Modeling MRI Brain Aging in Autosomal Dominant Alzheimer's Disease

We will measure how “old” a person’s brain appears, and test whether these age predictions are older than expected in participants with a rare genetic mutation that causes Alzheimer disease.

Recent machine learning tools can measure how “old” a person’s brain appears, compared to other healthy brains. In symptomatic Alzheimer’s disease (AD), brains appear older than expected, e.g., a 75-year-old AD brain might resemble a healthy 84-year-old’s brain. We will study people with a rare genetic mutation for early-onset Alzheimer’s disease, to test if their brains begin to appear older as they approach their expected dementia onset. These analyses will evaluate whether clinicians can use this brain-age approach to identify people with very early AD pathology and predict AD risk.

Brain-age gap (BAG) models may yield novel MRI biomarkers of AD progression, but most BAG studies have focused on sporadic Alzheimer disease (AD). Thus, applying BAG to preclinical autosomal dominant AD (ADAD) is a relatively novel approach. We will extend upon recent work in this area by using more comprehensive, multimodal MRI features, which capture more variance in healthy age differences. This approach also allows us to compare functional and structural age predictions to established MRI markers of AD progression.

Brain age gap (BAG) estimates may reflect comprehensive, easily-interpretable, non-invasive biomarkers of brain health and may improve sensitivity to Alzheimer disease (AD) over established MRI measures (e.g., hippocampal volume). Thus, they may offer clinical utility as an AD screening or staging tool or as a clinical trial endpoint. This proposal aims to validate BAG by testing whether BAG patterns previously demonstrated in sporadic AD are consistently observed in autosomal dominant AD participants, who are younger and lack most confounding age-related pathologies.