def test_first_level_model_design_creation():
# Test processing of FMRI inputs
with InTemporaryDirectory():
shapes = ((7, 8, 9, 10),)
mask, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# basic test based on basic_paradigm and glover hrf
t_r = 10.0
slice_time_ref = 0.
events = basic_paradigm()
model = FirstLevelModel(t_r, slice_time_ref, mask=mask,
drift_model='polynomial', drift_order=3)
model = model.fit(func_img, events)
frame1, X1, names1 = check_design_matrix(model.design_matrices_[0])
# check design computation is identical
n_scans = func_img.get_data().shape[3]
start_time = slice_time_ref * t_r
end_time = (n_scans - 1 + slice_time_ref) * t_r
frame_times = np.linspace(start_time, end_time, n_scans)
design = make_first_level_design_matrix(frame_times, events,
drift_model='polynomial', drift_order=3)
frame2, X2, names2 = check_design_matrix(design)
assert_array_equal(frame1, frame2)
assert_array_equal(X1, X2)
assert_array_equal(names1, names2)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del FUNCFILE, mask, model, func_img
def test_first_level_model_design_creation():
# Test processing of FMRI inputs
with InTemporaryDirectory():
shapes = ((7, 8, 9, 10),)
mask, FUNCFILE, _ = write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# basic test based on basic_paradigm and glover hrf
t_r = 1.0
slice_time_ref = 0.
paradigm = basic_paradigm()
model = FirstLevelModel(t_r, slice_time_ref, mask=mask,
drift_model='polynomial', drift_order=3)
model = model.fit(func_img, paradigm)
frame1, X1, names1 = check_design_matrix(model.design_matrices_[0])
# check design computation is identical
n_scans = func_img.get_data().shape[3]
start_time = slice_time_ref * t_r
end_time = (n_scans - 1 + slice_time_ref) * t_r
frame_times = np.linspace(start_time, end_time, n_scans)
design = make_design_matrix(frame_times, paradigm,
drift_model='polynomial', drift_order=3)
frame2, X2, names2 = check_design_matrix(design)
assert_array_equal(frame1, frame2)
assert_array_equal(X1, X2)
assert_array_equal(names1, names2)
def test_first_level_model_design_creation():
# Test processing of FMRI inputs
with InTemporaryDirectory():
shapes = ((7, 8, 9, 10), )
mask, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# basic test based on basic_paradigm and glover hrf
t_r = 10.0
slice_time_ref = 0.
events = basic_paradigm()
model = FirstLevelModel(t_r,
slice_time_ref,
mask_img=mask,
drift_model='polynomial',
drift_order=3)
model = model.fit(func_img, events)
frame1, X1, names1 = check_design_matrix(model.design_matrices_[0])
# check design computation is identical
n_scans = get_data(func_img).shape[3]
start_time = slice_time_ref * t_r
end_time = (n_scans - 1 + slice_time_ref) * t_r
frame_times = np.linspace(start_time, end_time, n_scans)
design = make_first_level_design_matrix(frame_times,
events,
drift_model='polynomial',
drift_order=3)
frame2, X2, names2 = check_design_matrix(design)
assert_array_equal(frame1, frame2)
assert_array_equal(X1, X2)
assert_array_equal(names1, names2)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del FUNCFILE, mask, model, func_img
def test_csv_io():
# test the csv io on design matrices
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
paradigm = modulated_event_paradigm()
DM = make_design_matrix(frame_times, paradigm, hrf_model="glover", drift_model="polynomial", drift_order=3)
path = "design_matrix.csv"
with InTemporaryDirectory():
DM.to_csv(path)
DM2 = pd.DataFrame().from_csv(path)
_, matrix, names = check_design_matrix(DM)
_, matrix_, names_ = check_design_matrix(DM2)
assert_almost_equal(matrix, matrix_)
assert_equal(names, names_)
def test_design_matrix0c():
# test design matrix creation when regressors are provided manually
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
_, X, names = check_design_matrix(
make_design_matrix(frame_times, drift_model="polynomial", drift_order=3, add_regs=ax)
)
assert_almost_equal(X[:, 0], ax[:, 0])
ax = np.random.randn(127, 4)
assert_raises_regex(
AssertionError,
"Incorrect specification of additional regressors:.",
make_design_matrix,
frame_times,
add_regs=ax,
)
ax = np.random.randn(128, 4)
assert_raises_regex(
ValueError,
"Incorrect number of additional regressor names.",
make_design_matrix,
frame_times,
add_regs=ax,
add_reg_names="",
)
def test_csv_io():
# test the csv io on design matrices
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
events = modulated_event_paradigm()
DM = make_first_level_design_matrix(frame_times, events, hrf_model='glover',
