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