Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer’s Disease related neurodegeneration

Abstract

ABSTRACTNeuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one’s estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer’s Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Identifying how an individual’s BAG temporal pattern changes over time would enable improved prediction of clinical outcome based on neurophysiological changes and better understanding of AD progression. To fill this gap, our study conducted predictive modeling using large neuroimaging data with up to 8 years of follow-up to examine the temporal patterns of the BAG’s trajectory and how it varies by subject-level characteristics and disease status. To the best of our knowledge, this is the first effort to take a longitudinal approach to investigate the pattern and rate of change in BAG over time in individuals who progress from mild cognitive impairment (MCI) to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance than single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by gender and APOε4 carriership. Findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.