Machine‐learning‐based Alzheimer's disease dementia score using structural MRI neurodegeneration patterns: Independent validation on ADNI, AIBL, OASIS and MIRIAD

Abstract

AbstractBackgroundBiomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1‐weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in‐vivo, potentially providing information enabling the design of biomarkers for DAT.MethodWe propose a novel biomarker using structural MRI volume‐based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty, and then validated on several independent datasets for subjects at various stages along the longitudinal trajectory from NC to DAT.ResultIndependent validation on 8834 un‐seen images from ADNI, AIBL, OASIS, MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time‐to‐conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months, and 0.73 for TTC of up to 7 years, achieving the state‐of‐the‐art results.ConclusionWe propose and validate a novel ensemble‐learning based biomarker score based on 3D topographic patterns of MRI‐observed structural volumetric features. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along the disease course.