Susan A. Gauthier

This study examines age- and sex-related changes in glymphatic function and meningeal lymphatic vessels (mLVs) and their association with processing speed in humans.

The paper uses TSPO-PET to show higher inflammation in white matter disrupted by paramagnetic rim lesions in MS patients, linked to disability.

This study uses estimated structural and functional connectivity networks from MRI and AI to predict cognition in multiple sclerosis patients, finding strong predictive accuracy for follow-up scores.

The paper uses multi-compartment T2 relaxometry to show that parenchymal cerebrospinal fluid fraction increases quadratically with age in brain tissue.

The paper identifies early structural disconnection biomarkers in multiple sclerosis linked to disability and cognitive impairment progression.

This study examines how brain lesion volume in multiple sclerosis patients correlates with changes in brain activity dynamics and entropy.

QSMRim-Net is a deep learning model that identifies chronic active multiple sclerosis lesions on QSM images by addressing class imbalance, achieving high accuracy and correlation with expert annotations.

The paper uses a three-compartment T2 relaxometry model to show that CSFF increases with age in non-active MS patients, indicating higher perivascular space load in cerebral cortical regions.

This paper compares models using observed and estimated connectivity networks to classify multiple sclerosis patients into disability groups, finding estimated networks perform better.