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 either the canonical shape with two derivatives, or a (smoothed) Finite Impulse Response.

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.