Dogs' sleep waves correlate to their intelligence - the dog g-factor

Sleep spindles are the name for distinct brain wave patterns observed during sleep.

These waves, lasting up to six seconds, are common in non-rapid eye movement (non-REM) sleep and are generated by a specific circuit in the brain (the thalamocortical circuit).

While studies on sleep-dependent learning assume a direct, causal involvement of sleep spindles in the formation of memory, the spindles have recently also been linked to the general cognitive performance in awake animals, including dogs.

In a new study, researchers investigated whether sleep spindle activity in sleeping dogs could be used to predict their general attention and general intelligence or "g-factor".

The study
A study involving 58 companion dogs aimed to investigate two main types of sleep spindle activity: the occurrence of slow spindles and the power within the sigma frequency range (9–16 Hz), which appears to be conserved across mammals.

The dog´s age varied from 7 to 14 years. They had dognitive (:D) tests and polysomnographic recordings performed on two occasions each, with a three-month break in between.

Polysomnography is a sleep study that records brain waves, eye movements, muscle activity, heart rate, and breathing patterns while a person or animal sleeps. The data provides insights into different sleep stages and physiological changes throughout the night.

Sleep spindle activity was found to correlate positively with attention, a key cognitive skill. Additionally, the study found that sleep spindle activity, especially slow spindles, was also tied to general cognitive abilities like problem-solving and memory.

Interestingly, while slower spindles were more strongly linked to attention, faster spindles and their frequency were associated with lower intelligence scores. This finding aligns with previous studies in humans and animals, suggesting that too much fast spindle activity affects cognitive performance negatively.

The current findings, along with behavioral tests, support the new way of measuring general intelligence - g-factor - in dogs and suggest that dogs' intelligence works similarly to human intelligence.

Sleep spindles as a marker for aging
The research's relevance extends beyond cognitive performance, highlighting how sleep spindle activity may be an indicator of cognitive aging.

As dogs age, their ability to filter out irrelevant information, a function associated with the thalamus and sleep spindles, may decline, leading to a reduction in attention and intelligence.

In the study, the oldest dogs showed a decrease in spindle density over time, indicating potential age-related changes in cognitive function. This finding suggests that sleep spindles could serve as biomarkers for aging processes in the brain.

The study points to a broader application of these findings to other species, including humans, where sleep spindle activity might offer a measurable correlation between cognitive health and aging.

Implications for neuroscience and cross-species research
This research on dogs elucidates the neurophysiological mechanisms underlying brain function, showing that sleep spindle activity—particularly the slow type—can serve as a reliable indicator of intelligence.

While previous studies have linked spindles to learning and memory-building during sleep, this work offers a fresh perspective by exploring how they might be predictive of more general, real-world cognitive performance, such as attention span.

The findings also suggest that the thalamus, which is involved in generating both sleep spindles and attention-related brain waves, plays a critical role in the cognitive processes that underpin cognition in both sleep and in wakefulness.

This research revealed that certain features of sleep spindles, such as their density and power, were associated with both attention span and intelligence, providing an exciting cross-species link between our sleep waves at night time and intelligence.

About the scientific paper:

First author: Ivaylo Borislavov Iotchev, Hungary
Published in: NeuroImage, December 2024
Link to paper: https://www.sciencedirect.com/science/article/pii/S1053811924004130