GLM on surface data in NiLearn

jyeatman · May 19, 2022, 4:18pm

Does anyone know why NiLearn uses different functions to fit the glm for surface data versus volume data? Based on the tutorials run_glm is the only function that takes surface data whereas the main function nilearn.glm.first_level.FirstLevelModel only accepts niftis. Am I overlooking something or is this by design?

bthirion · May 20, 2022, 4:07pm

Hi,
Indeed in the example, we use low-level functions to run the GLM on the surface because… we have not yet implemented a high-level API that would be the equivalent of FirsLevelModel for surface data.
So I hope that we’ll propose something better in a future release.
HTH,
Bertrand

jyeatman · May 20, 2022, 4:51pm

Thanks @bthirion . In future releases will FirstLevelModel take volume or surface data? Is there an open PR or issue on this? If no one is currently working on it I could try

Related question: is this tutorial still up to date for the current best practices in surface glm? I didn’t see any handling of confounds

bthirion · May 20, 2022, 5:50pm

Yes, the tutorial you mention is fine. In that case, you would need to handle confounds ‘manually’ by creating the design matrix first.
Let me suggest that you open an issue on Nilearn first: this is a way to get more people’s opinions.
Thx !

Bertrand

jyeatman · June 8, 2022, 7:07pm

@bthirion - Can you offer any guidance on analyzing multiple runs of surface data? This tutorial only covers one run. And unlike the volume functions, it doesn’t seem that the surface functions are set up to handle multiple runs of data

bthirion · June 8, 2022, 7:40pm

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

github.com

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