RT Journal Article
SR Electronic
T1 Applicability of the Sparse Temporal Acquisition Technique in Resting-State Brain Network Analysis
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
DO 10.3174/ajnr.A4554
A1 N. Yakunina
A1 T.S. Kim
A1 W.S. Tae
A1 S.S. Kim
A1 E.C. Nam
YR 2015
UL http://www.ajnr.org/content/early/2015/11/19/ajnr.A4554.abstract
AB BACKGROUND AND PURPOSE: The ability of sparse temporal acquisition to minimize the effect of scanner background noise is of utmost importance in auditory fMRI; however, it has considerably lower temporal efficiency and resolution than the conventional continuous acquisition method. The purpose of this study was to determine whether sparse sampling could be applied to resting-state research by comparing its results with those obtained by using continuous acquisition.MATERIALS AND METHODS: We identified resting-state networks by using independent component analysis and measured their functional connectivity strength in 14 healthy subjects who underwent two 6-minute sparse (60 volumes) and continuous (360 volumes) imaging sessions. To account for the sample size difference, an additional continuous dataset was generated by temporally matching the continuous dataset to 60 volumes of the sparse dataset.RESULTS: Consistent resting-state network maps were produced through all 3 datasets. Scanner background noise did not appear to affect the spatial constitution of the networks, whereas a larger sample size influenced it substantially. The strength of the intranetwork connectivity was similar through the 3 datasets.CONCLUSIONS: Our results indicated that continuous acquisition is a recommended technique that should be applied in most of the resting-state studies due to its superior temporal efficiency and increased statistical power. The use of sparse temporal acquisition should be restricted to very particular conditions when continuous scanner noise is unacceptable.AbbreviationsCAcontinuous acquisitionDMNdefault mode networkRSNresting-state networkSBNscanner background noiseSTAsparse temporal acquisitionICAindependent component analysis