Enhanced subject-specific resting-state network detection and extraction with fast fMRI

Akin, Burak, Lee, Hsu-Lei, Hennig, Juergen and LeVan, Pierre (2017) Enhanced subject-specific resting-state network detection and extraction with fast fMRI. Human Brain Mapping, 38 2: 817-830. doi:10.1002/hbm.23420

Author Akin, Burak
Lee, Hsu-Lei
Hennig, Juergen
LeVan, Pierre
Title Enhanced subject-specific resting-state network detection and extraction with fast fMRI
Journal name Human Brain Mapping   Check publisher's open access policy
ISSN 1097-0193
Publication date 2017-02-01
Sub-type Article (original research)
DOI 10.1002/hbm.23420
Open Access Status Not yet assessed
Volume 38
Issue 2
Start page 817
End page 830
Total pages 14
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Language eng
Subject 2702 Anatomy
3614 Radiological and Ultrasound Technology
2741 Radiology Nuclear Medicine and imaging
2808 Neurology
2728 Clinical Neurology
Abstract Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations.
Keyword Fast fMRI
High frequency fluctuations
Resting state networks
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
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Queensland Brain Institute Publications
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Created: Mon, 26 Jun 2017, 14:25:26 EST by Kirstie Asmussen on behalf of Learning and Research Services (UQ Library)