Scientists find small regions of the brain can take micro-naps while the rest of the brain is awake and vice versa

Scientists find small regions of the brain can take micro-naps while the rest of the brain is awake and vice versa

by University of California – Santa Cruz Hengen’s artistic interpretation of the varied brain wave patterns that produce the fundamental states of sleep and wake. Credit: Keith Hengen Sleep and wake: They’re totally distinct states of being that define the boundaries of our daily lives. For years, scientists have measured the difference between these instinctual brain processes by observing brain waves, with sleep characteristically defined by slow, long-lasting waves measured in tenths of seconds that travel across the whole organ.

For the first time, scientists have found that sleep can be detected by patterns of neuronal activity just milliseconds long, 1,000 times shorter than a second, revealing a new way to study and understand the basic brain wave patterns that govern consciousness. They also show that small regions of the brain can momentarily “flicker” awake while the rest of the brain remains asleep, and vice versa from wake to sleep.

These findings, described in a new study published in the journal Nature Neuroscience , are from a collaboration between the laboratories of Assistant Professor of Biology Keith Hengen at Washington University in St. Louis and Distinguished Professor of Biomolecular Engineering David Haussler at UC Santa Cruz. The research was carried out by Ph.D. students David Parks (UCSC) and Aidan Schneider (WashU).

Over four years of work, Parks and Schneider trained a neural network to study the patterns within massive amounts of brain wave data, uncovering patterns that occur at extremely high frequencies that have never been described before and challenge foundational, long-held conceptions of the neurological basis of sleep and wake.

“With powerful tools and new computational methods, there’s so much to be gained by challenging our most basic assumptions and revisiting the question of ‘what is a state?'” Hengen said. “Sleep or wake is the single greatest determinant of your behavior, and then everything else falls out from there. So if we don’t understand what sleep and wake actually are, it seems like we’ve missed the boat.”

“It was surprising to us as scientists to find that different parts of our brains actually take little naps when the rest of the brain is awake, although many people may have already suspected this in their spouse, so perhaps a lack of male-female bias is what is surprising,” Haussler quipped. Understanding sleep

Neuroscientists study the brain via recordings of the electrical signals of brain activity , known as electrophysiology data, observing voltage waves as they crest and fall at different paces. Mixed into these waves are the spike patterns of individual neurons.

The researchers worked with data from mice at the Hengen Lab in St. Louis. The freely-behaving animals were equipped with a very lightweight headset that recorded brain activity from 10 different brain regions for months at a time, tracking voltage from small groups of neurons with microsecond precision.

This much input created petabytes—which are one million times larger than a gigabyte—of data. David Parks led the effort to feed this raw data into an artificial neural network, which can find highly complex patterns, to differentiate sleep and wake data and find patterns that human observation may have missed. A collaboration with the shared academic computer infrastructure located at UC San Diego enabled the team to work with this much data, which was on the scale of what large companies like Google or Facebook might use.

Knowing that sleep is traditionally defined by slow-moving waves, Parks began to feed smaller and smaller chunks of data into the neural network and asked it to predict whether the brain was asleep or awake.

The team found that the model could differentiate between sleep and wake from just milliseconds of brain activity data. This was shocking to the research team—it showed that the model couldn’t have been relying on the slow-moving waves to learn the difference between sleep and wake. Just as listening to a thousandth of a second of a song couldn’t tell you if it had a slow rhythm, it would be impossible for the model to learn a rhythm that occurs over several seconds by just looking at random isolated milliseconds of information.

“We’re seeing information at a level of detail that’s unprecedented,” Haussler said. “The previous feeling was that nothing would be found there, that all the relevant information was in the slower frequency waves. This paper says, if you ignore the conventional measurements, and you just look at the details of the high frequency measurement over just a thousandth of a second, there is enough there to tell if the tissue is asleep or not. This tells us that there is something going on a very fast scale—that’s a new hint to what might be going on in sleep.”

Hengen, for his part, was convinced that Parks and Schneider had missed something, as their results were so contradictory to bedrock concepts drilled into him over many years of neuroscience education. He asked Parks to produce more and more evidence that this phenomenon could be real.

“This challenged me to ask myself, ‘To what extent are my beliefs based on evidence, and what evidence would I need to see to overturn those beliefs?'” Hengen said. “It really did feel like a game of cat and mouse, because I’d ask David [Parks] over and over to produce more evidence and prove things to me, and he’d come back and say, ‘Check this out.’ It was a really interesting process as a scientist to have my students tear down these towers brick by brick, and for me to have to be okay with that.” Local patterns

Because an artificial neural network is fundamentally a black box and does not report back on what it learns from, Parks began stripping away layers of temporal and spatial information to try to understand what patterns the model could be learning from.

Eventually, they got down to the point where they were looking at chunks of brain data just a millisecond long and at the highest frequencies of brain voltage fluctuations.

“We’d taken out all the information that neuroscience has used to understand, define, and analyze sleep for […]

Read more at medicalxpress.com

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