Life Science

How Does Your Brain Keep Track of Time?

A new finding challenges conventional wisdom about the mind’s internal clock

February 22, 2010

Researchers have found some misbehaving neurons in the part of the brain that tells time. The startling discovery could change how neuroscientists think about the brain’s influence on the body’s biological rhythms, and has raised questions about the very definition of a functioning neuron.

At the University of Manchester in England, electrophysiologist Mino Belle studies the electrical activity of neurons in the mammalian suprachiasmatic nucleus, or SCN. Although no bigger than a grain of rice, the SCN is the brain’s central timekeeper and command center in charge of coordinating the cycles, called circadian rhythms, that determine the crucial timing of processes like hormone release, the sleep-wake cycle, and metabolism.

Neuroscientists like Belle are busy trying to figure out how and why the cells in the SCN behave the way they do, because insights at the cellular level could lead to more effective therapies for sleep disorders, like insomnia and narcolepsy, as well as disorders linked to disrupted circadian rhythms, like shift work sleep disorder and jet lag. Understanding what makes the SCN tick could also lead to better therapies for metabolism disorders like diabetes.

For decades, conventional wisdom has said that SCN neurons fire frequently during the day, while an individual is awake, and rest at night. But researchers now know that not all SCN neurons are created equal. Specifically, not all of them contain genes that code for a specific type of “molecular clock.”

In 2007, Belle began experimenting with mice that were genetically engineered so their clock-containing SCN cells would glow fluorescent green, standing out amongst their neighbors. When he recorded only from clock-containing cells, he observed a perplexing pattern in the data: instead of firing during the day, these cells were actually at rest.

Even stranger: they were resting in an electrical state never before recorded in healthy neurons.

“When we started to see the behavior, it took me several months to accept,” said Belle. “But it was overwhelmingly the case.” Belle spent those months not only replicating the result, but also performing various tests to make sure the cells were healthy. His findings were published in the October 9 issue of the journal Science.

The SCN uses environmental cues, most importantly light, to determine the time of day and year. But Belle’s findings shake up everything researchers thought they knew about how SCN neurons respond to those cues.

Furthermore, the results fly in the face of the current working definition of a healthy neuron.

A resting neuron usually has a membrane potential — the voltage difference between the interior and exterior of the cell — of about -70 mV. It “fires” when an influx of positive charge, usually in the form of charged sodium or calcium particles, causes sufficient “depolarization.” The threshold is usually around -50 mV. But Belle reports observing neurons with a resting membrane potential of -25 mV during the day.

Barnard College neuroscientist Joseph LeSauter, who has studied the SCN for 16 years, has a hard time making sense of that finding. “At  minus 30 [mV] or minus 35, maybe it’s OK. But at minus 25, that cell, in order to survive, has to eat up so much energy that it would be very strange to be in that state. Why would a brain be designed to eat so much energy? I cannot conceptualize it,” LeSauter said.

The unique firing pattern and strange membrane potential are so contrary to conventional thinking about the SCN that Belle and University of Manchester colleague Hugh Piggins, a co-author of the Science paper who has studied the SCN for 18 years, were not surprised when it was next to impossible to convince colleagues it was real.

Fortunately, they got some help.

Unbeknownst to them, a research group led by University of Michigan mathematician Daniel Forger had built a model of the SCN, based on data collected from existing literature, that predicted the very electrical behavior they had observed.

Mathematical models can help answer questions about complex systems like the SCN, said Forger, because they can simulate and analyze multiple variables simultaneously. While laboratory experiments can only look at one or two parts of a system at a time, a mathematical model can combine the individual observations and then predict how a system will behave when all the parts are working together.

The match between the predictions made by the University of Michigan model and the findings from Belle’s research is dead-on, Forger said. “A model is never perfect, but I think you’d be hard pressed to say the model didn’t predict certainly qualitatively — and I think quantitatively — what was found experimentally.”

After connecting at a 2008 conference on biological rhythms in Florida, the two research teams began a collaboration that continues today.

But with or without the model, the result is surprising, said electrophyiologist Charles Allen of Oregon Health and Science University. “We’re all trying to figure out exactly where it fits in what we know about how neurons function. There is no other example in any nerve cell for a functional state that is that depolarized,” he said.

Allen, like LeSauter, has a hard time understanding why such a depolarized state would ever occur, given its high energy cost. He also said the data raises important questions about how these neurons could be surviving such a large influx of positive charge, most likely in the form of calcium, which at high amounts is toxic to the cell.

Belle, meanwhile, welcomes the uncertainty. “Now that we have seen cells reside outside of normal neurophysiology, we may ask the next questions: Is that all that we know about the central nervous system? Is that all that we know about neurons?”

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Katrina says:

Beautifully written article! How exciting it is to open a door to countless new discoveries in the brain and central nervous system. This article was easy for laymen to understand, namely me :) Vividly written!

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