Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians

Details

Ressource 1Download: BIB_71BA4BF71B47.P001.pdf (1591.03 [Ko])
State: Public
Version: author
Serval ID
serval:BIB_71BA4BF71B47
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians
Journal
NeuroImage: Clinical
Author(s)
Lambert Christian, Lutti Antoine, Helms Gunther, Frackowiak Richard, Ashburner John
ISSN
2213-1582 (Print)
Publication state
Published
Issued date
2013
Volume
2
Pages
684-694
Language
english
Abstract
The human brainstem is a densely packed, complex but highly organised structure. It not only serves as a conduit for long projecting axons conveying motor and sensory information, but also is the location of multiple primary nuclei that control or modulate a vast array of functions, including homeostasis, consciousness, locomotion, and reflexive and emotive behaviours. Despite its importance, both in understanding normal brain function as well as neurodegenerative processes, it remains a sparsely studied structure in the neuroimaging literature. In part, this is due to the difficulties in imaging the internal architecture of the brainstem in vivo in a reliable and repeatable fashion.
A modified multivariate mixture of Gaussians (mmMoG) was applied to the problem of multichannel tissue segmentation. By using quantitative magnetisation transfer and proton density maps acquired at 3 T with 0.8 mm isotropic resolution, tissue probability maps for four distinct tissue classes within the human brainstem were created. These were compared against an ex vivo fixated human brain, imaged at 0.5 mm, with excellent anatomical correspondence. These probability maps were used within SPM8 to create accurate individual subject segmentations, which were then used for further quantitative analysis. As an example, brainstem asymmetries were assessed across 34 right-handed individuals using voxel based morphometry (VBM) and tensor based morphometry (TBM), demonstrating highly significant differences within localised regions that corresponded to motor and vocalisation networks. This method may have important implications for future research into MRI biomarkers of pre-clinical neurodegenerative diseases such as Parkinson's disease.
Web of science
Open Access
Yes
Create date
11/09/2013 9:16
Last modification date
20/08/2019 14:30
Usage data