Automated detection of sleep states from olfactory brain waves

Understanding the transition between wake and sleep is important for medical and clinical applications ranging from surgical anesthesia to sleep disorders such as insomnia. Although much has been learned from mouse models, tracking sleep/wake states in rodents and the transitions between the two is currently labor-intensive and suffers from variability both between scoring methods and between individual scorers. Using real-time local field potential recordings from the mouse olfactory bulb, the new method relies entirely on brain activity and a preset algorithm, thus making it more efficient, more objective and more reliable than current methods.

After discovering that changes in gamma waves from the olfactory bulb are a reliable marker for sleep/wake states, the team developed an automated sleep-scoring algorithm that performed better than standard classification methods. Unlike standard methods that rely on recordings of muscle activity, the new method did not misclassify rodent "freezing" behavior as sleep. They also found that while brain activity from the hippocampus was the best signal for distinguishing REM and non-REM sleep, beta waves from the olfactory bulb could do the job almost as well, meaning that the automated system only requires one implanted wire per mouse.

Once the system was established, the scientists were able to use it for several applications, including detecting the depth of anesthesia and characterizing the differences between wake-sleep and sleep-wake transitions. The automated real-time classification system thus has the potential to have wide-ranging applications in sleep research.