Abstract Background and Objectives: Recently, longitudinal studies of Alzheimer's disease
have gathered a substantial amount of neuroimaging data. New methods are needed to …

Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging
of the human brain. We employed a recently validated method for robust cross-sectional and …

Accurate prediction of Alzheimer's disease (AD) is important for the early diagnosis and
treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD …

Current models of progression in neurodegenerative diseases use neuroimaging measures
that are averaged across pre-defined regions of interest (ROIs). Such models are unable to …

Alzheimer's disease is a common and complex brain disorder that primarily affects the
elderly. Because it is progressing and has few effective therapies, it requires a thorough …

Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR)
imaging has been commonly used to assess knee joint degeneration due to its distinct …

The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result
of complex mechanisms interacting across multiple spatial and temporal scales …

Blind source separation (BSS) aims to separate latent source signals from their mixtures. For
spatially dependent signals in high-dimensional and large-scale data, such as …

Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for
accurately visualizing the change and development of the brain's subcortical structures (eg …

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that
allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis …