurred spontaneously, and whether a phase-locked auditory click effectively enlarged them with good accuracy for predicting the SO trough (∼70%) and SO peak values (∼80%). Also, we were able to predict when stimulation would elicit spindle activity with an accuracy of ∼60%. Finally, we evaluate the importance of the various SO features used to make these predictions. Our results offer new insight into SO and spindle dynamics and may suggest techniques for developing future methods for online optimization of stimulation.

Recovery of consolidation after sleep following stroke-interaction of slow waves, spindles, and GABA

Sleep is known to promote recovery after stroke. Yet it remains unclear how stroke affects neural processing during sleep. Using an experimental stroke model in rats along with electrophysiological monitoring of neural firing and sleep microarchitecture, here we show that sleep processing is altered by stroke. We find that the precise coupling of spindles to global slow oscillations (SOs), a phenomenon that is known to be important for memory consolidation, is disrupted by a pathological increase in "isolated" local delta waves. The transition from this pathological to a physiological state-with increased spindle coupling to SO-is associated with sustained performance gains during recovery. Interestingly, post-injury sleep could be pushed toward a physiological state via a pharmacological reduction of tonic γ-aminobutyric acid (GABA). Together, our results suggest that sleep processing after stroke is impaired due to an increase in delta waves and that its restoration can be important for recovery.

Boosting Recovery During Sleep by Means of Auditory Stimulation

Sufficient recovery during sleep is the basis of physical and psychological well-being. Understanding the physiological mechanisms underlying this restorative function is essential for developing novel approaches to promote recovery during sleep. Phase-targeted auditory stimulation (PTAS) is an increasingly popular technique for boosting the key electrophysiological marker of recovery during sleep, slow-wave activity (SWA, 1-4 Hz EEG power). However, it is unknown whether PTAS induces physiological sleep. In this study, we demonstrate that, when applied during deep sleep, PTAS accelerates SWA decline across the night which is associated with an overnight improvement in attentional performance. Thus, we provide evidence that PTAS enhances physiological sleep and demonstrate under which conditions this occurs most efficiently. These findings will be important for future translation into clinical populations suffering from insufficient recovery during sleep.

Covering the Gap Between Sleep and Cognition – Mechanisms and Clinical Examples

A growing number of studies have shown the strong relationship between sleep and different cognitive processes, especially those that involve memory consolidation. Traditionally, these processes were attributed to mechanisms related to the macroarchitecture of sleep, as sleep cycles or the duration of specific stages, such as the REM stage. More recently, the relationship between different cognitive traits and specific waves (sleep spindles or slow oscillations) has been studied. We here present the most important physiological processes induced by sleep, with particular focus on brain electrophysiology. In addition, recent and classical literature were reviewed to cover the gap between sleep and cognition, while illustrating this relationship by means of clinical examples. Finally, we propose that future studies may focus not only on analyzing specific waves, but also on the relationship between their characteristics as potential biomarkers for multiple diseases.

Conscious experiences and high-density EEG patterns predicting subjective sleep depth

What accounts for feeling deeply asleep? Standard sleep recordings only incompletely reflect subjective aspects of sleep and some individuals with so-called sleep misperception frequently feel awake although sleep recordings indicate clear-cut sleep. To identify the determinants of sleep perception, we performed 787 awakenings in 20 good sleepers and 10 individuals with sleep misperception and interviewed them about their subjective sleep depth while they underwent high-density EEG sleep recordings. Surprisingly, in good sleepers, sleep was subjectively lightest in the first 2 h of non-rapid eye movement (NREM) sleep, generally considered the deepest sleep, and deepest in rapid eye movement (REM) sleep. Compared to good sleepers, sleep misperceptors felt more frequently awake during sleep and reported lighter REM sleep. At the EEG level, spatially widespread high-frequency power was inversely related to subjective sleep depth in NREM sleep in both groups and in REM sleep in misperceptors. Subjective sleep depth positively correlated with dream-like qualities of reports of mental activity. These findings challenge the widely held notion that slow wave sleep best accounts for feeling deeply asleep. Instead, they indicate that subjective sleep depth is inversely related to a neurophysiological process that predominates in early NREM sleep, becomes quiescent in REM sleep, and is reflected in high-frequency EEG activity. In sleep misperceptors, this process is more frequently active, more spatially widespread, and abnormally persists into REM sleep. These findings help identify the neuromodulatory systems involved in subjective sleep depth and are relevant for studies aiming to improve subjective sleep quality.

