DETROIT — Wayne State University researchers are using photoacoustic imaging to observe brain activity and, in the process, discovering more about how it responds to different types of learning and experiences.

The team’s findings were recently published in the science journal Photoacoustics. The study stemmed from a project by Wayne State University School of Medicine alumnus, James Matchynski, M.D., Ph.D., and was led by School of Medicine faculty members Shane Perrine, Ph.D., associate professor of psychiatry and behavioral neurosciences, and Alana Conti, Ph.D., professor of psychiatry and behavioral neurosciences and director of the Translational Neuroscience Program. The team collaborated with colleagues in the Department of Biomedical Engineering at the University of Illinois Chicago.

“The research we recently published was a follow-up study,” said Perrine. “We started using photoacoustic imaging as a neuroimaging method to investigate conditioned fear behavior and mechanisms of learned behaviors in general. This current paper used pattern recognition analysis to further explore ambiguous results from the previous studies.”

Photoacoustic imaging uses optics and acoustics to generate a signal in biological tissues — in this case, the brain — where the signal provides structural and functional data and allows researchers to track factors such as brain activity. Pattern recognition and similarity-based metrics are components of artificial intelligence that have been used in a number of computational methods in neuroscience and brain imaging.

“Using photoacoustic imaging alone, we saw that when we put animals in a novel environment, their prefrontal cortex lit up the same amount as if we put them in a dedicated learning experience,” said Perrine. “When we examined the findings using pattern recognition, we found that a common pattern of brain activity within the prefrontal cortex only emerged in animals in the learning context but not for those in the novel context.”

Of note was the use of artificial intelligence in analyzing the data.

“The previous publications quantified brain activity, which led us to observe that a learning task equally activated the prefrontal cortex compared to novelty exposure,” said Perrine. “Pattern recognition, which is a principal component of artificial intelligence designs, allowed us to disentangle the brain activity in the prefrontal cortex and show that only the learning task — which is a more highly coordinated task — engaged prefrontal cortex in a homogenous pattern of activity.”

This research was supported by the National Institutes of Health, grant numbers R21DA052657, R01DA042057, F30MH122093, R01EB027769 and R01EB028661.

Read the full article at pubmed.ncbi.nlm.nih.gov/39811065/.

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• Shane Perrine, Ph.D., Alana Conti, Ph.D., and James Matchynski, M.D., Ph.D. (png)