Alzheimerâ??s Disease (AD) is a neurological disorder characterized by a progressive\ndeterioration of brain functions that affects, above all, older adults. It can be difficult to make an early\ndiagnosis because its first symptoms are often associated with normal aging. Electroencephalography\n(EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose\nof this paper is to study the brain network parameters through the estimation of Lagged Linear\nConnectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for\n84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT),\n21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to\ncompare the results obtained using a 256-channel EEG, the corresponding 10-10 system 64-channel\nEEG and the corresponding 10-20 system 18-channel EEG, both of which are extracted from the\n256-electrode configuration. The computation of the Characteristic Path Length, the Clustering\nCoefficient, and the Connection Density from HD-EEG configuration reveals a weakening of smallworld\nproperties of MCI and AD patients in comparison to healthy subjects. On the contrary, the\nvariation of the network parameters was not detected correctly when we employed the standard\n10-20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the\nAD brain network.
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