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.compute_regressor

nilearn.glm.first_level.compute_regressor(exp_condition, hrf_model, frame_times, con_id='cond', oversampling=50, fir_delays=None, min_onset=-24)

Convolve regressors with HRF model.

Parameters:

exp_conditionarray-like of shape (3, n_events)

yields description of events for this condition as a (onsets, durations, amplitudes) triplet

hrf_modelstr, function, list of functions, or None

This parameter defines the HRF model to be used. It can be a string if you are passing the name of a model implemented in Nilearn. Valid names are:

• "spm": This is the HRF model used in SPM. See spm_hrf.

• "spm + derivative": SPM model plus its time derivative. This gives 2 regressors. See spm_hrf, and spm_time_derivative.

• "spm + derivative + dispersion": Same as above plus dispersion derivative. This gives 3 regressors. See spm_hrf, spm_time_derivative, and spm_dispersion_derivative.

• "glover": This corresponds to the Glover HRF. See glover_hrf.

• "glover + derivative": The Glover HRF + time derivative. This gives 2 regressors. See glover_hrf, and glover_time_derivative.

• "glover"+ derivative + dispersion": Same as above plus dispersion derivative. This gives 3 regressors. See glover_hrf, glover_time_derivative, and glover_dispersion_derivative.

• "fir": Finite impulse response basis. This is a set of delayed dirac models.

It can also be a custom model. In this case, a function should be provided for each regressor. Each function should behave as the other models implemented within Nilearn. That is, it should take both t_r and oversampling as inputs and return a sample numpy array of appropriate shape.

Note

It is expected that "spm" standard and "glover" models would not yield large differences in most cases.

Note

In case of "glover" and "spm" models, the derived regressors are orthogonalized with respect to the main one.

frame_timesarray of shape (n_scans)

the desired sampling times

con_idstr, default=’cond’

Identifier of the condition

oversamplingint, default=50

Oversampling factor to perform the convolution.

fir_delays[int] 1D-array-like or None, default=None

Delays (in scans) used in case of a finite impulse response model.

min_onsetfloat, default=-24

Minimal onset relative to frame_times[0] (in seconds) events that start before frame_times[0] + min_onset are not considered.

Returns:

computed_regressorsarray of shape(n_scans, n_reg)

Computed regressors sampled at frame times.

reg_nameslist of strings

Corresponding regressor names.

Examples using nilearn.glm.first_level.compute_regressor

Example of MRI response functions

Example of MRI response functions