Editorial: Quantitative imaging methods and analysis in Alzheimer’s disease assessment

Abstract

Alzheimer’s disease (AD) is a complex condition gradually eroding memory and cognitive abilities. While cognitive tests aid diagnosis, their limitations include an inability to detect subtle early-stage changes, subjective interpretation influenced by various factors, and inconsistent predictive accuracy. Imaging techniques play a pivotal role in AD detection by revealing structural and functional brain changes associated with the disease. Quantitative MRI highlights brain atrophy, ruling out other causes, while PET scans visualize hallmark proteins like beta-amyloid and tau. Cerebrospinal fluid (CSF) analysis assesses biomarkers linked to AD, notably Aβ42 and tau proteins. The APOE gene variants, specifically ε4, influence AD susceptibility. EEG, measuring brain electrical activity, offers supplementary information but isn’t a primary diagnostic tool.

The goal of this Research Topic was to investigate the impact of quantitative imaging in the assessment of AD by using structural MRI or molecular neuroimaging such as PET as results from cognitive tests are not sufficient to accurately and reliably make the diagnosis of AD. Also, it evaluated the performance of deep learning methods in the classification and prediction of AD. Early detection is crucial for effective intervention and treatment.