Timothy S. Coalson

DRUMS improves cortical surface alignment by using deep learning with multimodal supervision, outperforming existing methods in accuracy and reproducibility.

This paper introduces a surface-intrinsic framework for hippocampal unfolding that improves alignment accuracy and morphology preservation compared to volumetric methods.

This paper develops a method to compensate for cortical folding effects on cortical thickness, improving its biological interpretability and reducing variability in brain morphology studies.

This paper introduces an automated individual cortical parcellation method using a novel weakly supervised classifier, improving performance over previous models and identifying new atypical language-related brain areas.

The paper develops a processing pipeline for arterial spin labelling MRI data from the Human Connectome Project Lifespan and finds age-related differences in cerebral perfusion and transit times.

This paper introduces an automated tICA pipeline for fMRI data to remove artifacts and improve signal detection, achieving high accuracy in component classification.

QuNex provides a platform for reproducible neuroimaging analytics, integrating multi-modal tools and enabling scalable, high-throughput processing across species and datasets.

This paper generalises the HCP multimodal cortical parcellation to UK Biobank data using a geometric deep learning framework, improving detection rates across cortical areas.

This paper introduces two empirical methods to correct B1+ transmit field biases in T1w/T2w myelin maps, improving their accuracy for neuroanatomical and statistical analyses.

The paper develops a 24-channel MRI coil and adapts HCP protocols for macaques, enabling high-quality in vivo imaging and preprocessing comparable to human studies.

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

The paper describes a large-scale retinotopy dataset from the HCP and presents population receptive field analysis results from both individual subjects and group averages.

This paper introduces temporal ICA to remove global structured noise from fMRI data while preserving global neural signals, improving upon traditional global signal regression methods.

This paper compares traditional neuroimaging methods with surface-based approaches, showing traditional methods significantly degrade cortical area localization.

This paper estimates local cortical growth patterns in preterm infants using aMSM, revealing regional and temporal differences in expansion linked to folding and injury detection.

This paper introduces a new regularization method for cortical surface alignment in brain imaging, improving the robustness and biological interpretability of multi-modal data alignment.