Gregory Kiar

The paper examines sex differences in functional brain connectivity across youth, finding that males show greater network integration while females show greater network segregation along the sensorimotor-association axis.

Resting-state fMRI intrinsic timescale lengthens with age in association, not sensorimotor, cortex during ages 8-22, following the sensorimotor-association axis in two cohorts; it is largely age-stable in young adults.

The paper develops a deep-learning model to translate spatial topography maps into individualized connectomes, confirming its accuracy and ability to retain brain-cognition associations.

This study links childhood environmental exposures to white matter structure in children, showing that advantaged environments correlate with larger white matter tracts and reduced orientation dispersion.

The paper evaluates numerical variability in functional MRI graph measures using the fMRIPrep pipeline and finds measurable impact on network outcomes.

This study identifies two axes of white matter development in human cortico-cortical tracts using diffusion MRI across three datasets, revealing non-uniform maturation patterns.

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.

Styx and NiWrap provide language-native wrappers for command-line tools, reducing complexity in pipeline development and enhancing portability across programming languages and environments.

This paper introduces causal methods to detect batch effects in multi-site data, showing they provide more accurate or 'no answer' results compared to non-causal methods.

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

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.

The paper introduces a framework to quantify and optimize measurement reliability for studying individual differences in neuroscience and clinical research.

This study evaluates the reliability of dMRI-based tractometry by assessing reproducibility, test-retest reliability, and robustness using software and datasets from the Human Connectome Project and clinical-grade measurements.

This paper investigates how numerical uncertainty in brain imaging pipelines affects connectome stability and shows that induced variance can improve network reliability and classification of individual differences.

This paper introduces a low-resource pipeline for estimating connectomes from MRI data, demonstrating its reliability across diverse datasets without manual intervention.

The paper uses Monte Carlo Arithmetic to perturb structural connectome pipelines, creating augmented datasets that improve age classification performance across multiple classifiers and preprocessing strategies.

This paper introduces a new statistic called discriminability to improve replicability by distinguishing accidental from systematic deviations in data, showing its effectiveness in both simulated and real datasets.

The paper develops a pipeline to estimate connectomes from neuroimaging data, showing high consistency within individuals but significant variability across studies and sites.

This paper introduces BIDS Apps, a framework for creating portable neuroimaging analysis tools compatible with BIDS data, improving reproducibility and ease of use.