A visual aid for computer-based analysis of sleep–wake state in rats

Sleep scoring in rodents: Criteria, automatic approaches and outstanding issues

There is nothing we spend as much time on in our lives as we do sleeping, which makes it even more surprising that we currently do not know why we need to sleep. Most of the research addressing this question is performed in rodents to allow for invasive, mechanistic approaches. However, in contrast to human sleep, we currently do not have shared and agreed upon standards on sleep states in rodents. In this article, we present an overview on sleep stages in humans and rodents and a historical perspective on the development of automatic sleep scoring systems in rodents. Further, we highlight specific issues in rodent sleep that also call into question some of the standards used in human sleep research.

Identification of the sleep-wake states in rats using the high-frequency activity of the electroencephalogram

The electroencephalographic signal constitutes the main sign classically used for the identification of states of alertness. However, activities in the high frequency (>100 Hz) range have not been properly studied despite their high potential for sleep scoring in rodents. In the present study, we designed a method for the identification of the sleep-wake states in rats by exclusively using high-frequency activities of the electroencephalogram. By calculating the ratio between the amplitude of the electroencephalographic signal from 110 to 200 Hz and from 110 to 300 Hz, we obtained an index that had values that were low during wakefulness, intermediate during non-REM sleep and high during REM sleep. This high-frequency index (HiFI) allowed the identification of each state without the need to study other signs such as muscle activity or eye movements. To evaluate the performance of the index, we compared it with the conventional scoring of the sleep-wake cycle based upon the study of the electromyogram and delta (0.5-4 Hz), theta (6-9 Hz) and sigma (10-14 Hz) bands of the electroencephalogram. The index had an accuracy of 90.43 ± 1.91% (Cohen's kappa value of 0.82), confirming that the study of the high-frequency activities of the electroencephalogram was sufficient to reliably identify alertness states in the rat. Compared to other sleep-scoring methods, the HiFI has several advantages. It only requires one electroencephalography electrode, thus reducing the severity of the surgical preparation of the experimental animal, and its calculation is very simple, so it can be easily implemented online to classify sleep-wake states in real time.

Investigation of sleep-wake rhythm in non-human primates without restraint during data collection

To understand sleep mechanisms and develop treatments for sleep disorders, investigations using animal models are essential. The sleep architecture of rodents differs from that of diurnal mammals including humans and non-human primates. Sleep studies have been conducted in non-human primates; however, these sleep assessments were performed on animals placed in a restraint chair connected via the umbilical area to the recording apparatus. To avoid restraints, cables, and other stressful apparatuses and manipulations, telemetry systems have been developed. In the present study, sleep recordings in unrestrained cynomolgus monkeys (Macaca fascicularis) and common marmoset monkeys (Callithrix jacchus) were conducted to characterize normal sleep. For the analysis of sleep-wake rhythms in cynomolgus monkeys, telemetry electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG) signals were used. For the analysis of sleep-wake rhythms in marmosets, telemetry EEG and EOG signals were used. Both monkey species showed monophasic sleep patterns during the dark phase. Although non-rapid eye movement (NREM) deep sleep showed higher levels at the beginning of the dark phase in cynomolgus monkeys, NREM deep sleep rarely occurred during the dark phase in marmosets. Our results indicate that the use of telemetry in non-human primate models is useful for sleep studies, and that the different NREM deep sleep activities between cynomolgus monkeys and common marmoset monkeys are useful to examine sleep functions.

Unsupervised online classifier in sleep scoring for sleep deprivation studies

STUDY OBJECTIVE

This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents.

DESIGN

We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD).

SETTINGS

Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD.

PARTICIPANTS

Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings.

MEASUREMENTS

The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours.

RESULTS

Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes).

CONCLUSIONS

Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method.

Optimized single electroencephalogram channel sleep staging in rats

Most studies of sleep staging in rats use both multichannels electroencephalogram (EEG) and electromyogram (EMG), so it would be convenient and meaningful in some fields if sleep staging in rats could be realized using a single EEG channel. In this study, we used a single bipolar cortical EEG electrode at the frontal–parietal location with a 0.5–30 Hz filter band and a clustering sleep-staging algorithm including seven classification parameters. The agreements between the computer and two independent raters were 96.9 ± 1.1% for Wake, 97.1 ± 1.4% for non-rapid eye movement (NREM) sleep, and 91.4 ± 2.5% for rapid eye movement (REM) sleep, and the overall agreement was 96.7 ± 0.7%. These results indicate that the accuracies of sleep staging remain high even though only a single EEG channel was used and that a system based on this scheme would be suitable for realtime and long-term studies of sleep.

Automated analysis of sleep-wake state in rats

A fully automated computer-based sleep scoring system is described and validated for use in rats. The system was designed to emulate visual sleep scoring by using the same basic features of the electroencephalogram (EEG) and electromyogram (EMG), and a similar set of decision-making rules. State indices are calculated for each 5s epoch by combination of amplitudes (microVrms) of 6 filtered EEG frequency bands (EEGlo, d.c.-1.5Hz; delta, 1.5-6Hz; theta, 6-9Hz; alpha, 10.5-15Hz; beta, 22-30Hz; gamma, 35-45Hz; Sigma(EEG)=delta+theta+alpha+beta+gamma) and EMG (10-100Hz) yielding dimensionless ratios: WAKE-index=(EMGxgamma)/theta; NREM-index=(deltaxalpha)/gamma(2); REM-index=theta(3)/(deltaxalphaxEMG); artifact-index=[(2xEEG(lo))+beta]*(gamma/Sigma(EEG)). The index values are re-scaled and normalized, thereby dispensing with the need for animal-specific threshold values. The system was validated by direct comparison with visually scored data in 9 rats. Overall, the computer and visual scores were 96% concordant, which is similar to inter-rater agreement in visual scoring. False-positive error rate was <5%. A re-test protocol in 7 rats confirmed the long-term stability of the system in studies lasting 5 weeks. The system was implemented and further validated in a study of sleep architecture in 7 rats under a 12:12h LD cycle.

High dose CART peptide induces abnormal EEG activity and behavioral seizures

Cocaine- and amphetamine-regulated transcript (CART) peptides are neurotransmitters found throughout the nervous system and in the periphery. CART has an important role in the regulation of food intake, anxiety, endocrine function, and in mesolimbic-mediated reward and reinforcement. This short report casts light upon previous descriptions of presumed behavioral seizure and tremor activity following administration of CART into the central nervous system. By employing electroencephalographic (EEG) recordings, we document the state of cerebrocortical activity. We find that intracerebroventricular (icv) administration of 5 microg of CART 55-102 readily produces an abnormal EEG characterized initially by high amplitude hypersynchronous alpha in the 8-10 Hz range during behavioral wakefulness as manifest in both cortical and hippocampal theta EEG channels. This reliably progressed in three of three animals tested to unequivocal epileptiform activity accompanied by tremors and assumption of a rigid, tonic body posture. The neural substrates underlying this finding are unclear. This novel description of the epileptogenic quality of CART should lend caution to interpretations of the behaviors attributed to CART in other experimental paradigms.