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Abstract #4183

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

1 Medical Physics, University Medical Center, Freiburg, Germany

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 inter-network connectivity.

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