GLM on surface data in NiLearn

bthirion · June 8, 2022, 7:40pm

You have to do the fixed effects analysis “mnaually”.
You have an example in this file:

hbp-brain-charting/public_analysis_code/blob/master/ibc_public/utils_pipeline.py#L556

      
        
                                write_dir, 'effect_variance_maps/%s.nii.gz' % contrast))
                            save(ffx_stat, os.path.join(
                                write_dir, 'stat_maps/%s.nii.gz' % contrast))
                            plot_stat_map(
                                ffx_stat, bg_img=subject_dic['anat'], display_mode='z',
                                dim=0, cut_coords=7, title=contrast, threshold=3.0,
                                output_file=os.path.join(write_dir,
                                                         'stat_maps/%s.png' % contrast))
            
            

            
def fixed_effects_surf(con_imgs, var_imgs):
                """Idem fixed_effects_img but for surfaces"""
                from nibabel import load
                from nibabel.gifti import GiftiDataArray, GiftiImage
                con, var = [], []
                for (con_img, var_img) in zip(con_imgs, var_imgs):
                    con.append(np.ravel([
                        darrays.data for darrays in load(con_img).darrays]))
                    var.append(np.ravel([
                        darrays.data for darrays in load(var_img).darrays]))

HTH,
Bertrand