Sleep is a vital part of our existence–we spend a third of our lives asleep, our memory and motor skills fade with lack of sleep, various diseases are associated with disorders of sleep–and yet no one truly knows why we need it. Scientists at the University of California, San Diego, hope to change that. They have developed a method of analyzing brain activity during sleep, which they say is much simpler than current techniques and could potentially be used to study a myriad of sleep disturbances.
“Sleep is a major health problem and scientific mystery,” says Terry Sejnowski, a head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies, in La Jolla, CA. “We have a tool here that might help us get to the bottom of that mystery.”
To be diagnosed with a sleep disorder, a patient typically spends the night at a specialized clinic, hooked up to machines that record brain activity, muscle activity, and other factors. A technician or doctor then pores over the data, searching for signs that the patient’s peaceful slumber has gone awry. However, results of the test can vary widely, depending on who analyzes the data.
A new analysis method developed by Sejnowski and Philip Low, a graduate student at the Salk, could make the process much easier and more automated. The researchers have created an algorithm that can detect subtle but statistically significant changes in brain activity. The result is a program that can differentiate the phases of sleep, such as REM (rapid eye movement) sleep, which is typically when we dream, and deep sleep, using less data than other methods do. “The technique has a unique temporal resolution that may be able to define sleep states more accurately,” says Jean-Paul Spire, a neurologist and scientist at the University of Chicago who was not involved in the research.
The ability to better define sleep states could shed light on the various diseases, such as depression and Alzheimer’s, that have been linked to sleep disorders. “We know, for example, that kidney disease is associated with sleep disruption,” says Sejnowski. “I suspect we will be able to pick that up and maybe diagnose these diseases before they become so serious that they require surgery or dialysis.”
Because the algorithm allows automatic analysis, the technology might be useful for studies that requires long-term recording, such as readings of epilepsy patients, or large numbers of patients, which are more labor-intensive to analyze by hand. “It would be ideal for studying drug effects on sleep,” says Jerome Siegel, a neuroscientist who studies sleep at the University of California, Los Angeles. (Other automated sleep-analysis methods do exist, but many sleep centers still rely on a human interpretation.)
Researchers say that simpler sleep-analysis technologies would be useful. But they caution that more testing is needed to determine if this particular method will prove more reliable than previous analysis algorithms, as well as to show when it may be most useful. “A computer program might not understand patients with severe disabilities,” says Mark Eric Dyken, director of the Sleep Disorders Center at the University of Iowa.
Such studies are in the works. The researchers plan to analyze sleep records from patients with Alzheimer’s and narcolepsy. They are also working on preliminary research into the basis of sleep. “Why do we need sleep?” asks Sejnowski. “Why does it have the effect it does? Clearly sleeping does something to the brain to help fix it. We want to find out what that is.”