Recent evidence has shown that structural magnetic resonance imaging (MRI) is
an effective tool for Alzheimer's disease (AD) prediction and diagnosis. While
traditional MRI-based diagnosis uses images acquired at a single time point, a
longitudinal study is more sensitive and accurate in detecting early
pathological changes of the AD. Two main difficulties arise in longitudinal
MRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects
(i.e., different scanning time and different total number of scans); (2) the
heterogeneous progressions of high-dimensional regions of interest (ROIs) in
MRI. In this work, we propose a novel feature selection and estimation method
which can be applied to extract features from the heterogeneous longitudinal
MRI. A key ingredient of our method is the combination of smoothing splines and
the l1​-penalty. We perform experiments on the Alzheimer's Disease
Neuroimaging Initiative (ADNI) database. The results corroborate the advantages
of the proposed method for AD prediction in longitudinal studies
