Chronic escitalopram treatment attenuated the accelerated rapid eye movement sleep transitions after selective rapid eye movement sleep deprivation: a model-based analysis using Markov chains

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

BACKGROUND

Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI).

OBJECTIVE

This study aimed to use data collected from fitness trackers to predict MCI status.

METHODS

In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status.

RESULTS

Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time.

CONCLUSIONS

Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies.

Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram: a pilot study

BACKGROUND

Previous studies have shown escitalopram is related to sleep quality. However, effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. This study may help to reveal pharmacological mechanism underlying escitalopram treatment.

METHODS

The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed eight weeks' treatment of escitalopram were included in the final statistics. Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment.

RESULTS

For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) of δ1 band (0.5 - 2 Hz), accompanied by a significant decrease in the RE of β2 band (20 - 30 Hz). Lempel-Ziv complexity and Co - complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as throughout the night sleep. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry. Regarding band-specific EEG complexity estimations, δ1 and β2 in stage-1 and δ1 in stage-2 sleep stage in frontal cortex is found to be much more sensitive to escitalopram treatment in comparison to central and occipital cortices.

CONCLUSIONS

The sleep quality of MDD patients improved, EEG response occurred asymmetry in left and right hemispheres due to escitalopram treatment, and frontal cortex is found to be much more sensitive to escitalopram treatment. These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.

AM-251, A Cannabinoid Antagonist, Modifies the Dynamics of Sleep-Wake Cycles in Rats

Study Objectives: (a) To describe the microarchitecture of wakefulness and sleep following administrations of 5- and 10-mg/kg AM-251 in rats. (b) To develop a new statistical method to follow bout-to-bout dynamics. Method: Wistar rats (n = 6) had been equipped with electroencephalography (EEG) and electromyography (EMG) electrodes. Following their recovery and habituation after the surgery, the animals were injected with vehicle and 5- and 10-mg/kg AM-251 intraperitoneally and EEG, EMG, and motor activity were analyzed for the subsequent 3 h. Results: AM-251 induced a dose- and time-dependent increase in the number of bouts in active wake (AW), and it decreased this number in all other vigilance states except in passive wake (PW). In contrast, the bout duration in PW compensatory decreased. The effect of AM-251 on the sleep transition dynamics was monitored with a new tool we call "transition heatmap." The analysis of bout trajectories with transition heatmaps reveals a highly organized pattern. Conclusion: AM-251 selectively influences the frequency of vigilance state transitions, but it has no direct impact on the state lengths. AM-251 markedly changed the state transition dynamics, which was visualized with the help of state transition heatmaps.

Proteomic analysis of rat serum revealed the effects of chronic sleep deprivation on metabolic, cardiovascular and nervous system

Sleep is an essential and fundamental physiological process that plays crucial roles in the balance of psychological and physical health. Sleep disorder may lead to adverse health outcomes. The effects of sleep deprivation were extensively studied, but its mechanism is still not fully understood. The present study aimed to identify the alterations of serum proteins associated with chronic sleep deprivation, and to seek for potential biomarkers of sleep disorder mediated diseases. A label-free quantitative proteomics technology was used to survey the global changes of serum proteins between normal rats and chronic sleep deprivation rats. A total of 309 proteins were detected in the serum samples and among them, 117 proteins showed more than 1.8-folds abundance alterations between the two groups. Functional enrichment and network analyses of the differential proteins revealed a close relationship between chronic sleep deprivation and several biological processes including energy metabolism, cardiovascular function and nervous function. And four proteins including pyruvate kinase M1, clusterin, kininogen1 and profilin-1were identified as potential biomarkers for chronic sleep deprivation. The four candidates were validated via parallel reaction monitoring (PRM) based targeted proteomics. In addition, protein expression alteration of the four proteins was confirmed in myocardium and brain of rat model. In summary, the comprehensive proteomic study revealed the biological impacts of chronic sleep deprivation and discovered several potential biomarkers. This study provides further insight into the pathological and molecular mechanisms underlying sleep disorders at protein level.

Mathematical modeling of sleep state dynamics in a rodent model of shift work

Millions of people worldwide are required to work when their physiology is tuned for sleep. By forcing wakefulness out of the body’s normal schedule, shift workers face numerous adverse health consequences, including gastrointestinal problems, sleep problems, and higher rates of some diseases, including cancers. Recent studies have developed protocols to simulate shift work in rodents with the intention of assessing the effects of night-shift work on subsequent sleep (Grønli et al., 2017). These studies have already provided important contributions to the understanding of the metabolic consequences of shift work (Arble et al., 2015; Marti et al., 2016; Opperhuizen et al., 2015) and sleep-wake-specific impacts of night-shift work (Grønli et al., 2017). However, our understanding of the causal mechanisms underlying night-shift-related sleep disturbances is limited. In order to advance toward a mechanistic understanding of sleep disruption in shift work, we model these data with two different approaches. First we apply a simple homeostatic model to quantify differences in the rates at which sleep need, as measured by slow wave activity during slow wave sleep (SWS) rises and falls. Second, we develop a simple and novel mathematical model of rodent sleep and use it to investigate the timing of sleep in a simulated shift work protocol (Grønli et al., 2017). This mathematical framework includes the circadian and homeostatic processes of the two-process model, but additionally incorporates a stochastic process to model the polyphasic nature of rodent sleep. By changing only the time at which the rodents are forced to be awake, the model reproduces some key experimental results from the previous study, including correct proportions of time spent in each stage of sleep as a function of circadian time and the differences in total wake time and SWS bout durations in the rodents representing night-shift workers and those representing day-shift workers. Importantly, the model allows for deeper insight into circadian and homeostatic influences on sleep timing, as it demonstrates that the differences in SWS bout duration between rodents in the two shifts is largely a circadian effect. Our study shows the importance of mathematical modeling in uncovering mechanisms behind shift work sleep disturbances and it begins to lay a foundation for future mathematical modeling of sleep in rodents.

