Prashanthi Vemuri

The paper compares three harmonization methods for multisite diffusion MRI data to reduce protocol-related variability while preserving associations with cognitive impairment and PET biomarkers.

The paper develops an automated method combining MRI contrasts to improve tissue segmentation and perivascular space detection using data from aging and Alzheimer's studies.

The paper identifies that diffusion MRI biomarkers show microstructural damage up to 16 years before white matter hyperintensities in cerebral small vessel disease.

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 develops a Bayesian longitudinal clustering framework to identify distinct AD brain atrophy patterns and their progression, revealing heterogeneity in disease progression.

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.