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Abstract #2110

Reliable Detection of Default Mode Network in Resting-State Perfusion FMRI Using PCASL 3D GRASE with Background Suppression

MAGNA25Lirong Yan1, Yong Fan2, Danny JJ Wang1

1Neurology, UCLA, Los Angeles, CA, United States; 2Institute of Automation, Chinese Academy of Sciences, Beijing, China

Resting-state functional connectivity (RSFC) analysis of ASL perfusion fMRI may improve the understanding of the biophysical mechanism of resting connectivity, and may provide a quantitative alternative to resting state BOLD fMRI. In this study, pseudo-continuous ASL (pCASL) with 3D background suppressed (BS) GRASE readout was used to detect RSFC arising from spontaneous perfusion fluctuations. By comparison with a standard resting-state BOLD fMRI scan and a pCASL GRASE scan with compromised BS, the present study shows that the default mode network can be reliably detected in resting-state perfusion image series without BOLD contamination.