Hi,

I was looking into the orthogonalization for the design matrix used in `nilearn.glm.first_level`

, and there is something I need help figuring out. I am using Nilearn version 0.10.4.

When creating a design matrix in `nilearn/glm/first_level/design_matrix.py`

, the `make_first_level_design_matrix`

function calls `_convolve_regressors`

. In `_convolve_regressors`

, we iterate over each condition in the events and use the `compute_regressor`

function from `hemodynamic_models.py`

.

From what I checked in my code, `compute_regressor`

works on a single vector, which is a single regressor of the condition, and it performs orthogonalization over a single regressor each time, which doesn’t make sense.

I would appreciate any help to understand if I am missing something here.

Thanks!

Tamir Scherf