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

Recent years have seen a proliferation of studies in which computational models are used to spec- ify precisely a set of hypotheses regarding reinforcement learning and decision making in humans, which are then tested against data from functional magnetic resonance imaging (fMRI). fMRI research proceeds by using information provided by the blood oxygenation level dependent (BOLD) signal to make inferences about the underlying neural activation. The focus of much of this model-based fMRI effort has been on the ventral striatum (VS), where the BOLD response has been shown to reflect reward prediction error signals (momentary differences between expected and obtained outcomes) from dopaminergic afferents. To make sensible inferences from fMRI data it is important to accurately model the hemodynamic response function (HRF), i. e. , the hemodynamic response evoked by a punctate neural event. A canonical HRF, mapped for sensory cortical regions, is commonly used for analyzing activity throughout the brain despite the fact that hemodynamics are known to vary across regions, in particular in subcortical areas such as the VS. Here we use data from an experiment focused on learning from prediction errors (Niv et al. , 2010) to fit a VS-specific HRF function. Our results show that the VS HRF differs significantly from the canonical HRF, most im- portantly peaking at 6 sec rather than at 5 sec. We demonstrate the superiority of the VS HRF in modeling data by showing that it increases statistical power. This result is particularly relevant to fMRI studies of reinforcement learning and decision making as many of these rely on fine analysis of the VS BOLD activity to distinguish between important but subtle differences in computational models of learning and choice. We therefore recommend the use of this new HRF for future fMRI studies of the ventral striatum.

Authors

• Gecia Bravo Hermsdorff
• Yael Niv

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