Slide Slam P2
Demographic, Health, and Neural Factors Associated with Chronic Aphasia Severity
Lisa Johnson1, Alexandra Basilakos1, Sameneh Nemati1, Chris Rorden1, Roger Newman-Norlund1, Leonardo Bonilha2, Argye Hillis3, Gregory Hickok4, Julius Fridriksson1; 1University of South Carolina, 2Medical University of South Carolina, 3Johns Hopkins University, 4University of California, Irvine
Introduction Lesion size and location are often reported as the most reliable factors that predict severity of language impairment in persons with post-stroke aphasia. Several studies have also found that demographic and health factors are related aphasia severity. The extent to which these factors predict language impairment, beyond traditional cortical measures, remains unknown. Identifying and understanding the contributions of factors to predictive models of severity constitutes critical knowledge for clinicians interested in charting the likely course of aphasia in their patients and designing effective treatment approaches in light of those predictions. Methods Utilizing neuroimaging and language testing data from 224 individuals with chronic aphasia, we conducted a lesion symptom mapping analysis (LSM) to identify regions which predict overall aphasia severity scores. We used residual values from a linear model between severity and proportion damage to these critical regions as the dependent factor in three models: 1) Demographic Model; 2) Health Model; and 3) Overall Model. Results Two regions were identified to be associated with aphasia severity: left posterior insula and left superior longitudinal fasciculus. The Demographic Model revealed cognitive reserve and time post-stroke as significant predictors of severity (p = 0.004; p = 0.03), and the Health Model found that the extent of periventricular hyperintensities was associated with severity (p = 0.01). An interaction between presence of diabetes and exercise frequency was also found (p = 0.04), indicating that those with comorbid diabetes who exercise more had less severe aphasia than those who do not exercise. Finally, the Overall Model showed a relationship between aphasia severity and time post-stroke (p = 0.02), periventricular hyperintensities (p = 0.001), and a significant interaction between diabetes and exercise frequency post-stroke (p = 0.03). Summary Results from this study add to the growing literature suggesting demographic variables can shed light on individual differences in aphasia severity beyond lesion profile. Additionally, our results emphasize the importance of cognitive reserve and brain health in aphasia recovery.