Document Detail

Fast eigenvector centrality mapping of voxel-wise connectivity in functional MRI: implementation, validation and interpretation.
MedLine Citation:
PMID:  23016836     Owner:  NLM     Status:  Publisher    
Eigenvector centrality mapping (ECM) has recently emerged as a measure to spatially characterise connectivity in functional brain imaging by attributing network properties to voxels. The main obstacle for widespread use of ECM in functional MRI (fMRI) is the cost of computing and storing the connectivity matrix. This paper presents fast ECM (fECM), an efficient algorithm to estimate voxel-wise eigenvector centralities from fMRI time series. Instead of explicitly storing the connectivity matrix, fECM computes matrix-vector products directly from the data, achieving high accelerations for computing voxel-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware. We demonstrate the validity of fECM at cluster and voxel levels, using synthetic and in vivo data. Results from synthetic data are compared to the theoretical 'gold standard', and local centrality changes in fMRI data are measured after experimental intervention. A simple scheme is presented to generate time series with prescribed covariances that represent a connectivity matrix. These time series are used to construct a 4D data set, whose volumes consist of separate regions with known intra- and interregional connectivities. The fECM method is tested and validated on these synthetic data. Resting-state fMRI data acquired after real vs. sham repetitive transcranial magnetic stimulation (rTMS) show fECM connectivity changes in resting-state network regions. A comparison of analyses with and without accounting for motion parameters demonstrates a moderate effect of these parameters on the centrality estimates. Its computational speed and statistical sensitivity make fECM a good candidate for connectivity analyses of multi-modality and high-resolution functional neuroimaging data.
Alle Meije Wink; Jan C de Munck; Ysbrand D van der Werf; Odile A van den Heuvel; Frederik Barkhof
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-9-27
Journal Detail:
Title:  Brain connectivity     Volume:  -     ISSN:  2158-0022     ISO Abbreviation:  Brain Connect     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-9-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101550313     Medline TA:  Brain Connect     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
VU University Medical Centre, Radiology, Boelelaan 1118, Amsterdam, Noord-Holland, Netherlands, 1081 HZ, +31.20.444.0316, +31.20.444.0397;
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