Possible association between spindle frequency and reversal-learning in aged family dogs

Sleep-physiological correlates of brachycephaly in dogs

The shape of the cranium is one of the most notable physical changes induced in domestic dogs through selective breeding and is measured using the cephalic index (CI). High CI (a ratio of skull width to skull length > 60) is characterized by a short muzzle and flat face and is referred to as brachycephaly. Brachycephalic dogs display some potentially harmful changes in neuroanatomy, and there are implications for differences in behavior, as well. The path from anatomy to cognition, however, has not been charted in its entirety. Here, we report that sleep-physiological markers of white-matter loss (high delta power, low frontal spindle frequency, i.e., spindle waves/s), along with a spectral profile for REM (low beta, high delta) associated with low intelligence in humans, are each linked to higher CI values in the dog. Additionally, brachycephalic subjects spent more time sleeping, suggesting that the sleep apnea these breeds usually suffer from increases daytime sleepiness. Within sleep, more time was spent in the REM sleep stage than in non-REM, while REM duration was correlated positively with the number of REM episodes across dogs. It is currently not clear if the patterns of sleep and sleep-stage duration are mainly caused by sleep-impairing troubles in breathing and thermoregulation, present a juvenile-like sleeping profile, or are caused by neuro-psychological conditions secondary to the effects of brachycephaly, e.g., frequent REM episodes are known to appear in human patients with depression. While future studies should more directly address the interplay of anatomy, physiology, and behavior within a single experiment, this represents the first description of how the dynamics of the canine brain covary with CI, as measured in resting companion dogs using a non-invasive sleep EEG methodology. The observations suggest that the neuroanatomical changes accompanying brachycephaly alter neural systems in a way that can be captured in the sleep EEG, thus supporting the utility of the latter in the study of canine brain health and function.

Central nodes of canine functional brain networks are concentrated in the cingulate gyrus

Compared to the field of human fMRI, knowledge about functional networks in dogs is scarce. In this paper, we present the first anatomically-defined ROI (region of interest) based functional network map of the companion dog brain. We scanned 33 awake dogs in a "task-free condition". Our trained subjects, similarly to humans, remain willingly motionless during scanning. Our goal is to provide a reference map with a current best estimate for the organisation of the cerebral cortex as measured by functional connectivity. The findings extend a previous spatial ICA (independent component analysis) study (Szabo et al. in Sci Rep 9(1):1.25. https://doi.org/10.1038/s41598-019-51752-2 , 2019), with the current study including (1) more subjects and (2) improved scanning protocol to avoid asymmetric lateral distortions. In dogs, similarly to humans (Sacca et al. in J Neurosci Methods. https://doi.org/10.1016/j.jneumeth.2021.109084 , 2021), ageing resulted in increasing framewise displacement (i.e. head motion) in the scanner. Despite the inherently different approaches between model-free ICA and model-based ROI, the resulting functional networks show a remarkable similarity. However, in the present study, we did not detect a designated auditory network. Instead, we identified two highly connected, lateralised multi-region networks extending to non-homotropic regions (Sylvian L, Sylvian R), including the respective auditory regions, together with the associative and sensorimotor cortices and the insular cortex. The attention and control networks were not split into two fully separated, dedicated networks. Overall, in dogs, fronto-parietal networks and hubs were less dominant than in humans, with the cingulate gyrus playing a central role. The current manuscript provides the first attempt to map whole-brain functional networks in dogs via a model-based approach.

Sleep and cognition in aging dogs. A polysomnographic study

Introduction

Sleep is fundamental for cognitive homeostasis, especially in senior populations since clearance of amyloid beta (key in the pathophysiology of Alzheimer's disease) occurs during sleep. Some electroencephalographic characteristics of sleep and wakefulness have been considered a hallmark of dementia. Owners of dogs with canine cognitive dysfunction syndrome (a canine analog to Alzheimer's disease) report that their dogs suffer from difficulty sleeping. The aim of this study was to quantify age-related changes in the sleep-wakefulness cycle macrostructure and electroencephalographic features in senior dogs and to correlate them with their cognitive performance.

Methods

We performed polysomnographic recordings in 28 senior dogs during a 2 h afternoon nap. Percentage of time spent in wakefulness, drowsiness, NREM, and REM sleep, as well as latency to the three sleep states were calculated. Spectral power, coherence, and Lempel Ziv Complexity of the brain oscillations were estimated. Finally, cognitive performance was evaluated by means of the Canine Dementia Scale Questionnaire and a battery of cognitive tests. Correlations between age, cognitive performance and sleep-wakefulness cycle macrostructure and electroencephalographic features were calculated.

Results

Dogs with higher dementia scores and with worse performance in a problem-solving task spent less time in NREM and REM sleep. Additionally, quantitative electroencephalographic analyses showed differences in dogs associated with age or cognitive performance, some of them reflecting shallower sleep in more affected dogs.

