  INTRODUCTION: Previous studies have applied normative modeling on a single
neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We
employed a deep learning-based multimodal normative framework to analyze
individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging
biomarkers.
  METHODS: We selected cross-sectional discovery (n = 665) and replication
cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET.
Normative modeling estimated individual-level abnormal deviations in
amyloid-positive individuals compared to amyloid-negative controls. Regional
abnormality patterns were mapped at different clinical group levels to assess
intra-group heterogeneity. An individual-level disease severity index (DSI) was
calculated using both the spatial extent and magnitude of abnormal deviations
across ATN.
  RESULTS: Greater intra-group heterogeneity in ATN abnormality patterns was
observed in more severe clinical stages of AD. Higher DSI was associated with
worse cognitive function and increased risk of disease progression.
  DISCUSSION: Subject-specific abnormality maps across ATN reveal the
heterogeneous impact of AD on the brain.