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Millions of people worldwide are required to work when their physiology is tuned for sleep.

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Enforcing wakefulness during this time leads to numerous adverse health consequences including sleep problems and higher rates of some diseases.

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Rodent models of shift work have illuminated some of the effects of night shift work on subsequent sleep.

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This study uses mathematical modeling to accurately simulate rodent sleep during baseline and shift work conditions.

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A simple mathematical framework can help us understand possible mechanisms underlying the sleep disturbances seen in shift work.

Caffeine and Modafinil Ameliorate the Neuroinflammation and Anxious Behavior in Rats during Sleep Deprivation by Inhibiting the Microglia Activation

Background: Sleep deprivation (SD) plagues modern society due to the professional demands. It prevails in patients with mood and neuroinflammatory disorders. Although growing evidence suggests the improvement in the cognitive performance by psychostimulants during sleep-deprived conditions, the impending involved mechanism is rarely studied. Thus, we hypothesized that mood and inflammatory changes might be due to the glial cells activation induced modulation of the inflammatory cytokines during SD, which could be improved by administering psychostimulants. The present study evaluated the role of caffeine/modafinil on SD-induced behavioral and inflammatory consequences.

Methods: Adult male Sprague-Dawley rats were sleep deprived for 48 h using automated SD apparatus. Caffeine (60 mg/kg/day) or modafinil (100 mg/kg/day) were administered orally to rats once every day during SD. Rats were subjected to anxious and depressive behavioral evaluation after SD. Subsequently, blood and brain were collected for biochemical, immunohistochemical and molecular studies.

Results: Sleep deprived rats presented an increased number of entries and time spent in closed arms in elevated plus maze test and decreased total distance traveled in the open field (OF) test. Caffeine/modafinil treatment significantly improved these anxious consequences. However, we did not observe substantial changes in immobility and anhedonia in sleep-deprived rats. Caffeine/modafinil significantly down-regulated the pro- and up-regulated the anti-inflammatory cytokine mRNA and protein expression in the hippocampus during SD. Similar outcomes were observed in blood plasma cytokine levels. Caffeine/modafinil treatment significantly decreased the microglial immunoreactivity in DG, CA1 and CA3 regions of the hippocampus during SD, however, no significant increase in immunoreactivity of astrocytes was observed. Sholl analysis signified the improvement in the morphological alterations of astrocytes and microglia after caffeine/modafinil administration during SD. Stereological analysis demonstrated a significant improvement in the number of ionized calcium binding adapter molecule I (Iba-1) positive cells (different states) in different regions of the hippocampus after caffeine or modafinil treatment during SD without showing any significant change in total microglial cell number. Eventually, the correlation analysis displayed a positive relationship between anxiety, pro-inflammatory cytokines and activated microglial cell count during SD.

Conclusion: The present study suggests the role of caffeine or modafinil in the amelioration of SD-induced inflammatory response and anxious behavior in rats.

Highlights

- SD induced mood alterations in rats.

- Glial cells activated in association with the changes in the inflammatory cytokines.

- Caffeine or modafinil improved the mood and restored inflammatory changes during SD.

- SD-induced anxious behavior correlated with the inflammatory consequences.

REM sleep homeostasis in the absence of REM sleep: Effects of antidepressants

Most antidepressants suppress rapid eye movement (REM) sleep, which is thought to be important to brain function, yet the resulting REM sleep restriction is well tolerated. This study investigated the impact of antidepressants with different mechanisms of action, such as selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCA), on the regulation of REM sleep in rats. REM sleep was first demonstrated to be homeostatically regulated using 5, 8 and 10 h of REM-sleep specific restriction through EEG-triggered arousals, with an average of 91 ± 10% of lost REM sleep recovered following a 26-29 -hour recovery period. Acute treatment with the antidepressants paroxetine, citalopram and imipramine inhibited REM sleep by 84 ± 8, 84 ± 8 and 69 ± 9% respectively relative to vehicle control. The pharmacologically-induced REM sleep deficits by paroxetine and citalopram were not fully recovered, whereas, after imipramine the REM sleep deficit was fully compensated. Given the marked difference between REM sleep recovery following the administration of paroxetine, citalopram, imipramine and REM sleep restriction, the homeostatic response was further examined by pairing REM sleep specific restriction with the three antidepressants. Surprisingly, the physiologically-induced REM sleep deficits incurred prior to suppression of REM sleep by all antidepressants was consistently recovered. The data indicate that REM sleep homeostasis remains operative following subsequent treatment with antidepressants and is unaffected by additional pharmacological inhibition of REM sleep.