Rutgers–Camden Researchers Discover Two New Wave Bands for EEG

brain image

By Jeanne Leong

Researchers led by a Rutgers University–Camden team have discovered two new bands of high-frequency electroencephalographic (EEG) activity that could lead to better understanding about brain activity and its role in memory.

Joe Martin

Joe Martin

The two high-frequency waves they discovered can be useful in clinical measurements in EEG, which doctors use to understand sleeplessness and wakefulness in patients. Other EEG waves include the well-known lower frequency alpha waves, discovered in the 1920s and popularized for biofeedback in the 1970s.

Led by Rutgers–Camden professors Dawei Hong, Joe Martin, and Sean O’Malley, Rutgers–Camden doctoral student Steve Moffett, and Shushuang Man, a professor at Southwest Minnesota State University, the researchers measured EEG activity in the brains of healthy, spontaneously behaving rats.

The team’s research revealed the new high-frequency EEG bands—the first band, named Psi, centered between 260-280 Hz, and the second band, named Omega, in the 400-500 Hz range.

The study of high-frequency EEG was inspired by the 2016 theoretical paper, “A Stochastic Mechanism for Signal Propagation in the Brain: Force of rapid random fluctuations in membrane potentials of individual neurons,” in the Journal of Theoretical Biology by Hong, Man, and Martin.

Dawei Hong

Dawei Hong

“The idea is that they vary with sleeping and waking,” says Martin, a professor of biology and associate dean for science, mathematics, technology, and health sciences. “We measured EEG-defined sleeping and waking and we found that the Psi and Omega waves increased with waking.”

The discovery of variations in the waves with sleep and waking reveals that the waves could have significant roles in brain physiology. This finding might be used by sleep researchers as another indicator to use in analyzing sleep studies.

The study also found so-called “1/f-type noise” in the high-frequency range. This noise is found in many systems and has different forms described by an exponent “b.” Systems in which the noise is independent of past activity have an exponent of 2, which is what was found during waking. Systems in which the noise is altered by past activity can be said to have a type of “memory” and have a higher exponent. This was found for 1/f-type noise in REM sleep.

Sean O'Malley

Sean O’Malley

“There’s a lot of evidence that especially REM sleep is really important in coding memories, and repairing memories,” says O’Malley, an associate professor of physics.

Through Rutgers–Camden’s Center for Computational and Integrative Biology, the multidisciplinary group of faculty worked together to make the discovery. “Without CCIB, these results would not have been possible,” says Dawei Hong, associate professor of computer science.

“Being able to reach out to faculty in different disciplines under the CCIB umbrella has resulted in a deeper understanding of all aspects of the project, not just the parts which were related to my previous research,” says Moffett, a doctoral student in computational and integrative biology at Rutgers–Camden.

“The new experimental finding opens up a new avenue of research on what these new waves are,” says Martin.

Steve Moffett

Steve Moffett

The study, “Dynamics of High-Frequency Brain Activity,” is published in Scientific Reports, a journal issued by the Nature Publishing Group.

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