Neil P. Oxtoby

The paper uses data-driven analysis of amyloid-beta peptides to identify biomarkers for familial Alzheimer's disease and predict age-at-onset in PSEN1 mutation carriers.

The paper improves cognitive decline prediction by integrating multi-threshold polygenic scores into hippocampal-based normative models using UK Biobank data.

This paper introduces the parsimonious event-based model (P-EBM) to infer disease progression sequences with fewer stages than biomarkers, showing improved performance in Alzheimer's disease analysis.

The paper identifies a tau-first subtype of Alzheimer's disease through analysis of post mortem and in vivo data, showing higher soluble TREM2 levels in tau-first cases.

Fed-ComBat harmonizes batch effects in decentralized data, showing comparable results to centralized methods for linear and nonlinear cases.

This study uses data-driven modeling to show that white matter microstructural changes precede cortical volume loss in Alzheimer's disease, suggesting earlier detection through microstructural markers.

pySuStaIn provides a Python implementation of the SuStaIn algorithm to infer disease subtypes and stages from cross-sectional data.

The paper proposes a multi-view workflow for integrating imaging and genetic data in Parkinson's Disease to identify potential genetic biomarkers.

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.

This paper introduces KDEMM for disease progression modeling, showing it outperforms GMMs in simulating cognitive data and aligns better with clinical phenotypes in Alzheimer's subtypes.

This paper develops data-driven models to predict Alzheimer’s disease progression in dominantly-inherited cases, showing improved accuracy over traditional estimates of years until onset.