This study represents a collaborative effort among investigators from various departments at the University of South Carolina. This research involves experts from the VP Office of Research, including first author, Dr. Alex Teghipco, the Department of Communication Sciences and Disorders, the Department of Psychology, and the Department of Neurology at the School of Medicine, including Dr. Leo Bonilha. This study demonstrates that AI can accurately measure the integrity of residual brain tissue after a stroke, which has direct implications for neuroplasticity and the severity of language impairments after the stroke (aphasia).

Teghipco A, Newman-Norlund R, Fridriksson J, Rorden C, Bonilha L. Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity. Commun Med (Lond). 2024 Jun 12;4(1):115. doi: 10.1038/s43856-024-00541-8. PMID: 38866977; PMCID: PMC11169346.