Brain Age in Aphasia

Abstract Post-stroke chronic aphasia is a prevalent language processing problem commonly associated with significantly reduced quality of life. Unfortunately, our knowledge of the personalized factors underlying aphasia severity remains incomplete, and only 50% of the variance can be explained by comprehensive

models that incorporate lesion characteristics, demographic variables, and cognitive factors. Importantly, age is a predictor of aphasia severity, but this relationship is inconsistent and the interplay between age, age-related brain integrity and aphasia is not well-understood. A better understanding of how aging affects brain integrity

and interferes with aphasia would clarify an important mechanism related to aphasia severity and reduce the unexplained variance in clinical trajectories. A new breakthrough in neuroimaging can now bridge this knowledge gap: brain age is a novel machine learning approach that can accurately measure age-related

neurodegeneration. Premature brain aging (PBA) relative to chronological age is strongly associated with cardiovascular risk factors and is a powerful marker of decreased cognition and lowered brain plasticity in the general population. Our team pioneered novel neuroimaging methods to measure PBA among stroke survivors

and our preliminary studies demonstrated that PBA is a common but underappreciated factor among stroke survivors with aphasia. Many stroke survivors with aphasia have cardiovascular risk factors and PBA accounts for a considerable proportion of the hitherto unexplained variability in aphasia severity and recovery. Crucially,

novel findings that significantly expand our understanding of aphasia severity are rare and it is therefore important to better understand the mechanistic relationship between PBA and aphasia. We will leverage one of the largest comprehensive demographic, behavioral and neuroimaging datasets in chronic aphasia (the Center

for the Study of Aphasia Recovery – C-STAR) and in healthy aging (the Aging Brain Cohort at University of South Carolina – ABC@USC) to examine: 1) the influence of cardiovascular risk factors versus protective cognitive variables such as education and multilingualism on PBA and aphasia (Specific Aim 1); 2) the

association between PBA confined to regional cortical areas and linguistic symptoms (Specific Aim 2); 3) the importance of PBA affecting remote functional and structural networks and language impairments (Specific Aim 3); and 4) whether stroke and chronic aphasia are associated with accelerated PBA in longitudinal cohorts

(Specific Aim 4). This research will provide pivotal insights into the recognized but inadequately understood relationship between aging and aphasia and it will clarify factors that influence personalized aphasia trajectories among many stroke survivors. Our team is uniquely positioned to perform this research given our

track record of multidisciplinary research in aphasia, neurology, neuroimaging and machine learning.