Alzheimer's disease and models of computation: Imaging, classification, and neural models

Abstract

Prediction or early-stage diagnosis of Alzheimer's disease (AD) requires a comprehensive understanding of the underlying mechanisms of the disease and its progression. Researchers in this area have approached the problem from multiple directions by attempting to develop (a) neurological (neurobiological and neurochemical) models, (b) analytical models for anatomical and functional brain images, (c) analytical feature extraction models for electroencephalograms (EEGs), (d) classification models for positive identification of AD, and (e) neural models of memory and memory impairment in AD. This article presents a state-of-the-art review of research performed on computational modeling of AD and its markers. The review covers the following approaches: computer imaging, classification models, connectionist neural models, and biophysical neural models. It is concluded that a mixture of markers and a combination of novel computational techniques such as neural computing, chaos theory, and wavelets can increase the accuracy of algorithms for automated detection and diagnosis of AD.