Christopher Patterson

The paper uses machine learning to predict autopsy-confirmed neuropathology from clinical, neuroimaging, and CSF biomarkers, finding that different modalities best predict specific pathologies.

This study compares deep learning methods to predict cognitive decline using brain MRI and clinical data over two years, showing the value of integrating image and tabular data.

This study evaluates deep learning models that predict multiple dementia-related neuropathologies using MRI, clinical, and genetic data, showing the potential of multimodal AI for non-invasive profiling.

The paper develops a method to impute missing mitochondrial DNA variants using a large reference alignment, improving haplogroup assignment and enabling better integration of mtDNA data from different sources.

The paper investigates how mitochondrial DNA haplogroups and a polygenic risk score interact to influence dementia risk and age of onset.