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Understanding the Brain to Improve Mental Health
When Assistant Professor of Data Science Aiying Zhang describes her research, her enthusiasm is unmistakable. “I’m passionate about my work,” she said, beaming with energy. “Data science allows us to uncover patterns in the brain that we simply couldn’t see before.”
Zhang’s journey to data science has been anything but traditional — a winding path that mirrors the complexity of the questions she now studies. Trained first in statistics, she learned to see patterns in numbers. A Ph.D. in biomedical engineering introduced her to the power of neuroimaging, machine learning, and artificial intelligence. Postdoctoral work at Columbia University and the New York State Psychiatric Institute immersed her in biostatistics and psychiatry, where data met the human mind. Each step built toward her current focus at the University of Virginia: using data science to decode how the brain develops, and what happens when it doesn’t.
Exploring the Gender Gap in Autism Research
In a recent collaboration with School of Data Science colleagues Javier Rasero and Jack Van Horn, Zhang examines one of the most persistent questions in autism research: why boys are diagnosed with autism spectrum disorder more often than girls.
“Across nearly every dataset, about 75 percent of cases are boys,” she said. “That imbalance in data makes it difficult to understand what’s really happening in girls’ brains.”
Working with neurologist Kevin Pelphrey and other autism researchers at the University of Virginia, Zhang used neuroimaging to study both brain connectivity and morphology — features like cortical thickness and surface area — to better understand biological differences between boys and girls.
“When we study the brain, we look at both structure and function,” Zhang explained. “The brain’s shape and its connections tell us a lot about how it develops and where things may diverge.”
The team’s findings suggest that developmental differences between boys’ and girls’ brains during adolescence may help explain variations in autism as well as why certain mental health conditions, including anxiety, depression, and ADHD, tend to emerge differently in males and females.
Linking Brain Structure, Behavior, and Mental Health
Zhang’s research highlights how many mental health issues in adulthood can be traced to patterns that emerge during adolescence. By identifying these early signs of risk, her work aims to inform strategies that prevent or reduce the impact of psychiatric disorders later in life.
By integrating neuroimaging data with behavioral and genetic information, Zhang hopes to build more accurate models for early detection and intervention. “The future is in early diagnosis,” she said. “If we can detect risk factors in childhood — even before symptoms appear — we can guide treatment and support in ways that truly change lives.”
Although much of this work is still in the research stage, advances in brain imaging and computational modeling are moving it closer to clinical application. Zhang’s team is already using imaging to validate treatments and monitor how the brain changes in response to different forms of therapy.
A School Without Walls
For Zhang, the UVA’s School of Data Science provides the ideal environment for research that thrives on collaboration across disciplines.
“The faculty here come from so many backgrounds — engineering, medicine, social science — and that diversity makes collaboration easy and exciting,” she said. “We all bring complementary skill sets to the table.”
Surrounded by colleagues with complementary expertise, she sees firsthand how interdisciplinary teamwork strengthens the questions researchers ask and the impact of the answers they find. That spirit reflects the School of Data Science’s “School Without Walls” mission, which removes boundaries between disciplines to pursue knowledge for the common good.
This article was compiled from an in-person interview with Aiying Zhang in September 2025.
