At a sleep research symposium in January 2020, Janna Lendner presented findings that hint at a way to look at people’s brain activity for signs of the boundary between wakefulness and unconsciousness. For patients who are comatose or under anesthesia, it can be all-important that physicians make that distinction correctly. Doing so is trickier than it might sound, however, because when someone is in the dreaming state of rapid-eye movement (REM) sleep, their brain produces the same familiar, smoothly oscillating brain waves as when they are awake.
Lendner argued, though, that the answer isn’t in the regular brain waves, but rather in an aspect of neural activity that scientists might normally ignore: the erratic background noise.
Some researchers seemed incredulous. “They said, ‘So, you’re telling me that there’s, like, information in the noise?’” said Lendner, an anesthesiology resident at the University Medical Center in Tübingen, Germany, who recently completed a postdoc at the University of California, Berkeley. “I said, ‘Yes. Someone’s noise is another one’s signal.’”
Lendner is one of a growing number of neuroscientists energized by the idea that noise in the brain’s electrical activity could hold new clues to its inner workings. What was once seen as the neurological equivalent of annoying television static may have profound implications for how scientists study the brain.
Skeptics used to tell the neuroscientist Bradley Voytek that there was nothing worth studying in these noisy features of brain activity. But his own studies of changes in electrical noise as people age, as well as previous literature on statistical trends in irregular brain activity, convinced him that they were missing something. So he spent years working on a way to help scientists rethink their data.
“It’s insufficient to go up in front of a group of scientists and say, ‘Hey, I think we’ve been doing things wrong,’” said Voytek, an associate professor of cognitive science and data science at the University of California, San Diego. “You’ve got to give them a new tool to do things” differently or better.
In collaboration with neuroscientists at UC San Diego and Berkeley, Voytek developed software that isolates regular oscillations—like alpha waves, which are studied heavily in both sleeping and waking subjects—hiding in the aperiodic parts of brain activity. This gives neuroscientists a new tool to dissect both the regular waves and the aperiodic activity in order to disentangle their roles in behavior, cognition and disease.
The phenomenon that Voytek and other scientists are investigating in a variety of ways goes by many names. Some call it “the 1/f slope” or “scale-free activity”; Voytek has pushed to rebrand it “the aperiodic signal” or “aperiodic activity.”
It’s not just a quirk of the brain. The patterns that Lendner, Voytek and others look for are related to a phenomenon that scientists started noticing in complex systems throughout the natural world and technology in 1925. The statistical structure crops up mysteriously in so many different contexts that some scientists even think it represents an undiscovered law of nature.
Although published studies have looked at arrhythmic brain activity for more than 20 years, no one has been able to establish what it really means. Now, however, scientists have better tools for isolating aperiodic signals in new experiments and looking more deeply older data, too. Thanks to Voytek’s algorithm and other methods, a flurry of studies published in the last few years have run with the idea that aperiodic activity contain hidden treasures that may advance the study of aging, sleep, childhood development and more.
What Is Aperiodic Activity?
Our bodies groove to the familiar rhythms of heartbeats and breaths—persistent cycles essential to survival. But there are equally vital drumbeats in the brain that don’t seem to have a pattern, and they may contain new clues to the underpinnings of behavior and cognition.
When a neuron sends a chemical called glutamate to another neuron, it makes the recipient more likely to fire; this scenario is called excitation. Conversely, if a neuron spits out the neurotransmitter gamma-aminobutyric acid, or GABA, the recipient neuron becomes less likely to fire; that’s inhibition. Too much of either has consequences: Excitation gone haywire leads to seizures, while inhibition characterizes sleep and, in more extreme cases, coma.