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• W2890956950 abstract "Abstract Increasing interest is being directed to developing an objective marker that could be used for the assessment of symptom severity in Alzheimer’s disease (AD). This study assessed the utility of graph theory, an emerging topic in statistical physics, to identify the changes of brain topology in AD patients. A total of 108 AD patients were recruited and their scalp electroencephalogram (EEG) recordings were analyzed retrospectively. Weighted and undirected networks were constructed from EEG signals in different frequency bands and two fundamental measures of the whole-brain network, the average clustering coefficient (CC) and global efficiency (GE), were calculated. Meanwhile, the local structure of the network was investigated by nodal CC. We then examined the group differences of those measures and their association with cognitive assessments of AD patients. The results revealed a topological reorganization of alpha band network in AD patients. The nodal CCs from Fz and Pz electrodes seemed to be preserved in AD while those from frontal and central-parietal regions, such as F3, F4, C3, Cz, C4, P3 and P4, were affected significantly by the disease. Furthermore, significant correlations have been found between the global topological measures and the severity of AD, while the altered local structure was revealed to associate with cognitive impairment measured by the verbal fluency and digit-backward tests in AD patients. Overall, topological reorganization of the functional brain network is involved in the evolution of AD. Network measures, i.e., CC and GE, might serve as objective biomarkers for the evaluation of symptom severity in AD." @default.
• W2890956950 created "2018-09-27" @default.
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• W2890956950 date "2019-01-01" @default.
• W2890956950 modified "2023-09-30" @default.
• W2890956950 title "Topological reorganization of EEG functional network is associated with the severity and cognitive impairment in Alzheimer’s disease" @default.
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• W2890956950 doi "https://doi.org/10.1016/j.physa.2018.09.043" @default.
• W2890956950 hasPublicationYear "2019" @default.
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