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

TL;DR

This paper introduces a novel method combining smoothing splines and l1-penalty for feature extraction from high-dimensional, heterogeneous longitudinal MRI data, improving Alzheimer's disease prediction accuracy.

Contribution

The paper develops a new feature selection and estimation technique tailored for heterogeneous longitudinal MRI data in AD prediction.

Findings

Enhanced prediction accuracy demonstrated on ADNI database

Effective handling of heterogeneous longitudinal MRI data

Improved detection of early AD pathological changes

Abstract

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 $l_1$-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.