Ruben C. Gur

This study examines how structure-function coupling in brain networks develops during youth, finding it aligns with cortical hierarchies and supports cognitive improvements.

The paper harmonizes 10,232 structural brain MRIs from 18 studies to create a normative reference of brain development and aging across 3 to 96 years.

This study identifies two neurobiologically distinct subtypes of youth with internalizing symptoms using structural brain imaging and machine learning.

This study finds no significant differences in brain structure between adolescent and young adult cannabis users and non-users using high-resolution MRI scans.

The paper uses machine learning to analyze brain states from fMRI data and shows that white matter connectivity influences their dynamics and development.

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 identifies brain-based dimensions of psychopathology using sCCA in youths, finding four dimensions linked to functional dysconnectivity that replicate in an independent dataset.

The paper finds that larger-brained humans show preferential expansion of fronto-parietal cortical networks and related subcortical regions, at the expense of primary sensory/motor systems.

The paper examines the heritability of neural activation in the nucleus accumbens during monetary incentive anticipation and its genetic covariance with self-reported pleasure experience in twin pairs.

The paper examines the association between cortical thickness and impulsive choice in adolescents, finding reduced cortical thickness in specific brain regions linked to impulsive decision-making.

The paper develops an automated method to assess structural brain image quality using Euler number and validates its effectiveness in identifying low-quality images and its relationship with cortical thickness and age-related associations.

This paper evaluates how in-scanner head motion affects structural connectivity estimates from diffusion tractography in a developmental cohort, showing motion biases connectivity strength in a consistency and length-dependent manner.

This paper shows that diffusion tensor imaging data are site-specific and demonstrates that ComBat effectively harmonizes them while preserving biological variability.

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.