Linked Life Data

18499266

Source:http://linkedlifedata.com/resource/pubmed/id/18499266

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2008-6-3
pubmed:abstractText
Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called "mass-univariate" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM's power to detect discriminative voxels.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0165-0270
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
172
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
94-104
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
The impact of functional connectivity changes on support vector machines mapping of fMRI data.
pubmed:affiliation
Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, Brazil. jrsatobr@gmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't