Results of the first large-scale assessment of radiological mental health in stroke patients

University of Cincinnati researchers are presenting abstracts at the European Stroke Organization Conference (ESOC) 2023, May 24-26 in Munich, Germany, including the results of the first large-scale assessment of radiological mental health in stroke patients in a single population.

According to UCA’s Achala Vagal, MD, professor of neuroradiology, extensive research has helped identify stroke risk factors for the first time, but there is limited understanding of what the brains of stroke patients look like at the population level.

“Images can be a realistic representation of the presence and severity of clinical conditions such as diabetes, high blood pressure, high cholesterol, and kidney failure,” she said. “However, most large epidemiologic studies of brain health have been conducted in non-stroke subjects.”

Vagal was co-principal investigator on the Evaluation of New Data on Neuroimaging Outcomes in Stroke Patients in the Population-Based Radiological Brain Health Assessment in Stroke Epidemiology (APRISE) study.

The research team reviewed clinical imaging data from nearly 3,500 patients who had a stroke in the Greater Cincinnati/Northern Kentucky region in 2015 to assess the imaging characteristics of small vessel disease in the brain following a prior injury, microbleeds, white. Other observations include tissue disease (wearing away of tissue) or cerebral infarction.

According to Vagal, the team identified three distinct visual imaging features that were associated with a specific set of clinical variables.

“This will help us understand the biology of mental health in stroke patients and help guide future interventions,” she said. “We expected all imaging parameters of brain health to be closely clustered due to small vessel disease, but we found a lack of clustering of microvessels with white matter disease.”

Based on the findings from the study, Vagal said the team is now working to build a repeatable stroke prediction model using brain health imaging data.

“Such a large-scale characterization of mental health care can help identify new observable behaviors that can guide further research,” she said.

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