Omar Narvaez

This study uses unsupervised deep learning to analyze rat brain histology, revealing tissue organization and detecting pathological changes without manual annotations.

This study uses multidimensional diffusion MRI to show increased microstructural heterogeneity in 5xFAD mouse brains compared to wild-type, associated with amyloid pathology but not widespread degeneration.

This study introduces frequency-dependent diffusion tensor distribution imaging (ωDTD) combined with machine learning to better characterize ischemic tissue damage in rats, showing improved representation of microstructural changes compared to conventional DTI.

This paper uses unsupervised clustering on diffusion-relaxation MRI data to classify tissue water populations in rat and human brains, revealing more complex tissue structures than traditional methods.

The paper develops a protocol to characterize epileptogenic tissue using multimodal data from surgical samples, aiming to improve diagnosis and treatment of drug-resistant epilepsy.

This study evaluates the reproducibility of MMD-MRI metrics in ex vivo mouse brains and identifies fixation time effects.

This study investigates whether frequency-dependent diffusion-relaxation MRI can provide meaningful information or if it leads to overfitting in different tissue types and frequency ranges.

The paper demonstrates in vivo rat-brain mapping using nonparametric D(omega)-R1-R2 distributions MRI to identify multiple water pools in gray matter with distinct diffusion and relaxation properties.

The paper validates per-bundle dMRI metrics in a fiber crossing region using histological data from axonally degenerated optic nerves in rats.