Pedro M. Gordaliza

This study introduces an automated 3D MRI segmentation method for the human eye and orbit, providing large-scale unbiased atlases for clinical research.

This study links sulcal widening in schizophrenia to network hubs and genes involved in synaptic signaling and energy metabolism.

The paper analyzes whole-blood RNA-seq data from TB patients to identify rRNA transcriptomic signatures that change during anti-TB treatment, suggesting potential biomarkers for monitoring treatment response.

The paper examines morphometric similarity networks in health and schizophrenia, finding reduced default mode network similarity in schizophrenia but minimal individual deviations over time.

This study uses deep learning and normative modeling to analyze MRI data from MS patients and identify subcortical volume deviations that improve understanding of disease progression.

The paper introduces a synthetic fetal brain MRI dataset generated with improved realism and used to evaluate automated segmentation and AI model development.

The paper introduces an algorithm to estimate vertex-wise sulcal width maps on cortical surfaces, validated against simulations and showing correlations with age in an aging population.

ABLE extracts sulcal fundi and gyral crown lines from brain surfaces using Laplacian surface collapse, validated on HCP data to show reliable and accurate sulcal topology representation.

The paper examines longitudinal allometric scaling of sulcal morphology in health and schizophrenia, finding steeper age-related declines in SZ compared to healthy individuals.

The paper examines sex differences in lifespan trajectories and variability of human sulcal and gyral morphology using longitudinal neuroimaging data.

The paper identifies a subgroup of schizophrenia patients with extreme cognitive deficits by analyzing sulcal width dissimilarity using the PBSI-SW metric.