Boris A. Gutman

This study evaluates how sample size affects machine learning performance in classifying dementia stages using subcortical brain features from 2,511 ADNI samples.

The paper introduces a 3D brain geometry toolkit for multisite neuroimaging analysis, showing subcortical shape measures improve diagnostic and treatment prediction compared to traditional volume features.

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 paper applies a spherical harmonics-based deep learning model to MRI-derived brain shape metrics to predict age, sex, and Alzheimer's disease diagnosis, showing strong performance in classification and regression tasks.

This study examines subcortical brain alterations in CNV carriers across multiple neuropsychiatric disorders, finding shared and distinct patterns of structural changes.

This study identifies subcortical shape differences in MDD patients using ENIGMA data, highlighting region-specific changes in surface area and thickness.

The paper identifies genetic loci associated with human cortical structure and finds genetic correlations with neuropsychiatric disorders.

The paper maps cognitive performance to brain structure using voxel-based morphometry and subcortical shape analysis in a large population sample.

This study finds smaller left hippocampal CA1 volume in PTSD patients compared to trauma-exposed controls, with associations dependent on covariates.

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

The paper evaluates 35 methods for generating structural connectomes using pairwise classification on diffusion MRI data from three test-retest datasets.

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.