Presentation
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Regional brain aging: premature aging of the domain general system and aphasia severity
Poster A57 in Poster Session A, Tuesday, October 24, 10:15 am - 12:00 pm CEST, Espace Vieux-Port
Natalie Hetherington1, Sarah Newman-Norlund1, Sara Sayers1, Chris Rorden1, Roger Newman-Norlund1, Janina Wilmskoetter2, Rebecca Roth3, Deena Schwen Blackett2, Julius Fridriksson1, Leonardo Bonilha3; 1University of South Carolina, 2Medical University of South Carolina, 3Emory University
Introduction: Brain age is becoming increasingly recognized as a marker for cognition and cognitive reserve. It is estimated by comparing regional gray matter tissue volumes from one individual against a normative database of healthy individuals. The difference between chronological age and estimated brain age (BrainGAP) provides information about the health of the brain tissue relative to typical brain aging. Advanced brain aging (increased BrainGAP) is associated with poorer cognitive reserve and lower resilience to injury. However, brain aging is a concept that has been largely defined based on healthy individuals without focal brain lesions. It is possible that focal brain lesions (e.g., strokes) are associated with differential levels of brain aging within the same person (i.e., some brain regions may age faster than others). Therefore, we hypothesize that atrophy within specific brain systems commonly associated with language recovery may be important determinants of variance in long-term aphasia severity. Methods: Eighty-nine participants with stroke aphasia (PWA) and 232 healthy control participants underwent T1-weighted MRI scanning. The BrainAgeR pipeline was used to estimate brain age for controls. Those with an estimated brain age within 5% of their chronological age were used to create future linear models (n=126). Gray matter volume of each region of interest (ROI) in the Johns Hopkins University (JHU) atlas was calculated for all PWA and controls. ROIs from the JHU atlas were grouped into brain regions, including left hemisphere, right hemisphere, domain-general, and language-specific. For each PWA, non-lesioned ROIs were identified in each region (e.g., domain-general ROIs). In controls, the gray matter volume of the identified ROIs were used to generate a model to estimate brain age (from BrainAgeR). Then, by entering the gray matter volume of these ROIs in PWA into the model, it was possible to estimate the regional brain age of each PWA (i.e., the combined brain age of domain general regions). We then evaluated the relationship between regional brain age and aphasia severity (WAB-R AQ) using multiple linear regression models in which the WAB-R AQ was the dependent variable, and the following were independent variables: regional BrainGAP, atrophy (average gray matter volume), lesion volume, participant age, and number of ROIs used in the regression model. We focus on the domain-general region as participants had large lesions encompassing most of the language-specific ROIs. Results: PWA had an increased BrainGAP compared to controls, specifically to the left hemisphere. Multiple linear regression analysis between left domain-general regions and WAB-AQ revealed that BrainGAP (p=0.008), atrophy (p=0.009), lesion volume (p<0.001), and age (p<0.001) were significant predictors of aphasia severity (WAB-R AQ). Discussion: The results corroborate previous research suggesting that individuals have increased brain age following a stroke, but also extends this notion by demonstrating that it is the lesioned hemisphere driving the increased BrainGAP in PWA. These results suggest that isolated aging matters for behavior, and degradation to specific brain regions may be associated with behavioral outcomes.
Topic Areas: Disorders: Acquired,