drift_model='polynomial', drift_order=3)
path = 'design_matrix.csv'
with InTemporaryDirectory():
DM.to_csv(path)
DM2 = pd.DataFrame().from_csv(path)
_, matrix, names = check_design_matrix(DM)
_, matrix_, names_ = check_design_matrix(DM2)
assert_almost_equal(matrix, matrix_)
assert_equal(names, names_)
def design_matrix_light(
frame_times,
paradigm=None,
hrf_model="glover",
drift_model="cosine",
period_cut=128,
drift_order=1,
fir_delays=[0],
add_regs=None,
add_reg_names=None,
min_onset=-24,
path=None,
):
""" Idem make_design_matrix, but only returns the computed matrix
and associated names """
dmtx = make_design_matrix(
frame_times,
paradigm,
hrf_model,
drift_model,
period_cut,
drift_order,
fir_delays,
add_regs,
add_reg_names,
min_onset,
)
_, matrix, names = check_design_matrix(dmtx)
return matrix, names
def test_design_matrix0():
# Test design matrix creation when no paradigm is provided
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
_, X, names = check_design_matrix(make_design_matrix(frame_times, drift_model="polynomial", drift_order=3))
assert_equal(len(names), 4)
x = np.linspace(-0.5, 0.5, 128)
assert_almost_equal(X[:, 0], x)
def test_design_matrix0():
# Test design matrix creation when no experimental paradigm is provided
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
_, X, names = check_design_matrix(make_first_level_design_matrix(
frame_times, drift_model='polynomial', drift_order=3))
assert_equal(len(names), 4)
x = np.linspace(- 0.5, .5, 128)
assert_almost_equal(X[:, 0], x)
def test_design_matrix0d():
# test design matrix creation when regressors are provided manually
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
_, X, names = check_design_matrix(make_first_level_design_matrix(
frame_times, drift_model='polynomial', drift_order=3, add_regs=ax))
assert_equal(len(names), 8)
assert_equal(X.shape[1], 8)
def test_design_matrix0d():
# test design matrix creation when regressors are provided manually
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
_, X, names = check_design_matrix(make_design_matrix(
frame_times, drift_model='polynomial', drift_order=3, add_regs=ax))
assert_equal(len(names), 8)
assert_equal(X.shape[1], 8)
def design_matrix_light(
frame_times, events=None, hrf_model='glover',
drift_model='cosine', period_cut=128, drift_order=1, fir_delays=[0],
add_regs=None, add_reg_names=None, min_onset=-24, path=None):
""" Idem make_first_level_design_matrix, but only returns the computed matrix
and associated names """
dmtx = make_first_level_design_matrix(frame_times, events, hrf_model,
drift_model, period_cut, drift_order, fir_delays,
add_regs, add_reg_names, min_onset)
_, matrix, names = check_design_matrix(dmtx)
return matrix, names
def test_design_matrix0c():
# test design matrix creation when regressors are provided manually
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
_, X, names = check_design_matrix(
make_design_matrix(frame_times,
drift_model='polynomial',
drift_order=3,
add_regs=ax))
assert_almost_equal(X[:, 0], ax[:, 0])
def test_spm_1():
# Check that the nistats design matrix is close enough to the SPM one
# (it cannot be identical, because the hrf shape is different)
frame_times = np.linspace(0, 99, 100)
conditions = ["c0", "c0", "c0", "c1", "c1", "c1", "c2", "c2", "c2"]
onsets = [30, 50, 70, 10, 30, 80, 30, 40, 60]
paradigm = pd.DataFrame({"name": conditions, "onset": onsets})
X1 = make_design_matrix(frame_times, paradigm, drift_model="blank")
_, matrix, _ = check_design_matrix(X1)
spm_design_matrix = DESIGN_MATRIX["arr_0"]
assert_true(((spm_design_matrix - matrix) ** 2).sum() / (spm_design_matrix ** 2).sum() < 0.1)
def test_spm_1():
# Check that the nistats design matrix is close enough to the SPM one
# (it cannot be identical, because the hrf shape is different)
frame_times = np.linspace(0, 99, 100)
conditions = ['c0', 'c0', 'c0', 'c1', 'c1', 'c1', 'c2', 'c2', 'c2']
onsets = [30, 50, 70, 10, 30, 80, 30, 40, 60]
paradigm = pd.DataFrame({'name': conditions, 'onset': onsets})
X1 = make_design_matrix(frame_times, paradigm, drift_model='blank')
_, matrix, _ = check_design_matrix(X1)
spm_design_matrix = DESIGN_MATRIX['arr_0']
assert_true(((spm_design_matrix - matrix)**2).sum() /
(spm_design_matrix**2).sum() < .1)
def plot_design_matrix(design_matrix, rescale=True, ax=None, output_file=None):
"""Plot a design matrix provided as a DataFrame
Parameters
----------
design matrix : pandas DataFrame,
Describes a design matrix.
