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_img=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 report_flm_fiac(): # pragma: no cover
data = datasets.func.fetch_fiac_first_level()
fmri_img = [data['func1'], data['func2']]
from nilearn.image import mean_img
mean_img_ = mean_img(fmri_img[0])
design_files = [data['design_matrix1'], data['design_matrix2']]
design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files]
fmri_glm = FirstLevelModel(mask_img=data['mask'], minimize_memory=True)
fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices)
n_columns = design_matrices[0].shape[1]
contrasts = {
'SStSSp_minus_DStDSp': _pad_vector([1, 0, 0, -1], n_columns),
'DStDSp_minus_SStSSp': _pad_vector([-1, 0, 0, 1], n_columns),
'DSt_minus_SSt': _pad_vector([-1, -1, 1, 1], n_columns),
'DSp_minus_SSp': _pad_vector([-1, 1, -1, 1], n_columns),
'DSt_minus_SSt_for_DSp': _pad_vector([0, -1, 0, 1], n_columns),
'DSp_minus_SSp_for_DSt': _pad_vector([0, 0, -1, 1], n_columns),
'Deactivation': _pad_vector([-1, -1, -1, -1, 4], n_columns),
'Effects_of_interest': np.eye(n_columns)[:5]
}
report = make_glm_report(
fmri_glm,
contrasts,
bg_img=mean_img_,
height_control='fdr',
)
output_filename = 'generated_report_flm_fiac.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
def report_flm_bids_features(): # pragma: no cover
data_dir = _fetch_bids_data()
model, subject = _make_flm(data_dir)
title = 'FLM Bids Features Stat maps'
report = make_glm_report(
model=model,
contrasts='StopSuccess - Go',
title=title,
cluster_threshold=3,
)
output_filename = 'generated_report_flm_bids_features.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
def report_flm_adhd_dmn(): # pragma: no cover
t_r = 2.
slice_time_ref = 0.
n_scans = 176
pcc_coords = (0, -53, 26)
adhd_dataset = nilearn.datasets.fetch_adhd(n_subjects=1)
seed_masker = NiftiSpheresMasker([pcc_coords],
radius=10,
detrend=True,
standardize=True,
low_pass=0.1,
high_pass=0.01,
t_r=2.,
memory='nilearn_cache',
memory_level=1,
verbose=0)
seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])
frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)
design_matrix = make_first_level_design_matrix(frametimes,
hrf_model='spm',
add_regs=seed_time_series,
add_reg_names=["pcc_seed"])
dmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))
contrasts = {'seed_based_glm': dmn_contrast}
first_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)
first_level_model = first_level_model.fit(run_imgs=adhd_dataset.func[0],
design_matrices=design_matrix)
report = make_glm_report(
first_level_model,
contrasts=contrasts,
title='ADHD DMN Report',
cluster_threshold=15,
height_control='bonferroni',
min_distance=8.,
plot_type='glass',
report_dims=(1200, 'a'),
)
output_filename = 'generated_report_flm_adhd_dmn.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
plot_abs=False,
display_mode='z',
figure=plt.figure(figsize=(4, 4)))
plt.show()
###############################################################################
# We can get a latex table from a Pandas Dataframe for display and publication purposes
from nilearn.reporting import get_clusters_table
print(get_clusters_table(z_map, norm.isf(0.001), 10).to_latex())
#########################################################################
# Generating a report
# -------------------
# Using the computed FirstLevelModel and contrast information,
# we can quickly create a summary report.
from nilearn.reporting import make_glm_report
report = make_glm_report(
model=model,
contrasts='StopSuccess - Go',
)
#########################################################################
# We have several ways to access the report:
# report # This report can be viewed in a notebook
# report.save_as_html('report.html')
# report.open_in_browser()
plotting.plot_stat_map(
z_map, threshold=threshold, colorbar=True,
title='sex effect on grey matter density (FDR = .05)')
