def test_strategy_scrubbing(tmp_path):
"""Check user specified input for scrubbing strategy."""
file_nii, _ = create_tmp_filepath(tmp_path,
image_type="regular",
copy_confounds=True,
copy_json=True)
confounds, sample_mask = load_confounds_strategy(
file_nii, denoise_strategy="scrubbing", fd_threshold=0.15)
# out of 30 vols, should have 6 motion outliers from scrubbing,
# and 2 vol removed by srubbing strategy "full"
assert len(sample_mask) == 22
# shape of confound regressors untouched
assert confounds.shape[0] == 30
# also load confounds with very liberal scrubbing thresholds
# this should not produce an error
confounds, sample_mask = load_confounds_strategy(
file_nii,
denoise_strategy="scrubbing",
fd_threshold=1,
std_dvars_threshold=5)
assert len(sample_mask) == 29 # only non-steady volumes removed
# maker sure global signal works
confounds, sample_mask = load_confounds_strategy(
file_nii, denoise_strategy="scrubbing", global_signal="full")
for check in [
"global_signal",
"global_signal_derivative1",
"global_signal_power2",
"global_signal_derivative1_power2",
]:
assert check in confounds.columns
def test_irrelevant_input(tmp_path):
"""Check invalid input raising correct warning or error message."""
file_nii, _ = create_tmp_filepath(tmp_path,
image_type="regular",
copy_confounds=True,
copy_json=True)
warning_message = (r"parameters accepted: \['motion', 'wm_csf', "
"'global_signal', 'demean']")
with pytest.warns(UserWarning, match=warning_message):
load_confounds_strategy(file_nii,
denoise_strategy="simple",
ica_aroma="full")
# invalid strategy
with pytest.raises(KeyError, match="blah"):
load_confounds_strategy(file_nii, denoise_strategy="blah")
def test_load_confounds_strategy(tmp_path, denoise_strategy, image_type):
"""Smoke test with no extra inputs."""
file_nii, _ = create_tmp_filepath(tmp_path,
image_type=image_type,
copy_confounds=True,
copy_json=True)
confounds, _ = load_confounds_strategy(file_nii,
denoise_strategy=denoise_strategy)
assert isinstance(confounds, pd.DataFrame)
def test_strategy_compcor(tmp_path):
"""Check user specified input for compcor strategy."""
file_nii, _ = create_tmp_filepath(tmp_path,
image_type="regular",
copy_confounds=True,
copy_json=True)
confounds, _ = load_confounds_strategy(file_nii,
denoise_strategy="compcor")
compcor_col_str_anat = "".join(confounds.columns)
assert "t_comp_cor_" not in compcor_col_str_anat
assert ("a_comp_cor_57" not in compcor_col_str_anat
) # this one comes from the white matter mask
def test_strategies(tmp_path, denoise_strategy, image_type):
"""Check defaults setting of each preset strategy."""
file_nii, _ = create_tmp_filepath(tmp_path,
image_type=image_type,
copy_confounds=True,
copy_json=True)
confounds, _ = load_confounds_strategy(file_nii,
denoise_strategy=denoise_strategy)
# Check that all fixed name model categories have been successfully loaded
list_check = _get_headers(denoise_strategy)
for col in confounds.columns:
# Check that all possible names exists
checker = [
re.match(keyword, col) is not None for keyword in list_check
]
assert sum(checker) == 1
# Using predefined strategies
# ---------------------------
# Instead of customising the strategy through
# :func:`nilearn.interfaces.fmriprep.load_confounds`, one can use a predefined
# strategy with :func:`nilearn.interfaces.fmriprep.load_confounds_strategy`.
# Based on the confound variables generated through :term:`fMRIPrep`, and past
# benchmarks studies (:footcite:`Ciric2017`, :footcite:`Parker2018`): `simple`,
# `scrubbing`, `compcor`, `ica_aroma`.
# The following examples shows how to use the `simple` strategy and overwrite
# the motion default to basic.
from nilearn.interfaces.fmriprep import load_confounds_strategy
# use default parameters
confounds, sample_mask = load_confounds_strategy(fmri_filenames,
denoise_strategy="simple",
motion="basic")
time_series = masker.fit_transform(fmri_filenames,
confounds=confounds,
sample_mask=sample_mask)
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(correlation_matrix,
figure=(10, 8),
labels=labels[1:],
vmax=0.8,
vmin=-0.8,
title='simple',