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

Displacement and functional ultrasound (fUS) imaging of displacement-guided focused ultrasound (FUS) neuromodulation in mice

Focused ultrasound (FUS) stimulation is a promising neuromodulation technique with the merits of non-invasiveness, high spatial resolution, and deep penetration depth. However, simultaneous imaging of FUS-induced brain tissue displacement and the subsequent effect of FUS stimulation on brain hemodynamics has proven challenging thus far. In addition, earlier studies lack in situ confirmation of targeting except for the magnetic resonance imaging-guided FUS system-based studies. The purpose of this study is 1) to introduce a fully ultrasonic approach to in situ target, modulate neuronal activity, and monitor the resultant neuromodulation effect by respectively leveraging displacement imaging, FUS, and functional ultrasound (fUS) imaging, and 2) to investigate FUS-evoked cerebral blood volume (CBV) response and the relationship between CBV and displacement. We performed displacement imaging on craniotomized mice to confirm the in situ targeting for neuromodulation site. We recorded hemodynamic responses evoked by FUS while fUS imaging revealed an ipsilateral CBV increase that peaks at 4 s post-FUS. We report a stronger hemodynamic activation in the subcortical region than cortical, showing good agreement with a brain elasticity map that can also be obtained using a similar methodology. We observed dose-dependent CBV responses with peak CBV, activated area, and correlation coefficient increasing with the ultrasonic dose. Furthermore, by mapping displacement and hemodynamic activation, we found that displacement colocalized and linearly correlated with CBV increase. The findings presented herein demonstrated that FUS evokes ipsilateral hemodynamic activation in cortical and subcortical depths while the evoked hemodynamic responses colocalize and correlate with FUS-induced displacement. We anticipate that our findings will help consolidate accurate targeting as well as shedding light on one of the mechanisms behind FUS modulation, i.e., how FUS mechanically displaces brain tissue affecting cerebral hemodynamics and thereby its associated connectivity.

Intervention effects of telenursing based on M-O-A model in empty-nest older adult individuals with chronic diseases: a randomized controlled trial

Aim

This study aims to verify the effectiveness of M-O-A telenursing intervention model in improving the health status and quality of life of the empty-nest older adult individuals with chronic diseases by a randomized comparative trial.

Methods

M-O-A telenursing intervention model was constructed based on the needs of the participants. The control group (N = 39) received routine nursing, the experimental group (N = 39) received M-O-A telenursing intervention in addition to routine nursing. After 12 weeks of intervention, the intervention effects of being a participant in the two groups were evaluated. SPSS 26.0 was used for data analysis.

Results

After 12 weeks of intervention, for the experimental group, each dimension of quality of life based on EQ-5D-3L became better, especially for "pain/discomfort," "anxiety/depression," "HRQoL" and "EQ-VAS" (all p < 0.05) and each dimension of quality of life based on SF-36 became better too, especially for "GH," "BP," "RE," "MH," "VT," "SF," "PCS," "MCS," "SF-36" (all p < 0.05). In addition, there was a statistical downward trend in blood pressure, blood glucose, weight, BMI, fat rate, nap duration, number of nocturnal awakenings, light sleep rate and a statistical upward trend in water rate, basal metabolic rate, nighttime sleep duration, deep sleep rate, rapid eye movement sleep rate, especially at the end of intervention (all p < 0.05). While for the control group, there was no statistical improvement in all these aspects.

Conclusion

The M-O-A telenursing model could effectively regulate quality of life and health condition of the empty-nest older adult individuals with chronic diseases, making it worthy of further promotion and application.

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16-hour fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Functional ultrasound (fUS) imaging of displacement-guided focused ultrasound (FUS) neuromodulation in mice

Focused ultrasound (FUS) stimulation is a promising neuromodulation technique with the merits of non-invasiveness, high spatial resolution, and deep penetration depth. However, simultaneous imaging of FUS-induced brain tissue displacement and the subsequent effect of FUS stimulation on brain hemodynamics has proven challenging thus far. In addition, earlier studies lack in situ confirmation of targeting except for the magnetic resonance imaging-guided FUS system-based studies. The purpose of this study is 1) to introduce a fully ultrasonic approach to in situ target, modulate neuronal activity, and monitor the resultant neuromodulation effect by respectively leveraging displacement imaging, FUS, and functional ultrasound (fUS) imaging, and 2) to investigate FUS-evoked cerebral blood volume (CBV) response and the relationship between CBV and displacement. We performed displacement imaging on craniotomized mice to confirm the in targeting for neuromodulation site. We recorded hemodynamic responses evoked by FUS and fUS revealed an ipsilateral CBV increase that peaks at 4 s post-FUS. We saw a stronger hemodynamic activation in the subcortical region than cortical, showing good agreement with the brain elasticity map that can also be obtained using a similar methodology. We observed dose-dependent CBV response with peak CBV, activated area, and correlation coefficient increasing with ultrasonic dose. Furthermore, by mapping displacement and hemodynamic activation, we found that displacement colocalizes and linearly correlates with CBV increase. The findings presented herein demonstrated that FUS evokes ipsilateral hemodynamic activation in cortical and subcortical depths and the evoked hemodynamic responses colocalized and correlate with FUS-induced displacement. We anticipate that our findings will help consolidate accurate targeting as well as an understanding of how FUS displaces brain tissue and affects cerebral hemodynamics.

Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness

The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.

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.

Functional differences in cerebral activation between slow wave-coupled and uncoupled sleep spindles

Spindles are often temporally coupled to slow waves (SW). These SW-spindle complexes have been implicated in memory consolidation that involves transfer of information from the hippocampus to the neocortex. However, spindles and SW, which are characteristic of NREM sleep, can occur as part of this complex, or in isolation. It is not clear whether dissociable parts of the brain are recruited when coupled to SW vs. when spindles or SW occur in isolation. Here, we tested differences in cerebral activation time-locked to uncoupled spindles, uncoupled SW and coupled SW-spindle complexes using simultaneous EEG-fMRI. Consistent with the "active system model," we hypothesized that brain activations time-locked to coupled SW-spindles would preferentially occur in brain areas known to be critical for sleep-dependent memory consolidation. Our results show that coupled spindles and uncoupled spindles recruit distinct parts of the brain. Specifically, we found that hippocampal activation during sleep is not uniquely related to spindles. Rather, this process is primarily driven by SWs and SW-spindle coupling. In addition, we show that SW-spindle coupling is critical in the activation of the putamen. Importantly, SW-spindle coupling specifically recruited frontal areas in comparison to uncoupled spindles, which may be critical for the hippocampal-neocortical dialogue that preferentially occurs during sleep.

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.

Mice Lacking Cerebellar Cortex and Related Structures Show a Decrease in Slow-Wave Activity With Normal Non-REM Sleep Amount and Sleep Homeostasis

In addition to the well-known motor control, the cerebellum has recently been implicated in memory, cognition, addiction, and social behavior. Given that the cerebellum contains more neurons than the cerebral cortex and has tight connections to the thalamus and brainstem nuclei, it is possible that the cerebellum also regulates sleep/wakefulness. However, the role of the cerebellum in sleep was unclear, since cerebellar lesion studies inevitably involved massive inflammation in the adjacent brainstem, and sleep changes in lesion studies were not consistent with each other. Here, we examine the role of the cerebellum in sleep and wakefulness using mesencephalon- and rhombomere 1-specific Ptf1a conditional knockout (Ptf1a cKO) mice, which lack the cerebellar cortex and its related structures, and exhibit ataxic gait. Ptf1a cKO mice had similar wake and non-rapid eye movement sleep (NREMS) time as control mice and showed reduced slow wave activity during wakefulness, NREMS and REMS. Ptf1a cKO mice showed a decrease in REMS time during the light phase and had increased NREMS delta power in response to 6 h of sleep deprivation, as did control mice. Ptf1a cKO mice also had similar numbers of sleep spindles and fear memories as control mice. Thus, the cerebellum does not appear to play a major role in sleep-wake control, but may be involved in the generation of slow waves.

Local sleep: A new concept in brain plasticity

Traditionally, sleep and wakefulness have been considered as two global, mutually exclusive states. However, this view has been challenged by the discovery that sleep and wakefulness are actually locally regulated and that islands of these two states may often coexist in the same individual. Importantly, such a local regulation seems to be the key for many essential functions of sleep, including the maintenance of cognitive efficiency and the consolidation of new skills and memories. Indeed, local changes in sleep-related oscillations occur in brain areas that are used and involved in learning during wakefulness. In turn, these changes directly modulate experience-dependent brain adaptations and the consolidation of newly acquired memories. In line with these observations, alterations in the regional balance between wake- and sleep-like activity have been shown to accompany many pathologic conditions, including psychiatric and neurologic disorders. In the last decade, experimental research has started to shed light on the mechanisms involved in the local regulation of sleep and wakefulness. The results of this research have opened new avenues of investigation regarding the function of sleep and have revealed novel potential targets for the treatment of several pathologic conditions.

Sleep Deprivation Impairs Learning-Induced Increase in Hippocampal Sharp Wave Ripples and Associated Spike Dynamics during Recovery Sleep

Sleep deprivation (SD) causes deficits in off-line memory consolidation, but the underlying network oscillation mechanisms remain unclear. Hippocampal sharp wave ripple (SWR) oscillations play a critical role in off-line memory consolidation. Therefore, we trained mice to learn a hippocampus-dependent trace eyeblink conditioning (tEBC) task and explored the influence of 1.5-h postlearning SD on hippocampal SWRs and related spike dynamics during recovery sleep. We found an increase in hippocampal SWRs during postlearning sleep, which predicted the consolidation of tEBC in conditioned mice. In contrast, sleep-deprived mice showed a loss of tEBC learning-induced increase in hippocampal SWRs during recovery sleep. Moreover, the sleep-deprived mice exhibited weaker reactivation of tEBC learning-associated pyramidal cells in hippocampal SWRs during recovery sleep. In line with these findings, tEBC consolidation was impaired in sleep-deprived mice. Furthermore, sleep-deprived mice showed augmented fast excitation from pyramidal cells to interneurons and enhanced participation of interneurons in hippocampal SWRs during recovery sleep. Among various interneurons, parvalbumin-expressing interneurons specifically exhibited overexcitation during hippocampal SWRs. Our findings suggest that altered hippocampal SWRs and associated spike dynamics during recovery sleep may be candidate network oscillation mechanisms underlying SD-induced memory deficits.