Alejandra Sierra

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.

The paper develops a custom microelectrode array for recording extracellular action potentials and local field potentials from human hippocampal slices to study temporal lobe epilepsy.

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.

This study shows chronic stress and neuronal activity remodel nodes of Ranvier in mice, linking structural changes to anxiety-like behavior.

DeepACSON segments myelinated axons, cell nuclei, and mitochondria in low-resolution 3D-EM datasets of rat brain tissue, capturing morphological changes post-injury.

The paper introduces MU-Net, a convolutional neural network for automated skull-stripping and segmentation in mouse brain MRI, achieving high accuracy and speed.

This study investigates the role of the meningeal lymphatic system in migraine pain by analyzing inflammatory and nociceptive responses in mice lacking this system.

The paper develops an automated 3D segmentation pipeline for axonal morphology analysis in rat white matter and finds reduced axonal diameter in chronic brain injury.