Linked Life Data

15784426

Source:http://linkedlifedata.com/resource/pubmed/id/15784426

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2005-3-23
pubmed:abstractText
A major challenge for neuroscience is to map accurately the spatiotemporal patterns of activity of the large neuronal populations that are believed to underlie computing in the human brain. To study a specific example, we selected the mismatch negativity (MMN) brain wave (an event-related potential, ERP) because it gives an electrophysiological index of a "primitive intelligence" capable of detecting changes, even abstract ones, in a regular auditory pattern. ERPs have a temporal resolution of milliseconds but appear to result from mixed neuronal contributions whose spatial location is not fully understood. Thus, it is important to separate these sources in space and time. To tackle this problem, a two-step approach was designed combining the independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. Here we implement this approach to analyze the subsecond spatiotemporal dynamics of MMN cerebral sources using trial-by-trial experimental data. We show evidence that a cerebral computation mechanism underlies MMN. This mechanism is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, which activate at distinct time intervals and are grouped in six main statistically independent components.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1053-8119
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
471-7
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Combined ICA-LORETA analysis of mismatch negativity.
pubmed:affiliation
Neurodynamics Laboratory, Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Passeig de la Vall d'Hebron 171, 08035 Barcelona, Catalonia, Spain.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't