title: Enhanced subject-specific resting-state network detection and extraction with fast fMRI.
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. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.
authors.forename: Burak
authors.initials: B
authors.lastname: Akin
authors.affiliation: Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.
authors.forename: Hsu-Lei
authors.initials: HL
authors.lastname: Lee
authors.affiliation: Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Hennig
authors.affiliation: Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.
authors.forename: Pierre
authors.initials: P
authors.lastname: LeVan
mesh_terms: D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging* / Q000502:physiology*; D001931:Brain Mapping; D017352:Echo-Planar Imaging; D004569:Electroencephalography; D056344:Executive Function / Q000502:physiology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000502:physiology*; D010100:Oxygen / Q000097:blood; D025341:Principal Component Analysis; D012146:Rest; D055815:Young Adult
chemical_list: D010100:Oxygen
keywords: ICA; MREG; fast fMRI; high frequency fluctuations; resting state networks