Journal ArticleDOI
Directed Functional Networks in Alzheimer's Disease: Disruption of Global and Local Connectivity Measures
Saeedeh Afshari,Mahdi Jalili +1 more
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In this paper, the authors studied EEG-based directed functional networks in Alzheimer's disease (AD) and found that functional networks of AD brains have significantly reduced global connectivity in alpha and beta bands (P < 0.05).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.read more
Citations
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Journal ArticleDOI
Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?
TL;DR: Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods, which might explain contradictory results reported in the literature for network properties specific to AD symptoms.
Journal ArticleDOI
Reduced integration and improved segregation of functional brain networks in Alzheimer's disease.
TL;DR: Electroencephalography data recorded during resting state revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local informationprocessing (segregation) in terms of brain network segregation and integration.
Journal ArticleDOI
Human brain connectivity: Clinical applications for clinical neurophysiology.
Mark Hallett,Willem de Haan,Gustavo Deco,Reinhard Dengler,Riccardo Di Iorio,Cecile Gallea,Christian Gerloff,Christian Grefkes,Rick C. Helmich,Morten L. Kringelbach,Francesca Miraglia,Ivan Rektor,Ondřej Strýček,Fabrizio Vecchio,Lukas J. Volz,Tao Wu,Paolo Maria Rossini +16 more
TL;DR: This state-of-the-art review makes clear the value of networks and brain models for understanding symptoms and signs of disease and can serve as a foundation for further work.
Journal ArticleDOI
Graph theoretical analysis of Alzheimer's disease
TL;DR: This work considers resting-state electroencephalography signals recorded from healthy subjects and patients suffering from Alzheimer's disease in two conditions: eyes-open and eyes-closed to use the network metrics as features for discriminating AD from healthy controls.
Journal ArticleDOI
Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis.
S.J.T. van Montfort,E. van Dellen,Cornelis J. Stam,A.H. Ahmad,L.J. Mentink,C.W. Kraan,Andrew Zalesky,Arjen J. C. Slooter +7 more
TL;DR: Lower structural connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.
References
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Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
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Investigating causal relations by econometric models and cross-spectral methods
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