A Biophysical Theory of Beta Waves

September 12, 2016

In 1929, Hans Berger, a German psychiatrist, introduced the electroencephalogram (EEG), a tool that measures electrical activity in the brain via electrodes placed on the scalp. It records neural oscillations, or rhythmic neural activity, allowing scientists and clinicians to detect abnormal brain activity arising from brain injury or disease. While they use the technique for both diagnostic and research purposes, scientists remain puzzled over what the variety of different neural oscillation patterns mean.  What do differences in frequency band (as measured in Hertz), shape, and time pattern signify? A new theory from scientists at Brown University suggests that one type of oscillation, the beta wave, arises from the thalamus, the part of the brain that relays sensory information to the cortex, and in doing so, may help inhibit sensory and motor information processing.

Understanding EEG waves

EEG has been in use for nearly 100 years. And, in that time, it has provided unparalleled guidance into our understanding of brain activity, the diagnosis of neurological disease and injury, and in the planning and execution of neurosurgical interventions. Jan Wessel, a cognitive neurologist at the University of Iowa, says the technique is still used frequently used today, even in the era of functional magnetic resonance imaging (fMRI).

“fMRI gives you great spatial resolution—and the ability to visualize the brain. But the nice thing about EEG is the temporal resolution,” he says. “The brain processes information super-rapidly. And cognitive control functions, like motor inhibition, which I study, sets off a cascade of processes that happen within a few hundred milliseconds. The relative standard protocol in fMRI only takes a sample of activity about every two seconds. So you can’t really break down these different processes and what corresponds to what. EEG allows you to clearly map the exact timing of these different things.”

But while laboratories like Wessel’s use EEG to statistically contrast two conditions to find the oscillatory patterns that may distinguish them, as well as measure synchronous and dynamic activity patterns, he says we still don’t know much about the physiology behind those underlying signals.

“We still don’t really know a lot about where these different rhythms are generated, or how they are generated, or why they are even there,” he says. “It’s an open field of science—and one that could better inform our knowledge of cognition.”

Some of the rhythms that EEG, as well as its close counterpart the magnetoencephalagram (MEG), a newer technique that measures the magnetic fields produced by the brain’s electrical activity instead of the electrical activity directly, detect are different neural oscillation patterns called alpha, beta, delta, gamma, and theta waves. Each rhythm differs by shape, time, and frequency band. For example, alpha waves run at between 7.5 and 12.5 hertz (cycles per second), while delta measures in at 1–4 hertz, and beta tracks at 13–30 hertz. The majority of research has focused on alpha, delta, and theta waves; beta waves have received less attention because they are more unpredictable and not quite as fluid as other bands.

“Beta waves are prominent throughout the brain but they don’t have the signature of activity that you might expect a wave to have, this kind of regular, up-and-down oscillating phenomena,” says Stephanie Jones, a neuroscientist at Brown University. “In our data, they are more stochastic, brief events. When you look at averaged data, you can get the sense of a wave. But in unfiltered signals, they have a rather unintuitive wave form.”

That doesn’t mean what beta waves are unimportant. Many studies have linked enhanced beta oscillations to Parkinson’s disease, a neurological disorder with hallmark symptoms involving motor control issues. Further work suggested that the presence of these waves predicted both perception and shifts of perceptual attention. Jones wanted to better understand how this specific wave form emerges from the underlying neural circuits.

Characterizing beta oscillations

Thomas Baumgarten, a researcher at the Institute of Clinical Neuroscience and Medical Psychology in Düsseldorf, Germany, says that Hans Berger, the psychiatrist who invented EEG, looked into beta activity nearly 100 years ago, but ended up focusing most of his efforts on alpha band activity.

“Berger didn’t really know what to do with it. He tried to formulate theories on beta but because alpha-band activity was much more prominent and responded to simpler manipulations, he focused on alpha,” he says. “Other researchers have also tried, but the only thing we’ve really been able to do is pinpoint its activity in motor and sensorimotor cortex. And while we now know beta is tightly linked to motor and somatosensory domains, recent studies also point towards a role of beta in cognitive domains like decision making and memory formation, too.”

Jones and her colleagues wanted to take a deeper look. The group used multiple techniques to better understand from where in the brain beta might hail, starting with MEG to measure beta band activity in the somatosensory cortex and inferior frontal cortex of human study participants. After characterizing the waves, in time and shape, they then used a computer model of cortical circuitry, meant to simulate a cortical column that included layers of multiple cell types, including pyramidal neurons, and tried to recreate the beta waves.

“To study the neural mechanisms creating the observed beta waveform, we developed a computational model of a neural circuit that took into account the physics of the actual MEG sources. And including the biophysics that contribute to the MEG signal in our model, we were able to play around with the synaptic inputs to the model to test how we could get the output to look like typical beta waves. We predicted that the signal is coming from precisely timed excitatory synaptic inputs that create big electrical currents that flow up and down the dendrites of large cortical pyramidal neurons,” she says. “It really was getting the biophysics of the modeling right that led to our predictions and this theory.”

That theory was that beta events are born from bursts of activity in the thalamus that drive electrical currents in the cortex—and, based on correlative evidence, the researchers posit that these beta events help shift attention to optimally filter sensory information and help inhibit movements. The group tested the theory in both mice and rhesus macaques, and saw similar patterns of response when they recorded from cells in cortical layers. The research was reported in the Proceedings of the National Academy of Sciences in June.

“One of the strengths of this study is that Dr. Jones addresses the problem from several different perspectives,” says Baumgarten. “And it allows us to ask further questions about beta and what it is doing in the brain.”

Moving towards understanding—and intervention

Jones cautions that this study is a foundational work that sets the stage for further research. But she says that by offering this theory of where beta comes from, researchers can now ask further questions about how and why beta activity is relevant to sensory and motor function.

“We know that brain rhythms correlate with functions such as attention, perception, and motor action. We know that they change in disease states. But we don’t know why,” she says. “Is it really the rhythm that is important?  Is it causally changing the information processing in the brain?  Or is the rhythm just a signature of some other activity that we need to look at?  This study gets at where these rhythms come from. But the next question needs to be whether they are causally important. And, if so, whether we can causally manipulate them to improve function when they are disrupted in diseases like Parkinson’s disease.”

The answer to those questions, she says, may one day help improve interventions like deep brain stimulation to help relieve Parkinsonian symptoms and improve function. But Wessel says that the contribution to our understanding of brain activity, and what the different brain rhythms measured by EEG may represent, is equally important.

“The functional consequences that these kinds of studies could potentially have are tremendous. If we know how a beta process evolves, we can have a better understanding of how different cognitive functions come about in the brain,” he says. “Many of us have models about how different processes are anatomically implemented in the brain. If we could map stereotypic signatures onto certain neural circuits, we could have a better indication if those theories are right or wrong. It could inspire the development of new and more accurate models that account for the presence or absence of these different oscillations, helping us better understand how the brain is doing all the things that it does.”