Navona Calarco

This study characterizes the human claustrum using multiscale methods, revealing MRI resolution-dependent features and defining practical measurement limits.

The paper curates and provides analysis-ready diffusion MRI data for pediatric brain white-matter research, including automated quality control using a trained neural network.

This study evaluates variability in white matter bundle segmentation across 42 groups using the same dataset, finding protocol variability exceeds other sources of inconsistency.

The paper develops a transcriptome-based polygenic risk score (T-PRS) linking depression-related gene expression changes to neural face processing and anhedonia in a non-clinical sample.

The paper introduces ciftify, a framework to adapt HCP surface-based analysis methods to legacy MR data using CIFTI format.

This study introduces PINT, a method to identify individual-specific brain network variations in ASD and TD controls, finding greater variability in ASD and reduced hypo-connected regions after PINT application.