Spatiotemporal Organization and Cross-Frequency Coupling of Sleep Spindles in Primate Cerebral Cortex

Neurophysiological features of STN LFP underlying sleep fragmentation in Parkinson's disease

BACKGROUND

Sleep fragmentation is a persistent problem throughout the course of Parkinson's disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear.

METHOD

We recorded subthalamic nucleus (STN) local field potentials (LFPs) using deep brain stimulation (DBS) with real-time wireless recording capacity from 13 patients with PD undergoing a one-night polysomnography recording, 1 month after DBS surgery before initial programming and when the patients were off-medication. The STN LFP features that characterised different sleep stages, correlated with arousal and sleep fragmentation index, and preceded stage transitions during N2 and REM sleep were analysed.

RESULTS

Both beta and low gamma oscillations in non-rapid eye movement (NREM) sleep increased with the severity of sleep disturbance (arousal index (ArI)-betaNREM: r=0.9, p=0.0001, sleep fragmentation index (SFI)-betaNREM: r=0.6, p=0.0301; SFI-gammaNREM: r=0.6, p=0.0324). We next examined the low-to-high power ratio (LHPR), which was the power ratio of theta oscillations to beta and low gamma oscillations, and found it to be an indicator of sleep fragmentation (ArI-LHPRNREM: r=-0.8, p=0.0053; ArI-LHPRREM: r=-0.6, p=0.0373; SFI-LHPRNREM: r=-0.7, p=0.0204; SFI-LHPRREM: r=-0.6, p=0.0428). In addition, long beta bursts (>0.25 s) during NREM stage 2 were found preceding the completion of transition to stages with more cortical activities (towards Wake/N1/REM compared with towards N3 (p<0.01)) and negatively correlated with STN spindles, which were detected in STN LFPs with peak frequency distinguishable from long beta bursts (STN spindle: 11.5 Hz, STN long beta bursts: 23.8 Hz), in occupation during NREM sleep (β=-0.24, p<0.001).

CONCLUSION

Features of STN LFPs help explain neurophysiological mechanisms underlying sleep fragmentations in PD, which can inform new intervention for sleep dysfunction.

TRIAL REGISTRATION NUMBER

NCT02937727.

Spindle oscillations emerge at the critical state of electrically coupled networks in the thalamic reticular nucleus

Spindle oscillation is a waxing-and-waning neural oscillation observed in the brain, initiated at the thalamic reticular nucleus (TRN) and typically occurring at 7-15 Hz. Experiments have shown that in the adult brain, electrical synapses, rather than chemical synapses, dominate between TRN neurons, suggesting that the traditional view of spindle generation via chemical synapses may need reconsideration. Based on known experimental data, we develop a computational model of the TRN network, where heterogeneous neurons are connected by electrical synapses. The model shows that the interplay between synchronizing electrical synapses and desynchronizing heterogeneity leads to multiple synchronized clusters with slightly different oscillation frequencies whose summed-up activity produces spindle oscillation as seen in local field potentials. Our results suggest that during spindle oscillation, the network operates at the critical state, which is known for facilitating efficient information processing. This study provides insights into the underlying mechanism of spindle oscillation and its functional significance.

Entrainment to sleep spindles reflects dissociable patterns of connectivity between cortex and basal ganglia

Sleep spindles are crucial for learning in the cortex and basal ganglia (BG) because they facilitate the reactivation of previously active neuronal ensembles. Studying field potentials (FPs) and spiking in the cortex and BG during sleep in non-human primates following pre-sleep learning, we show that FP sleep spindles are widespread in the BG and are similar to cortical spindles in morphology, spectral content, and response to the pre-sleep task. Further, BG spindles are concordant with electroencephalogram (EEG) spindles and associated with increased cortico-BG correlation. However, spindles across the BG differ markedly in their entrainment of local spiking. The spiking activity of striatal projection neurons exhibits consistent phase locking to striatal and EEG spindles, producing phase windows of peaked cross-region spindling. In contrast, firing in other BG nuclei is not entrained to either local or EEG sleep spindles. These results suggest corticostriatal synapses as the main hub for offline cortico-BG communication.

