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Updated: Jun 21, 2025
Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
Sayantan Kumar1,2, Tom Earnest3, Braden Yang3
1Department of Computer Science and Engineering, Washington University in St Louis; 1 Brookings Drive, Saint Louis, MO 63130.
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.
