Conor Owens‐Walton

This paper evaluates automated white matter lesion segmentation methods in Parkinson's disease and healthy controls, finding U-Net-pgs to be the most accurate and reliable approach across multiple metrics and datasets.

This study evaluates deep learning models that predict multiple dementia-related neuropathologies using MRI, clinical, and genetic data, showing the potential of multimodal AI for non-invasive profiling.

This study evaluates parameter-efficient fine-tuning methods for vision transformers in neuroimaging, showing they outperform full fine-tuning and training from scratch with fewer parameters.

This study identifies subregional cerebellar atrophy patterns in epilepsy syndromes using MRI data from 1,602 patients and 1,022 controls, finding significant volume loss in posterior lobe regions.

This study compares anatomical and diffusion MRI for Parkinson's disease detection using deep convolutional neural networks, finding that dMRI-based models show promise for classification.

This paper uses a ConvVAE to reduce the dimensionality of white matter tractography data, identifies 6 tracts with significant differences between Alzheimer's patients and controls, and preserves distances in a 6-dimensional space.

FiberNeat filters false positive and implausible streamlines from tractograms using unsupervised methods, improving tract quality and tractometry.

This paper proposes a 3D CNN for classifying Alzheimer's and Parkinson's disease using T1-weighted MRI, achieving higher ROC-AUC than random forest on multiple datasets.