Sandra E. Black

This study identifies carotid intraplaque hemorrhage as an independent predictor of asymmetric white matter lesion pathology in older men, highlighting a novel imaging marker for stroke risk.

The paper identifies NEUROG2's role in regulating a human-specific gene regulatory program in neural progenitor cells through transcriptomic analysis of knock-in organoids.

This study examines the relationship between amyloid-PET signals in white matter and microstructural changes like free water and fiber integrity, finding that free water and WMH volume are more closely linked to Aβ-PET in WMH regions.

The paper introduces a visual quality control protocol for FreeSurfer cortical parcellations using VisualQC, showing it effectively detects errors and has reliable inter-rater agreement.

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.

This paper identifies five new genetic risk loci for Lewy body dementia through whole-genome sequencing and shows shared risk profiles with Alzheimer’s and Parkinson’s disease.

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.

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.