Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.glm.first_level.spm_dispersion_derivative#

nilearn.glm.first_level.spm_dispersion_derivative(tr, oversampling=50, time_length=32.0, onset=0.0)[source]#

Implementation of the SPM dispersion derivative hrf model

Parameters:
trfloat

Scan repeat time, in seconds.

oversamplingint, optional

Temporal oversampling factor in seconds. Default=50.

time_lengthfloat, optional

hrf kernel length, in seconds. Default=32.

onsetfloat, optional

Onset of the response in seconds. Default=0.

Returns:
dhrfarray of shape(length / tr * oversampling), dtype=float

dhrf sampling on the oversampled time grid