Characterising ongoing brain aging from cross-sectional data

Abstract

\"Brain age delta\" is the difference between age estimated from brain imaging data and actual age. Positive delta in adults is normally interpreted as implying that an individual is aging (or has aged) faster than the population norm, an indicator of unhealthy aging. Unfortunately, from cross-sectional (single timepoint) imaging data, it is impossible to know whether a single individual\'s positive delta reflects a state of faster ongoing aging, or an unvarying trait (in other words, a \"historical baseline effect\" in the context of the population being studied). However, for a cross-sectional dataset comprising many individuals, one could attempt to disambiguate the overall relative contributions of varying aging rates vs. fixed baseline effects. We present a method for doing this, and show that for the most common approaches, which estimate a single delta per subject, baseline effects are likely to dominate. If instead one estimates multiple biologically distinct modes of brain aging, we find that some modes do reflect aging rates varying strongly across subjects. We demonstrate this, and verify our modelling, using longitudinal (two timepoint) data from 4,400 participants in UK Biobank. In addition, whereas previous work found incompatibility between cross-sectional and longitudinal brain aging, we show that careful data processing does show consistency between cross-sectional and longitudinal results.