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The new method classifies brain cells based on electrical signals



For decades, neurologists have relied on the technique of reading electrical "spikes" of live brain activity, behaving as subjects tell little about the types of cells they follow. In a new study, researchers from the University of Tubingen and the MIT's Picauer Institute for Learning and Memory demonstrate a way to increase their insights by distinguishing four different cell classes from this vast information.

Advances provide brain researchers with an opportunity to better understand how different types of neurons contribute to behavior, perception and memory and how they are defective in cases of psychiatric or neurological disorders. Similar to mechanics, it can better understand and change the machine by looking at how each part works, so neuroscientists can better understand the brain when they can separate the roles played by different cells while thinking.

"From the anatomical studies we know there are more types of cells in the brain and if they are there, they must be there for a reason," said Earl Miller, a professor of neuroscience at Pickover in the MIT's Department of Brain and Cognitive Sciences and co-author of the study. senior author of the paper at Current Biology. "We can't really understand the functioning of the brain until we fully understand the roles these different types of cells can play."

Miller collaborated with the lead team in Tubingen, with lead author Catherine Trentio, Constantine von Nicolei and Professor Marcus Siegel, co-senior author and former postman at Miller Lab, to develop a new way to break down more neuro-type information. electrophysiological measurements. These measures follow the rapid changes in voltage or spikes that neurons show as they communicate in circuits, a phenomenon necessary for brain function.

"Identifying different types of cells will be crucial to understanding both the local and large information processes in the brain," Siegel said.

Four is greater than two

At best, neurologists have so far only been able to determine from electrophysiology whether the neuron was excitatory or inhibitory. This is because they only analyzed the difference in the width of the spike. A typical amount of data in a study of electrophysiology – spikes of several hundred neurons – only backed that single degree of differentiation, Miller said.

But the new study could go further because it comes from a set of images of nearly 2,500 neurons. Miller and Siegel collected MIT data years ago from three regions in the animal cortex that performed experimental tasks that integrated perception and decision making.

"We recognized the unusually rich resource available," Siegel said.

So the team decided to set up the database via a ringer of sophisticated statistical and computing tools to analyze the waveforms of the spikes. Their analysis showed that the waveforms can actually be sorted into two dimensions: how fast the waveform moves between its lowest and highest voltages ("duration to peak") and how quickly the voltage changes again afterwards, returning from top to normal levels ("repolarization time"). The plot of these two factors against each other neatly sorted the cells into four different "clusters". Not only were the clusters visible throughout the database, but also individually within each of the three cortical regions.

For the difference to have any meaning, the four classes of cells must have functional differences. To try this, the researchers decided to sort the cells based on other criteria, such as their "firing rate" (how often they spin), whether they tend to shoot and how variable their intervals are between Spikes – all factors in how they participate and affect the circuits to which they are connected. Indeed, mobile classes remained different from these measures.

In another phase of analysis, classical cells also differed, as the researchers watched as they responded to the animals watching and processing visual stimulation. But in this case, they saw that cells play different roles in different regions. For example, a Class 1 cell may react differently in one region than in another.

"These cell types are really different cell types that have different properties," Miller said. "But they have different functions in different cortical areas, because different cortical areas have different functions."

New research capability

In the study, the authors speculate on which true neuron writes their four mathematically defined classes that closely resemble but do not yet offer definitive determination. Miller, however, said the more qualitative delineations drawn by the study were enough to make him want to analyze old data on nerve disease to see what new things he could learn.

One of Miller's main research interests is the nature of working memory – our ability to keep information as a guide in mind when using it. His research revealed that it was made possible by complex brain regions mixing and precise timing bursts of nerve activity. He can now understand how different classes of neurons play specific roles in specific regions to provide us with this useful mental ability.

Both Miller and Siegel's laboratories are particularly interested in different brain rhythms, which are abundant in the brain and likely play a key role in organizing communication between neurons. The new results open a powerful new window to discover what role different classes of neurons play in these brain rhythms.

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The US National Institutes of Health, European Research Council, Deutsche Forschungsgemeinschaft, and the Center for Integrative Neuroscience provided funding for the study.

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