Linked Life Data

19188113

Source:http://linkedlifedata.com/resource/pubmed/id/19188113

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2009-2-3
pubmed:abstractText
A novel approach is proposed for quantitatively characterizing the spatial patterns of activation statistics in functional magnetic resonance imaging (fMRI) activation maps. Specifically, we propose using 3-D invariant moment descriptors, as opposed to the traditionally-employed magnitude-based features such as mean voxel statistics or percentage of activated voxels, to characterize the task-specific spatial distribution of activation statistics within a given region of interest (ROI). The proposed method is applied to real fMRI data collected from 21 healthy subjects performing previously-learned right-handed finger tapping sequences that are either externally guided (EG) by a cue or internally guided (IG)--tasks expected to incur subtle differences in motor-related cortical and subcortical ROIs. Voxel-based activation statistics contrasting EG versus rest and IG versus rest are examined in multiple manually-drawn ROIs on unwarped brain images. Analyzing the activation statistics within each ROI using the proposed 3-D invariant moment descriptors detected significant group differences between the two tasks, thus quantitatively demonstrating that the spatial distribution of activation statistics within an ROI represent an important task-related attribute of brain activation. In contrast, conventional methods that solely rely on activation statistic magnitudes and disregard spatial information showed reduced discriminability. Normally, incorporating spatial information would merely increase inter-subject variability partly due to differences in brain size and subject's orientation in the scanner. Yet, our results suggest that the proposed spatial features, which are invariant to similarity transformations, can effectively account for such inter-subject variability, while enhancing the sensitivity in detecting task-specific activation. Thus, we argue that this novel quantitative description of the "3-D texture" of activation maps provides new directions to explore for ROI-based fMRI analysis.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1558-0062
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
261-8
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Spatial characterization of FMRI activation maps using invariant 3-D moment descriptors.
pubmed:affiliation
Biomedical Signal and Image Computing Laboratory (BiSICL), The University of British Columbia, Vancouver, BC, V6T1Z4 Canada. bernardn@ece.ubc.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't