Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …

Alzheimer's disease (AD) is considered as one of the leading causes of death among
people over the age of 70 that is characterized by memory degradation and language …

Objectives Glymphatic function has not yet been explored in behavioral variant
frontotemporal dementia (bvFTD). The spatial correlation between regional glymphatic …

Computerized clinical decision support systems can help to provide objective, standardized,
and timely dementia diagnosis. However, current computerized systems are mainly based …

Graphical, voxel, and region-based analysis has become a popular approach to studying
neurodegenerative disorders such as Alzheimer's disease (AD) and its prodromal stage …

Neuroimaging-based brain-age estimation via machine learning has emerged as an
important new approach for studying brain aging. The difference between one's estimated …

High-frequency oscillations of the frontal cortex are involved in functions of the brain that
fuse processed data from different sensory modules or bind them with elements stored in the …

Diffusion MRI is the modality of choice to study alterations of white matter. In past years,
various works have used diffusion MRI for automatic classification of Alzheimer's disease …

Positron emission tomography (PET) neuroimaging provides unique possibilities to study
biological processes in vivo under basal and interventional conditions. For quantification of …

Early diagnosis of Alzheimer's helps a doctor to decide the treatment for the patient based
on the stages. The existing methods involve applying the deep learning methods for …