Christos A. Davatzikos

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

This study identifies scanner effects in brain community detection and proposes new harmonization methods to reduce these effects in functional connectivity analysis.

This paper introduces PeDecURe, a method to adjust for nuisance variables in multivariate pattern analysis, showing it improves accuracy and generalizability in neuroimaging studies.

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

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.

The paper introduces longitudinal ComBat to harmonize multi-scanner imaging data, showing it improves detection of longitudinal changes and controls type I error better than existing methods.

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.

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.

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.

This study links accelerated cortical thinning in structural brain networks to irritability in youth, highlighting associations in emotion-related brain regions.

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.