def report_slm_oasis(): # pragma: no cover
n_subjects = 5 # more subjects requires more memory
oasis_dataset = nilearn.datasets.fetch_oasis_vbm(n_subjects=n_subjects)
# Resample the images, since this mask has a different resolution
mask_img = resample_to_img(
nilearn.datasets.fetch_icbm152_brain_gm_mask(),
oasis_dataset.gray_matter_maps[0],
interpolation='nearest',
)
design_matrix = _make_design_matrix_slm_oasis(oasis_dataset, n_subjects)
second_level_model = SecondLevelModel(smoothing_fwhm=2.0, mask=mask_img)
second_level_model.fit(oasis_dataset.gray_matter_maps,
design_matrix=design_matrix)
contrast = [[1, 0, 0], [0, 1, 0]]
report = make_glm_report(
model=second_level_model,
contrasts=contrast,
bg_img=nilearn.datasets.fetch_icbm152_2009()['t1'],
height_control=None,
)
output_filename = 'generated_report_slm_oasis.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
def test_high_level_glm_with_paths():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1),)
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# Ordinary Least Squares case
model = SecondLevelModel(mask_img=mask)
# asking for contrast before model fit gives error
with pytest.raises(ValueError):
model.compute_contrast([])
# fit model
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
c1 = np.eye(len(model.design_matrix_.columns))[0]
z_image = model.compute_contrast(c1, output_type='z_score')
assert isinstance(z_image, Nifti1Image)
assert_array_equal(z_image.affine, load(mask).affine)
# try with target_shape
target_shape = (10, 10, 10)
target_affine = np.eye(4)
target_affine[0, 3] = 1
model = SecondLevelModel(mask_img=mask, target_shape=target_shape,
target_affine=target_affine)
z_image = model.fit(Y, design_matrix=X).compute_contrast(c1)
assert_array_equal(z_image.shape, target_shape)
assert_array_equal(z_image.affine, target_affine)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del Y, FUNCFILE, func_img, model
def test_slm_reporting():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1),)
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
model = SecondLevelModel()
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
c1 = np.eye(len(model.design_matrix_.columns))[0]
report_slm = glmr.make_glm_report(model, c1)
# catches & raises UnicodeEncodeError in HTMLDocument.get_iframe()
report_iframe = report_slm.get_iframe()
# So flake8 doesn't complain about not using variable (F841)
report_iframe
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del Y, FUNCFILE, func_img, model
def test_second_level_model_glm_computation():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1),)
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# Ordinary Least Squares case
model = SecondLevelModel(mask_img=mask)
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
model.compute_contrast()
labels1 = model.labels_
results1 = model.results_
labels2, results2 = run_glm(
model.masker_.transform(Y), X.values, 'ols')
assert_almost_equal(labels1, labels2, decimal=1)
assert len(results1) == len(results2)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del func_img, FUNCFILE, model, X, Y
def test_make_headings_with_contrasts_title_custom():
model = SecondLevelModel()
test_input = ({'contrast_0': [0, 0, 1],
'contrast_1': [0, 1, 1],
},
'Custom Title for report',
model,
)
expected_output = ('Custom Title for report',
'Custom Title for report',
'Second Level Model',
)
actual_output = glmr._make_headings(*test_input)
assert actual_output == expected_output
def test_make_headings_with_contrasts_title_none():
model = SecondLevelModel()
test_input = ({'contrast_0': [0, 0, 1],
'contrast_1': [0, 1, 1],
},
None,
model
)
expected_output = (
'Report: Second Level Model for contrast_0, contrast_1',
'Statistical Report for contrast_0, contrast_1',
'Second Level Model',
)
actual_output = glmr._make_headings(*test_input)
assert actual_output == expected_output
def test_second_level_model_contrast_computation_with_memory_caching():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1),)
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# Ordinary Least Squares case
model = SecondLevelModel(mask_img=mask, memory='nilearn_cache')
# fit model
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
ncol = len(model.design_matrix_.columns)
c1 = np.eye(ncol)[0, :]
# test memory caching for compute_contrast
model.compute_contrast(c1, output_type='z_score')
# or simply pass nothing
model.compute_contrast()
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del func_img, FUNCFILE, model, X, Y
def test_fmri_inputs():
# Test processing of FMRI inputs
with InTemporaryDirectory():
# prepare fake data
p, q = 80, 10
X = np.random.randn(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)
flms = [flm, flm, flm]
# 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'])
# smoke tests with correct input
# First level models as input
SecondLevelModel(mask_img=mask).fit(flms)
SecondLevelModel().fit(flms)
# Note : the following one creates a singular design matrix
SecondLevelModel().fit(flms, confounds)
SecondLevelModel().fit(flms, None, sdes)
# dataframes as input
SecondLevelModel().fit(niidf)
SecondLevelModel().fit(niidf, confounds)
SecondLevelModel().fit(niidf, confounds, sdes)
SecondLevelModel().fit(niidf, None, sdes)
# niimgs as input
SecondLevelModel().fit(niimgs, None, sdes)
