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Describe your proposed enhancement in detail.

We recently fit GLMs to edge timeseries (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369861/) in order to look at fluctuations of attention at the network level. We believe it's an exciting approach that offers novel information and which is really easy to implement.

Almost all of the code was based off of nilearn - it mostly involved just changing FirstLevelModel().fit to take numpy arrays, rather than niimgs, so that we could give it the 2D arrays (n_features x n_scans).

It would be really great to bake this feature directly into nilearn, so that it can be easily kept up to date with other nilearn features and bug-fixes. I believe the changes to the code would be minimal, and add little-to-no maintenance, and I would be happy to submit the pull request myself.

I have some example pieces of potential changes below.

Benefits to the change

This would add flexibility to the nilearn code for fitting first-level models - letting it fit edges, or even just data that is in a significantly different format (e.g., open data that was already preprocessed and parcellated with some atlas). It would cost very little to maintain.

Pseudocode for the new behavior, if applicable

# insert your code below

def fit(...):

if (self.mask_img is None) & (type(run_imgs[0])==np.ndarray):
    assert run_imgs[0].shape==2  # nFeatures x nScans
...

if type(run_img)==np.ndarray:
    Y = run_img
else:
    run_img = check_niimg(run_img, ensure_ndim=4)
    Y = self.masker_.transform(run_img, sample_mask=sample_mask)

if design_matrices is None:
     n_scans = Y.shape[-1]
     
     
def compute_contrast(...):

...

if self.masker_ is None:
    output = estimate_
else:
    output = self.masker_.inverse_transform(estimate_)