Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
<|reference_start|>Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers: 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.<|reference_end|>
@article{kumar2024analyzing,
    title={Analyzing heterogeneity in Alzheimer Disease using multimodal normative
  modeling on imaging-based ATN biomarkers},
    author={Sayantan Kumar, Tom Earnest, Braden Yang, Deydeep Kothapalli, Andrew
  J. Aschenbrenner, Jason Hassenstab, Chengie Xiong, Beau Ances, John Morris,
  Tammie L. S. Benzinger, Brian A. Gordon, Philip Payne, Aristeidis Sotiras},
    journal={arXiv preprint arXiv:2404.05748},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05748},
    primaryClass={q-bio.NC cs.LG}
}