Maxime Chamberland

The paper develops an automated pipeline for segmenting and parcellating brainstem and subcortical white matter pathways using diffusion MRI data.

The paper introduces a subject-specific cortical folding model that generates fiber orientations from cortical geometry, validated with fetal MRI and applied to adult data without diffusion inputs or machine learning.

Radial diffusivity asymmetry driving DTI-ALPS indices pervades white matter, persists at high b-values, and tracks crossing fibers, axonal undulation and dispersion rather than perivascular diffusion.

This study compares the orientation of white matter nerve fibers and vasculature in the human brain using diffusion MRI and susceptibility weighted imaging, finding that vasculature aligns with some but not all white matter tracts.

The paper uses MRI to show age-related changes in cortical microstructure that align with oligodendrocyte gene expression during development.

The paper applies implicit neural representations to improve diffusion MRI modeling by leveraging spatial correlations and generating continuous FODs.

This study examines age-related changes in microstructure-informed cortical networks using diffusion MRI data from 8-18 year olds, finding increased global efficiency and mean strength in specific networks.

The paper characterizes lifespan changes in white matter microstructure, macrostructure, and associated cortical morphology using large datasets, finding unique developmental and aging trajectories across brain pathways.

This study investigates the role of the fornix in sequence memory by correlating its microstructure with memory performance in healthy adults.

This paper evaluates the reproducibility of multi-compartment relaxometry for myelin water imaging across different protocols and optimizes acquisition settings to reduce scan time.

This paper develops a surface-based tractography method for short association fibres in the human brain, showing it produces longer streamlines and high cortical coverage but with limited reliability and repeatability.

This study uses ultra-strong gradient MRI to show that myelin changes, not axon density, are linked to premanifest Huntington's disease, with mutation size correlating with myelin levels.

This study evaluates variability in white matter bundle segmentation across 42 groups using the same dataset, finding protocol variability exceeds other sources of inconsistency.

This study assesses the repeatability of soma and neurite metrics in grey matter using diffusion MRI across five scans of six healthy adults, finding high reliability in key measures.

This paper introduces a method to estimate orientation-specific relaxation-diffusion features using nonparametric Monte Carlo MRI signal inversion, enabling detailed analysis of white matter microstructure.

The paper uses multimodal PCA on white matter imaging data to identify structural features linked to reading and age, finding no direct reading skill associations but age-related changes in microstructure.

This study examines how different b-value settings affect apparent fibre density estimates in diffusion MRI, finding that higher b-values improve accuracy in capturing intra-axonal signal properties and age-related microstructural changes.

This paper reduces the dimensionality of diffusion MRI measures to identify biologically interpretable components that capture age-related changes in white matter microstructure.

This paper introduces diffusion profile realignment to reduce variability in along-tract dMRI analysis by improving alignment across subjects, enhancing detection of regional changes.

This study assesses the reproducibility of manual segmentation of the pyramidal tract in diffusion MRI tractography across experts and non-experts, finding voxel-wise measures more reproducible than streamline-wise ones.

This study evaluates modern diffusion tractography methods using a standardized challenge, finding no significant improvement in anatomical accuracy despite methodological advances.

The paper evaluates tractography methods by analyzing a simulated dataset, finding that most algorithms generate many false-positive connections.