Sophia Frangou

This study shows that brain-age gaps in ADHD vary with medication use and developmental stage, with abnormalities more common in unmedicated children than adolescents.

The paper develops and validates age- and sex-specific normative models of brain morphometry across diverse ethnoracial groups, showing high accuracy in multiple populations.

The paper evaluates factors affecting brain-age prediction models using structural neuroimaging data from 35,683 individuals, finding optimal model performance with no site harmonization, age-binning, and a sample size of over 1,600.

This study examines brain morphometric deviations in individuals at clinical high-risk for psychosis and finds they largely overlap with healthy variation and are not strongly associated with symptom severity or conversion to psychosis.

This study evaluates 21 machine learning algorithms for predicting brain age from sMRI data in youth, finding Extreme Gradient Boosting, Random Forest Regression, and Support Vector Regression with Radial Basis Function Kernel as the top performers.

The paper examines sex differences in brain-age-gap estimates using neuroimaging data from the Human Connectome Project, finding sex-specific patterns in regional brain ageing and predictors of global brainAGE.

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

The paper investigates the conservation of rare schizophrenia risk variants across diverse populations and finds consistent burden in constrained genes.

This study compares machine learning algorithms for predicting brain age in schizophrenia, finding that most linear methods perform similarly but OLS regression is less accurate, leading to variability in brainPAD estimates.

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.

SS-Detect improves source-based morphometry in multi-scanner studies by detecting both common and scanner-specific brain patterns with higher accuracy and sensitivity than conventional methods.

The paper constructs a brain functional atlas for late adulthood, showing spatial differences from younger adult atlases and providing a publicly available resource.