Michael Donohue

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 identifies gene associations with plasma pTau217 in preclinical Alzheimer's disease, showing modulation by APOEε4, Aβ-PET, and sex, with X-linked genes playing a role.

The paper applies a Bayesian multivariate growth mixture model to identify cognitive decline patterns in Alzheimer's Disease Neuroimaging Initiative data.

The paper estimates the time-course of pathological markers and cognitive dysfunction in Alzheimer's disease relative to Aβ-positivity, finding tau and cognitive changes occur years before significant Aβ accumulation.

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.

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.