The nature of the hemodynamic response (HDR) is still not fully understood due to the multifaceted processes involved. Aside from the overall amplitude, the response may vary across cognitive states, tasks, brain regions, and subjects with respect to characteristics such as rise and fall speed, peak duration, undershoot shape, and overall duration. Here we demonstrate that the fixed-shape or adjusted-shape methods may fail to detect some shape subtleties. In contrast, the estimated-shape method (ESM) through multiple basis functions can provide the opportunity to identify some subtle shape differences and achieve higher statistical power at both individual and group levels. Previously, some dimension reduction approaches focused on the peak magnitude, or made inferences based on the area under the curve or interaction, which can lead to potential misidentifications. By adopting a generic framework of multivariate modeling (MVM), we showcase a hybrid approach that is validated by simulations and real data. Unlike the few analyses that were limited to main effect, two- or three-way interactions, we extend the approach to an inclusive platform that is more adaptable than the conventional GLM, achieving a practical equipoise among representation, false positive control, statistical power, and modeling flexibility.
Chen, G., Saad, Z. S., Adleman, N. E., Leibenluft, E., & Cox, R. W. (2015). Detecting the subtle shape differences in hemodynamic responses at the group level. Frontiers in Neuroscience, 9(OCT). https://doi.org/10.3389/fnins.2015.00375