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Recent evidence has shown that structural magnetic resonance imaging (MRI) isan effective tool for Alzheimer's disease (AD) prediction and diagnosis. Whiletraditional MRI-based diagnosis uses images acquired at a single time point, alongitudinal study is more sensitive and accurate in detecting earlypathological changes of the AD. Two main difficulties arise in longitudinalMRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects(i.e., different scanning time and different total number of scans); (2) theheterogeneous progressions of high-dimensional regions of interest (ROIs) inMRI. In this work, we propose a novel feature selection and estimation methodwhich can be applied to extract features from the heterogeneous longitudinalMRI. A key ingredient of our method is the combination of smoothing splines andthe $l_1$-penalty. We perform experiments on the Alzheimer's DiseaseNeuroimaging Initiative (ADNI) database. The results corroborate the advantagesof the proposed method for AD prediction in longitudinal studies.

High-Dimensional Smoothing Splines and Application in Alzheimer's Disease Prediction Using Magnetic Resonance Imaging