Data-driven disease progression models are an emerging set of computational tools that
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …

Neurodegenerative diseases of the brain pose a major and increasing global health
challenge, with only limited progress made in developing effective therapies over the last …

Higher educational attainment is observationally associated with lower risk of Alzheimer's
disease. However, the biological mechanisms underpinning this association remain unclear …

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development
of statistical models that relate changes in biomarkers with neurodegeneration and …

The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of
secondary prevention measures thought to modify the disease trajectory. However, it is …

Despite the acceleration of knowledge and data accumulation in neuroscience over the last
years, the highly prevalent neurodegenerative disease of AD remains a growing problem …

Disease progression models are crucial to understanding degenerative diseases. Mixed-
effects models have been consistently used to model clinical assessments or biomarkers …

Variability in neurodegenerative disease progression poses great challenges for the
evaluation of potential treatments. Identifying the persons who will experience significant …

Studying small effects or subtle neuroanatomical variation requires large-scale sample size
data. As a result, combining neuroimaging data from multiple datasets is necessary …

Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve
Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging …