Dokumentdetails
ID
oai:arXiv.org:2404.05748
Thema
Quantitative Biology - Neurons and... Computer Science - Machine Learnin...Autor
Kumar, Sayantan Earnest, Tom Yang, Braden Kothapalli, Deydeep Aschenbrenner, Andrew J. Hassenstab, Jason Xiong, Chengie Ances, Beau Morris, John Benzinger, Tammie L. S. Gordon, Brian A. Payne, Philip Sotiras, AristeidisKategorie
Computer Science
Jahr
2024
Auflistungsdatum
10.07.2024
Schlüsselwörter
ad individual-level modeling alzheimer abnormality normative disease atnZusammenfassung
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.
;Comment: Under review in Alzheimer's & Dementia
Kumar, Sayantan,Earnest, Tom,Yang, Braden,Kothapalli, Deydeep,Aschenbrenner, Andrew J.,Hassenstab, Jason,Xiong, Chengie,Ances, Beau,Morris, John,Benzinger, Tammie L. S.,Gordon, Brian A.,Payne, Philip,Sotiras, Aristeidis, 2024, Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
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