Begim Fayzullobekova

The paper presents a curated dataset of processed diffusion MRI data from the ABCD Study, showing that specific diffusion metrics are sensitive to adolescent white matter development and robust to image quality variation.

BIBSNet is a deep learning model for infant brain segmentation in MRI scans that outperforms JLF in accuracy and speed, with compatible outputs for further analysis.

This study identifies distinct cortical thickness and curvature patterns in autism and ADHD using harmonized, large-scale brain imaging data.

The paper estimates heritability of subcortical brain volumes in adolescents using a multi-site cohort, finding high heritability in cerebellum cortex and hippocampus.

The paper extends fMRIPrep to handle MRI data from infancy to old age, demonstrating high-quality preprocessing across a wide age range.