Rapid eye movement density during REM sleep in dogs (Canis familiaris)

Kovács, Enikő; Kosztolányi, András; Kis, Anna

doi:10.3758/s13420-018-0355-9

Cited by 10 publications

(11 citation statements)

References 32 publications

(38 reference statements)

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“…Even within this group of healthy dogs, we found that age significantly affected the activity pattern across the day, with effects also seen for mass and sex. Recent work has provided insights into sleep architecture in dogs 19,21 , with the development of sophisticated non-invasive polysomnography tools. Surprisingly however, this basic question about sleep-wake cycle in dogs had not been fully characterized.…”

Section: Discussionmentioning

confidence: 99%

“…Companion dogs offer a distinctive perspective when studying sleep patterns, not only due to shared environmental influences in the home, but also similarities shared by humans and dogs in sleep architecture and sleep-wake cycles 4,[15][16][17][18][19][20] . Both species cycle through phases of wakefulness, drowsiness, rapid eye movement (REM) sleep, and non-rapid eye movement (NREM) sleep 4,15,16,20,21 . As with activity, companion dogs and humans follow a similar circadian rhythm and diurnal sleep pattern 8,21,22 .…”

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confidence: 99%

“…Both species cycle through phases of wakefulness, drowsiness, rapid eye movement (REM) sleep, and non-rapid eye movement (NREM) sleep 4,15,16,20,21 . As with activity, companion dogs and humans follow a similar circadian rhythm and diurnal sleep pattern 8,21,22 . However, the majority of our understanding of dog sleep patterns comes from studies conducted only at night, or are based on older observational studies of dogs whose primary housing is no longer comparable to the majority of pet dogs 23 .…”

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confidence: 99%

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A functional linear modeling approach to sleep–wake cycles in dogs

Woods

Patel

et al. 2020

Sci Rep

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The study of companion (pet) dogs is an area of great translational potential, as they share a risk for many conditions that afflict humans. Among these are conditions that affect sleep, including chronic pain and cognitive dysfunction. Significant advancements have occurred in the ability to study sleep in dogs, including development of non-invasive polysomnography; however, basic understanding of dog sleep patterns remains poorly characterized. The purpose of this study was to establish baseline sleep–wake cycle and activity patterns using actigraphy and functional linear modeling (FLM), for healthy, adult companion dogs. Forty-two dogs were enrolled and wore activity monitors for 14 days. FLM demonstrated a bimodal pattern of activity with significant effects of sex, body mass, and age; the effect of age was particularly evident during the times of peak activity. This study demonstrated that FLM can be used to describe normal sleep–wake cycles of healthy adult dogs and the effects of physiologic traits on these patterns of activity. This foundation makes it possible to characterize deviations from normal patterns, including those associated with chronic pain and cognitive dysfunction syndrome. This can improve detection of these conditions in dogs, benefitting them and their potential as models for human disease.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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A functional linear modeling approach to sleep–wake cycles in dogs

Woods

Patel

et al. 2020

Sci Rep

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“…The electrophysiological recordings were carried out according to the completely non-invasive polysomnography method developed and validated by Kis et al [14] and applied in many studies since (e.g. [15,44,45]). According to the procedure, we recorded the EEG (including electrodes next to the eyes, used as eletrooculogram (EOG); mainly for detecting artefactual muscle movements), electrocardiogram and the respiratory signal of dogs, but only used the EEG signal in these analyses.…”

Section: Electrophysiological Recordingsmentioning

confidence: 99%

Differences in dogs’ event-related potentials in response to human and dog vocal stimuli; a non-invasive study

et al. 2022

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Recent advances in the field of canine neuro-cognition allow for the non-invasive research of brain mechanisms in family dogs. Considering the striking similarities between dog's and human (infant)'s socio-cognition at the behavioural level, both similarities and differences in neural background can be of particular relevance. The current study investigates brain responses of n = 17 family dogs to human and conspecific emotional vocalizations using a fully non-invasive event-related potential (ERP) paradigm. We found that similarly to humans, dogs show a differential ERP response depending on the species of the caller, demonstrated by a more positive ERP response to human vocalizations compared to dog vocalizations in a time window between 250 and 650 ms after stimulus onset. A later time window between 800 and 900 ms also revealed a valence-sensitive ERP response in interaction with the species of the caller. Our results are, to our knowledge, the first ERP evidence to show the species sensitivity of vocal neural processing in dogs along with indications of valence sensitive processes in later post-stimulus time periods.

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“…The previous literature used to estimate priors for each model are shown in Table 1. (Carskadon & Dement, 2005;Yetton et al, 2018) Spindles (Purcell et al, 2016;Warby et al, 2014;Yetton et al, n.d.) Slow Oscillations (Carrier et al, 2011;Massimini, 2004) REM Density (Ficca et al, 1999;Khalsa, Conroy, Duffy, Czeisler, & DIJK, 2002;Kovács, Kosztolányi, & Kis, 2018;Reynolds III et al, 1985;Yetton et al, 2016) Evaluation Criteria…”

Section: Priorsmentioning

confidence: 99%

P(Sleep) 2.0: Capturing the dynamics of nighttime sleep using probabilistic models and big data

Yetton¹

2019

Preprint

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Sleep, a critical necessity for health, cognition, and well-being, consumes 25 years of the average human’s life. Alterations in sleep’s macro and microstate have been observed in multiple illnesses, including depression and Alzheimer’s, and sleep may represent a biomarker of these difficult to diagnose illnesses. Despite this, adequate models of the dynamics of sleep able to capture the fine-grained minutia of sleep’s many states and processes are lacking. The current work leverages over 1000 nights of healthy sleep polysomnography recordings from multiple open-source datasets. Data were automatically analyzed through a machine learning and data engineering pipeline to extract important features of the electroencephalogram, such as sleep spindles, slow oscillations (SO), and Rapid Eye Movement (REM) events. The counts of these sleep features and transition probabilities of sleep stages were modeled across the night in 30-second epoch intervals, using a flexible yet principled Bayesian cognitive modeling approach. The model, christened P(Sleep) 2.0, defines “typical” patterns of healthy sleep, and how they are affected by time, circadian processes and individual differences (e.g., age, sex). Transition probability dynamics were found to best be predicted by the previous 2 stages, time spent in stage, time across the night, age and sex, but not an age*sex interaction. Spindles, SO and REM events were also affected by the same features, but an age*sex interaction was additionally present. N2 spindles and both N2 and N3 slow oscillations were found to increase with time spent in a stage, whereas N3 spindles and REM decreased. Spindles and SO were more present in the evening compared to the morning; in contrast, REM increased over the night. REM counts and N3 spindles increased with age, while N2 spindles and SO reduced. Females had more N2 spindles, and SO in both N2 and N3 than men. The current work replicates a body of previous literature based on small sample sizes, and the increased power in this study provides firm conclusions to arguments on the patterns of healthy sleep across the night, both at the macroscale (stages) and microscale (feature events). The proposed work has broader impacts on the detection of abnormal (and potentially pathologic) sleep patterns.

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Rapid eye movement density during REM sleep in dogs (Canis familiaris)

Cited by 10 publications

References 32 publications

A functional linear modeling approach to sleep–wake cycles in dogs

A functional linear modeling approach to sleep–wake cycles in dogs

Differences in dogs’ event-related potentials in response to human and dog vocal stimuli; a non-invasive study

P(Sleep) 2.0: Capturing the dynamics of nighttime sleep using probabilistic models and big data

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