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bioRxiv · 10.1101/2023.08.15.553412

Normative Modeling Using Multimodal Variational Autoencoders to Identify Individual Heterogeneity in Alzheimer Disease

Kumar, S.·Earnest, T.·Payne, P.·Sotiras, A.
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Abstract

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSPrevious 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. METHODSWe 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. RESULTSGreater 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. DISCUSSIONSubject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

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Kumar, S., Earnest, T., Payne, P., Sotiras, A.. 2023-08-17. Normative Modeling Using Multimodal Variational Autoencoders to Identify Individual Heterogeneity in Alzheimer Disease. https://doi.org/10.1101/2023.08.15.553412

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