Resting State Hemodynamic Response Function Retrieval and Deconvolution (RS-HRF)
Once that the HRF has been retrieved for each voxel/vertex, it can be deconvolved from the time series (for example to improve lag-based connectivity estimates), or one can map the shape parameters everywhere in the brain (including white matter), and use it as a pathophysiological indicator.
Input can be 2D GIfTI, 3D or 4D NIfTI images, but also on time series matrices/vectors.
The output are three HRF shape parameters for each voxel/vertex, plus the deconvolved time series, and the number of retrieved pseudo-events. All can be written back to GIfTI or NIfTI images.
Please refer to the links on the left for the different versions and relative documentation.