Previous studies [1,2] have shown that slow variations in the cardiac cycle are coupled with signal changes in the blood-oxygen level dependent (BOLD) contrast. The detection of neurophysiological hemodynamic changes, driven by neuronal activity, is hampered by such physiological noise. It is therefore of great importance to model and remove these physiological artifacts. The cardiac cycle causes pulsatile arterial blood flow. This pulsation is translated into brain tissue and fluids bounded by the cranial cavity [3]. We exploit this pulsality effect in BOLD fMRI volumes to build a reliable cardio surrogate estimate. We propose a Gaussian Process (GP) heart rate model to build physiological noise regressors for the General Linear Model (GLM) used in fMRI analysis. The proposed model can also incorporate information from physiological recordings such as photoplethysmogram or electrocardiogram, and is able to learn the temporal interdependence of individual modalities.
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