Laurel A. Beckett

This paper develops a deep-learning method to segment neurofibrillary tangles from brain tissue images using manual annotations, achieving high accuracy and scalability.

This study uses machine learning to show that integrating brain connectome data with amyloid-beta PET imaging improves prediction of cognitive decline in older adults.

The paper investigates whether hemispheric asymmetry in tau pathology in Alzheimer's disease is linked to reduced inter-hemispheric connectivity or asymmetric amyloid-beta distribution, finding a strong association with amyloid-beta laterality.

This paper evaluates the polygenic risk score for Alzheimer's disease in Japanese individuals, showing its utility despite genetic differences from European populations.

This study uses an epidemic spreading model to show that tau spreads through neuronal connections in Alzheimer's disease, with beta-amyloid accelerating this process in some brain regions.

This study compares whole hippocampus and subfield biomarkers for Alzheimer’s disease detection and prediction using multimodal MRI data, finding subiculum biomarkers improve prediction accuracy.

This paper develops a blood-based machine learning model to predict cerebral spinal fluid Aβ1-42 levels, showing high accuracy in identifying Alzheimer's disease risk.

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.

The paper examines the heritability of regional brain volumes using genetic and neuroimaging data from multiple cohorts, finding high heritability and consistent patterns across populations.