def test_high_level_non_parametric_inference_with_paths():
with InTemporaryDirectory():
n_perm = 100
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
c1 = np.eye(len(X.columns))[0]
neg_log_pvals_img = non_parametric_inference(Y,
design_matrix=X,
second_level_contrast=c1,
mask=mask,
n_perm=n_perm)
neg_log_pvals = get_data(neg_log_pvals_img)
assert isinstance(neg_log_pvals_img, Nifti1Image)
assert_array_equal(neg_log_pvals_img.affine, load(mask).affine)
assert np.all(neg_log_pvals <= -np.log10(1.0 / (n_perm + 1)))
assert np.all(0 <= neg_log_pvals)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory
del X, Y, FUNCFILE, func_img, neg_log_pvals_img
def test_non_parametric_inference_cluster_level():
"""Test non-parametric inference with cluster-level inference."""
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
out = non_parametric_inference(
Y,
design_matrix=X,
mask=mask,
n_perm=10,
threshold=0.001,
)
assert isinstance(out, dict)
assert "t" in out.keys()
assert "logp_max_t" in out.keys()
assert "logp_max_size" in out.keys()
assert "logp_max_mass" in out.keys()
assert get_data(out["logp_max_t"]).shape == shapes[0][:3]
del func_img, FUNCFILE, out, X, Y
def test_high_level_non_parametric_inference_with_paths():
with InTemporaryDirectory():
n_perm = 100
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
df_input = pd.DataFrame({
'subject_label': [f'sub-{i}' for i in range(4)],
'effects_map_path': [FUNCFILE] * 4,
'map_name': [FUNCFILE] * 4
})
func_img = load(FUNCFILE)
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
c1 = np.eye(len(X.columns))[0]
neg_log_pvals_imgs = [
non_parametric_inference(second_level_input,
design_matrix=X,
second_level_contrast=c1,
first_level_contrast=FUNCFILE,
mask=mask,
n_perm=n_perm,
verbose=1)
for second_level_input in [Y, df_input]
]
assert all(
[isinstance(img, Nifti1Image) for img in neg_log_pvals_imgs])
for img in neg_log_pvals_imgs:
assert_array_equal(img.affine, load(mask).affine)
neg_log_pvals_list = [get_data(i) for i in neg_log_pvals_imgs]
for neg_log_pvals in neg_log_pvals_list:
assert np.all(neg_log_pvals <= -np.log10(1.0 / (n_perm + 1)))
assert np.all(0 <= neg_log_pvals)
masker = NiftiMasker(mask, smoothing_fwhm=2.0)
with pytest.warns(UserWarning,
match="Parameter smoothing_fwhm "
"of the masker overridden"):
non_parametric_inference(Y,
design_matrix=X,
second_level_contrast=c1,
smoothing_fwhm=3.0,
mask=masker,
n_perm=n_perm)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory
del X, Y, FUNCFILE, func_img, neg_log_pvals_imgs
def test_non_parametric_inference_permutation_computation():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
neg_log_pvals_img = non_parametric_inference(Y,
design_matrix=X,
mask=mask,
n_perm=100)
assert get_data(neg_log_pvals_img).shape == shapes[0][:3]
del func_img, FUNCFILE, neg_log_pvals_img, X, Y
# than the previous one.
threshold = 1
title = ('Group left-right button press: \n'
'parametric test (FWER < 10%)')
display = plotting.plot_glass_brain(
neg_log_pval, colorbar=True, display_mode='z', plot_abs=False, vmax=3,
cut_coords=cut_coords, threshold=threshold, title=title)
plotting.show()
###########################################################################
# Now, we compute the (corrected) p-values with a permutation test.
from nilearn.glm.second_level import non_parametric_inference
neg_log_pvals_permuted_ols_unmasked = \
non_parametric_inference(second_level_input,
design_matrix=design_matrix,
model_intercept=True, n_perm=1000,
two_sided_test=False,
smoothing_fwhm=8.0, n_jobs=1)
###########################################################################
# Let us plot the (corrected) negative log p-values for the nonparametric test.
title = ('Group left-right button press: \n'
'permutation test (FWER < 10%)')
display = plotting.plot_glass_brain(
neg_log_pvals_permuted_ols_unmasked, colorbar=True, vmax=3,
display_mode='z', plot_abs=False, cut_coords=cut_coords,
threshold=threshold, title=title)
plotting.show()
