Linked Life Data

17633723

Source:http://linkedlifedata.com/resource/pubmed/id/17633723

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-7-18
pubmed:abstractText
Inferring the position of functionally active regions from a multi-subject fMRI dataset involves the comparison of the individual data and the inference of a common activity model. While voxel-based analyzes, e.g. Random Effect statistics, are widely used, they do not model each individual activation pattern. Here, we develop a new procedure that extracts structures individually and compares them at the group level. For inference about spatial locations of interest, a Dirichlet Process Mixture Model is used. Finally, inter-subject correspondences are computed with Bayesian Network models. We show the power of the technique on both simulated and real datasets and compare it with standard inference techniques.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
482-94
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
High level group analysis of FMRI data based on Dirichlet process mixture models.
pubmed:affiliation
INRIA Futurs, Neurospin, Gif-sur-Yvette cedex, France. bertrand.thirion@inria.fr
pubmed:publicationType
Journal Article, Evaluation Studies