We applied the THETA score to investigate cognitive resilience mechanisms in individuals with heterogeneous global tau burden.
We evaluated ComBat and Deming regression for harmonizing white matter hyperintensity measurements across scanners and demonstrated that harmonization challenges persist.
We developed brain age prediction models designed to be invariant to MRI scanner changes for use in aging and dementia studies.
We developed an automatic classification system for head MR series to streamline neuroimaging data management.
We validated the THETA score using longitudinal PET data and histopathology, demonstrating its association with disease progression and neuropathological staging.
We developed a machine learning method to identify transition points in Alzheimer's disease biomarkers as a supplement to the reliable worsening method.
We used brain age models to quantify the impact of chronic medical conditions on dementia risk.
We evaluated non-imaging measures for predicting variability in white matter changes relevant to vascular contributions to cognitive impairment and dementia.
We compared NODDI diffusion imaging with structural MRI for sensitivity to neurodegenerative changes.