Tracking dynamic resting-state networks at higher frequencies using MR-encephalography

Lee, Hsu-Lei, Zahneisen, Benjamin, Hugger, Thimo, LeVan, Pierre and Hennig, Juergen (2013) Tracking dynamic resting-state networks at higher frequencies using MR-encephalography. Neuroimage, 65 216-222. doi:10.1016/j.neuroimage.2012.10.015

Author Lee, Hsu-Lei
Zahneisen, Benjamin
Hugger, Thimo
LeVan, Pierre
Hennig, Juergen
Title Tracking dynamic resting-state networks at higher frequencies using MR-encephalography
Journal name Neuroimage   Check publisher's open access policy
ISSN 1053-8119
Publication date 2013-01-15
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2012.10.015
Open Access Status Not yet assessed
Volume 65
Start page 216
End page 222
Total pages 7
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract Current resting-state network analysis often looks for coherent spontaneous BOLD signal fluctuations at frequencies below 0.1 Hz in a multiple-minutes scan. However hemodynamic signal variation can occur at a faster rate, causing changes in functional connectivity at a smaller time scale. In this study we proposed to use MREG technique to increase the temporal resolution of resting-state fMRI. A three-dimensional single-shot concentric shells trajectory was used instead of conventional EPI, with a TR of 100 ms and a nominal spatial resolution of 4 × 4 × 4 mm3. With this high sampling rate we were able to resolve frequency components up to 5 Hz, which prevents major physiological noises from aliasing with the BOLD signal of interest. We used a sliding-window method on signal components at different frequency bands, to look at the non-stationary connectivity maps over the course of each scan session. The aim of the study paradigm was to specifically observe visual and motor resting-state networks. Preliminary results have found corresponding networks at frequencies above 0.1 Hz. These networks at higher frequencies showed better stability in both spatial and temporal dimensions from the sliding-window analysis of the time series, which suggests the potential of using high temporal resolution MREG sequences to track dynamic resting-state networks at sub-minute time scale.
Keyword Functional connectivity
Resting-state networks
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 232908
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Brain Institute Publications
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