FROM THE NEST | FALL 2023 | 7 Yu Zhang, right, and Ph.D. student Alex Zhao test the lab’s electroencephalography (EEG) system. The study of biomarkers in the brain— powered by cutting-edge machine learning techniques—could redefine the way mental health conditions are categorized and diagnosed and lead to more effective, personalized treatments. That’s the goal of Yu Zhang, an assistant professor of bioengineering and electrical and computer engineering who recently received major support from the National Institute of Mental Health, a division of the National Institutes of Health. The two grants, which total nearly $4 million, will fund projects using brain imaging and machine learning to search for biomarkers in the brain to improve diagnosis and treatment. Redefining Classifications “Our goal is to build objective biomarkers using brain imaging and machine learning that better capture the brain’s dysfunction,” Zhang said. “Those biomarkers will essentially enable us to predict whether an individual patient will respond to medication based on their brain circuits, and that will help guide personalized intervention.” One study aims to improve the treatment of depression. The research team includes collaborators from Dell Medical School at the University of Texas at Austin, the Perelman School of Medicine at the University of Pennsylvania and Stanford University School of Medicine. The other study aims to identify biomarkers in the brain to help in redefining the classification of mental disorders. Currently, mental health conditions are grouped according to subjective behavioral and clinical assessments and self-reported questionnaires, Zhang said. As a result, the range of symptoms within a single diagnostic category such as autism can be vast. Redefining the classification system could facilitate the development of more effective treatment for patients, he said. “This work has the potential to redefine mental health conditions and would be a major breakthrough in the field,” said Zhang. “It could help us establish more effective therapeutics for individual patients, which is something traditional clinical diagnoses can’t achieve.”—Christine Fennessy Identifying Biomarkers NIH grants support AI-driven approach to diagnosing mental health conditions. RESEARCH COPING WITH STRESS Research published in the journal PLOS ONE sheds light on how individuals coped with stress during the dark days of the pandemic and which strategies were associated with better quality of life. The study found that problem- and emotion-focused coping strategies were associated with a higher quality of life, while avoidant coping strategies had a negative effect. “People use different types of coping to deal with different stressors, and people may use all three strategies at different times,” said lead author Fathima Wakeel, College of Health. “What this study demonstrates is how those strategies work during a large-scale traumatic event.” The researchers say the results, while not entirely surprising, provide insights that may help inform individual and societal responses in the future.—Dan Armstrong RESEARCH RICHARD MIA / DOUGLAS BENEDICT
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