### Summary of what happened:

I have about ~75 fMRI scans, all of which have been analysed with nilearn first level analysis (very easy to use!). I then take 1 contrast (let’s call it contrast X) from this analysis for the below:

- Prior to fitting, I create a design matrix using make_second_level_design_matrix.
- I then fit SecondLevelModel, supplying it with the runs and design matrix.
- I then use second_level_model.compute_contrast. Easy for one-sample tests, however I am quite confused as to how interactions are represented. I think for my sake and the documentation, having straightforward examples with various scenarios would be very helpful. However, the case I’m interested in is: 2 groups (healthy v. schizophrenia), 1 covariate (age), 1 variable of interest (for this example, let’s use number of cigarettes smoked per month).

### Command used (and if a helper script was used, a link to the helper script or the command generated):

```
1.
design_matrix = make_second_level_design_matrix(alldat['subject_label'], alldat[['subject_label','group','age','n_cigs']]), This produces a matrix where group.healthy == 1, and group.smoker == -1, age is continuous, n_cigs is continuous, and intercept == 1.
2.
second_level_model = SecondLevelModel(smoothing_fwhm=12, mask_img=mask)
second_level_model = second_level_model.fit(
runs,
design_matrix=design_matrix
)
3.
z_map = second_level_model.compute_contrast(
second_level_contrast=[?],
output_type='z_score',
)
```

Now my question is, what should I specify for the contrast in step 3, if I want to compare the relationship between n_cigs and contrast X between groups, while controlling for age. In other words, does the relationship between number of cigarettes and brain activity during task X differ between those with and without schizophrenia? I understand both binary i.e. [0,0,1,-1,0] and string i.e. [‘group’ * ‘n_cigs’] can be used to specify the second level contrast. I would appreciate examples with both.

### Version:

Name: nilearn

Version: 0.10.4

Summary: Statistical learning for neuroimaging in Python

Home-page:

Author: Nilearn developers

Author-email:

License: new BSD

Location: /home/mrspecial/anaconda3/lib/python3.11/site-packages

Requires: joblib, lxml, nibabel, numpy, packaging, pandas, requests, scikit-learn, scipy

Required-by: neuromaps, niworkflows

Thank you for any help!

Rami