Amirhossein Modabbernia

The paper benchmarks and optimizes a normative modeling framework for brain morphometry across the lifespan using MFPR, showing excellent accuracy and stability.

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 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.

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.