Astrocytes take the initiative: revealed hidden stars of brain rhythms

Summary: A new research highlights how astrocytes, they consider mere support cells for a long time, actively give the dynamics of the brain network. Using computational models and automatic learning, researchers showed that synchronized neuronal activity of astrocytes synchronized crucial for memory, attention and sleep.

These glial cells subtly influence rhythmic brain states, undetectable by conventional metrics but revealed through advanced analysis. The findings suggest a more prominent role for astrocytes in brain function and possible therapies aimed at neurons -Glia interactions.

Key facts:

Astrocytes actively modulate synchronized brain rhythms, not just support neurons. The learning of the machine revealed the subtle influence of astrocytes lost by traditional measures. Its role in the coordination of the network could inform new treatments for brain disorders.

Source: Fau

It is overlooked and underestimated, glial cells, non -neuronal cells that support, protect and communicate with neurons, are finally entering the center of neuroscience care.

A new study by the University of Atlantic of Florida highlights the surprising influence of a particular glial cell, revealing that it plays a much more active and dynamic role in the brain function of what was previously thought.

Using a sophisticated computational modeling and automatic learning, researchers discovered how astrocytes, a “star” shaped glial cell, subtly, but significantly, modulate communication between neurons, especially during highly coordinated synchronous brain activity.

“Clearly, glial cells are significantly involved in several brain functions, which makes it identify their presence among neurons is an attractive and important problem,” said Rodrigo Pena, Ph.D., principal author, assistant professor of biological sciences within the Charles E. Schmidt College of Science in the John D. MacArthur campus in Jupiter, and member of the Fau Stiles-Nicholson Institute Brain Institute.

“To that end, modeling can be useful. However, the simulation of complex interactions between glial cells and neurons is a challenging task that requires advanced computer approaches.”

The research, in collaboration with the Federal University of São Carlos and the University of São Paulo in Brazil, addresses a fundamental gap in neuroscience.

“While neurons have long dominated conversation, glial cells, and predominantly astrocytes, have been treated as passive support structures.

“But recent discoveries have challenged this vision centered on neurons, suggesting that astrocytes are active participants in processes such as synaptic modulation, energy regulation and even the coordination of the network,” said Laura Fontennas, Ph.D., co -author, assistant teacher of biological sciences within the Charles E. Schmidt College of Science in the John Campus MacArthur, and a member of the FAU Biological Tamad.

The study, published in the Cognitive Neurodynics magazine, takes those ideas beyond, which shows that astrocytes influence how neurons groups shoot, especially when the brain is in a “synchronous” state, where the large populations of neurons shoot in a coordinated rhythm, a crucial condition for functions, memory formation and sleep cycles.

To explore this, the equipment generated data from artificial brain networks and applied a set of automatic learning models that include decision -making trees, gradient impulse, random forests and neuronal advances to classify and detect the influence of astrocytes under different network states.

The results reveal that neuronal feed networks arose as the most effective, especially in asynchronous conditions (less coordinated), where the capture of subtle patterns required richer and more complex data.

“Our goal was to identify the presence of glial cells in synaptic transmission using different automatic learning methods, which do not require strong assumptions about the data,” Pena said.

“We found that the average shooting rate, a common experimental measure, was particularly effective in helping these models to detect glial influences, especially when combined with robust algorithms such as neuronal feed networks.”

According to Fontennas, researchers can now investigate these computational findings in appropriate animal models, such as in the zebra fish.

One of the key findings of the study is that astrocytes exert their strongest influence during synchronous brain states. Under these conditions, advanced statistical tools, such as the coherence of spike training, which measures time relationships between neuronal signals, detected a change towards more coordinated and frequency frequency shots when astrocytes were present.

This suggests that astrocytes not only admit, but can also adjust the rhythmic dynamics of brain networks, which can contribute to stability and information flow.

“Even with the difficulties of identifying the presence of glial cells, our study highlights the usefulness of automatic learning in the detection of its influence within neural networks, particularly taking advantage of the average shooting rate as an effective method of data collection,” Pena said.

Traditional brain activity metrics such as the shooting rate and the coefficient of variation often lose these subtleties. The study shows that although astrocytes affect the behavior of the network, their contributions do not always produce great changes in conventional measures.

As a result, the detection of their influence requires more nuanced tools, which can see beyond the obvious and identify the deepest patterns in brain activity.

As science continues to unravel the complexities of the human mind, this study is a reminder that some of the most important brain taxpayers have gone unnoticed.

Thanks to automatic learning and computational neuroscience, the invisible influence of astrocytes is now appearing in sight, and with it, a richer and more complete image of how the brain really works.

“By improving our ability to detect glial influence through advanced statistical methods, we open new ways to explore how neurons shape the function of the brain,” Pena said.

“It is a critical step to understand neurological disorders and could inform future therapies that are directed not only to neurons, but to the entire cellular ecosystem of the brain.”

The co -authors of the study are João Pedro Pirola, first author and student of the Federal University of São Carlos who works in the Pena Laboratory; Paige Deforest, a newly graduated from Wilkes Honors College from Fau; Paulo R. Protochevicz, Ph.D., University of São Paulo; and Ricardo F. Ferreira, Ph.D., Federal University of São Carlos.

About this research news of AI and neuroscience

Author: Gisele Galoustian
Source: Fau
Contact: Gisele Galoustian – Fau
Image: The image is accredited to Neuroscience News

Original research: open access.
“Astrocytic signatures in neuronal activity: an identification approach based on automatic learning” by Rodrigo Pena et al. Cognitive neurodynamics

Abstract

Astrocytic signatures in neuronal activity: an identification approach based on automatic learning

This study investigates the expanding role of astrocytes, predominant glial cells, in brain function, focus on their presence influences the activity of the neuronal network and how.

We focus on private network activities identified as synchronous and asynchronous.

Using computational modeling to generate synthetic data, we examine these network states and find that astrocytes significantly affect synaptic communication, mainly in synchronous states.

We use different methods to extract data from a network and compare it, the best thing to identify glial cells, with an average firing speed that emerges with greater precision.

To reach the aforementioned conclusions, we apply several automatic learning techniques, including decision trees, random forests, bags, gradient impulse and neuronal advances of progress, the latter exceed other models.

Our findings reveal that glial cells play a crucial role in the modulation of synaptic activity, especially in synchronous networks, highlighting possible ways for detection with automatic learning models through accessible experimental measures.