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.

The brain processes memories as we sleep, generating rhythms of electrical activity called ‘sleep spindles’. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas.

Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys.

The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes.

In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer’s disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings.

Communication from the cerebellum to the neocortex during sleep spindles

Surprisingly little is known about neural activity in the sleeping cerebellum. Using long-term wireless recording, we characterised dynamic cerebro-thalamo-cerebellar interactions during natural sleep in monkeys. Similar sleep cycles were evident in both M1 and cerebellum as cyclical fluctuations in firing rates as well as a reciprocal pattern of slow waves and sleep spindles. Directed connectivity from motor cortex to the cerebellum suggested a neocortical origin of slow waves. Surprisingly however, spindles were associated with a directional influence from the cerebellum to motor cortex, conducted via the thalamus. Furthermore, the relative phase of spindle-band oscillations in the neocortex and cerebellum varied systematically with their changing amplitudes. We used linear dynamical systems analysis to show that this behaviour could only be explained by a system of two coupled oscillators. These observations appear inconsistent with a single spindle generator within the thalamo-cortical system, and suggest instead a cerebellar contribution to neocortical sleep spindles. Since spindles are implicated in the off-line consolidation of procedural learning, we speculate that this may involve communication via cerebello-thalamo-neocortical pathways in sleep.

Shared and unique features of mammalian sleep spindles - insights from new and old animal models

Sleep spindles are phasic events observed in mammalian non-rapid eye movement sleep. They are relevant today in the study of memory consolidation, sleep quality, mental health and ageing. We argue that our advanced understanding of their mechanisms has not exhausted the utility and need for animal model work. This is both because some topics, like cognitive ageing, have not yet been addressed sufficiently in comparative efforts and because the evolutionary history of this oscillation is still poorly understood. Comparisons across species often are either limited to referencing the classical cat and rodent models, or are over-inclusive, uncritically including reports of sleep spindles in rarely studied animals. In this review, we discuss the emergence of new (dog and sheep) models for sleep spindles and compare the strengths and shortcomings of new and old models based on the three validation criteria for animal models - face, predictive, and construct validity. We conclude that an emphasis on cognitive ageing might dictate the future of comparative sleep spindle studies, a development that is already becoming visible in studies on dogs. Moreover, reconstructing the evolutionary history of sleep spindles will require more stringent criteria for their identification, across more species. In particular, a stronger emphasis on construct and predictive validity can help verify if spindle-like events in other species are actual sleep spindles. Work in accordance with such stricter validation suggests that sleep spindles display more universally shared features, like defining frequency, than previously thought.

Coupling of gamma band activity to sleep spindle oscillations - a combined EEG/MEG study

Sleep spindles are crucial to memory consolidation. Cortical gamma oscillations (30-100 Hz) are considered to reflect processing of memory in local cortical networks. The temporal and regulatory relationship between spindles and gamma activity might therefore provide clues into how sleep strengthens cortical memory representations. Here, combining EEG with MEG recordings during sleep in healthy humans (n = 12), we investigated the temporal relationships of cortical gamma band activity, always measured by MEG, during fast (12-16 Hz) and slow (8-12 Hz) sleep spindles detected in the EEG or MEG. Time-frequency distributions did not show a consistent coupling of gamma to the spindle oscillation, although activity in the low gamma (30-40 Hz) and neighboring beta range (<30 Hz) was generally increased during spindles. However, more fine-grained analyses of cross-frequency interactions revealed that both low and high gamma power (30-100 Hz) was coupled to the phase of slow and fast EEG spindles, importantly, with this coupling at a fixed phase only for the oscillations within an individual spindle, but with variable phase across spindles. We did not observe any coupling of gamma activity for spindles detected solely in the MEG and not in parallel EEG recordings, raising the possibility that these are more local spindles of different quality. Similar to fast spindle activity, low gamma band power followed a ~0.025 Hz infraslow rhythm during sleep whose frequency, however, was significantly faster than that of spindle activity. Our findings suggest a general function of fast and slow spindles that by spanning larger cortical networks might serve to synchronize gamma band activity occurring in more local but distributed networks. Thereby, spindles might help linking local memory processing between distributed networks.