###########################################################################
# Note that there does not seem to be any significant effect of sex on
# grey matter density on that dataset.
###########################################################################
# Generating a report
# -------------------
# It can be useful to quickly generate a
# portable, ready-to-view report with most of the pertinent information.
# This is easy to do if you have a fitted model and the list of contrasts,
# which we do here.
from nilearn.reporting import make_glm_report
icbm152_2009 = datasets.fetch_icbm152_2009()
report = make_glm_report(model=second_level_model,
contrasts=['age', 'sex'],
bg_img=icbm152_2009['t1'],
)
#########################################################################
# We have several ways to access the report:
# report # This report can be viewed in a notebook
# report.save_as_html('report.html')
# report.open_in_browser()
def first_level(subject_dic,
additional_regressors=None,
compcorr=False,
smooth=None,
mesh=False,
mask_img=None):
""" Run the first-level analysis (GLM fitting + statistical maps)
in a given subject
Parameters
----------
subject_dic: dict,
exhaustive description of an individual acquisition
additional_regressors: dict or None,
additional regressors provided as an already sampled
design_matrix
dictionary keys are session_ids
compcorr: Bool, optional,
whether confound estimation and removal should be done or not
smooth: float or None, optional,
how much the data should spatially smoothed during masking
"""
start_time = time.ctime()
# experimental paradigm meta-params
motion_names = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz']
hrf_model = subject_dic['hrf_model']
high_pass = subject_dic['high_pass']
drift_model = subject_dic['drift_model']
tr = subject_dic['TR']
slice_time_ref = 1.
if not mesh and (mask_img is None):
mask_img = masking(subject_dic['func'], subject_dic['output_dir'])
if additional_regressors is None:
additional_regressors = dict([
(session_id, None) for session_id in subject_dic['session_id']
])
for session_id, fmri_path, onset, motion_path in zip(
subject_dic['session_id'], subject_dic['func'],
subject_dic['onset'], subject_dic['realignment_parameters']):
task_id = _session_id_to_task_id([session_id])[0]
if mesh is not False:
from nibabel.gifti import read
n_scans = np.array(
[darrays.data for darrays in read(fmri_path).darrays]).shape[0]
else:
n_scans = nib.load(fmri_path).shape[3]
# motion parameters
motion = np.loadtxt(motion_path)
# define the time stamps for different images
frametimes = np.linspace(slice_time_ref,
(n_scans - 1 + slice_time_ref) * tr, n_scans)
if task_id == 'audio':
mask = np.array([1, 0, 1, 1, 0, 1, 1, 0, 1, 1])
n_cycles = 28
cycle_duration = 20
t_r = 2
cycle = np.arange(0, cycle_duration, t_r)[mask > 0]
frametimes = np.tile(cycle, n_cycles) +\
np.repeat(np.arange(n_cycles) * cycle_duration, mask.sum())
frametimes = frametimes[:-2] # for some reason...
