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Neuroimaging Biomarkers of Successful Cognitive Aging


While aging is typically associated with cognitive decline, some individuals are able to diverge from the characteristic downward slope and maintain very high levels of cognitive performance. By examining morphological characteristics of the brain using structural neuroimaging, several studies have attempted to understand these trajectories and elucidate what neurobiological factors contribute to preserved cognition throughout advanced aging. Using data from the National Alzheimer’s Coordinating Center (NACC) and Alzheimer's Disease Neuroimaging Initiative (ADNI), I examined individuals aged 60 and above who demonstrated a combined performance at or above the top 50th percentile in memory and executive function, deemed as Top Cognitive Performers (TCP). In this thesis, we aimed to understand how well structural (cortical thickness, cortical volume, and white matter hyperintensity volume) and pathological (amyloid and tau burden) characteristics can be used to model TCP. As these relationships are sparsely studied in the oldest segment of our population, those 90 and above, we also utilized existing structural magnetic resonance imaging (MRI) and neuropsychological assessment data collected by The 90+ Study. Results showed that regional network-style cortical thickness both outperformed localist cingulate models and was sufficient in predicting TCP. Additionally, we were able to show that while both cortical thickness and volume models preformed similarly, cortical thickness did slightly better at predicting TCP. Though we were able to see group differences in white matter intensities in some age groups, this measure did not independently perform as well as cortical thickness. Lastly, unlike differences seen in structural correlates, TCP individuals did not exhibit group differences in PET measures of amyloid or tau. Taken together, this shows that neuroimaging biomarkers are useful in the identification of successfully aging cohorts, particularly when examining structural correlates of aging. These relationships were seen in younger and older datasets, further suggesting that MRI biomarkers are useful across the full adult lifespan.

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