Sydney C. Covitz

This paper develops deep learning models to detect neuromuscular diseases and quantify movement impairment using video-derived biomechanics data from smartphone recordings.

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.

Smartphone-video biomechanics (OpenCap) reproduced four timed function tests (r > 0.98) and separated facioscapulohumeral from myotonic dystrophy better than test times (balanced accuracy 82% vs 50%; 129 participants).

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.

This paper introduces a framework for applying network control theory to the human structural connectome to study neural dynamics and control energy, controllability, and their correlations with behavior and mental health.

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

The paper identifies proteins and molecular processes related to brain connectivity by integrating neuroimaging, proteomic, and gene expression data from human brain regions.

This study tracks cortical activity propagations in youth, finding increased top-down propagations with cognitive control demands and development.

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.

The paper shows that intrinsic brain activity decreases along a sensorimotor-association cortical axis during human development, with reduced activity in disadvantaged youth.

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

A curated collection of processed ABCD neuroimaging derivatives plus utilities for access, sharing and analysis, letting researchers exchange derivatives they cannot share through the central archive.