D. Louis Collins

The paper examines the temporal relationships between white matter hyperintensities, neurodegeneration, amyloid β, and cognition using longitudinal data from Alzheimer's Disease Neuroimaging Initiative participants.

This study uses MRI to show cerebral atrophy patterns in ALS patients, linking them to clinical disability and a proposed pathological staging model.

The paper develops a random forest classifier to diagnose behavioral variant frontotemporal dementia using MRI and cognitive scores, achieving high accuracy in both training and validation cohorts.

The paper predicts Alzheimer’s dementia onset 2-7 years before clinical diagnosis using combined cognitive scores and MRI biomarkers from a single baseline visit.

This paper develops an unsupervised domain adaptation method to improve automated neuroanatomy segmentation in MRI across different domains without target domain annotations.

The paper identifies distinct morphometric network changes in aging and Alzheimer’s disease, showing additive effects of aging and AD on GM loss, with unique whole-brain pattern variability in AD.

The paper shows that combining cortical and subcortical white/gray contrast improves age prediction and reveals cognitive performance differences through residual analysis.

This study compares hippocampal volume and SNIPE score to predict dementia onset in cognitively normal individuals, finding SNIPE score has a larger effect size in distinguishing progressors.

This paper identifies a brain atrophy pattern from MRI that predicts disease progression in early Parkinson's disease better than other biomarkers.

This study uses deformation-based morphometry to quantify cerebral atrophy in bvFTD patients, showing ventricular expansion as a sensitive marker for disease progression and providing sample size estimates for clinical trials.

This study investigates the genetic overlap between obesity and neurobehavioural factors using brain imaging data from the Human Connectome Project, finding significant genetic correlations between obesity and brain morphology, cognitive function, and personality traits.

The paper introduces a deep learning method for automatic quality control of stereotaxic MRI registration, achieving higher accuracy than manual assessment.

The paper presents a deep learning method for tissue classification in infant MRI scans and applies it to the iSeg-2017 challenge data.

This study compares diffeomorphic and non-diffeomorphic methods for creating average anatomical templates and finds that diffeomorphic frameworks do not necessarily improve accuracy.

This study uses voxel-wise T2 relaxometry to analyze brain development in healthy infants and toddlers, providing maps of T2 evolution and comparing them to manual estimates.

The paper develops a defacing algorithm for MRI data to anonymize faces while preserving data integrity for subsequent processing.

This study examines the link between multiple concussions and brain atrophy in retired Canadian football players, finding greater hippocampal atrophy and poorer verbal memory performance compared to controls.

This study finds that high baseline white matter hyperintensities (WMHs) in de novo Parkinson’s disease patients are linked to greater cognitive decline and cortical thinning compared to low WMHs or controls.

The paper shows that cortical thinning in early Parkinson’s Disease correlates with connectivity to subcortical disease reservoirs, suggesting disease spread along neural networks.

This study identifies a clinical-anatomical signature of Parkinson’s Disease using partial least squares and MRI, showing that atrophy patterns predict clinical progression.

The paper creates a composite atlas of subcortical brain structures for DBS targets using multimodal MRI, histology, and structural connectivity to improve electrode placement analysis.