Linked Life Data

15206009

Source:http://linkedlifedata.com/resource/pubmed/id/15206009

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2004-6-18
pubmed:abstractText
Biological research about dyslexia has been conducted using various neuroimaging methods like functional Magnetic Resonance Imaging (fMRI) or Electroencephalography (EEG). Since language functions are characterized by both distributed network activities and speed of processing within milliseconds, high temporal as well as high spatial resolution of activation profiles are of interest: "where" can dyslexia specific activations be detected and "when" do language processes start to diverge between dyslexics and controls? Due to the network character of language processing, fMRI-constrained distributed source models based on EEG-data were computed for multimodal data integration. First single-case results show that this method could be a promising approach for the understanding of a repeatedly described experimental finding for dyslexia like that of an overactivation in inferior frontal language areas. Multimodal data analysis for the subjects presented here could probably demonstrate that inferior frontal overactivations are the consequence of a phonological deficit and could represent ongoing articulation processes used to solve phonologically challenging tasks.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0300-9564
pubmed:author
pubmed:issnType
Print
pubmed:volume
111
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
951-69
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Dyslexia: the possible benefit of multimodal integration of fMRI- and EEG-data.
pubmed:affiliation
Department of Child and Adolescent Psychiatry, University of Jena, Germany. Carolin.Gruenling@med.uni-jena.de
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't