# The neg-log p-values obtained with nonparametric testing are capped at 3
# since the number of permutations is 1e3.
def test_non_parametric_inference_contrast_computation():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# asking for contrast before model fit gives error
with pytest.raises(ValueError):
non_parametric_inference(None, None, None, 'intercept', mask)
# fit model
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
# formula should work without second-level contrast
neg_log_pvals_img = non_parametric_inference(Y,
design_matrix=X,
mask=mask,
n_perm=100)
ncol = len(X.columns)
c1, cnull = np.eye(ncol)[0, :], np.zeros(ncol)
# formula should work with second-level contrast
neg_log_pvals_img = non_parametric_inference(Y,
design_matrix=X,
second_level_contrast=c1,
mask=mask,
n_perm=100)
# formula should work passing variable name directly
neg_log_pvals_img = \
non_parametric_inference(Y, design_matrix=X,
second_level_contrast='intercept',
mask=mask, n_perm=100)
# passing null contrast should give back a value error
with pytest.raises(ValueError):
non_parametric_inference(Y, X, cnull, 'intercept', mask)
# passing wrong parameters
with pytest.raises(ValueError):
non_parametric_inference(Y, X, [], 'intercept', mask)
# check that passing no explicit contrast when the design
# matrix has more than one columns raises an error
rng = np.random.RandomState(42)
X = pd.DataFrame(rng.uniform(size=(4, 2)), columns=["r1", "r2"])
with pytest.raises(ValueError):
non_parametric_inference(Y, X, None)
del func_img, FUNCFILE, neg_log_pvals_img, X, Y
def test_fmri_inputs_for_non_parametric_inference():
# Test processing of FMRI inputs
with InTemporaryDirectory():
# prepare fake data
rng = np.random.RandomState(42)
p, q = 80, 10
X = rng.standard_normal(size=(p, q))
shapes = ((7, 8, 9, 10), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
T = func_img.shape[-1]
des = pd.DataFrame(np.ones((T, 1)), columns=['a'])
des_fname = 'design.csv'
des.to_csv(des_fname)
# prepare correct input first level models
flm = FirstLevelModel(subject_label='01').fit(FUNCFILE,
design_matrices=des)
# prepare correct input dataframe and lists
shapes = ((7, 8, 9, 1), )
_, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
dfcols = ['subject_label', 'map_name', 'effects_map_path']
dfrows = [['01', 'a', FUNCFILE], ['02', 'a', FUNCFILE],
['03', 'a', FUNCFILE]]
niidf = pd.DataFrame(dfrows, columns=dfcols)
niimgs = [FUNCFILE, FUNCFILE, FUNCFILE]
niimg_4d = concat_imgs(niimgs)
confounds = pd.DataFrame([['01', 1], ['02', 2], ['03', 3]],
columns=['subject_label', 'conf1'])
sdes = pd.DataFrame(X[:3, :3], columns=['intercept', 'b', 'c'])
# test missing second-level contrast
# niimgs as input
with pytest.raises(ValueError):
non_parametric_inference(niimgs, None, sdes)
with pytest.raises(ValueError):
non_parametric_inference(niimgs, confounds, sdes)
# 4d niimg as input
with pytest.raises(ValueError):
non_parametric_inference(niimg_4d, None, sdes)
# test wrong input errors
# test first level model
with pytest.raises(ValueError):
non_parametric_inference(flm)
# test list of less than two niimgs
with pytest.raises(ValueError):
non_parametric_inference([FUNCFILE])
# test dataframe
with pytest.raises(ValueError):
non_parametric_inference(niidf)
# test niimgs requirements
with pytest.raises(ValueError):
non_parametric_inference(niimgs)
with pytest.raises(ValueError):
non_parametric_inference(niimgs + [[]], confounds)
with pytest.raises(ValueError):
non_parametric_inference([FUNCFILE])
# test other objects
with pytest.raises(ValueError):
non_parametric_inference('random string object')
del X, FUNCFILE, func_img
# cluster-level inference.
# Performing cluster-level inference will increase the computation time of
# the permutation procedure.
# Increasing the number of parallel jobs (``n_jobs``) can reduce the time
# cost.
#
# .. hint::
# If you wish to only run voxel-level correction, set ``threshold`` to None
# (the default).
from nilearn.glm.second_level import non_parametric_inference
out_dict = non_parametric_inference(
second_level_input,
design_matrix=design_matrix,
model_intercept=True,
n_perm=500, # 500 for the sake of time. Ideally, this should be 10,000.
two_sided_test=False,
smoothing_fwhm=8.0,
n_jobs=1,
threshold=0.001,
)
###############################################################################
# Let us plot the (corrected) negative log p-values for the both tests.
#
# We will use a negative log10 p threshold of 1, which corresponds to p<0.1.
# This threshold indicates that there is less than 10% probability to make a
# single false discovery (90% chance that we make no false discovery at all).
# This threshold is much more conservative than an uncorrected threshold, but
# is still more liberal than a typical corrected threshold for this kind of
# analysis, which tends to be ~0.05.
#
title = ('Group-level association between motor activity and reading: \n'
'neg-log of parametric corrected p-values (FWER < 10%)')
plotting.plot_stat_map(neg_log_pval,
colorbar=True,
cut_coords=cut_coords,
threshold=threshold,
title=title)
plotting.show()
##############################################################################
# Computing the (corrected) negative log p-values with permutation test
from nilearn.glm.second_level import non_parametric_inference
neg_log_pvals_permuted_ols_unmasked = \
non_parametric_inference(contrast_map_filenames,
design_matrix=design_matrix,
second_level_contrast='fluency',
model_intercept=True, n_perm=1000,
two_sided_test=False, mask=None,
smoothing_fwhm=5.0, n_jobs=1)
###########################################################################
# Let us plot the (corrected) negative log p-values
title = ('Group-level association between motor activity and reading: \n'
'neg-log of non-parametric corrected p-values (FWER < 10%)')
plotting.plot_stat_map(neg_log_pvals_permuted_ols_unmasked,
colorbar=True,
cut_coords=cut_coords,
threshold=threshold,
title=title)
plotting.show()
# The neg-log p-values obtained with non parametric testing are capped at 3