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

OBJECTIVE

In multiple sclerosis (MS), MRI measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven patterns of covarying regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS).

METHODS

We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale, Nine-Hole Peg Test (9HPT) and Symbol Digit Modalities Test (SDMT), from a clinical trial in 988 people with SPMS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA). We repeated ICA on 400 healthy controls. We used survival models to determine whether baseline patterns of covarying GM volume measures predict cognitive and motor worsening.

RESULTS

We identified 15 patterns of regionally covarying GM features. Compared with whole brain GM, deep GM and lesion volumes, some ICA components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR=1.29, 95% CI 1.09 to 1.52, p<0.005). Two ICA components were associated with 9HPT worsening (HR=1.30, 95% CI 1.06 to 1.60, p<0.01 and HR=1.21, 95% CI 1.01 to 1.45, p<0.05). ICA measures could better predict SDMT and 9HPT worsening (C-index=0.69-0.71) compared with models including only whole and regional MRI measures (C-index=0.65-0.69, p value for all comparison <0.05).

CONCLUSIONS

The disability progression was better predicted by some of the covarying GM regions patterns, than by single regional or whole-brain measures. ICA, which may represent structural brain networks, can be applied to clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.

Sleeping at the switch

Sleep slow waves are studied for their role in brain plasticity, homeostatic regulation, and their changes during aging. Here, we address the possibility that two types of slow waves co-exist in humans. Thirty young and 29 older adults underwent a night of polysomnographic recordings. Using the transition frequency, slow waves with a slow transition (slow switchers) and those with a fast transition (fast switchers) were discovered. Slow switchers had a high electroencephalography (EEG) connectivity along their depolarization transition while fast switchers had a lower connectivity dynamics and dissipated faster during the night. Aging was associated with lower temporal dissipation of sleep pressure in slow and fast switchers and lower EEG connectivity at the microscale of the oscillations, suggesting a decreased flexibility in the connectivity network of older individuals. Our findings show that two different types of slow waves with possible distinct underlying functions coexist in the slow wave spectrum.

Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep

The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography (hd-EEG) in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out to different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.

Memory-relevant nap sleep physiology in healthy and pathological aging

STUDY OBJECTIVES

Aging is associated with detrimental changes in sleep physiology, a process accelerated in Alzheimer's disease. Fine-tuned temporal interactions of non-rapid eye movement slow oscillations and spindles were shown to be particularly important for memory consolidation, and to deteriorate in healthy older adults. Whether this oscillatory interaction further decline in early stages of Alzheimer's disease such as mild cognitive impairment has not been investigated to date, but may have important therapeutic implications.

METHODS

Here, we assessed differences in sleep architecture and memory-relevant slow oscillation, sleep spindles and their functional coupling during a 90-min nap between healthy young and older adults, and in older patients with mild cognitive impairment. Furthermore, associations of nap-sleep characteristics with sleep-dependent memory performance change were evaluated.

RESULTS

We found significant differences between young and older healthy adults, and between young adults and patients with mild cognitive impairment, but not between healthy older adults and patients for several sleep metrics, including slow oscillation-spindle coupling. Moreover, sleep-dependent retention of verbal memories was significantly higher in young healthy adults versus older adults with and without mild cognitive impairment, but no difference between the two older groups was observed. Associations with sleep metrics were only found for pre-nap memory performances.

CONCLUSIONS

In conclusion, our results indicate changes in nap sleep physiology and sleep-related memory consolidation in older adults with and without mild cognitive impairment. Thus, interventions targeted at improving sleep physiology may help to reduce memory decline in both groups, but our study does not indicate additional benefits for patients with mild cognitive impairment.

CLINICAL TRAIL REGISTRATION

Effects of Brain Stimulation During Daytime Nap on Memory Consolidation in Younger, Healthy Subjects: https://clinicaltrials.gov/ct2/show/NCT01840865; NCT01840865. Effects of Brain Stimulation During a Daytime Nap on Memory Consolidation in Older Adults; https://clinicaltrials.gov/ct2/show/study/NCT01840839?term=01840839&draw=2&rank=1; NCT01840839. Effects of Brain Stimulation During a Daytime Nap on Memory Consolidation in Patients With Mild Cognitive Impairment; https://clinicaltrials.gov/ct2/show/NCT01782365?term=01782365&draw=2&rank=1; NCT01782365.

