# Brain Age in Aphasia

> **NIH NIH R01** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2024 · $601,672

## Abstract

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.

## Key facts

- **NIH application ID:** 10945028
- **Project number:** 1R01DC022458-01
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Leonardo F Bonilha
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $601,672
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10945028

## Citation

> US National Institutes of Health, RePORTER application 10945028, Brain Age in Aphasia (1R01DC022458-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10945028. Licensed CC0.

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