Magnetic Resonance Imaging (MRI) has revolutionized the study of brain aging. It provides non-invasive, detailed images of brain structure and function, allowing researchers to observe changes associated with normal aging and neurodegenerative diseases. MRI results have shown promise in predicting longitudinal brain functions in aging through the following: Volumetry – Measures changes in brain volume, particularly in regions like the hippocampus and prefrontal cortex, which are vulnerable to age-related decline; cortical thickness – assesses the thickness of the cerebral cortex, which can thin with age; white matter integrity – diffusion-tensor MRI (DTI) measures the diffusion of water molecules in white matter tracts, revealing changes in microstructure and connectivity. In this project, we will focus on the use of the MRI and cognitive data from the Baltimore Longitudinal Study of Aging (BLSA), and aim to determine a predictive modeling approach for estimating longitudinal changes in cognitive function in older adults. Methods include but are not limited to linear mixed-effects model, support-vector machines, neural networks and deep learning. The outcome of this project will enable more effective use of MRI in early diagnosis.