The domestic dog (Canis familiaris) is a promising animal model. Yet, the canine neuroscience literature is predominantly comprised of studies wherein (semi-)invasive methods and intensive training are used to study awake dog behavior. Given prior findings with humans and/or dogs, our goal was to assess, in 16 family dogs (1.5–7 years old; 10 males; 10 different breeds) the effects of pre-sleep activity and timing and location of sleep on sleep electrophysiology. All three factors had a main and/or interactive effect on sleep macrostructure. Following an active day, dogs slept more, were more likely to have an earlier drowsiness and NREM, and spent less time in drowsiness and more time in NREM and REM. Activity also had location- and time of day-specific effects. Time of day had main effects; at nighttime, dogs slept more and spent less time in drowsiness and awake after first drowsiness, and more time in NREM and in REM. Location had a main effect; when not at home, REM sleep following a first NREM was less likely. Findings are consistent with and extend prior human and dog data and have implications for the dog as an animal model and for informing future comparative research on sleep.

Non-REM bursts of activity in the sigma range (9–16 Hz) typical of sleep spindles predict learning in dogs, similar to humans and rats. Little is known, however, about the age-related changes in amplitude, density (spindles/minute) and frequency (waves/second) of canine spindles. We investigated a large sample (N = 155) of intact and neutered pet dogs of both sexes, varying in breed and age, searching for spindles in segments of non-REM sleep. We recorded EEG from both a frontal midline electrode (Fz) and a central midline electrode (Cz) in 55.5% of the dogs, in the remaining animals only the Fz electrode was active (bipolar derivation). A similar topography was observed for fast (≥13 Hz) spindle occurrence as in humans (fast spindle number, density on Cz > Fz). For fast spindles, density was higher in females, and increased with age. These effects were more pronounced among intact animals and on Fz. Slow spindle density declined and fast spindle frequency increased with age on Cz, while on Fz age-related amplitude decline was observed. The frequency of fast spindles on Fz and slow spindles on Cz was linked to both sex and neutering, suggesting modulation by sexual hormones. Intact females displayed higher frequencies than males and neutered females. Our findings support the argument that sigma bursts in the canine non-REM sleep are analogous to human sleep spindles, and suggest that slow and fast spindles display different trajectories related to age, of which an increase in frontal fast spindles is unique to dogs.

The dog (Canis familiaris) has been proved to be an interesting and valid animal model of human socio-cognitive skills not just at the behavioural level (Miklósi & Topál, 2013), but also in the area of neurocognitive research, including sleep-related cognition (Bunford, Andics, Kis, Miklósi, & Gácsi, 2017). One prominent line of canine neuroscience literature focuses on awake functioning, mainly using

Age-related differences in dog sleep and the age at which dogs reach adulthood as indexed by sleep electrophysiology are unknown. We assessed, in (1) a Juvenile sample (n = 60) of 2–14-month-old dogs (weight range: 4–68 kg), associations between age, sleep macrostructure, and non-rapid eye movement (NREM) EEG power spectrum, whether weight moderates associations, and (2) an extended sample (n = 91) of 2–30-months-old dogs, when sleep parameters stabilise. In Juvenile dogs, age was positively associated with time in drowsiness between 2 and 8 months, and negatively with time in rapid eye movement (REM) sleep between 2 and 6 months. Age was negatively associated with delta and positively with theta and alpha power activity, between 8 and 14 months. Older dogs exhibited greater sigma and beta power activity. Larger, > 8-month-old dogs had less delta and more alpha and beta activity. In extended sample, descriptive data suggest age-related power spectrum differences do not stabilise by 14 months. Drowsiness, REM, and delta power findings are consistent with prior results. Sleep electrophysiology is a promising index of dog neurodevelopment; some parameters stabilise in adolescence and some later than one year. Determination of the effect of weight and timing of power spectrum stabilisation needs further inquiry. The dog central nervous system is not fully mature by 12 months of age.