if mesh is not False:
compcorr = False # XXX Fixme
if compcorr:
confounds = high_variance_confounds(fmri_path, mask_img=mask_img)
confounds = np.hstack((confounds, motion))
confound_names = ['conf_%d' % i for i in range(5)] + motion_names
else:
confounds = motion
confound_names = motion_names
if onset is None:
warnings.warn('Onset file not provided. Trying to guess it')
task = os.path.basename(fmri_path).split('task')[-1][4:]
onset = os.path.join(
os.path.split(os.path.dirname(fmri_path))[0], 'model001',
'onsets', 'task' + task + '_run001', 'task%s.csv' % task)
if not os.path.exists(onset):
warnings.warn('non-existant onset file. proceeding without it')
paradigm = None
else:
paradigm = make_paradigm(onset, task_id)
# handle manually supplied regressors
add_reg_names = []
if additional_regressors[session_id] is None:
add_regs = confounds
else:
df = read_csv(additional_regressors[session_id])
add_regs = []
for regressor in df:
add_reg_names.append(regressor)
add_regs.append(df[regressor])
add_regs = np.array(add_regs).T
add_regs = np.hstack((add_regs, confounds))
add_reg_names += confound_names
# create the design matrix
design_matrix = make_first_level_design_matrix(
frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
high_pass=high_pass,
add_regs=add_regs,
add_reg_names=add_reg_names)
_, dmtx, names = check_design_matrix(design_matrix)
# create the relevant contrasts
contrasts = make_contrasts(task_id, names)
if mesh == 'fsaverage5':
# this is low-resolution data
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_fsaverage5_%s' % session_id)
elif mesh == 'fsaverage7':
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_fsaverage7_%s' % session_id)
elif mesh == 'individual':
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_individual_%s' % session_id)
else:
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_stats_%s' % session_id)
if not os.path.exists(subject_session_output_dir):
os.makedirs(subject_session_output_dir)
np.savez(os.path.join(subject_session_output_dir, 'design_matrix.npz'),
design_matrix=design_matrix)
if mesh is not False:
run_surface_glm(design_matrix, contrasts, fmri_path,
subject_session_output_dir)
else:
z_maps, fmri_glm = run_glm(design_matrix,
contrasts,
fmri_path,
mask_img,
subject_dic,
subject_session_output_dir,
tr=tr,
slice_time_ref=slice_time_ref,
smoothing_fwhm=smooth)
# do stats report
anat_img = nib.load(subject_dic['anat'])
stats_report_filename = os.path.join(subject_session_output_dir,
'report_stats.html')
report = make_glm_report(
fmri_glm,
contrasts,
threshold=3.0,
bg_img=anat_img,
cluster_threshold=15,
title="GLM for subject %s" % session_id,
)
report.save_as_html(stats_report_filename)
def generate_report(self,
contrasts,
title=None,
bg_img="MNI152TEMPLATE",
threshold=3.09,
alpha=0.001,
cluster_threshold=0,
height_control='fpr',
min_distance=8.,
plot_type='slice',
display_mode=None,
report_dims=(1600, 800)):
""" Returns HTMLDocument object
for a report which shows all important aspects of a fitted GLM.
The object can be opened in a browser, displayed in a notebook,
or saved to disk as a standalone HTML file.
The GLM must be fitted and have the computed design matrix(ces).
Parameters
----------
A fitted first or second level model object.
contrasts: Dict[string, ndarray] or String or List[String] or ndarray or
List[ndarray]
Contrasts information for a first or second level model.
Example:
Dict of contrast names and coefficients,
or list of contrast names
or list of contrast coefficients
or contrast name
or contrast coefficient
Each contrast name must be a string.
Each contrast coefficient must be a list or numpy array of ints.
Contrasts are passed to ``contrast_def`` for FirstLevelModel
(:func:`nilearn.glm.first_level.FirstLevelModel.compute_contrast`)
& second_level_contrast for SecondLevelModel
(:func:`nilearn.glm.second_level.SecondLevelModel.compute_contrast`)
title: String, optional
If string, represents the web page's title and primary heading,
model type is sub-heading.
If None, page titles and headings are autogenerated
using contrast names.
bg_img: Niimg-like object
Default is the MNI152 template
See http://nilearn.github.io/manipulating_images/input_output.html
The background image for mask and stat maps to be plotted on upon.
To turn off background image, just pass "bg_img=None".
threshold: float
Default is 3.09
Cluster forming threshold in same scale as `stat_img` (either a
t-scale or z-scale value). Used only if height_control is None.
alpha: float
Default is 0.001
Number controlling the thresholding (either a p-value or q-value).
Its actual meaning depends on the height_control parameter.
This function translates alpha to a z-scale threshold.
cluster_threshold: int, optional
Default is 0
Cluster size threshold, in voxels.
height_control: string or None
false positive control meaning of cluster forming
threshold: 'fpr' (default) or 'fdr' or 'bonferroni' or None
min_distance: `float`
For display purposes only.
