Alexandra J. Golby

This paper develops a deep learning method to map white matter tracts from CT scans in intracerebral hemorrhage patients, showing high anatomical plausibility and correlation with motor scores.

This study examines the role of superficial white matter connections of Broca's area in language performance across children and adults, finding bilateral associations with language tasks.

This study examines cerebellar pathway microstructure and cognitive performance in over 9,000 participants, finding consistent associations across pre-adolescents and young adults.

This paper introduces FMQ Regression to analyze white matter microstructure and its relationship to cognitive and motor factors, finding stronger associations in bundle periphery and sex-specific differences.

This paper introduces DeepRGVP, a deep learning framework for automatically identifying the retinogeniculate visual pathway from diffusion MRI tractography data, showing superior performance compared to existing methods.

This study evaluates variability in white matter bundle segmentation across 42 groups using the same dataset, finding protocol variability exceeds other sources of inconsistency.

This study compares four tractography methods for reconstructing the retinogeniculate visual pathway using diffusion MRI data from 57 subjects, finding that UKF-2T and iFOD1 provide the best results.

This paper creates a novel trigeminal tractography atlas for automated trigeminal nerve identification using dMRI data, showing comparable results to manual methods with advantages in efficiency and robustness.

This study examines how hormone levels in prolactinoma patients affect brain functional connectivity, finding increased connectivity and sex-dependent hormone correlations.