Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians

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Etat: Public
Version: de l'auteur⸱e
ID Serval
serval:BIB_71BA4BF71B47
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians
Périodique
NeuroImage: Clinical
Auteur⸱e⸱s
Lambert Christian, Lutti Antoine, Helms Gunther, Frackowiak Richard, Ashburner John
ISSN
2213-1582 (Print)
Statut éditorial
Publié
Date de publication
2013
Volume
2
Pages
684-694
Langue
anglais
Résumé
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
Oui
Création de la notice
11/09/2013 10:16
Dernière modification de la notice
20/08/2019 15:30
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