Recently dogs (Canis familiaris) have been demonstrated to be a promising model species for studying human behavior as they have adapted to the human niche and developed human-like socio-cognitive skills. Research on dog behavior, however, has so far almost exclusively focused on awake functioning.Here we present a self-developed non-invasive easily replicable canine polysomnography method. N=22 adult pet dogs (with their owners present) and N=12 adult humans participated in Study I. From these subjects N=7 dogs returned on two more occasions for Study II.In Study I. we give a descriptive analysis of the sleep electroencephalogram of the dog and compare it to human data. In order to validate our canine polysomnography method in Study II. we compare the sleep macrostructure and the EEG spectrum of dogs after a behaviorally active versus passive day.In Study I. we found that dogsʼ sleep EEG resembled that of human subjects and was generally in accordance with previous literature using invasive technology. In Study II. we show that similarly to previous results on humans daytime load of novel experiences affects the macrostructural and spectral aspects of subsequent sleep.Our results validate the family dog as a model species for studying the effects of pre-sleep activities on the EEG pattern under natural conditions and thus broaden the perspectives of the rapidly growing fields of canine cognition and sleep research.

Sleep spindles are thalamocortical oscillations in nonrapid eye movement sleep, which play an important role in sleep-related neuroplasticity and offline information processing. Sleep spindle features are stable within and vary between individuals, with, for example, females having a higher number of spindles and higher spindle density than males. Sleep spindles have been associated with learning potential and intelligence; however, the details of this relationship have not been fully clarified yet. In a sample of 160 adult human subjects with a broad IQ range, we investigated the relationship between sleep spindle parameters and intelligence. In females, we found a positive age-corrected association between intelligence and fast sleep spindle amplitude in central and frontal derivations and a positive association between intelligence and slow sleep spindle duration in all except one derivation. In males, a negative association between intelligence and fast spindle density in posterior regions was found. Effects were continuous over the entire IQ range. Our results demonstrate that, although there is an association between sleep spindle parameters and intellectual performance, these effects are more modest than previously reported and mainly present in females. This supports the view that intelligence does not rely on a single neural framework, and stronger neural connectivity manifesting in increased thalamocortical oscillations in sleep is one particular mechanism typical for females but not males.

Procedural learning is a fundamental cognitive function that facilitates efficient processing of and automatic responses to complex environmental stimuli. Here, we examined training-dependent and off-line changes of two sub-processes of procedural learning: namely, sequence learning and statistical learning. Whereas sequence learning requires the acquisition of order-based relationships between the elements of a sequence, statistical learning is based on the acquisition of probabilistic associations between elements. Seventy-eight healthy young adults (58 females and 20 males) completed the modified version of the Alternating Serial Reaction Time task that was designed to measure Sequence and Statistical Learning simultaneously. After training, participants were randomly assigned to one of three conditions: active wakefulness, quiet rest, or daytime sleep. We examined off-line changes in Sequence and Statistical Learning as well as further improvements after extended practice. Performance in Sequence Learning increased during training, while Statistical Learning plateaued relatively rapidly. After the off-line period, both the acquired sequence and statistical knowledge was preserved, irrespective of the vigilance state (awake, quiet rest or sleep). Sequence Learning further improved during extended practice, while Statistical Learning did not. Moreover, within the sleep group, cortical oscillations and sleep spindle parameters showed differential associations with Sequence and Statistical Learning. Our findings can contribute to a deeper understanding of the dynamic changes of multiple parallel learning and consolidation processes that occur during procedural memory formation.

Sleep spindles are frequently studied for their relationship with state and trait cognitive variables, and they are thought to play an important role in sleep-related memory consolidation. Due to their frequent occurrence in NREM sleep, the detection of sleep spindles is only feasible using automatic algorithms, of which a large number is available. We compared subject averages of the spindle parameters computed by a fixed frequency (FixF) (11–13 Hz for slow spindles, 13–15 Hz for fast spindles) automatic detection algorithm and the individual adjustment method (IAM), which uses individual frequency bands for sleep spindle detection. Fast spindle duration and amplitude are strongly correlated in the two algorithms, but there is little overlap in fast spindle density and slow spindle parameters in general. The agreement between fixed and manually determined sleep spindle frequencies is limited, especially in case of slow spindles. This is the most likely reason for the poor agreement between the two detection methods in case of slow spindle parameters. Our results suggest that while various algorithms may reliably detect fast spindles, a more sophisticated algorithm primed to individual spindle frequencies is necessary for the detection of slow spindles as well as individual variations in the number of spindles in general.