Linked Life Data

16929548

Source:http://linkedlifedata.com/resource/pubmed/id/16929548

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2006-11-27
pubmed:abstractText
Structural equation modeling (SEM) is a statistical method that can assess changes in effective connectivity across tasks or between groups. In its initial application to neuroimaging data, anatomical connectivity provided the constraints to decompose interregional covariances to estimate effective connections. There have been concerns expressed, however, with the validity of interpreting effective connections for a model that does not adequately fit the data. We sought to address this concern by creating two population networks with different patterns of effective connectivity, extracting three samples sizes (N = 100, 60, 20), and then assessing whether the ability to detect effective connectivity differences depended on absolute model fit. Four scenarios were assessed: (1) elimination of a region showing no task differences; (2) elimination of connections with no task differences; (3) elimination of connections that carried task differences, but could be expressed through alternative indirect routes; (4) elimination of connections that carried task differences, and could not be expressed through indirect routes. We were able to detect task differences in all four cases, despite poor absolute model fit. In scenario 3, total effects captured the overall task differences even though the direct effect was no longer present. In scenario 4, task differences that were included in the model remained, but the missing effect was not expressed. In conclusion, it seems that when independent information (e.g., anatomical connectivity) is used to define the causal structure in SEM, inferences about task- or group-dependent changes are valid regardless of absolute model fit.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1065-9471
pubmed:author
pubmed:copyrightInfo
(c) 2006 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:volume
27
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
935-47
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Testing effective connectivity changes with structural equation modeling: what does a bad model tell us?
pubmed:affiliation
Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Ontario, Canada.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't