# An eLORETA Longitudinal Analysis of Resting State EEG Rhythms in Alzheimer's Disease
## 1 Introduction  
Alzheimer's disease (AD) is a brain disorder causing a progressive decline in cognitive functions. The diagnosis of the disease is based, initially, on the evaluation of neuropsychological and behavioral symptoms related to disorders of memory, language, and visual-spatial perception [1]. There are four stages which characterize the evolution of the disease: In the first stage, called Mild Cognitive Impairment (MCI), the subject shows short-term memory loss and difficulty in remembering even words of daily use. These symptoms, however, do not substantially affect everyday life. This condition is not always irreversible because not all MCI subjects convert to AD--it happens for 50-60% of them. During the next two stages (Mild and Moderate AD) the patient shows difficulty in recognizing relatives and friends, and suffers from spatial and temporal disorientation. Moreover, the first behavioral changes appear, such as anxiety, insomnia, and personality changes. During the last stage--Severe AD--the patient no longer recognizes anyone; does not understand words; and has difficulty in dressing, eating, and swallowing. The subject is completely dependent on caregivers. Death occurs because of the further worsening of these symptoms [2; 3; 4]. To date, there is no cure for [PERSON]'s disease, but several medications are used for delaying the onset of the symptoms and improving the patient's quality of life. Therefore, early diagnosis of AD is very important because themedications are effective only if taken during the first stage. However, the diagnosis is hard because the first symptoms are often mistaken for normal aging [5]. During the last decades, neuroimaging methods--such as magnetic resonance imaging (MRI) and positron emission tomography (PET)--have played an even more important role in diagnosing AD [6; 7; 8]. Nevertheless, most of them are expensive and rather invasive, thus the need of a tool which overcomes these drawbacks. Electroencephalography (EEG) represents a valid technique to detect any pathological alterations in the brain [9; 10]. EEG is a low cost and noninvasive instrument, which records the brain electrical activity by means of electrodes distributed on the scalp. In particular, the signal is produced by the sum of postsynaptic potentials generated simultaneously by population of cortical pyramidal neurons. The EEG signal derives from the contribution of all active sources because of the conductivity of the scalp, the skull and the brain. Therefore, the choice of a realistic head model is essential for a correct EEG analysis [11]. EEG is characterized by an excellent temporal resolution, on the order of milliseconds; conversely, the spatial resolution is poor. EEG has been successfully employed for investigating AD [12; 13; 14; 15; 16]. Several studies have proven that the EEG frequency bands are affected by AD in a specific way. In particular, the spectral analysis shows a \"slowing\" of EEG signals in MCI and AD patients: the power increases in low frequencies (delta and theta) and decreases in higher frequency (alpha and beta), compared to normal elderly (Nold) [17; 18; 19; 20; 21; 22]. Moreover, the EEG signals of AD patients are characterized by less complexity and a reduction of synchronization between different areas of the brain [5; 23; 24]. The current flows generated by neurons can be modeled with dipoles. The localization of cortical sources which produce the EEG rhythms requires the solution of the so-called inverse problem. It is an ill-posed problem which has infinite possible solutions, because the number of the known parameters (electrodes) is lower than the number of the unknown parameters (current density of the sources). Therefore, the solutions are regularized using mathematical and neuroanatomical constraints, in order to choose the most likely solution. One of the most employed algorithm for solving the EEG inverse problem is Low-Resolution Electromagnetic Tomography (LORETA) [25].