Discussion

Polysomnographic recordings in dogs can detect sleep-wakefulness cycle changes associated with dementia. Further studies should evaluate polysomnography's potential clinical use to monitor the progression of canine cognitive dysfunction syndrome.

Two valid and reliable tests for monitoring age-related memory performance and neophobia differences in dogs

The prolonged lifespan of companion dogs has resulted in increased behavioural and physical challenges linked to old age. The development of behavioural tests to identify and monitor age-related differences has begun. However, standardised testing requires validation. The present study aimed to assess external validity, interobserver reliability, and test-retest reliability of an indoor test battery for the rapid assessment of age-related behavioural differences in dogs. Two experimenters tested young dogs (N = 20, mean age ± SD = 2.7 ± 0.4 years) and old dogs (N = 18, mean age ± SD = 11.8 ± 1.3 years) in the test battery once and then again after two weeks. Our results found external validity for two subtests out of six. On both test occasions, old dogs committed more errors than young dogs in a memory subtest and showed more object avoidance when encountering a novel object. Interobserver reliability and test-retest reliability was high. We conclude that the Memory and Novel object subtests are valid and reliable for monitoring age-related memory performance and object neophobic differences in dogs.

How Old Is My Dog? Identification of Rational Age Groupings in Pet Dogs Based Upon Normative Age-Linked Processes

Behavioral development is a lifelong process where cognitive traits such as learning and memory may be expected to take quadratic or linear trajectories. It is common practice for operational purposes to reduce study subjects into chronological categories when conducting research. However, there are no agreed-upon thresholds for this practice, and the lack of standardization may hinder comparison between studies of normative and pathological aging. In this perspective review, chronological categories have been identified that can be considered to represent normative cognitive and neurological aging in domestic family dogs. These categories work to capture age-related developmental trajectories for the majority of dog breeds. It is encouraged that researchers studying cognition and behavior, pathological cognitive deficits, or welfare of dogs across age categories utilize the categories presented here to best enable comparison between studies. The proposed groups could also support education programs informing owners of what behavioral changes to expect in their dog as they age, but they cannot be used to reflect health-based needs associated with breed-specific morbidity. The use of the age categories proposed here highlights significant welfare issues for breeds with the shortest average lifespans (e.g., the Great Dane). Studies show no evidence of an increased rate of behavioral or cognitive aging in short-lived breeds, and the shortest-lived breeds are most likely to die when classified by the proposed categories as Mature Adults. Adoption of these chronological categories in future research would aid comparison between studies and identification of non-normative age-related pathologies.

Shared and unique features of mammalian sleep spindles - insights from new and old animal models

Sleep spindles are phasic events observed in mammalian non-rapid eye movement sleep. They are relevant today in the study of memory consolidation, sleep quality, mental health and ageing. We argue that our advanced understanding of their mechanisms has not exhausted the utility and need for animal model work. This is both because some topics, like cognitive ageing, have not yet been addressed sufficiently in comparative efforts and because the evolutionary history of this oscillation is still poorly understood. Comparisons across species often are either limited to referencing the classical cat and rodent models, or are over-inclusive, uncritically including reports of sleep spindles in rarely studied animals. In this review, we discuss the emergence of new (dog and sheep) models for sleep spindles and compare the strengths and shortcomings of new and old models based on the three validation criteria for animal models - face, predictive, and construct validity. We conclude that an emphasis on cognitive ageing might dictate the future of comparative sleep spindle studies, a development that is already becoming visible in studies on dogs. Moreover, reconstructing the evolutionary history of sleep spindles will require more stringent criteria for their identification, across more species. In particular, a stronger emphasis on construct and predictive validity can help verify if spindle-like events in other species are actual sleep spindles. Work in accordance with such stricter validation suggests that sleep spindles display more universally shared features, like defining frequency, than previously thought.

Averaging sleep spindle occurrence in dogs predicts learning performance better than single measures

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.

Gut Microbiome Composition is Associated with Age and Memory Performance in Pet Dogs

Gut microbiota can crucially influence behavior and neurodevelopment. Dogs show unique similarities to humans in their physiology and may naturally develop dementia-like cognitive decline. We assessed 29 pet dogs' cognitive performance in a memory test and analyzed the bacterial 16S rRNA gene from fecal samples collected right after the behavioral tests. The major phyla identified in the dog microbiomes were Bacteroidetes, Firmicutes, and Fusobacteria, each represented by >20% of the total bacterial community. Fewer Fusobacteria were found in older dogs and better memory performance was associated with a lower proportion of Actinobacteria. Our preliminary findings support the existence of links between gut microbiota, age, and cognitive performance in pet dogs.

Reliability of Family Dogs' Sleep Structure Scoring Based on Manual and Automated Sleep Stage Identification

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.