Ultra-fast fMRI using MREG improves subject specific extraction of Resting State Networks
Burak Akin 1 , Hsu-Lei Lee 1 , Nadine Beck 1 , Jrgen Hennig 1 , and Pierre Levan 1
Medical Physics, University Medical Center,
Resting-state networks (RSN) are becoming an important
tool for the study of brain function. The advent of
novel fast fMRI sequences has led to improved
sensitivity in the statistical analysis of fMRI data. In
this study an ultra-fast imaging technique called
MR-encephalography (MREG) is compared with standard EPI.
RSN analysis is assessed for both datasets using ICA.
The 25-fold increase in sampling rate of MREG relative
to conventional fMRI resulted in improved sensitivity
and a higher number of components associated with
standard RSN in individual subjects. Compared to EPI,
MREG might thus greatly improve analyses of intra- and
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