[FIX] ensure that dataframes can be used as input for second level GL…

…M (estimation and contrast) (#3879)

* regression test and fix

* Apply suggestions from code review

* update chngelog

* fix

* rm test files

* Apply suggestions from code review

Co-authored-by: Yasmin <63292494+ymzayek@users.noreply.github.com>

* use write_fake_bold_img

---------

Co-authored-by: Yasmin <63292494+ymzayek@users.noreply.github.com>

doc/changes/latest.rst

@@ -16,6 +16,8 @@ Fixes
 
 - Fix bug that would prevent loading the confounds of a gifti file in actual fmriprep datasets (:gh:`3819` by `Rémi Gau`_).
 
+- Fix bug that prevented using dataframes as input for second level GLM when computing contrasts (:gh:`3879` by `Rémi Gau`_).
+
 - Fix bug in :func:`~glm.first_level.first_level_from_bids` that returned no confound files if the corresponding bold files contained derivatives BIDS entities (:gh:`3742` by `Rémi Gau`_).
 
 - Fix bug in :func:`~glm.first_level.first_level_from_bids` that would throw a warning about ``slice_time_ref`` not being provided even when it was (:gh:`3811` by `Rémi Gau`_).

nilearn/glm/second_level/second_level.py

@@ -179,15 +179,17 @@ def _check_confounds(confounds):
 
 
 def _check_first_level_contrast(second_level_input, first_level_contrast):
-    if isinstance(second_level_input[0], FirstLevelModel):
-        if first_level_contrast is None:
-            raise ValueError(
-                "If second_level_input was a list of "
-                "FirstLevelModel, then first_level_contrast "
-                "is mandatory. It corresponds to the "
-                "second_level_contrast argument of the "
-                "compute_contrast method of FirstLevelModel"
-            )
+    if (
+        isinstance(second_level_input, list)
+        and isinstance(second_level_input[0], FirstLevelModel)
+        and first_level_contrast is None
+    ):
+        raise ValueError(
+            "If second_level_input was a list of FirstLevelModel,"
+            " then first_level_contrast is mandatory. "
+            "It corresponds to the second_level_contrast argument "
+            "of the compute_contrast method of FirstLevelModel."
+        )
 
 
 def _check_output_type(output_type, valid_types):

nilearn/glm/tests/test_second_level.py

@@ -16,6 +16,7 @@
 from nilearn._utils import testing
 from nilearn._utils.data_gen import (
     generate_fake_fmri_data_and_design,
+    write_fake_bold_img,
     write_fake_fmri_data_and_design,
 )
 from nilearn.glm.first_level import FirstLevelModel, run_glm
@@ -1178,3 +1179,25 @@ def test_second_level_contrast_computation_with_memory_caching():
         # Delete objects attached to files to avoid WindowsError when deleting
         # temporary directory (in Windows)
         del func_img, model, X, Y
+
+
+def test_second_lvl_dataframe_computation(tmp_path):
+    """Check that contrast can be computed when using dataframes as input.
+
+    See bug https://github.com/nilearn/nilearn/issues/3871
+    """
+    shape = (7, 8, 9, 1)
+    file_path = write_fake_bold_img(
+        file_path=tmp_path / "img.nii.gz", shape=shape
+    )
+
+    dfcols = ["subject_label", "map_name", "effects_map_path"]
+    dfrows = [
+        ["01", "a", file_path],
+        ["02", "a", file_path],
+        ["03", "a", file_path],
+    ]
+    niidf = pd.DataFrame(dfrows, columns=dfcols)
+
+    model = SecondLevelModel().fit(niidf)
+    model.compute_contrast(first_level_contrast="a")