Role of corpus callosum in sleep spindle synchronization and coupling with slow waves

Sleep spindles of non-REM sleep are transient, waxing-and-waning 10–16 Hz EEG oscillations, whose cortical synchronization depends on the engagement of thalamo-cortical loops. However, previous studies in animal models lacking the corpus callosum due to agenesis or total callosotomy and in humans with agenesis of the corpus callosum suggested that cortico-cortical connections may also have a relevant role in cortical (inter-hemispheric) spindle synchronization. Yet, most of these works did not provide direct quantitative analyses to support their observations. By studying a rare sample of callosotomized, split-brain patients, we recently demonstrated that the total resection of the corpus callosum is associated with a significant reduction in the inter-hemispheric propagation of non-REM slow waves. Interestingly, sleep spindles are often temporally and spatially grouped around slow waves (0.5–4 Hz), and this coordination is thought to have an important role in sleep-dependent learning and memory consolidation. Given these premises, here we set out to investigate whether total callosotomy may affect the generation and spreading of sleep spindles, as well as their coupling with sleep slow waves. To this aim, we analysed overnight high-density EEG recordings (256 electrodes) collected in five patients who underwent total callosotomy due to drug-resistant epilepsy (age 40–53, two females), three non-callosotomized neurological patients (age 44–66, two females), and in a sample of 24 healthy adult control subjects (age 20–47, 13 females). Individual sleep spindles were automatically detected using a validated algorithm and their properties and topographic distributions were computed. All analyses were performed with and without a regression-based adjustment accounting for inter-subject age differences. The comparison between callosotomized patients and healthy subjects did not reveal systematic variations in spindle density, amplitude or frequency. However, callosotomized patients were characterized by a reduced spindle duration, which could represent the result of a faster desynchronization of spindle activity across cortical areas of the two hemispheres. In contrast with our previous findings regarding sleep slow waves, we failed to detect in callosotomized patients any clear, systematic change in the inter-hemispheric synchronization of sleep spindles. In line with this, callosotomized patients were characterized by a reduced extension of the spatial association between temporally coupled spindles and slow waves. Our findings are consistent with a dependence of spindles on thalamo-cortical rather than cortico-cortical connections in humans, but also revealed that, despite their temporal association, slow waves and spindles are independently regulated in terms of topographic expression.

Spontaneous slow oscillation - slow spindle features predict induced overnight memory retention

Study Objectives

Synchronization of neural activity within local networks and between brain regions is a major contributor to rhythmic field potentials such as the EEG. On the other hand, dynamic changes in microstructure and activity are reflected in the EEG, for instance slow oscillation (SO) slope can reflect synaptic strength. SO-spindle coupling is a measure for neural communication. It was previously associated with memory consolidation, but also shown to reveal strong interindividual differences. In studies, weak electric current stimulation has modulated brain rhythms and memory retention. Here, we investigate whether SO-spindle coupling and SO slope during baseline sleep are associated with (predictive of) stimulation efficacy on retention performance.

Methods

Twenty-five healthy subjects participated in three experimental sessions. Sleep-associated memory consolidation was measured in two sessions, in one anodal transcranial direct current stimulation oscillating at subjects individual SO frequency (so-tDCS) was applied during nocturnal sleep. The third session was without a learning task (baseline sleep). The dependence on SO-spindle coupling and SO-slope during baseline sleep of so-tDCS efficacy on retention performance were investigated.

Results

Stimulation efficacy on overnight retention of declarative memories was associated with nesting of slow spindles to SO trough in deep nonrapid eye movement baseline sleep. Steepness and direction of SO slope in baseline sleep were features indicative for stimulation efficacy.

Conclusions

Findings underscore a functional relevance of activity during the SO up-to-down state transition for memory consolidation and provide support for distinct consolidation mechanisms for types of declarative memories.

The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan

STUDY OBJECTIVES

Slow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan.

METHODS

Coupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles.

RESULTS

Two different subtypes of spindles were identified during the upstates of (distinct) slow waves: an "early-fast" spindle, more common in stage N2 sleep, and a "late-fast" spindle, more common in stage N3. We further found stages N2 and N3 sleep contain a mixture of discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails with increasing age.

CONCLUSIONS

Distinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface

A passive brain-computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects' data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.

