Evolution characterization of alzheimer’s disease using eLORETA’s three-dimensional distribution of the current density and small-world network

Alzheimer’s disease (AD) is the most common neurodegenerative disorder characterized by cognitive and intellectual deficits and behavior disturbance. The electroencephalogram (EEG) has been used as a tool for diagnosing AD for several decades. In the pre-clinical stage of AD, no reliable and valid symptoms are detected to allow a very early diagnosis. There are four different stages associated with AD. The first stage is known as Mild Cognitive Impairment (MCI), and corresponds to a variety of symptoms which do not significantly alter daily life. In the mild stage, an impairment of learning and memory is usually notable. The next stages (Mild and Moderate AD) are characterized by increasing cognitive deficits and decreasing independence, culminating in the patient’s complete dependence on caregivers and a complete deterioration of personality (Severe AD). In this paper, we propose the study of the evolution of Alzheimer’s disease using eLORETA’s three-dimensional distribution of the current density and Small-world network. Our goal is to see the changes of MCI patients’ EEG (called EEG T0) after three months (EEG T1). The results show that small-world is a valid technique to see the temporal evolution of the disease.