Neural signatures of α2-Adrenergic agonist-induced unconsciousness and awakening by antagonist

How the brain dynamics change during anesthetic-induced altered states of consciousness is not completely understood. The α2-adrenergic agonists are unique. They generate unconsciousness selectively through α2-adrenergic receptors and related circuits. We studied intracortical neuronal dynamics during transitions of loss of consciousness (LOC) with the α2-adrenergic agonist dexmedetomidine and return of consciousness (ROC) in a functionally interconnecting somatosensory and ventral premotor network in non-human primates. LOC, ROC and full task performance recovery were all associated with distinct neural changes. The early recovery demonstrated characteristic intermediate dynamics distinguished by sustained high spindle activities. Awakening by the α2-adrenergic antagonist completely eliminated this intermediate state and instantaneously restored awake dynamics and the top task performance while the anesthetic was still being infused. The results suggest that instantaneous functional recovery is possible following anesthetic-induced unconsciousness and the intermediate recovery state is not a necessary path for the brain recovery.

Basal ganglia beta oscillations during sleep underlie Parkinsonian insomnia

Sleep disorders are among the most debilitating comorbidities of Parkinson's disease (PD) and affect the majority of patients. Of these, the most common is insomnia, the difficulty to initiate and maintain sleep. The degree of insomnia correlates with PD severity and it responds to treatments that decrease pathological basal ganglia (BG) beta oscillations (10-17 Hz in primates), suggesting that beta activity in the BG may contribute to insomnia. We used multiple electrodes to record BG spiking and field potentials during normal sleep and in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced Parkinsonism in nonhuman primates. MPTP intoxication resulted in severe insomnia with delayed sleep onset, sleep fragmentation, and increased wakefulness. Insomnia was accompanied by the onset of nonrapid eye movement (NREM) sleep beta oscillations that were synchronized across the BG and cerebral cortex. The BG beta oscillatory activity was associated with a decrease in slow oscillations (0.1-2 Hz) throughout the cortex, and spontaneous awakenings were preceded by an increase in BG beta activity and cortico-BG beta coherence. Finally, the increase in beta oscillations in the basal ganglia during sleep paralleled decreased NREM sleep, increased wakefulness, and more frequent awakenings. These results identify NREM sleep beta oscillation in the BG as a neural correlate of PD insomnia and suggest a mechanism by which this disorder could emerge.

Characterizing Sleep Spindles in Sheep

Sleep spindles are distinctive transient patterns of brain activity that typically occur during non-rapid eye movement (NREM) sleep in humans and other mammals. Thought to be important for the consolidation of learning, they may also be useful for indicating the progression of aging and neurodegenerative diseases. The aim of this study was to characterize sleep spindles in sheep (Ovis aries). We recorded electroencephalographs wirelessly from six sheep over a continuous period containing 2 nights and a day. We detected and characterized spindles using an automated algorithm. We found that sheep sleep spindles fell within the classical range seen in humans (10–16 Hz), but we did not see a further separation into fast and slow bands. Spindles were detected predominantly during NREM sleep. Spindle characteristics (frequency, duration, density, topography) varied between individuals, but were similar within individuals between nights. Spindles that occurred during NREM sleep in daytime were indistinguishable from those found during NREM sleep at night. Surprisingly, we also detected numerous spindle-like events during unequivocal periods of wake during the day. These events were mainly local (detected at single sites), and their characteristics differed from spindles detected during sleep. These “wake spindles” are likely to be events that are commonly categorized as “spontaneous alpha activity” during wake. We speculate that wake and sleep spindles are generated via different mechanisms, and that wake spindles play a role in cognitive processes that occur during the daytime.

