BOLD response is more than just magnitude: improving detection sensitivity through capturing hemodynamic profiles

Typical FMRI analyses assume a canonical hemodynamic response function (HRF) with a focus on the overshoot peak height, while other morphological aspects are largely ignored. Thus, in most reported analyses, the overall effect is reduced from a curve to a single scalar. Here, we adopt a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. Then, we estimate the response in its entirety with a roughness penalty at the population level to improve predictive accuracy, inferential efficiency, and cross-study reproducibility. Using a fast event-related FMRI dataset, we demonstrate the extent of under-fitting and information loss that occurs when adopting the canonical approach. We also address the following questions: How much does the HRF shape vary across regions, conditions, and groups? Does an agnostic approach improve sensitivity to detect an effect compared to an assumed HRF? Can examining HRF shape help validate the presence of an effect complementing statistical evidence? Could the HRF shape provide evidence for whole-brain BOLD response during a simple task?