Fully Bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Abstract

Tau pathology and regional brain atrophy are the closest correlate of cognitive decline in Alzheimers disease (AD). Understanding heterogeneity and longitudinal progression of brain atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows to study confounding effects, 5) provides clusters visualization, 6) measures clustering uncertainty, all these simultaneously. We used amyloid-{beta} positive AD and negative healthy subjects, three longitudinal sMRI scans (cortical thickness and subcortical volume) over two years. We found 3 distinct longitudinal AD brain atrophy patterns: a typical diffuse pattern (n=34, 47.2%), and 2 atypical patterns: Minimal atrophy (n=23 31.9%) and Hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.