Silvio Sarubbo

This paper develops a framework to distinguish radial and tangential fiber components in human superficial white matter and examines their microstructural and functional properties.

This paper evaluates transfer learning for adapting a deep learning model trained on healthy data to segment white matter bundles in glioma patients, finding performance drops due to domain shift and partial success of transfer learning.

This study examines how different temporal resolutions in resting-state fMRI affect subject identifiability through functional connectivity fingerprinting.

This study investigates whether transfer learning can improve segmentation of white matter bundles in glioma patients by addressing domain shift between healthy and patient data.

Classifyber improves white matter bundle segmentation by combining atlas, connectivity, and geometry data into a linear model, showing robustness across diverse datasets.

This study assesses the reproducibility of manual segmentation of the pyramidal tract in diffusion MRI tractography across experts and non-experts, finding voxel-wise measures more reproducible than streamline-wise ones.

The paper identifies a new white matter tract, the superoanterior fasciculus, using diffusion MRI tractography and validates it across multiple cohorts and techniques.