Model-based estimation of the population contrast response function in human visual cortex

Contrast determines how strong visual stimuli drive neural responses and, in turn, how well we can see. This contrast-response relationship is nonlinear, with gain control mechanisms acting to compress responses to high-contrast stimuli, effectively setting the contrast range that neural responses are most sensitive to. Yet, capturing these saturating responses noninvasively in human functional magnetic resonance imaging (fMRI) has proven challenging. Here, we introduce a model-based fMRI approach to more flexibly and efficiently estimate saturating contrast responses: population contrast response function (CRF) mapping. To vet this technique, we benchmarked pCRF mapping against traditional deconvolved BOLD responses for stimuli presented with a fast event-related design. We found tight correspondence in parameter estimates between the deconvolution-derived CRF and pCRF estimates, validating the model-based approach. We then asked whether pCRF mapping could push beyond conventional paradigms by employing a continuous, highly condition-rich stimulation design that affords greater efficiency and flexibility but does not lend itself to traditional fMRI analyses. As predicted by the model-based approach, voxel-wise pCRF estimates could still be recovered from this unconventional design. In sum, we demonstrated that model-based pCRF mapping dissociates CRF estimation from strict design constraints and broadens the space of usable experimental paradigms while still capturing the saturating nonlinearities that are fundamental to neural population responses.