pyhrf.glm.
glm_nipy
(fmri_data, contrasts=None, hrf_model='Canonical', drift_model='Cosine', hfcut=128, residuals_model='spherical', fit_method='ols', fir_delays=[0], rescale_results=False, rescale_factor=None)¶Perform a GLM analysis on fMRI data using the implementation of Nipy.
Parameters: |
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Returns: | (glm instance, design matrix, dict of contrasts of objects) |
Examples: >>> from pyhrf.core import FmriData >>> from pyhrf.glm import glm_nipy >>> g,dmtx,con = glm_nipy(FmriData.from_vol_ui()) >>> g,dmtx,con = glm_nipy(FmriData.from_vol_ui(), contrasts={‘A-V’:’audio-video’})
pyhrf.glm.
glm_nipy_from_files
(bold_file, tr, paradigm_csv_file, output_dir, mask_file, session=0, contrasts=None, con_test_baseline=0.0, hrf_model='Canonical', drift_model='Cosine', hfcut=128, residuals_model='spherical', fit_method='ols', fir_delays=[0])¶#TODO: handle surface data hrf_model : Canonical | Canonical with Derivative | FIR