The sleeping activity of family dogs has been studied increasingly in the past years. Recently, a validated, non-invasive polysomnographic method has been developed for dogs, enabling the parallel recording of several neurophysiological signals on non-anesthetized family dogs, including brain activity (EEG), eye movements (EOG), cardiac (ECG), and respiratory activity (PNG). In this study, we examined the ECG (N = 30) and respiratory signals (N = 19) of dogs during a 3-h sleep period in the afternoon, under laboratory conditions. We calculated four time-domain heart rate variables [mean heart rate (HR), SDNN, RMSSD, and pNN50] from the ECG and the estimated average respiratory frequency from the respiratory signal. We analyzed how these variables are affected by the different sleep-wake phases (wakefulness, drowsiness, NREM, and REM) as well as the dogs’ sex, age and weight. We have found that the sleep-wake phase had a significant effect on all measured cardiac parameters. In the wake phase, the mean HR was higher than in all other phases, while SDNN, RMSSD, and pNN50 were lower than in all other sleep phases. In drowsiness, mean HR was higher compared to NREM and REM phases, while SDNN and RMSSD was lower compared to NREM and REM phases. In REM, SDNN, and RMSSD was higher than in NREM. However, the sleep-wake phase had no effect on the estimated average respiratory frequency of dogs. The dogs’ sex, age and weight had no effect on any of the investigated variables. This study represents a detailed analysis of the cardiac and respiratory activity of dogs during sleep. Since variations in these physiological signals reflect the dynamics of autonomic functions, a more detailed understanding of their changes may help us to gain a better understanding of the internal/emotional processes of dogs in response to different conditions of external stimuli. As such, our results are important since they are directly comparable to human findings and may also serve as a potential basis for future studies on dogs.

Although a positive link between sleep spindle occurrence and measures of post-sleep recall (learning success) is often reported for humans and replicated across species, the test–retest reliability of the effect is sometimes questioned. The largest to date study could not confirm the association, however methods for automatic spindle detection diverge in their estimates and vary between studies. Here we report that in dogs using the same detection method across different learning tasks is associated with observing a positive association between sleep spindle density (spindles/minute) and learning success. Our results suggest that reducing measurement error by averaging across measurements of density and learning can increase the visibility of this effect, implying that trait density (estimated through averaged occurrence) is a more reliable predictor of cognitive performance than estimates based on single measures.

Summary The aim of this study was to investigate hyperarousal in individuals with frequent nightmares (NM participants) by calculating arousal events during nocturnal sleep. We hypothesized an increased number of arousals in NM participants compared with controls, especially during those periods where the probability of spontaneous arousal occurrence is already high, such as non‐rapid eye movement to rapid eye movement transitions (pre‐rapid eye movement periods). Twenty‐two NM participants and 23 control participants spent two consecutive nights in our sleep laboratory, monitored by polysomnography. Arousal number and arousal length were calculated only for the second night, for 10 min before rapid eye movement (pre‐rapid eye movement) and 10 min after rapid eye movement (post‐rapid eye movement) periods, as well as non‐rapid eye movement and rapid eye movement phases separately. Repeated‐measures ANOVA model testing revealed significant Group (NM participants, controls) × Phase (pre‐rapid eye movement, post‐rapid eye movement) interaction in case of the number of arousals. Furthermore, post hoc analysis showed a significantly increased number of arousals during pre‐rapid eye movement periods in NM participants, compared with controls, a difference that disappeared in post‐rapid eye movement periods. We propose that focusing the analyses of arousals specifically on state transitory periods offers a unique perspective into the fragile balance between the sleep‐promoting and arousal systems. This outlook revealed an increased number of arousals in NM participants, reflecting hyperarousal during pre‐rapid eye movement periods.

Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g., emotion processing) and cognitive (e.g., memory consolidation) domains, methodologically relevant questions about the reliability of sleep stage scoring still need to be addressed. In Study 1, we analyzed the effects of different coders and different numbers of visible EEG channels on the visual scoring of the same polysomnography recordings. The lowest agreement was found between independent coders with different scoring experience using full (3 h-long) recordings of the whole dataset, and the highest agreement within-coder, using only a fraction of the original dataset (randomly selected 100 epochs (i.e., 100 × 20 s long segments)). The identification of drowsiness was found to be the least reliable, while that of non-REM (rapid eye movement, NREM) was the most reliable. Disagreements resulted in no or only moderate differences in macrostructural and spectral variables. Study 2 targeted the task of automated sleep EEG time series classification. Supervised machine learning (ML) models were used to help the manual annotation process by reliably predicting if the dog was sleeping or awake. Logistic regression models (LogREG), gradient boosted trees (GBT) and convolutional neural networks (CNN) were set up and trained for sleep state prediction from already collected and manually annotated EEG data. The evaluation of the individual models suggests that their combination results in the best performance: ~0.9 AUC test scores.