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_time_derivative¶

nilearn.glm.first_level.spm_time_derivative(t_r, oversampling=50, time_length=32.0, onset=0.0)[source]¶

Implement the SPM time derivative HRF (dhrf) 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.
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 / t_r, dtype=float): dhrf sampling on the provided grid

Examples

>>> import numpy as np
>>> from nilearn.glm.first_level import spm_time_derivative
>>> dhrf = spm_time_derivative(t_r=2.0, oversampling=1, time_length=20.0)
>>> np.round(dhrf, 3).tolist()
[0.0, 0.0, 0.167, 0.04, -0.091, -0.072, -0.035, -0.013, -0.0, 0.005]