Cortical and subcortical hemodynamic changes during sleep slow waves in human light sleep

EEG slow waves, the hallmarks of NREM sleep are thought to be crucial for the regulation of several important processes, including learning, sensory disconnection and the removal of brain metabolic wastes. Animal research indicates that slow waves may involve complex interactions within and between cortical and subcortical structures. Conventional EEG in humans, however, has a low spatial resolution and is unable to accurately describe changes in the activity of subcortical and deep cortical structures. To overcome these limitations, here we took advantage of simultaneous EEG-fMRI recordings to map cortical and subcortical hemodynamic (BOLD) fluctuations time-locked to slow waves of light sleep. Recordings were performed in twenty healthy adults during an afternoon nap. Slow waves were associated with BOLD-signal increases in the posterior brainstem and in portions of thalamus and cerebellum characterized by preferential functional connectivity with limbic and somatomotor areas, respectively. At the cortical level, significant BOLD-signal decreases were instead found in several areas, including insula and somatomotor cortex. Specifically, a slow signal increase preceded slow-wave onset and was followed by a delayed, stronger signal decrease. Similar hemodynamic changes were found to occur at different delays across most cortical brain areas, mirroring the propagation of electrophysiological slow waves, from centro-frontal to inferior temporo-occipital cortices. Finally, we found that the amplitude of electrophysiological slow waves was positively related to the magnitude and inversely related to the delay of cortical and subcortical BOLD-signal changes. These regional patterns of brain activity are consistent with theoretical accounts of the functions of sleep slow waves.

Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways

Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways in vivo. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.

Sleep has many roles, from strengthening new memories to regulating mood and appetite. While we might instinctively think of sleep as a uniform state of reduced brain activity, the reality is more complex. First, over the course of the night, we cycle between a number of different sleep stages, which reflect different levels of sleep depth. Second, the amount of sleep depth is not necessarily even across the brain but can vary between regions.

These sleep stages consist of either rapid eye movement (REM) sleep or non-REM (NREM) sleep. REM sleep is when most dreaming occurs, whereas NREM sleep is particularly important for learning and memory and can vary in duration and depth. During NREM sleep, large groups of neurons synchronize their firing to create rhythmic waves of activity known as slow waves. The more synchronous the activity, the deeper the sleep.

Vaidyanathan et al. now show that brain cells called astrocytes help regulate NREM sleep. Astrocytes are not neurons but belong to a group of specialized cells called glia. They are the largest glia cell type in the brain and display an array of proteins on their surfaces called G-protein-coupled receptors (GPCRs). These enable them to sense sleep-wake signals from other parts of the brain and to generate their own signals. In fact, each astrocyte can communicate with thousands of neurons at once. They are therefore well-poised to coordinate brain activity during NREM sleep.

Using innovative tools, Vaidyanathan et al. visualized astrocyte activity in mice as the animals woke up or fell asleep. The results showed that astrocytes change their activity just before each sleep–wake transition. They also revealed that astrocytes control both the depth and duration of NREM sleep via two different types of GPCR signals. Increasing one of these signals (Gi-GPCR) made the mice sleep more deeply but did not change sleep duration. Decreasing the other (Gq-GPCR) made the mice sleep for longer but did not affect sleep depth.

Sleep problems affect many people at some point in their lives, and often co-exist with other conditions such as mental health disorders. Understanding how the brain regulates different features of sleep could help us develop better – and perhaps more specific – treatments for sleep disorders. The current study suggests that manipulating GPCRs on astrocytes might increase sleep depth, for example. But before work to test this idea can begin, we must first determine whether findings from sleeping mice also apply to people.

A high-density electroencephalography study reveals abnormal sleep homeostasis in patients with rapid eye movement sleep behavior disorder

Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by disrupting motor enactments during REM sleep, but also cognitive impairments across several domains. In addition to REM sleep abnormalities, we hypothesized that RBD patients may also display EEG abnormalities during NREM sleep. We collected all-night recordings with 256-channel high-density EEG in nine RBD patients, predominantly early-onset medicated individuals, nine sex- and age- matched healthy controls, and nine additional controls with matched medications and comorbidities. Power spectra in delta to gamma frequency bands were compared during both REM and NREM sleep, between phasic and tonic REM sleep, and between the first versus last cycle of NREM sleep. Controls, but not RBD patients, displayed a decrease in beta power during phasic compared to tonic REM sleep. Compared to controls, RBD patients displayed a reduced decline in SWA from early to late NREM sleep. Overnight changes in the distribution of the amplitude of slow waves were also reduced in RBD patients. Without suppression of beta rhythms during phasic REM sleep, RBD patients might demonstrate heightened cortical arousal, favoring the emergence of behavioral episodes. A blunted difference between REM sleep sub-stages may constitute a sensitive biomarker for RBD. Moreover, reduced overnight decline in SWA suggests a reduced capacity for synaptic plasticity in RBD patients, which may favor progression towards neurodegenerative diseases.

