Modeling functional and structural brain‐predicted age in relation to Alzheimer disease and cognition

Abstract

AbstractBackgroundEstimates of “brain‐predicted age” quantify apparent brain age compared to normative trajectories of neuroimaging features. Brain‐predicted age is elevated in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior studies have typically modeled brain‐predicted age with volumetric magnetic resonance imaging (MRI), but other modalities, including resting‐state functional connectivity (FC) and multimodal MRI, have more recently been explored. In the present study, we tested the sensitivity of brain‐age predictions derived from volumetric, FC, and multimodal MRI to group differences in AD status and individual differences in cognition.MethodWe trained three Gaussian process regression models to predict age from (1) FC, (2) regional brain volumes, and (3) multimodal MRI (FC+volumetric) in 493 control participants (cognitively normal, amyloid‐negative [A‐], 18‐89 years old). We applied the trained models to independent samples staged by the presence or absence of amyloid (A+/A‐) and tau (T+/T‐) using PET (Centiloid) and CSF (amyloid β‐42/40 ratio, phosphorylated tau 181): 122 A‐T‐, 62 A+T‐, 62 A+T+, as well as 154 symptomatic AD participants. Global cognition was assessed using an established composite of cognitive tests across multiple domains.ResultAll models successfully predicted age in the control training set, with the multimodal model (mean absolute error [MAE]=5.3 years) outperforming the unimodal volumetric (MAE=6.0 years) and FC models (MAE=8.3 years) (Figure 1). In all three models, the brain age gap (BAG) was significantly elevated in symptomatic AD compared to cognitively normal controls. In the FC and multimodal models, the BAG was significantly reduced in A+T+ participants compared to T‐ controls (Figure 2). In symptomatic AD, lower global cognitive composite scores were associated with elevated volumetric and multimodal BAGs (Figure 3).ConclusionBoth FC and volumetric estimates of brain age are sensitive to symptomatic AD, consistent with previous reports. Additionally, FC‐based brain age may exhibit a biphasic response to preclinical AD pathology while volumetric brain age may be sensitive to cognitive decline in the symptomatic stage. Multimodal estimates of brain age capture each of these modality‐specific relationships, and further, improve sensitivity to healthy age differences. Hence, multimodal neuroimaging models may be useful in staging early symptomatic and preclinical AD.