Arterial spin labeling-based functional connectivity (ASL-FC) is an emerging method to identify synchronous brain networks from perfusion fluctuations. ASL-FC may compensate for some susceptibility-induced limitations in blood-oxygenation-level-dependent (BOLD)-FC, however ASL-FC processing strategies are only beginning to be investigated. We evaluate optimized ASL-FC pre-processing for network detection, testing the effects of six pre-processing strategies by comparing spatial and temporal features with BOLD-FC in major brain networks. Spatial smoothing, surround subtraction, and global signal regression are necessary to increase ASL-FC sensitivity. ASL-FC also allows for low frequencies to be interrogated, which contain high power but are inaccessible to common BOLD-FC analyses.
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