Linked Life Data

19163728

Source:http://linkedlifedata.com/resource/pubmed/id/19163728

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-2-16
pubmed:abstractText
In this study we estimated time-varying cortical connectivity patterns from a group of Spinal Cord Injured (SCI) patients during the attempt to move a paralyzed limb. This data were compared with the time-varying connectivity patterns estimated in a control group during the effective execution of the movement. Connectivity was estimated from high resolution EEG recordings with the use of realistic head modelling and the linear inverse estimation of the cortical activity. Time-varying PDC was obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Such estimator is able to follow rapid changes in the connectivity between cortical areas during an experimental task. The obtained experimental evidences support the conclusion that the SCI population involved a larger cortical network than those generated by the healthy subjects during the task performance. Such network differs for the involvement of the parietal cortices, which increases in strength near to the EMG onset.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2008
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4551-4
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Study of the time-varying cortical connectivity during the attempt of a foot movement by Spinal Cord Injured patients.
pubmed:affiliation
Dep. of Human Physiology and Pharmacology of the Univ. of Rome 'La Sapienza', Dep. of Computer Science of the Univ. of Rome 'La Sapienza' and IRCCS 'Fondazione Santa Lucia', Italy. laura.astolfi@uniromal.it
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't