rescale : bool, optional
Rescale columns magnitude for visualization or not.
ax : axis handle, optional
Handle to axis onto which we will draw design matrix.
output_file: string or None, optional,
The name of an image file to export the plot to. Valid extensions
are .png, .pdf, .svg. If output_file is not None, the plot
is saved to a file, and the display is closed.
Returns
-------
ax: axis handle
The axis used for plotting.
"""
# We import _set_mpl_backend because just the fact that we are
# importing it sets the backend
from nilearn.plotting import _set_mpl_backend
# avoid unhappy pyflakes
_set_mpl_backend
# normalize the values per column for better visualization
_, X, names = check_design_matrix(design_matrix)
if rescale:
X = X / np.maximum(1.e-12, np.sqrt(
np.sum(X ** 2, 0))) # pylint: disable=no-member
if ax is None:
plt.figure()
ax = plt.subplot(1, 1, 1)
ax.imshow(X, interpolation='nearest', aspect='auto')
ax.set_label('conditions')
ax.set_ylabel('scan number')
ax.set_xticks(range(len(names)))
ax.set_xticklabels(names, rotation=60, ha='right')
plt.tight_layout()
if output_file is not None:
plt.savefig(output_file)
plt.close()
ax = None
return ax
def test_spm_1():
# Check that the nistats design matrix is close enough to the SPM one
# (it cannot be identical, because the hrf shape is different)
frame_times = np.linspace(0, 99, 100)
conditions = ['c0', 'c0', 'c0', 'c1', 'c1', 'c1', 'c2', 'c2', 'c2']
onsets = [30, 50, 70, 10, 30, 80, 30, 40, 60]
paradigm = pd.DataFrame({'name': conditions,
'onset': onsets})
X1 = make_design_matrix(frame_times, paradigm, drift_model='blank')
_, matrix, _ = check_design_matrix(X1)
spm_design_matrix = DESIGN_MATRIX['arr_0']
assert_true(((spm_design_matrix - matrix) ** 2).sum() /
(spm_design_matrix ** 2).sum() < .1)
def _hcp_regions_selection(fmri_img,
subject_id,
n_voxels=10,
n_jobs=1,
verbose=False):
"""GLM on HCP dataset."""
paradigm, t_frames = get_paradigm_hcp(subject_id)
d_m = make_first_level_design_matrix(t_frames,
paradigm,
hrf_model='spm',
drift_model='Cosine',
period_cut=2 * 2 * EPOCH_DUR_HCP)
glm = FirstLevelModel(t_r=TR_HCP,
slice_time_ref=0.0,
noise_model='ols',
min_onset=10.0,
signal_scaling=False,
smoothing_fwhm=6.0,
standardize=False,
memory_level=1,
memory='./.cachedir',
minimize_memory=False,
n_jobs=n_jobs)
glm.fit(run_imgs=fmri_img, design_matrices=d_m)
_, _, names = check_design_matrix(d_m)
n_names = len(names)
c_val = dict([(n, c) for n, c in zip(names, np.eye(n_names))])
c_val['rh-lh'] = c_val['rh'] - c_val['lh']
c_val['lh-rh'] = c_val['lh'] - c_val['rh']
z_maps = dict([(n, glm.compute_contrast(c, output_type='z_score'))
for n, c in c_val.iteritems()])
z_maps = {
'rh': z_maps['rh-lh'],
'lh': z_maps['lh-rh'],
'cue': z_maps['cue']
}
region_mask_imgs = {}
for name, _ in [('rh', 'rh-lh'), ('lh', 'lh-rh'), ('cue', 'cue')]:
z_map_vector_mask = glm.masker_.transform(z_maps[name]).flatten()
z_region_vector_mask = mask_n_max(z_map_vector_mask, n_voxels)
z_region_vector_mask = z_region_vector_mask.astype(float)
region_mask_imgs[name] = \
glm.masker_.inverse_transform(z_region_vector_mask)
return d_m, z_maps, region_mask_imgs
def test_spm_2():
# Check that the nistats design matrix is close enough to the SPM one
# (it cannot be identical, because the hrf shape is different)
frame_times = np.linspace(0, 99, 100)
conditions = ['c0', 'c0', 'c0', 'c1', 'c1', 'c1', 'c2', 'c2', 'c2']
onsets = [30, 50, 70, 10, 30, 80, 30, 40, 60]
durations = 10 * np.ones(9)
events = pd.DataFrame({'trial_type': conditions,
'onset': onsets,
'duration': durations})
X1 = make_first_level_design_matrix(frame_times, events, drift_model=None)
spm_design_matrix = DESIGN_MATRIX['arr_1']
_, matrix, _ = check_design_matrix(X1)
assert_true(((spm_design_matrix - matrix) ** 2).sum() /
(spm_design_matrix ** 2).sum() < .1)
def test_design_matrix0c():
# test design matrix creation when regressors are provided manually
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
