Difference between revisions of "Whether functional brain networks in Alzheimer's Disease(AD) are characterized by a loss of small-world features"
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| − | Cognitive dysfunction in Alzheimer's disease(AD) could be due, at least in part, to a functional disconnection between distant brain areas. Correlation between signals of brain activity reflects functional interactions between different brain areas. An approach to the characterization of a network as complex as human brain is to apply graph theory. Graphs with many local connections and a few random long distance connections are characterized by a high cluster coefficient and a short path length; such near-optimal networks are designated as ‘‘small-world’’ networks. | + | Cognitive dysfunction in Alzheimer's disease(AD) could be due, at least in part, to a functional disconnection between distant brain areas. Correlation between signals of brain activity reflects functional interactions between different brain areas. An approach to the characterization of a network as complex as human brain is to apply graph theory. Graphs with many local connections and a few random long distance connections are characterized by a high cluster coefficient and a short path length; such near-optimal networks are designated as ‘‘small-world’’ networks. many types of real networks have been shown to have small-world features. |
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| + | In this paper, the authors intend to address the question whether functional brain networks in AD are characterized by a loss of small-world features. | ||
Latest revision as of 20:43, 29 March 2011
Cognitive dysfunction in Alzheimer's disease(AD) could be due, at least in part, to a functional disconnection between distant brain areas. Correlation between signals of brain activity reflects functional interactions between different brain areas. An approach to the characterization of a network as complex as human brain is to apply graph theory. Graphs with many local connections and a few random long distance connections are characterized by a high cluster coefficient and a short path length; such near-optimal networks are designated as ‘‘small-world’’ networks. many types of real networks have been shown to have small-world features.
In this paper, the authors intend to address the question whether functional brain networks in AD are characterized by a loss of small-world features.
