Linked Life Data

21569856

Source:http://linkedlifedata.com/resource/pubmed/id/21569856

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2011-6-13
pubmed:abstractText
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1095-9572
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
423-30
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Biological parametric mapping with robust and non-parametric statistics.
pubmed:affiliation
Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural, Research Support, N.I.H., Intramural