The function non_parametric_inference with threshold=None and tfce=False returns neg_log10_vfwe_pvals_img. This image appears to be thresholded, in the sense that its value are non-trivial only in some particular regions (where it is large enough).

For instance, if I use the documentation Second-level fMRI model: one sample test - Nilearn and adapt the call to non_parametric_inference as follows:

```
neg_log10_vfwe_pvals_img = non_parametric_inference(
second_level_input,
design_matrix=design_matrix,
model_intercept=True,
n_perm=500, # 500 for the sake of time. Ideally, this should be 10,000.
two_sided_test=False,
smoothing_fwhm=8.0,
n_jobs=2
)
```

(basically I removed the threshold argument and renamed the output)

Then if I plot neg_log10_vfwe_pvals_img I get spots with non-trivial values (around the expected active regions) and then 0 values elsewhere.

This suggest that the map is already thresholded at some default significance level: indeed, without a threshold, I would expect the map to be non-trivial everywhere, and only after choosing some alpha-level should I restrict the values to consider for inference.

I am missing something? If there such a hidden default threshold? Note that the function does not take an alpha argument.