Kahini P. Mehta

The paper introduces a longitudinal data resource combining multimodal imaging and cognitive phenotyping to study brain development and EF variation in adolescents.

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.

The paper introduces an open data resource combining neuroimaging and behavioral measures to study mood and sleep in adolescents and young adults.

This study examines how spatiotemporal brain activity propagations change from childhood to early adulthood, finding that certain propagation patterns become more prevalent with age and correlate with cognitive performance.

The paper introduces Reproducible Brain Charts (RBC), an open data resource integrating brain development data from 6,346 youth, showing the benefits of harmonized phenotypes and reproducible processing for studying psychopathology.

This study shows that optimizing study design, such as increasing age variability and adding longitudinal measurements, improves replicability and effect sizes in brain-wide association studies.

The paper examines how morphometric similarity in brain regions changes during adolescence, finding distinct developmental patterns in paralimbic versus neocortical areas.

The paper shows thalamocortical connections develop along a sensorimotor-association gradient in youth, linking structural connectivity to cortical plasticity and environmental sensitivity.

The paper examines how functional connectivity develops along the sensorimotor-association axis during childhood to adolescence, finding that sensorimotor connectivity increases while association connectivity decreases, reinforcing cortical hierarchy.

XCP-D provides a standardized, reproducible post-processing pipeline for fMRI data, supporting multiple pre-processing pipelines and BIDS best practices.

The paper introduces BABS, a Python package for reproducible and scalable neuroimaging data processing using BIDS Apps and HPC systems.

The paper investigates how functional connectivity patterns in brain networks predict symptoms of borderline personality disorder in adolescents and young adults using fMRI data.

This study links individual differences in delay discounting to specific patterns of dorsal prefrontal cortex connectivity in youth using resting-state fMRI.

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.

CuBIDS streamlines reproducible curation of large BIDS datasets by validating metadata, managing variability, and preparing data for preprocessing pipelines.

ModelArray is an R package for efficient statistical analysis of fixel data, supporting linear and nonlinear models with reduced memory usage.