Dear Community,

The Second-level fMRI model: one sample test - Nilearn is very helpful, but one thing confuses me:

The example compares the result of the permutation test statistics with a very strict unconventional bonferroni method and not something like an FDR corrected result:

```
neg_log_pval = math_img("-np.log10(np.minimum(1, img * {}))"
.format(str(n_voxels)),
img=p_val)
```

Why aren’t the permutation results compared to an FDR corrected parametric test?

And is this possible with the math_img function? Or should I implement a custom function that works something like this: statistics - Calculating adjusted p-values in Python - Stack Overflow

Kind regards,

-Jelle