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Disentangling biologically distinct subgroups of Alzheimer’s disease (AD) may facilitate a deeper understanding of the neurobiology underlying clinical heterogeneity. We employed longitudinal [18F]FDG-PET standardized uptake value ratios (SUVRs) to map hypometabolism across cognitively-defined AD subgroups. Participants were 384 amyloid-positive individuals with an AD dementia diagnosis from ADNI who had a total of 1028 FDG-scans (mean time between first and last scan: 1.6 ± 1.8 years). These participants were categorized into subgroups on the basis of substantial impairment at time of dementia diagnosis in a specific cognitive domain relative to the average across domains. This approach resulted in groups of AD-Memory (n = 135), AD-Executive (n = 8), AD-Language (n = 22), AD-Visuospatial (n = 44), AD-Multiple Domains (n = 15) and AD-No Domains (for whom no domain showed substantial relative impairment; n = 160). Voxelwise contrasts against controls revealed that all AD-subgroups showed progressive hypometabolism compared to controls across temporoparietal regions at time of AD diagnosis. Voxelwise and regions-of-interest (ROI)-based linear mixed model analyses revealed there were also subgroup-specific hypometabolism patterns and trajectories. The AD-Memory group had more pronounced hypometabolism compared to all other groups in the medial temporal lobe and posterior cingulate, and faster decline in metabolism in the medial temporal lobe compared to AD-Visuospatial. The AD-Language group had pronounced lateral temporal hypometabolism compared to all other groups, and the pattern of metabolism was also more asymmetrical (left

Background: Grey matter (GM) atrophy in Alzheimer's disease (AD) is most commonly modeled as a function of time. However, this approach does not take into account inter-individual differences in initial disease severity or changes due to aging. Here, we modeled GM atrophy within individuals across the AD clinical spectrum as a function of time, aging and MMSE, as a proxy for disease severity, and investigated how these models influence estimates of GM atrophy. Methods: We selected 523 individuals from ADNI (100 preclinical AD, 288 prodromal AD, 135 AD dementia) with abnormal baseline amyloid PET/CSF and ≥1 year of MRI follow-up. We calculated total and 90 regional GM volumes for 2281 MRI scans (median [IQR]; 4 [3–5] scans per individual over 2 [1.6–4] years) and used linear mixed models to investigate atrophy as a function of time, aging and decline on MMSE. Analyses included clinical stage as interaction with the predictor and were corrected for baseline age, sex, education, field strength and total intracranial volume. We repeated analyses for a sample of participants with normal amyloid (n = 387) to assess whether associations were specific for amyloid pathology. Results: Using time or aging as predictors, amyloid abnormal participants annually declined −1.29 ± 0.08 points and − 0.28 ± 0.03 points respectively on the MMSE and −12.23 ± 0.47 cm3 and −8.87 ± 0.34 respectively in total GM volume (p