Eric Feczko

The paper examines whether increased salience network surface area in adolescents is specific to depression or a general marker of psychopathology, finding stronger associations with overall psychopathology than depression alone.

This study examines hippocampal subregion development in early life and links growth patterns to school-aged episodic memory performance.

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 a scalable method to estimate basal ganglia brain tissue iron using fMRI data, validated across developmental datasets and applicable to large-scale repositories.

This study compares brain network organization during resting state and executive function tasks, finding task-evoked networks show new and strengthened relationships with executive function ability in children.

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.

This study shows stimulant medications affect brain connectivity in action/motor and reward-related regions, not attention networks, and mimic the effects of sleep.

The paper introduces SHAMAN, a method to assess motion impact on brain-behavior associations in fMRI studies, finding significant motion overestimation and underestimation in various traits.

This study estimates the heritability of functional brain networks in adolescents using SNP and twin data from the ABCD study, finding low genetic contributions.

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

This study parcellates the cerebral cortex in toddlers using functional connectivity and shows high reproducibility across ages, with area boundaries more similar to adults than neonates.

This study identifies infant brain areas with adult-like functional network organization, showing less age-related variation and higher reliability, and suggests implications for using adult network models in infants.

The paper identifies unintended bias from collinearity reduction strategies in fMRI analysis and introduces a contrast-based variance inflation factor to assess their impact.

This paper introduces multi-echo acquisition and thermal denoising methods to improve precision functional MRI in adults, children, and newborns, showing increased signal-to-noise ratio and reliability.

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 introduces Baby Open Brains (BOBs), an open-source repository of manually curated infant brain segmentations to improve reproducibility in neuroimaging research.

The paper evaluates the impact of different preprocessing pipelines on functional MRI reproducibility and shows moderate inter-pipeline agreement, affecting brainwide association study consistency.

The paper tests motion-ordering and motion-ordering+resampling methods to retain minoritized youth in fMRI data analysis, improving representation and reproducibility of brain-behavior associations.

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.

The paper investigates how an individual's exposome influences functional brain network organization and cognitive functioning in youth, finding that exposome factors predict cognitive performance better than neuroimaging data.

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

This paper introduces Synth, a field map-less distortion correction method for fMRI data, showing comparable or better performance than field map-based approaches in pediatric and adult datasets.

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

The paper defines personalized functional networks in children to predict cognitive differences, finding that fronto-parietal network representation correlates with general cognition.

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

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

The paper develops a precision functional brain atlas to capture individual-specific network topography and demonstrates its utility in generating reproducible statistical maps of brain function.

This study characterizes cortical thickness development in Japanese macaques using latent growth models and identifies how maternal diet and inflammation influence regional cortical maturation patterns.