David M. Cash

This paper improves cerebrovascular brain age predictions by harmonizing arterial spin labeling (ASL) features across multiple cohorts using various methods, showing that ASL-specific harmonization enhances model performance.

This study identifies graded multidimensional variations in cognition and brain volumes between Alzheimer's disease and posterior cortical atrophy, suggesting shared phenotypic continua rather than distinct categories.

The paper describes MRI and PET protocols used in the DIAN study to investigate preclinical Alzheimer's disease and reports key imaging findings.

This paper introduces a tissue-weighted mean method to reduce bias in NODDI metrics by accounting for CSF partial volume, showing it improves accuracy in ROI analysis for neurodegenerative disease studies.

The paper describes an MRI dataset and processing pipeline for the EPAD study, identifying quality control features and image-derived phenotypes related to aging and dementia.

The paper investigates the relationship between gene expression and brain atrophy in genetic frontotemporal dementia, finding correlations between atrophy patterns and specific gene sets related to vascular, glial, and neuronal functions.

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 detects pre-symptomatic shape changes in the thalamus using spatiotemporal analysis of MRI scans from the GENFI study, finding differences five years before symptoms appear.

This paper develops data-driven models to predict Alzheimer’s disease progression in dominantly-inherited cases, showing improved accuracy over traditional estimates of years until onset.