The use of ASL fMRI to study brain function is constrained by its low signal-to-noise ratio and large temporal signal variations. We evaluated the influence of cardiorespiratory activity on the amount of variance in resting state and task based ASL data via several different physiological noise models. We further tested the utility of physiological noise correction approaches by pharmacologically inducing cardiorespiratory fluctuations and evaluating for improvements in the ASL signal. We found that regressing out these non-neuronal, cardiorespiratory-related signal variations substantially improved the ASL signal, offering an important advance for quantitative studies of cognitive processes.
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