Raymond M. Salvador

The paper assesses the polygenic burden of ubiquitin system genes in schizophrenia, finding enrichment of SZ-associated variation and associations with cognitive performance.

The paper develops fMRI classifiers to predict self- and other-related mentalizing, showing high accuracy and distinct neural patterns across different populations and conditions.

This study analyzes structural brain network changes in bipolar disorder, finding widespread connectivity disruptions linked to illness severity and medication use.

This study identifies a shared genetic factor underlying brain age gaps across 30 cohorts, finding 19 SNPs and associations with health traits.

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

This study identifies small but consistent anatomical correlates of negative symptom dimensions in schizophrenia, linking them to prefrontal cortical thickness and limbic subcortical volumes.

This study uses normative modeling to analyze cortical thickness deviations in MDD patients and healthy controls, finding large overlaps and significant heterogeneity in extreme deviations across individuals.

This study investigates how structural brain alterations in schizophrenia relate to network architecture, finding hub susceptibility and shared epicenters across disease stages and disorders.

This study examines structural brain heterogeneity in schizophrenia using multimodal MRI data from the ENIGMA dataset, finding higher frontotemporal heterogeneity and left parahippocampal homogeneity in patients.

The paper examines age-related variations in cortical thickness across different life stages using gene expression data from a large neuroimaging dataset.

This paper presents an automatic method to normalize and extract 3D facial meshes from head MRI scans, achieving high accuracy in orientation, alignment, and noise removal.

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.