Literature DB >> 21256220

fMRI of the brainstem using dual-echo EPI.

Florian Beissner1, Ralf Deichmann, Simon Baudrexel.   

Abstract

The brainstem is the part of the human brain that plays a pivotal role in the maintenance of many critical body functions. Due to the elevated level of cardiogenic noise, few fMRI studies have investigated the brainstem so far. Cardiac-gated echo-planar imaging with acquisition of two echoes per excitation (dual-echo EPI) is one method that significantly reduces cardiogenic noise and, thus, allows for fMRI measurements of the brainstem. As information on optimal preprocessing approaches for brainstem-fMRI data is still scarce, the goal of this study was to compare different combinations of normalization and smoothing procedures as implemented in standard fMRI software packages and to identify the combinations yielding optimal results for dual-echo EPI. 21 healthy subjects were measured while executing a simple motor paradigm to activate the facial and trigeminal motor nucleus in the brainstem. After motion correction and calculation of T(2)*-maps the data were preprocessed with 24 combinations of standard normalization (SPM classic, SPM unified, FSL, ABC) and smoothing procedures (pre-/post-smoothing with 3mm-, 4.5mm- and 6mm-kernel) before undergoing first- and second-level statistical analysis. Activation results were compared for first-level and second-level statistics using two anatomically defined regions of interest. Five methods were found to be sensitive for activation of both nuclei. These included FSL normalization with 3mm and 4.5mm pre-smoothing as well as 3mm post-smoothing, SPM unified normalization with 3mm pre-smoothing and ABC normalization with 4.5mm pre-smoothing. All these methods can be recommended for normalization and smoothing when analyzing fMRI data of the brainstem acquired by cardiac-gated dual-echo EPI.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21256220     DOI: 10.1016/j.neuroimage.2011.01.042

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


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