Minimum distance between subpeaks in mm. Default is 8 mm.
plot_type: String. ['slice' (default) or 'glass']
Specifies the type of plot to be drawn for the statistical maps.
display_mode: string
Default is 'z' if plot_type is 'slice'; '
ortho' if plot_type is 'glass'.
Choose the direction of the cuts:
'x' - sagittal, 'y' - coronal, 'z' - axial,
'l' - sagittal left hemisphere only,
'r' - sagittal right hemisphere only,
'ortho' - three cuts are performed in orthogonal directions.
Possible values are:
'ortho', 'x', 'y', 'z', 'xz', 'yx', 'yz',
'l', 'r', 'lr', 'lzr', 'lyr', 'lzry', 'lyrz'.
report_dims: Sequence[int, int]
Default is (1600, 800) pixels.
Specifies width, height (in pixels) of report window within a notebook.
Only applicable when inserting the report into a Jupyter notebook.
Can be set after report creation using report.width, report.height.
Returns
-------
report_text: HTMLDocument Object
Contains the HTML code for the GLM Report.
"""
from nilearn.reporting import make_glm_report
return make_glm_report(self,
contrasts,
title=title,
bg_img=bg_img,
threshold=threshold,
alpha=alpha,
cluster_threshold=cluster_threshold,
height_control=height_control,
min_distance=min_distance,
plot_type=plot_type,
display_mode=display_mode,
report_dims=report_dims)
plotting.plot_stat_map(
z_map, bg_img=mean_img_, threshold=3.0,
title='%s, fixed effects' % contrast_id)
plotting.show()
#########################################################################
# Not unexpectedly, the fixed effects version displays higher peaks than the
# input sessions. Computing fixed effects enhances the signal-to-noise ratio of
# the resulting brain maps.
#########################################################################
# Generating a report
# -------------------
# Since we have already computed the FirstLevelModel and
# and have the contrast, we can quickly create a summary report.
from nilearn.reporting import make_glm_report
report = make_glm_report(fmri_glm,
contrasts,
bg_img=mean_img_,
)
#########################################################################
# We have several ways to access the report:
# report # This report can be viewed in a notebook
# report.save_as_html('report.html')
# report.open_in_browser()
for condition_ in conditions:
z_maps.append(glm.compute_contrast(condition_))
condition_idx.append(condition_)
session_idx.append(session)
#########################################################################
# Generating a report
# -------------------
# Since we have already computed the FirstLevelModel
# and have the contrast, we can quickly create a summary report.
from nilearn.image import mean_img
from nilearn.reporting import make_glm_report
mean_img_ = mean_img(func_filename)
report = make_glm_report(glm,
contrasts=conditions,
bg_img=mean_img_,
)
#############################################################################
# In a jupyter notebook, the report will be automatically inserted, as above.
# We have several other ways to access the report:
# report # This report can be viewed in a notebook
# report.save_as_html('report.html')
# report.open_in_browser()
#############################################################################
# Transform the maps to an array of values
# ----------------------------------------
from nilearn.input_data import NiftiMasker
marker_color='g',
marker_size=300)
display.savefig(filename)
print("Save z-map in '{0}'.".format(filename))
###########################################################################
# Generating a report
# -------------------
# It can be useful to quickly generate a
# portable, ready-to-view report with most of the pertinent information.
# This is easy to do if you have a fitted model and the list of contrasts,
# which we do here.
from nilearn.reporting import make_glm_report
report = make_glm_report(
first_level_model,
contrasts=contrasts,
title='ADHD DMN Report',
cluster_threshold=15,
min_distance=8.,
plot_type='glass',
)
#########################################################################
# We have several ways to access the report:
# report # This report can be viewed in a notebook
# report.save_as_html('report.html')
# report.open_in_browser()