Primate somatosensory cortical neurons are entrained to both spontaneous and peripherally evoked spindle oscillations

Recurrent thalamocortical circuits produce a number of rhythms critical to brain function. In slow-wave sleep, spindles (7-16 Hz) are a prominent spontaneous oscillation generated by thalamic circuits and triggered by cortical slow waves. In wakefulness and under anesthesia, brief peripheral sensory stimuli can evoke 10-Hz reverberations due potentially to similar thalamic mechanisms. Functionally, sleep spindles and peripherally evoked spindles may play a role in memory consolidation and perception, respectively. Yet, rarely have the circuits involved in these two rhythms been compared in the same animals and never in primates. Here, we investigated the entrainment of primary somatosensory cortex (S1) neurons to both rhythms in ketamine-sedated macaques. First, we compared spontaneous spindles in sedation and natural sleep to validate the model. Then, we quantified entrainment with spike-field coherence and phase-locking statistics. We found that S1 neurons entrained to spontaneous sleep spindles were also entrained to the evoked spindles, although entrainment strength and phase systematically differed. Our results indicate that the spindle oscillations triggered by top-down spontaneous cortical activity and bottom-up peripheral input share a common cortical substrate.NEW & NOTEWORTHY Brief sensory stimuli evoke 10-Hz oscillations in thalamocortical neuronal activity and in perceptual thresholds. The mechanisms underlying this evoked rhythm are not well understood but are thought to be similar to those generating sleep spindles. We directly compared the entrainment of cortical neurons to both spontaneous spindles and peripherally evoked oscillations in sedated monkeys. We found that the entrainment strengths to each rhythm were positively correlated, although with differing entrainment phases, implying involvement of similar networks.

Dynamic modulation of theta–gamma coupling during rapid eye movement sleep

Theta phase modulates gamma amplitude in hippocampal networks during spatial navigation and rapid eye movement (REM) sleep. This cross-frequency coupling has been linked to working memory and spatial memory consolidation; however, its spatial and temporal dynamics remains unclear. Here, we first investigate the dynamics of theta–gamma interactions using multiple frequency and temporal scales in simultaneous recordings from hippocampal CA3, CA1, subiculum, and parietal cortex in freely moving mice. We found that theta phase dynamically modulates distinct gamma bands during REM sleep. Interestingly, we further show that theta–gamma coupling switches between recorded brain structures during REM sleep and progressively increases over a single REM sleep episode. Finally, we show that optogenetic silencing of septohippocampal GABAergic projections significantly impedes both theta–gamma coupling and theta phase coherence. Collectively, our study shows that phase-space (i.e. cross-frequency coupling) coding of information during REM sleep is orchestrated across time and space consistent with region-specific processing of information during REM sleep including learning and memory.

Individual spindle detection and analysis in high-density recordings across the night and in thalamic stroke

Sleep spindles are thalamocortical oscillations associated with several behavioural and clinical phenomena. In clinical populations, spindle activity has been shown to be reduced in schizophrenia, as well as after thalamic stroke. Automatic spindle detection algorithms present the only feasible way to systematically examine individual spindle characteristics. We took an established algorithm for spindle detection, and adapted it to high-density EEG sleep recordings. To illustrate the detection and analysis procedure, we examined how spindle characteristics changed across the night and introduced a linear mixed model approach applied to individual spindles in adults (n = 9). Next we examined spindle characteristics between a group of paramedian thalamic stroke patients (n = 9) and matched controls. We found a high spindle incidence rate and that, from early to late in the night, individual spindle power increased with the duration and globality of spindles; despite decreases in spindle incidence and peak-to-peak amplitude. In stroke patients, we found that only left-sided damage reduced individual spindle power. Furthermore, reduction was specific to posterior/fast spindles. Altogether, we demonstrate how state-of-the-art spindle detection techniques, applied to high-density recordings, and analysed using advanced statistical approaches can yield novel insights into how both normal and pathological circumstances affect sleep.