# 4d niimg as input
SecondLevelModel().fit(niimg_4d, None, sdes)
# test wrong input errors
# test first level model requirements
with pytest.raises(ValueError):
SecondLevelModel().fit(flm)
with pytest.raises(ValueError):
SecondLevelModel().fit([flm])
# test dataframe requirements
with pytest.raises(ValueError):
SecondLevelModel().fit(niidf['subject_label'])
# test niimgs requirements
with pytest.raises(ValueError):
SecondLevelModel().fit(niimgs)
with pytest.raises(ValueError):
SecondLevelModel().fit(niimgs + [[]],
confounds)
# test first_level_conditions, confounds, and design
with pytest.raises(ValueError):
SecondLevelModel().fit(flms, ['', []])
with pytest.raises(ValueError):
SecondLevelModel().fit(flms, [])
with pytest.raises(ValueError):
SecondLevelModel().fit(flms, confounds['conf1'])
with pytest.raises(ValueError):
SecondLevelModel().fit(flms, None, [])
def test_second_level_model_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)
# Ordinary Least Squares case
model = SecondLevelModel(mask_img=mask)
# asking for contrast before model fit gives error
with pytest.raises(ValueError):
model.compute_contrast('intercept')
# fit model
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
ncol = len(model.design_matrix_.columns)
c1, cnull = np.eye(ncol)[0, :], np.zeros(ncol)
# smoke test for different contrasts in fixed effects
model.compute_contrast(c1)
z_image = model.compute_contrast(c1, output_type='z_score')
stat_image = model.compute_contrast(c1, output_type='stat')
p_image = model.compute_contrast(c1, output_type='p_value')
effect_image = model.compute_contrast(c1, output_type='effect_size')
variance_image = \
model.compute_contrast(c1, output_type='effect_variance')
# Test output_type='all', and verify images are equivalent
all_images = model.compute_contrast(c1, output_type='all')
assert_array_equal(get_data(all_images['z_score']),
get_data(z_image))
assert_array_equal(get_data(all_images['stat']),
get_data(stat_image))
assert_array_equal(get_data(all_images['p_value']),
get_data(p_image))
assert_array_equal(get_data(all_images['effect_size']),
get_data(effect_image))
assert_array_equal(get_data(all_images['effect_variance']),
get_data(variance_image))
# formula should work (passing variable name directly)
model.compute_contrast('intercept')
# or simply pass nothing
model.compute_contrast()
# passing null contrast should give back a value error
with pytest.raises(ValueError):
model.compute_contrast(cnull)
# passing wrong parameters
with pytest.raises(ValueError):
model.compute_contrast([])
with pytest.raises(ValueError):
model.compute_contrast(c1, None, '')
with pytest.raises(ValueError):
model.compute_contrast(c1, None, [])
with pytest.raises(ValueError):
model.compute_contrast(c1, None, None, '')
# check that passing no explicit contrast when the design
# matrix has more than one columns raises an error
X = pd.DataFrame(np.random.rand(4, 2), columns=['r1', 'r2'])
model = model.fit(Y, design_matrix=X)
with pytest.raises(ValueError):
model.compute_contrast(None)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del func_img, FUNCFILE, model, X, Y
columns=['age', 'sex', 'intercept'])
#############################################################################
# Let's plot the design matrix.
from nilearn.reporting import plot_design_matrix
ax = plot_design_matrix(design_matrix)
ax.set_title('Second level design matrix', fontsize=12)
ax.set_ylabel('maps')
##########################################################################
# Next, we specify and fit the second-level model when loading the data and also
# smooth a little bit to improve statistical behavior.
from nilearn.stats.second_level_model import SecondLevelModel
second_level_model = SecondLevelModel(smoothing_fwhm=2.0, mask_img=mask_img)
second_level_model.fit(gray_matter_map_filenames, design_matrix=design_matrix)
##########################################################################
# Estimating the contrast is very simple. We can just provide the column
# name of the design matrix.
z_map = second_level_model.compute_contrast(second_level_contrast=[1, 0, 0],
output_type='z_score')
###########################################################################
# We threshold the second level contrast at uncorrected p < 0.001 and plot it.
from nilearn import plotting
from nilearn.stats import map_threshold
_, threshold = map_threshold(z_map, alpha=.05, height_control='fdr')
print('The FDR=.05-corrected threshold is: %.3g' % threshold)
fig.suptitle('subjects z_map language network (unc p<0.001)')
plotting.show()
#########################################################################
# Second level model estimation
# -----------------------------
# We just have to provide the list of fitted FirstLevelModel objects
# to the SecondLevelModel object for estimation. We can do this because
# all subjects share a similar design matrix (same variables reflected in
# column names).
from nilearn.stats.second_level_model import SecondLevelModel
second_level_input = models
#########################################################################
# Note that we apply a smoothing of 8mm.
second_level_model = SecondLevelModel(smoothing_fwhm=8.0)
second_level_model = second_level_model.fit(second_level_input)
