Hi,

After working for a while with nilearn, mostly with '‘z_score’ maps, I recently looked into the actual values of my beta maps using ‘output_type’ as ‘effect_size’. I discovered my beta values are extremely high. A lot of them are above a hundred and even above a thousand, which does not make sense to me.

When doing second-level analysis and looking at the ‘z_score’ maps, everything seems fine, so I don’t know what is wrong.

I would appreciate any help! Thanks!

Here is my code for creating the beta map and the design matrix:

```
fmri_glm = FirstLevelModel(t_r=2,
slice_time_ref = 0.5,
mask_img = no_vent_bin_img,
noise_model='ar1',
hrf_model='spm',
drift_model='cosine',
high_pass=1./180,
signal_scaling=False,
minimize_memory=False)
test_glm = fmri_glm.fit(subj_imgs, subj_events, subj_conf)
effect_map = test_glm.compute_contrast('feedback', output_type='effect_size')
plot_design_matrix(test_glm.design_matrices_[0])
```