Researchers from the University of Cincinnati will conduct the first large-scale assessment of radiation-induced brain health in a population of stroke patients at the European Stroke Organization Congress (ESOC) 2023 in Munich, Germany, May 24-26.
Extensive research has helped pinpoint risk factors for early stroke, but at the population level, there is little evidence of how the brains of stroke patients behave, according to Achara Veigal, M.D., Ph.D., a professor of neuroradiology at the University of California.
There is a limited understanding of what can be seen. “Imaging studies can objectively demonstrate the presence and severity of clinical factors such as diabetes, hypertension, high cholesterol, and renal failure,” she says. However, the majority of large-scale epidemiological studies of brain health have been conducted in subjects who have not had a stroke.
Vagal said, revealing new information from neuroimaging results in stroke patients and was co-principal investigator of the Population-Based Radiological Brain Health Assessment (APRISE) study in stroke epidemiology. The research team analyzed all available clinical imaging data from approximately 3,500 patients who had a stroke in the Greater Cincinnati/Northern Kentucky area in 2015, in the form of prior injury, microhemorrhages, white blood cells, etc.
The images were evaluated for signs of cerebral small vessel disease, material disorders (wear and tear of tissues), and brain atrophy. Vagal said the research team identified three distinct clusters of observable image characteristics, each associated with a specific set of clinical variables. “This will help us understand the biology of existing brain health conditions in stroke patients and help guide future interventions,” she said.
“While we expected all imaging parameters indicative of brain health due to small vessel disease to be closely clustered, we found the clustering of microhemorrhages due to white matter disease to be absent. With the knowledge gained from the study, Vagal said the team is now using brain health imaging data to build a predictive model for stroke recurrence. “Characterizing pre-existing brain health conditions at this scale will help identify new observable features to guide further research,” she said.