2024/04/04 by Sayantan Kumar, Kumar, Sayantan, Tom Earnest +14
Biochemistry, Genetics and Molecular Biology · #Biomedical Text Mining and Ontologies #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neurons and Cognition (q-bio.NC)
paper · pdf · doi:10.48550/arxiv.2404.05748
openalex publication_date 2024/04/04 · openalex created_date 2024/04/11 · openalex updated_date 2026/08/02
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.