Melissa F. Holmes

This study examines the relationship between white matter hyperintensities, cortical thickness, and empathy in neurodegenerative and cerebrovascular diseases, finding associations with specific brain regions and demographic factors.

This paper introduces a Bayesian 3D U-Net for WMH segmentation that provides uncertainty estimates and is robust to imaging variations, achieving high accuracy and efficiency.

The paper introduces CNN-based models for segmenting intracranial and ventricular volumes in neuroimaging data, showing improved accuracy and efficiency compared to existing methods.

This study improves FreeSurfer's cortical thickness estimation in patients with cerebral small vessel disease and stroke by applying correction procedures, leading to better segmentation and cognitive association results.

This paper describes the neuroimaging pipeline methods used in the ONDRI study to generate regional brain tissue volumes and neurovascular markers from structural MRI data.

The paper describes the quality assurance and quality control pipeline for multi-site MRI data in the ONDRI study, ensuring standardized data collection and high image quality.