Time and time again, scientists have shown that the brain's ideal state rests on the critical point between order and disorder, but only recently did it come to light that when the organ naturally deviates from this location, it behaves in a specific way that keeps its performance at maximum levels.
The brain can't statically remain at the sought-after critical point; its focus is forced in different directions as swarms of stimuli constantly provoke it. But according to a paper published March 1 in APS Physics, brains don't simply deviate from the critical point — they do so with quasicriticality, which preserves the optimal capacity that's found there.
John M. Beggs, a professor of biophysics at Indiana University and a coauthor of the study, believes that the discovery of quasicriticality can greatly benefit studies in mental health.
"This is expected to be relevant because departures from the critical point are associated with neuropathologies," he said. "If we can figure out how [the brain] moves near [the critical point], maybe we can figure out how to keep it closer."
The theory of operational value at the critical point, for all organized systems, emerged from a 1991 article in Scientific American by physicists Per Bak and Kan Chen. An inspiration to many, the theory's relationship to brains was pushed forth by Beggs' own 2003 paper.
Since then, scientists have been vigorously studying the brain at the critical point, including in monkeys, turtles, fish and even humans. Data experts are also trying to isolate that point to apply human-brain learning processes to artificial intelligence.
"The story that it's operating near the critical point has been around for nearly 20 years now," Beggs said. "The point of quasicriticality is that it can't actually operate exactly at that point, because it's a theoretical ideal. The question is, how does it deviate?"
In 2014, Viola Priesemann and colleagues from Germany tackled the question by saying brains consistently operate subcritically, slightly below optimal capacity.
In 2017, Miguel Muñoz, a Spanish researcher, suggested that the brain drifts around the critical point at random.
Nevertheless, Beggs and his team tried to put an end to the debate by testing the neural voltage spikesof lab-grown brains as they underwent various induced perturbations. The researchers arrived at a new conclusion.
The brain didn't behave with lower function, nor did it select a random route. It took the path of least operational sacrifice. From this behavior, the researchers termed the brain as acting quasicritically. The team believes this is the way brains manage to leave the critical point with very little consequence.
"Even if you drive it with all kinds of inputs, or if you drive it with noise, it's going to behave in a way that it's always as close to critical as possible," Beggs said.
He painted a scenario for The Academic Times to clearly illustrate what this means.
Suppose a person is in a square room with the critical point at the center. Upon straying from the center, it's possible to go north, south, east or west, but north is the path that has the capacity for maximal function. The brain will always choose north. That route is technically referred to as the Widom line.
The Widom line is often used to describe deviations from the critical point between "disorderly" fluids and more "orderly" solids. These points exist in many different aspects of nature and are commonly invoked when thinking about avalanches, earthquakes and even wind dynamics. But from a neurological perspective, the Widom line and associated critical point are imperative because of the two-fold job that brains have.
First, a brain needs to be extremely receptive to changes in the external, disorderly world. That includes things such as realizing sudden weather fluctuations or sensing a vicious tiger lurking in the grass nearby.
Second, it needs to be able to respond to those changes in a cohesive, orderly manner. That would mean increasing perspiration levels in high heat or heightening awareness to detect the tiger's movements.
Thus, the brain thrives when it can have both sides of the equation, a duality found on the critical point.
"We have this border between disorder and order. That's when all these information processing functions are optimized," said Beggs. "It transmits information best, stores information best, computes best — it's most sensitive to inputs right at the critical point."
Sensitivity was the primary variable that was tested for during Beggs' experiments. He grew real, but simplified, little brains in culture and laid them onto an array of more than 500 electrodes.
"The whole brain is too complex," Beggs said. "So we use a model system just like how Gregor Mendel used pea plants [to study trait inheritance], or Niels Bohr used the hydrogen atom to look at a more complex atom; he looked at the simple thing first."
The electrodes tracked how neurons reacted to various perturbations, and Beggs calculated each occurring voltage spike to determine a respective brain-state. If a network is extremely susceptible, it would react with a huge response, and if not, with a weak response. His findings supported the team's quasicriticality theory.
"Anytime you deviate from the critical point, the susceptibility is going to go down," Beggs said. "But what we found was that of all the different directions it could take, it took the direction that preserved susceptibility as much as it could."
The brain's responses were almost identical to those at the critical point.
Beggs continued to explain that beyond the brain replicas he tested, there have been other sets of data collected from actual brains that the team sifted through, too. The results were consistent.
"We don't disagree with the things people found," he said. "We agree with them, but as research develops, the questions become more refined. This question is, 'What's the nature of how we move around the critical point?' And, that's when quasicriticality comes in."
The paper, "Evidence for Quasicritical Brain Dynamics," was published March 1 in APS Physics. It was authored by Leandro J. Fosque, Rashid V. Williams-García, John M. Beggs and Gerardo Ortiz, Indiana University.