#########################################################################
# Computing contrasts at the second level is as simple as at the first level.
# Since we are not providing confounders we are performing a one-sample test
# at the second level with the images determined by the specified first level
# contrast.
zmap = second_level_model.compute_contrast(
first_level_contrast='language-string')
#########################################################################
# The group level contrast reveals a left lateralized fronto-temporal
# language network.
plotting.plot_glass_brain(zmap,
colorbar=True,
from nilearn import plotting
############################################################################
# Estimate second level model
# ---------------------------
# We define the input maps and the design matrix for the second level model
# and fit it.
import pandas as pd
second_level_input = data['cmaps']
design_matrix = pd.DataFrame([1] * len(second_level_input),
columns=['intercept'])
############################################################################
# Model specification and fit
from nilearn.stats.second_level_model import SecondLevelModel
second_level_model = SecondLevelModel(smoothing_fwhm=8.0)
second_level_model = second_level_model.fit(second_level_input,
design_matrix=design_matrix)
##########################################################################
# To estimate the contrast is very simple. We can just provide the column
# name of the design matrix.
z_map = second_level_model.compute_contrast(output_type='z_score')
###########################################################################
# We threshold the second level contrast at uncorrected p < 0.001 and plot
from scipy.stats import norm
p_val = 0.001
p001_uncorrected = norm.isf(p_val)
from nilearn.stats import cluster_level_inference
#########################################################################
# Get the set of individual statstical maps (contrast estimates)
cmap_filenames = localizer_dataset.cmaps
#########################################################################
# Perform the second level analysis
# ----------------------------------
#
# First, we define a design matrix for the model. As the model is trivial (one-sample test), the design matrix is just one column with ones.
import pandas as pd
design_matrix = pd.DataFrame([1] * n_samples, columns=['intercept'])
#########################################################################
# Next, we specify and estimate the model.
from nilearn.stats.second_level_model import SecondLevelModel
second_level_model = SecondLevelModel().fit(cmap_filenames,
design_matrix=design_matrix)
#########################################################################
# Compute the only possible contrast: the one-sample test. Since there
# is only one possible contrast, we don't need to specify it in detail.
z_map = second_level_model.compute_contrast(output_type='z_score')
#########################################################################
# Threshold the resulting map:
# false positive rate < .001, cluster size > 10 voxels.
from nilearn.stats import map_threshold
thresholded_map1, threshold1 = map_threshold(z_map,
alpha=.001,
height_control='fpr',
cluster_threshold=10)
############################################################################
# We then assemble those in a design matrix and
design_matrix = pd.DataFrame(np.hstack(
(condition_effect[:, np.newaxis], subject_effect)),
columns=['vertical vs horizontal'] + subjects)
############################################################################
# plot the design_matrix.
from nilearn.reporting import plot_design_matrix
plot_design_matrix(design_matrix)
############################################################################
# We formally specify the analysis model and fit it.
from nilearn.stats.second_level_model import SecondLevelModel
second_level_model = SecondLevelModel().fit(second_level_input,
design_matrix=design_matrix)
##########################################################################
# Estimating the contrast is very simple. We can just provide the column
# name of the design matrix.
z_map = second_level_model.compute_contrast('vertical vs horizontal',
output_type='z_score')
###########################################################################
# We threshold the second level contrast and plot it.
threshold = 3.1 # correponds to p < .001, uncorrected
display = plotting.plot_glass_brain(
z_map,
threshold=threshold,
colorbar=True,
plot_abs=False,
tested_var = tested_var[mask_quality_check].astype(float).reshape((-1, 1))
print("Actual number of subjects after quality check: %d" % n_samples)
############################################################################
# Estimate second level model
# ---------------------------
# We define the input maps and the design matrix for the second level model
# and fit it.
import pandas as pd
design_matrix = pd.DataFrame(np.hstack((tested_var, np.ones_like(tested_var))),
columns=['fluency', 'intercept'])
###########################################################################
# Fit of the second-level model
from nilearn.stats.second_level_model import SecondLevelModel
model = SecondLevelModel(smoothing_fwhm=5.0)
model.fit(contrast_map_filenames, design_matrix=design_matrix)
##########################################################################
# To estimate the contrast is very simple. We can just provide the column
# name of the design matrix.
z_map = model.compute_contrast('fluency', output_type='z_score')
###########################################################################
# We compute the fdr-corrected p = 0.05 threshold for these data
from nilearn.stats import map_threshold
_, threshold = map_threshold(z_map, alpha=.05, height_control='fdr')
###########################################################################
# Let us plot the second level contrast at the computed thresholds
from nilearn import plotting