Linked Life Data

18990648

Source:http://linkedlifedata.com/resource/pubmed/id/18990648

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2008-11-7
pubmed:abstractText
The extraction of the salient characteristics from brain connectivity patterns is an open challenging topic since often the estimated cerebral networks have a relative large size and complex structure. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach would extract significant information from the functional brain networks estimated through different neuroimaging techniques. The present work intends to support the development of the "brain network analysis:" a mathematical tool consisting in a body of indexes based on the graph theory able to improve the comprehension of the complex interactions within the brain. In the present work, we applied for demonstrative purpose some graph indexes to the time-varying networks estimated from a set of high-resolution EEG data in a group of healthy subjects during the performance of a motor task. The comparison with a random benchmark allowed extracting the significant properties of the estimated networks in the representative Alpha (7-12 Hz) band. Altogether, our findings aim at proving how the brain network analysis could reveal important information about the time-frequency dynamics of the functional cortical networks.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1558-0210
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
442-52
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Brain network analysis from high-resolution EEG recordings by the application of theoretical graph indexes.
pubmed:affiliation
IRCCS Fondazione Santa Lucia, 00179 Rome, Italy. fabrizio.devicofallani@uniroma1.it
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't