A predictive framework based on brain volume trajectories enabling early detection of Alzheimer's disease

Abstract

We present a framework for constructing predictive models of cognitive decline from longitudinal MRI examinations, based on mixed effects models and machine learning. We apply the framework to detect conversion from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to Alzheimer's disease (AD), using a large collection of subjects sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Aging (AIBL). We extract subcortical segmentation and cortical parcellation from corresponding T1-weighted images using FreeSurfer v.6.0, select bilateral 3D regions of interest relevant to neurodegeneration/dementia, and fit their longitudinal volume trajectories using linear mixed effects models. Features describing these model-based trajectories are then used to train an ensemble of machine learning classifiers to distinguish stable CN from converters to MCI, and stable MCI from converters to AD. On separate test sets the models achieved an average of accuracy/precision/recall score of 69/73/60% for converted to MCI and 75/74/77% for converted to AD, illustrating the framework's ability to extract predictive imaging-based biomarkers from routine T1-weighted MRI acquisitions.

Keywords: Alzheimer's disease; Longitudinal data analysis; MRI; Machine learning; Mild cognitive impairment; Mixed effects models.