Houston Daily

Researchers from the University of Houston (UH) and the University of Cincinnati (UC) have applied machine learning to study the impact of heroin on the brain. Their findings, published in "Science Advances" on April 30, provide insights that may advance addiction treatment.

Demetrio Labate, a UH mathematics professor, led the research with PhD students Michela Marini, Heng Zhao, and assistant professor Yabo Niu. They collaborated with Anna Kruyer from UC’s Department of Pharmacy. The team used object recognition technology to analyze changes in brain cell structure due to heroin use.

"Essentially the Holy Grail in the study of substance use disorder is how to find treatments that prevent opioid users from relapsing," Marini explained. Currently, there are treatments for alcohol intoxication but not for drugs like heroin.

Kruyer's lab at UC focuses on relapse processes. She noted that many overdose deaths occur when individuals misestimate their heroin tolerance during relapse. The team studied brain cell interactions, concentrating on astrocytes, which support neurons and regulate synaptic activity.

Kruyer explained, "Astrocytes are a kind of protective cell that can restore synaptic homeostasis." The research found that astrocyte subpopulations shrink and become less malleable after heroin exposure. Kruyer stated, "These data suggest that heroin is doing something molecularly that makes astrocytes less able to respond to synaptic activity and maintain homeostasis."

Labate and Marini employed machine learning to recognize structural features of astrocytes, revealing variations in structure by location. This suggests a link to the cells' different functions. Labate noted, "This approach opens the door to the development of novel techniques for identifying cellular or molecular biomarkers."

The research highlights the significance of interdisciplinary collaboration. Marini remarked, "By uncovering how astrocytes are altered by heroin use, we’re opening new doors not just for addiction research, but for understanding the brain’s response to a wide range of drugs and neurological conditions."

As the team continues its work, their findings could inform new addiction treatments aimed at restoring or replacing disrupted astrocyte functions. The machine learning techniques they developed may also be adaptable to study other cell types.