Intense debate in the neurology community before 2010 culminated in hypothetical models
of Alzheimer's disease progression: a pathophysiological cascade of biomarkers, each …

The rate of progression of Alzheimer's disease (AD) differs dramatically between patients.
Identifying the most is critical because when their numbers differ between treated and …

Data-driven disease progression models have provided important insight into the timeline of
brain changes in AD phenotypes. However, their utility in predicting the progression of pre …

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied
biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical …

Alzheimer's disease causes neural damage, including brain atrophy in the patient.
Consequently, ventricles that contain cerebral fluid a re e xpanded to filling th oseregions …

Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted
neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or …

Sequencing the regional progression of neurodegeneration in Alzheimer's disease (AD)
informs disease mechanisms and facilitates identification and staging of individuals at …

Computer-aided methods have shown added value for diagnosing and predicting brain
disorders and can thus support decision making in clinical care and treatment planning. This …

Data-driven Alzheimer's disease (AD) progression models are useful for clinical prediction,
disease mechanism understanding, and clinical trial design. Most dynamic models were …

Progressive diseases worsen over time and are characterised by monotonic change in
features that track disease progression. Here we connect ideas from two formerly separate …