Ruben C. Gur

The paper shows that intrinsic brain activity decreases along a sensorimotor-association cortical axis during human development, with reduced activity in disadvantaged youth.

The paper introduces CoCoA, a statistical framework to estimate conditional correlations and association sizes, demonstrating its effectiveness in neurocognitive data analysis.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

This paper introduces a method to analyze intermodal coupling between multiple brain imaging modalities and applies it to study neurodevelopmental patterns in adolescents.

The paper examines how individual differences in functional brain network topography relate to psychopathology dimensions in youth using machine learning and fMRI data.

The paper analyzes multi-scale functional brain networks in youths to identify developmental patterns of network re-organization and their cognitive implications.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

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

This study examines how cerebral blood flow and fMRI fluctuations are coupled in youth, finding age-related decreases, sex differences, and links to executive function.

This study uses pharmacological fMRI to show a reduction in the excitation:inhibition ratio in association cortex during adolescence, linked to psychopathology.

This paper introduces MAGIC, a multi-scale semi-supervised clustering method for brain imaging data to identify disease subtypes, and evaluates its performance on simulated and clinical datasets.

The paper investigates how structural brain networks influence functional dynamics through network controllability, finding that boundary controllability mediates the relationship between structural participation and functional flexibility.

The paper develops a theory of efficient coding in brain networks and shows that structural connectivity balances information fidelity and compression, predicting behavior and explaining biological organization.

This study identifies altered functional brain dynamics in 22q11.2DS during facial affect processing using fMRI, showing preserved early visual and insular activation but diminished motor feedback and emotional memory signals.

The paper introduces a permutation-based test to assess intermodal correspondence in neuroimaging data, showing it effectively detects known relationships and is conservative when none are expected.

This study examines how neighborhood socioeconomic status affects executive system activation during a working memory task in youth and identifies mediating brain activity patterns.

The paper uses machine learning to analyze functional topography in youth brains, finding sex differences linked to X-linked genes and specific brain networks.

The paper models cortical brain deviations from normative neurodevelopment to predict psychiatric symptoms, finding that transdiagnostic psychopathology dimensions better predict symptoms than specific disorder measures.

The paper analyzes cortical thickness trajectories in 17,075 individuals to show peak thickness in childhood, steep decline in early adulthood, and slower decline later, with notable exceptions and significant inter-individual variability.

The paper investigates genetic risk for psychosis spectrum symptoms in youth, finding a significant link with ADHD polygenic scores and age-specific effects.

The paper analyzes sex differences in brain structure variability across the lifespan using a large dataset, finding greater male variability in subcortical volumes, cortical surface area, and thickness in most regions.

This study identifies three neurocognitive subtypes of depression in youth using machine learning, showing distinct neural signatures despite similar clinical symptoms.

QSIPrep is a software platform for preprocessing diffusion MRI data that supports multiple sampling schemes and ensures reproducibility through standardized workflows and quality measures.

The study replicates and extends the Parieto-Frontal Integration Theory (PFIT) in youths, showing age-related strengthening of brain parameter-cognitive performance associations and suggesting an expanded network incorporating motivation and affect regions.

This paper develops a method to reduce technical variation in multisite MRI data by aligning intensity distributions across scanners.

The paper identifies statistical issues in brain age gap analysis, showing that modifying the brain age gap can inflate model accuracy statistics and suggests developing new methods to quantify deviation from normal developmental trajectories.

This study analyzes subcortical volume changes across the lifespan using MRI data from 18,605 individuals, finding age-related declines in some structures and increases in others.

The paper investigates how diagnosis and symptom domains affect cognitive performance in a case-control study of severe mental illness, finding distinct cognitive profiles linked to specific symptom domains.

The paper uses network control theory to show that structural brain networks mature during adolescence to reduce the energy cost of activating the frontoparietal system, supporting executive function development.

This study shows how functional topography of brain networks changes with age and relates to executive function and brain organization in youth.