Disentangling Alzheimer’s disease neurodegeneration from typical brain aging using MRI and machine learning

Abstract

Neuroimaging biomarkers that distinguish between changes due to typical brain aging and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multi-variate patterns of brain change related to the two processes (including the SPARE-AD [Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease] and SPARE-BA [of Brain Aging] scores investigated herein). However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, toward disentangling the two. T1-weighted MRI images of 4,054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment, or cognitively normal diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically-or molecularly-defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients (n = 718) and age-and sex-matched cognitively normal adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using cognitively normal individuals) and SPARE-AD1 (classification of cognitively normal versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group (n = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive mild cognitive impairment, and amyloid-and tau-positive cognitively normal individuals) and amyloid-negative cognitively normal group (n = 718).