Modeling the Hemodynamic Response Function for Prediction Errors in the Ventral Striatum

To make sensible inferences about neural activation from fMRI data it is important to accurately model the hemodynamic response function (HRF), i.e., the hemodynamic response evoked by a punctuate neural event. HRF models have been derived for sensory areas, where it is relatively clear what events cause a neural impulse response. However, this is obviously harder to do for higher order cortices such as prefrontal areas. Therefore, one HRF model is commonly used for analyzing activity throughout the brain, despite the fact that hemodynamics are known to vary across regions. For instance, several fMRI studies use a canonical HRF to analyze ventral striatum (VS) activity where converging evidence indicates that reward prediction error signals drive neural activity. However, the VS is a target of prominent dopaminergic projections, known to modulate vasculature and affect BOLD activity, suggesting that the HRF in the VS may be especially different from those in other brain areas. To address this, we use data from an experiment focused on learning from prediction-error signals to derive a VS-specific HRF model (VS-HRF). We show that this new VS-HRF increases statistical power in model comparison. Our result is of particular relevance to studies comparing computational models of learning and/or decision making in the VS, and for connectivity analyses, where the use of an (even slightly) inaccurate HRF model can lead to erroneous conclusions. More broadly, our study highlights the importance of the choice of HRF model in determining the significance of the results obtained in classical univariate fMRI analysis.