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Abstract #0679

Cardiorespiratory noise correction improves the ASL signal

Mahlega S Hassanpour1, Qingfei Luo1, W. Kyle Simmons1,2, Justin S Feinstein1,2, Martin Paulus1, Wenming Luh3, Jerzy Bodurka1,4, and Sahib S Khalsa1,2

1Laureate Institute for Brain Research, Tulsa, OK, United States, 2Oxley College of Health Sciences, University of Tulsa, 3Cornell MRI Facility, Cornell University, 4Stephenson School of Biomedical Engineering, University of Oklahoma

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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