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A probabilistic template of human mesopontine tegmental nuclei from in vivo 7T MRI.

Authors :
Bianciardi M
Strong C
Toschi N
Edlow BL
Fischl B
Brown EN
Rosen BR
Wald LL
Source :
NeuroImage [Neuroimage] 2018 Apr 15; Vol. 170, pp. 222-230. Date of Electronic Publication: 2017 May 03.
Publication Year :
2018

Abstract

Mesopontine tegmental nuclei such as the cuneiform, pedunculotegmental, oral pontine reticular, paramedian raphe and caudal linear raphe nuclei, are deep brain structures involved in arousal and motor function. Dysfunction of these nuclei is implicated in the pathogenesis of disorders of consciousness and sleep, as well as in neurodegenerative diseases. However, their localization in conventional neuroimages of living humans is difficult due to limited image sensitivity and contrast, and a stereotaxic probabilistic neuroimaging template of these nuclei in humans does not exist. We used semi-automatic segmentation of single-subject 1.1mm-isotropic 7T diffusion-fractional-anisotropy and T <subscript>2</subscript> -weighted images in healthy adults to generate an in vivo probabilistic neuroimaging structural template of these nuclei in standard stereotaxic (Montreal Neurological Institute, MNI) space. The template was validated through independent manual delineation, as well as leave-one-out validation and evaluation of nuclei volumes. This template can enable localization of five mesopontine tegmental nuclei in conventional images (e.g. 1.5T, 3T) in future studies of arousal and motor physiology (e.g. sleep, anesthesia, locomotion) and pathology (e.g. disorders of consciousness, sleep disorders, Parkinson's disease). The 7T magnetic resonance imaging procedure for single-subject delineation of these nuclei may also prove useful for future 7T studies of arousal and motor mechanisms.<br /> (Copyright © 2017 Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1095-9572
Volume :
170
Database :
MEDLINE
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
28476663
Full Text :
https://doi.org/10.1016/j.neuroimage.2017.04.070