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modalities.fmri.hrf

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modalities.fmri.hemodynamic_models

Module: modalities.fmri.hemodynamic_models

This module is for canonical hrf specification. Here we provide for SPM, Glover hrfs and FIR models. This module closely follows SPM implementation

Author: Bertrand Thirion, 2011

Functions

nipy.modalities.fmri.hemodynamic_models.compute_regressor(exp_condition, hrf_model, frametimes, con_id='cond', oversampling=16, fir_delays=None)

This is the main function to convolve regressors with hrf model

Parameters :

exp_condition: descriptor of an experimental condition :

hrf_model: string, the hrf model to be used. Can be chosen among: :

‘spm’, ‘spm_time’, ‘spm_time_dispersion’, ‘canonical’, ‘canonical_derivative’, ‘fir’

frametimes: array of shape (n):the sought :

con_id: string, optional identifier of the condition :

oversampling: int, optional, oversampling factor to perform the convolution :

fir_delays: array-like of int, onsets corresponding to the fir basis :

Returns :

creg: array of shape(n_scans, n_reg): computed regressors sampled :

at frametimes

reg_names: list of strings, corresponding regressor names :

nipy.modalities.fmri.hemodynamic_models.gamma_difference_hrf(tr, oversampling=16, time_length=32.0, onset=0.0, delay=6, undershoot=16.0, dispersion=1.0, u_dispersion=1.0, ratio=0.167)

Compute an hrf as the difference of two gamma functions

Parameters :

tr: float, scan repeat time, in seconds :

oversampling: int, temporal oversampling factor, optional :

time_length: float, hrf kernel length, in seconds :

onset: float, onset of the hrf :

Returns :

hrf: array of shape(length / tr * oversampling, float), :

hrf sampling on the oversampled time grid

nipy.modalities.fmri.hemodynamic_models.glover_hrf(tr, oversampling=16, time_length=32.0, onset=0.0)

Implementation of the Glover hrf model

Parameters :

tr: float, scan repeat time, in seconds :

oversampling: int, temporal oversampling factor, optional :

time_length: float, hrf kernel length, in seconds :

onset: float, onset of the response :

Returns :

hrf: array of shape(length / tr * oversampling, float), :

hrf sampling on the oversampled time grid

nipy.modalities.fmri.hemodynamic_models.glover_time_derivative(tr, oversampling=16, time_length=32.0, onset=0.0)

Implementation of the flover time derivative hrf (dhrf) model

Parameters :

tr: float, scan repeat time, in seconds :

oversampling: int, temporal oversampling factor, optional :

time_length: float, hrf kernel length, in seconds :

onset: float, onset of the response :

Returns :

dhrf: array of shape(length / tr, float), :

dhrf sampling on the provided grid

nipy.modalities.fmri.hemodynamic_models.resample_regressor(hr_regressor, hr_frametimes, frametimes, kind='linear')

this function samples the regressors at frametimes

Parameters :

hr_regressor: array of shape(n), :

the regressor time course sampled at high temporal resolution

hr_frametimes: array of shape(n), :

the corresponding time stamps

frametimes: array of shape(p), :

the desired time stamps

kind: string, optional, the kind of desired interpolation :

Returns :

regressor: array of shape(p), the resampled regressor :

nipy.modalities.fmri.hemodynamic_models.sample_condition(exp_condition, frametimes, oversampling=16)

this function samples the experimental condition at frametimes

Parameters :

exp_condition: a tuple of 3 arrays of shape n, corresponding :

to (onsets, duration, value), describing the experimental condition

frametimes: array of shape(n) :

over_sampling: int, over_sampling factor :

Returns :

regressor: array of shape(n) :

nipy.modalities.fmri.hemodynamic_models.spm_dispersion_derivative(tr, oversampling=16, time_length=32.0, onset=0.0)

Implementation of the SPM dispersion derivative hrf model

Parameters :

tr: float, scan repeat time, in seconds :

oversampling: int, temporal oversampling factor, optional :

time_length: float, hrf kernel length, in seconds :

onset: float, onset of the response :

Returns :

dhrf: array of shape(length / tr * oversampling, float), :

dhrf sampling on the oversampled time grid

nipy.modalities.fmri.hemodynamic_models.spm_hrf(tr, oversampling=16, time_length=32.0, onset=0.0)

Implementation of the SPM hrf model

Parameters :

tr: float, scan repeat time, in seconds :

oversampling: int, temporal oversampling factor, optional :

time_length: float, hrf kernel length, in seconds :

onset: float, onset of the response :

Returns :

hrf: array of shape(length / tr * oversampling, float), :

hrf sampling on the oversampled time grid

nipy.modalities.fmri.hemodynamic_models.spm_time_derivative(tr, oversampling=16, time_length=32.0, onset=0.0)

Implementation of the SPM time derivative hrf (dhrf) model

Parameters :

tr: float, scan repeat time, in seconds :

oversampling: int, temporal oversampling factor, optional :

time_length: float, hrf kernel length, in seconds :

onset: float, onset of the response :

Returns :

dhrf: array of shape(length / tr, float), :

dhrf sampling on the provided grid