Movement disorders in psychiatric patients

The observability of movement gives it advantages when trying to draw connections between brain and mind. Disturbed motor function pervades schizophrenia, though it is difficult now to subtract the effects of antipsychotic treatment. There is evidence from patients never exposed to these drugs that dyskinesia and even parkinsonism are to some degree innate to schizophrenia. Tardive dyskinesia and drug-induced parkinsonism are the most common movement disorders encountered in psychiatric practice. While D2 dopamine receptor blockade is a causative factor, both conditions defy straightforward neurochemical explanation. Balanced against the need to manage schizophrenic symptoms, neither prevention nor treatment is easy. Of all disorders classified as psychiatric, catatonia sits closest to organic neurology on the neuropsychiatric spectrum. Not only does it occur in the setting of unequivocally organic cerebral disease, but the alterations of consciousness it produces have 'organic' qualities even when the cause is psychiatric. No longer considered a subtype of schizophrenia, catatonia is defined by syndromic features based on motor phenomenology. Both severe depression and obsessive-compulsive disorder may be associated with 'soft' extrapyramidal signs that resemble parkinsonian bradykinesia. As functional neuroimaging studies suggest, movement and psychiatric disorders involve the same network connections between the basal ganglia and the cerebral cortex.

Local and Volume-Conducted Contributions to Cortical Field Potentials

Brain field potentials (FPs) can reach far from their sources, making difficult to know which waves come from where. We show that modern algorithms efficiently segregate the local and remote contributions to cortical FPs by recovering the generator-specific spatial voltage profiles. We investigated experimentally and numerically the local and remote origin of FPs in different cortical areas in anesthetized rats. All cortices examined show significant state, layer, and region dependent contribution of remote activity, while the voltage profiles help identify their subcortical or remote cortical origin. Co-activation of different cortical modules can be discriminated by the distinctive spatial features of the corresponding profiles. All frequency bands contain remote activity, thus influencing the FP time course, in cases drastically. The reach of different FP patterns is boosted by spatial coherence and curved geometry of the sources. For instance, slow cortical oscillations reached the entire brain, while hippocampal theta reached only some portions of the cortex. In anterior cortices, most alpha oscillations have a remote origin, while in the visual cortex the remote theta and gamma even surpass the local contribution. The quantitative approach to local and distant FP contributions helps to refine functional connectivity among cortical regions, and their relation to behavior.

Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands

Currently available prosthetic hands are capable of actuating anywhere from five to 30 degrees of freedom (DOF). However, grasp control of these devices remains unintuitive and cumbersome. To address this issue, we propose directly extracting finger commands from the neuromuscular system via electrodes implanted in residual innervated muscles and regenerative peripheral nerve interfaces (RPNIs). Two persons with transradial amputations had RPNIs created by suturing autologous free muscle grafts to their transected median, ulnar, and dorsal radial sensory nerves. Bipolar electrodes were surgically implanted into their ulnar and median RPNIs and into their residual innervated muscles. The implanted electrodes recorded local electromyography (EMG) with Signal-to-Noise Ratios ranging from 23 to 350 measured across various movements. In a series of single-day experiments, participants used a high speed pattern recognition system to control a virtual prosthetic hand in real-time. Both participants were able to transition between 10 pseudo-randomly cued individual finger and wrist postures in the virtual environment with an average online accuracy of 86.5% and latency of 255 ms. When the set was reduced to five grasp postures, average metrics improved to 97.9% online accuracy and 135 ms latency. Virtual task performance remained stable across untrained static arm positions while supporting the weight of the prosthesis. Participants also used the high speed classifier to switch between robotic prosthetic grips and complete a functional performance assessment. These results demonstrate that pattern recognition systems can use the high-quality EMG afforded by intramuscular electrodes and RPNIs to provide users with fast and accurate grasp control.

SUMMARY

Surgically implanted electrodes recorded finger-specific electromyography enabling reliable finger and grasp control of an upper limb prosthesis.