A. Barreto

Machine learning for the prediction of amyloid PET positivity using plasma biomarkers, cognition, APOE genotype, and structural imaging

Purpose Machine learning to enable precise, non-invasive detection of cerebral amyloid-beta (Aβ) pathology by integrating cognitive assessments, plasma biomarkers, and structural neuroimaging. Methods We developed an explainable multimodal machine-learning framework to predict amyloid PET visual read status using plasm...

Multiscale Organization of Amyloid-Associated Vulnerability Across the Hippocampal Memory System.

Alzheimer's disease (AD) selectively affects interconnected components of the hippocampal memory system, yet most MRI biomarkers characterize neurodegeneration using whole-hippocampal or isolated regional measurements. We tested whether amyloid-associated structural vulnerability is selectively concentrated in the corn...

Multimodal prediction of MCI-to-dementia conversion over a two-year window.