Reduced integration and improved segregation of functional brain networks in Alzheimer's disease

Objective. Emerging evidence shows that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induce...

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Bibliographic Details
Published in:Journal of neural engineering 2018-04, Vol.15 (2), p.026023-026023
Main Authors: Kabbara, A, Eid, H, El Falou, W, Khalil, M, Wendling, F, Hassan, M
Format: Article
Language:English
Subjects:
Alzheimer's disease
brain networks
Civil Engineering
EEG signal processing
Engineering Sciences
Mechanical engineering
Mechanics
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Summary:Objective. Emerging evidence shows that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. Approach. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Main results. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients' functional brain networks and their cognitive scores. Significance. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.
ISSN:1741-2560
1741-2552
DOI:10.1088/1741-2552/aaaa76