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(t_r, oversampling=50, time_length=32.0, onset=0.0)[source]¶

Implement the SPM dispersion derivative HRF model.

Parameters:

t_rfloat: Repetition time, in seconds (sampling period).

Changed in Nilearn 0.11.0: The old tr parameter was replaced by t_r.
oversamplingint, default=50: Temporal oversampling factor in seconds.
time_lengthfloat, default=32.0: HRF kernel length, in seconds.
onsetfloat, default=0.0: Onset of the response in seconds.

Returns:

dhrfarray of shape (length / tr * oversampling), dtype=float: dhrf sampling on the oversampled time grid

Examples

>>> import numpy as np
>>> from nilearn.glm.first_level import glover_dispersion_derivative
>>> ddhrf = glover_dispersion_derivative(
...     t_r=2.0, oversampling=1, time_length=20.0
... )
>>> np.round(ddhrf, 3).tolist()
[0.0, -0.0, -0.373, 0.282, 0.295, -0.04, -0.094, -0.048, -0.017, -0.005]