Abstract
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 profile at the individual level. Then, we estimate the BOLD response in its entirety with a smoothness constraint at the population level to improve predictive accuracy and inferential efficiency. Instead of using just the scalar that represents the effect magnitude, we assess the whole HRF shape, which reveals additional information that may prove relevant for many aspects of a study, as well as for cross-study reproducibility. Through 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 clinical 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?
Competing Interest Statement
The authors have declared no competing interest.