Erin W. Dickie

This study benchmarks four structural MRI foundation models for age, sex, and BMI prediction, finding that 3D-Neuro-SimCLR and AnatCL outperformed baselines overall.

Nipoppy standardizes neuroimaging data workflows to enable reproducible and comparable multi-center data sharing.

The paper shows that untrained convolutional neural networks can extract features from structural MRI data that perform as well as or better than pretrained models in three tasks across three datasets.

This study examines BOLD signal variability in autism and schizophrenia, finding reduced variability in these groups compared to controls, linked to poorer cognitive and functional outcomes.

IndivSTATIS integrates individualized brain parcellations into a shared component space to compare network configurations and predict individual differences.

The paper uses fALFF to compare brain activity patterns in ASD, SSD, and TDC groups, finding shared neurobiological features and variability differences.

BOONStim uses Bayesian optimization to improve TMS targeting by integrating fMRI and TMS simulation tools, showing faster convergence and accuracy compared to grid optimization.

The paper examines individual variability in functional connectivity during rest and an emotional-processing task in schizophrenia spectrum disorders and controls, identifying predictors like diagnosis and social cognition.

The paper introduces CLEAN-V, a method for testing variance components in neuroimaging data, showing improved power for detecting test-retest reliability and heritability.

This study compares volume-based, surface-based, and personalized surface-based approaches to identify dysconnectivity patterns in schizophrenia spectrum disorders using resting-state fMRI.

The paper examines age-related variations in cortical thickness across different life stages using gene expression data from a large neuroimaging dataset.

This study identifies white matter microstructural alterations in adult OCD patients and links them to clinical features, while finding no such abnormalities in pediatric patients.

The paper analyzes cortical thickness trajectories in 17,075 individuals to show peak thickness in childhood, steep decline in early adulthood, and slower decline later, with notable exceptions and significant inter-individual variability.

The paper analyzes brain activity variability across cognitive tasks using hierarchical clustering and dimensionality reduction, finding that individual differences are best represented multidimensionally.

This study evaluates how different preprocessing pipelines affect neuroimaging analyses, showing that software, parcellation, and quality control significantly impact results.

The paper integrates brain imaging and behavioral data to identify transdiagnostic subgroups in children with ASD, ADHD, or OCD, finding stable groups based on cortical thickness and inattention symptoms.

The paper analyzes sex differences in brain structure variability across the lifespan using a large dataset, finding greater male variability in subcortical volumes, cortical surface area, and thickness in most regions.

The paper shows that different analysis workflows by 70 teams on the same fMRI dataset led to varied results, but meta-analysis found consensus in activated regions.

This study analyzes subcortical volume changes across the lifespan using MRI data from 18,605 individuals, finding age-related declines in some structures and increases in others.

The paper examines sex differences in brain structure variability across the lifespan, finding greater regional variability in males and age-by-sex interactions in variability patterns.

The paper introduces ciftify, a framework to adapt HCP surface-based analysis methods to legacy MR data using CIFTI format.

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

This study introduces PINT, a method to identify individual-specific brain network variations in ASD and TD controls, finding greater variability in ASD and reduced hypo-connected regions after PINT application.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.