Directed functional networks in Alzheimer's disease: disruption of global and local connectivity measures

Afshari, S ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1109/JBHI.2016.2578954
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2017
  4. Abstract:
  5. 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. © 2013 IEEE
  6. Keywords:
  7. Alzheimer's disease (AD) ; Directed connectivity ; EEG ; Global and local efficiency ; Directed graphs ; Electroencephalography ; Neurodegenerative diseases ; Signal analysis ; Time series analysis ; Alzheimer's disease ; Functional relation ; Global connectivity ; In-network analysis ; Local connectivity ; Local efficiencies ; Long-range connection ; Graph theory ; Article ; Cell synchronization ; Clinical article ; Cluster analysis ; Connectome ; Controlled study ; Functional connectivity ; Human ; Mathematical parameters ; Modulation transfer function ; Alzheimer disease ; Brain mapping ; Diagnostic imaging ; Image processing ; Procedures ; Signal processing ; Theoretical model ; Very elderly ; Aged ; Aged, 80 and over ; Brain ; Female ; Humans ; Image Processing, Computer-Assisted ; Male ; Middle Aged ; Models, Theoretical ; Signal Processing, Computer-Assisted
  8. Source: IEEE Journal of Biomedical and Health Informatics ; Volume 21, Issue 4 , 2017 , Pages 949-955 ; 21682194 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/7488219