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More than 80 billion neurons in the human brain control our movement, perception, memory, decision-making, and emotions. These neurons can work independently, and they can also form dynamic, adaptable networks that operate both within and across dozens of brain regions, serving as the brain鈥檚 communication and computation system.

After having only been able to study one neuron at a time for many years, scientists and engineers can now study the interactions of many neurons within single regions of the brain, which has resulted in advances in neuroscience. But our ability to understand neuron interactions across those multiple regions still limits our understanding of the human brain.

The spiking activity of an individual neuron can be influenced by neurons within the same brain area, as well as neurons in other brain areas across wide areas of the brain. Many studies have examined the activity of populations of neurons within a single brain area. But since most brain functions rely on interactions across brain areas, it is likely that some component of the activity of each brain area is shaped by interactions with other brain areas.

Developments in neural recording technology have enabled simultaneous recordings of populations of neurons in multiple brain areas, which has given rise to the possibility of distinguishing interactions across brain areas from interactions solely within a single brain area. However, most statistical methods for analyzing populations of neurons are not designed to distinguish activity shared across areas from activity shared solely among neurons within each area.

The team, including lead authors Megan McDonnell, Akash Umakantha, and Ryan Williamson, developed a novel statistical approach called pCCA-FA that will allow researchers to leverage recordings from multiple areas of the brain at one time, revealing aspects of brain functions that are hidden in single-area recordings. This work was recently published in .

The relatively new ability to record neurons in multiple areas of the brain simultaneously allowed the team to record neurons in both hemispheres of the prefrontal cortex in animals performing a spatial memory task.

The prefrontal cortex serves as the brain鈥檚 control center and is responsible for many advanced mental abilities, including working memory.

By applying their statistical method to both data from their recordings, as well as synthetic data, they were able to tease apart the global processing from the local interactions, and to show whether fluctuations in neuron activity were shared within or across areas of the brain.

They found substantial activity that was shared among neurons within each population, much of which was actually shared across hemispheres of the prefrontal cortex and linked to an arousal process. Their work presents a path by which we can leverage multi-area recordings to reveal aspects of brain functions that are hidden in single-area recordings.

The pCCA-FA statistical method is a combination of probabilistic canonical correlation analysis and factor analysis. Using pCCA-FA to tease apart activity shared across hemispheres of prefrontal cortex from hemisphere-specific activity, they then correlated each type of activity with the animal鈥檚 behavior, which in this case was the animal鈥檚 arousal level over time.They found that arousal was more related to activity that was shared across brain areas than activity within a single brain area.

鈥淭he advantage of this work is that the insight is tightly paired with the method,鈥� according to , a professor of biomedical engineering and the Neuroscience Institute. He explained that changes in levels of arousal involved signals to both sides of the prefrontal cortex.

McDonnell, a Ph.D. student in the Neuroscience Institute, explained that their paper is not only about their new statistical method.

鈥淲e used the method to uncover that an arousal signal was shared across brain areas. We couldn鈥檛 have found that if we weren鈥檛 studying them all at once,鈥� said McDonnell.

鈥淏eing able to understand how these neurons work together across different areas of the brain can help us answer important questions,鈥� said , a professor of biomedical engineering, electrical and computer engineering, and the Neuroscience Institute.

The framework is general, so it can be applied to any two areas of the brain, which will enable others to apply the method to work in their own areas of study, explained Yu.

鈥淢any brain disorders involve abnormal communication in the brain, so the key to developing treatments and therapies for conditionslike ADHD, autism, and schizophrenia relies upon a better understanding of how these interactions work,鈥� explained Smith.

Co-lead authors Akash Umakantha and Ryan Williamson both recently earned their doctoral degrees from the Neuroscience Institute and the Machine Learning Department. McDonnell, Umakantha, and Williamson worked under the direction of Yu and Smith.