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• W2412178966 startingPage "949" @default.
• W2412178966 abstract "Techniques available in graph theory can be applied to signals recorded from human brain. In network analysis of EEG signals, the individual nodes are EEG sensor locations and the edges correspond to functional relations between them that are extracted from EEG time series. In this paper, we study EEG-based directed functional networks in Alzheimer's disease (AD). To this end, directed connectivity matrices of 25 AD patients and 26 healthy subjects are processed and a number of meaningful graph theory metrics are studied. Our data show that functional networks of AD brains have significantly reduced global connectivity in alpha and beta bands (P <; 0.05). The AD brains have significantly higher local connectivity than healthy controls in alpha and beta bands. This decreased profile in global connectivity can be linked to compensatory increased local connectivity as a result of wide-spread decline in the long-range connections. We also study resiliency of brain networks against targeted attack to hub nodes and find that AD networks are less resilient than healthy brains in alpha and beta bands." @default.
• W2412178966 created "2016-06-24" @default.
• W2412178966 creator A5029027705 @default.
• W2412178966 creator A5030903468 @default.
• W2412178966 date "2017-07-01" @default.
• W2412178966 modified "2023-10-06" @default.
• W2412178966 title "Directed Functional Networks in Alzheimer's Disease: Disruption of Global and Local Connectivity Measures" @default.
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• W2412178966 doi "https://doi.org/10.1109/jbhi.2016.2578954" @default.
• W2412178966 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27305688" @default.
• W2412178966 hasPublicationYear "2017" @default.
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