This toolbox is aimed to retrieve the onsets of pseudo-events triggering an hemodynamic response from resting state fMRI BOLD voxel-wise signal. It is based on point process theory, and fits a model to retrieve the optimal lag between the events and the HRF onset, as well as the HRF shape, using different shape parameters or combinations of basis functions.

Once that the HRF has been retrieved for each voxel, 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 3D or 4D nifti images, but also on time series matrices/vectors.
The output are three HRF shape parameters for each voxel, plus the deconvolved time series, and the number of retrieved pseudo-events. All can be written back to nifti images.

Please refer to the links on the left for the different versions and relative documentation.