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.glover_time_derivative¶

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

Implement the Glover time derivative HRF (dhrf) model.

Parameters:

t_rfloat: Repetition time, in seconds (sampling period).
tr:: Deprecated since Nilearn 0.11.0: Use t_r instead (see above).
oversamplingint, default=50: Temporal oversampling factor.
time_lengthfloat, default=32.0: HRF kernel length, in seconds.
onsetfloat, default=0.0: Onset of the response.

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 glover_time_derivative
>>> dhrf = glover_time_derivative(
...     t_r=2.0, oversampling=1, time_length=20.0
... )
>>> np.round(dhrf, 3).tolist()
[0.0, 0.0, 0.267, 0.076, -0.215, -0.168, -0.039, 0.027, 0.033, 0.019]