_, X, names = check_design_matrix(make_first_level_design_matrix(
frame_times, drift_model='polynomial',
drift_order=3, add_regs=ax))
assert_almost_equal(X[:, 0], ax[:, 0])
ax = np.random.randn(127, 4)
assert_raises_regex(
AssertionError,
"Incorrect specification of additional regressors:.",
make_first_level_design_matrix, frame_times, add_regs=ax)
ax = np.random.randn(128, 4)
assert_raises_regex(
ValueError,
"Incorrect number of additional regressor names.",
make_first_level_design_matrix, frame_times, add_regs=ax, add_reg_names='')
def first_level(subject_dic,
additional_regressors=None,
compcorr=False,
smooth=None,
surface=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']
hfcut = subject_dic['hfcut']
drift_model = subject_dic['drift_model']
tr = subject_dic['TR']
if not surface 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']):
paradigm_id = _session_id_to_task_id([session_id])[0]
if surface:
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(0, (n_scans - 1) * tr, n_scans)
if paradigm_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 surface:
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, paradigm_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,
period_cut=hfcut,
add_regs=add_regs,
add_reg_names=add_reg_names)
_, dmtx, names = check_design_matrix(design_matrix)
# create the relevant contrasts
contrasts = make_contrasts(paradigm_id, names)
if surface:
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_surf_%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 surface:
run_surface_glm(design_matrix, contrasts, fmri_path,
subject_session_output_dir)
else:
z_maps = run_glm(design_matrix,
contrasts,
fmri_path,
mask_img,
subject_dic,
subject_session_output_dir,
tr=tr,
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')
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
mask_img,
threshold=3.,
cluster_th=15,
anat=anat_img,
anat_affine=anat_img.affine,
design_matrices=[design_matrix],
subject_id=subject_dic['subject_id'],
start_time=start_time,
title="GLM for subject %s" % session_id,
# additional ``kwargs`` for more informative report
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
frametimes=frametimes,
drift_model=drift_model,
hrf_model=hrf_model,
)
if not surface:
ProgressReport().finish_dir(subject_session_output_dir)
print("Statistic report written to %s\r\n" % stats_report_filename)
def do_subject_glm(subject_id):
subject_output_dir = os.path.join(output_dir, subject_id)
# make design matrices
design_matrices = []
func = []
anat = os.path.join(subject_output_dir, "anatomy", "whighres001_brain.nii")
for run_path in sorted(
glob.glob(
os.path.join(data_dir, subject_id,
"model/model001/onsets/task*"))):
run_id = os.path.basename(run_path)
run_func = glob.glob(
os.path.join(subject_output_dir, "BOLD", run_id, "wrbold*.nii"))
assert len(run_func) == 1
run_func = run_func[0]
run_onset_paths = sorted(
glob.glob(
os.path.join(data_dir, subject_id,
"model/model001/onsets/%s/*" % run_id)))
onsets = map(np.loadtxt, run_onset_paths)
conditions = np.hstack([[condition_keys["cond%03i" % (c + 1)]] *
len(onsets[c])
for c in range(len(run_onset_paths))])
onsets = np.vstack((onsets))
onsets *= tr
run_func = nibabel.load(run_func)
func.append(run_func)
n_scans = run_func.shape[-1]
onset, duration, modulation = onsets.T
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
paradigm = pd.DataFrame(
dict(name=conditions,
onset=onset,
duration=duration,
modulation=modulation))
design_matrix = make_design_matrix(frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut)
design_matrices.append(design_matrix)
n_runs = len(func)
# specify contrasts
_, _, names = check_design_matrix(design_matrix)
n_columns = len(names)
contrast_matrix = np.eye(n_columns)
contrasts = {}
for c in range(len(condition_keys)):
contrasts[names[2 * c]] = contrast_matrix[2 * c]
contrasts["avg"] = np.mean(contrasts.values(), axis=0)
# more interesting contrasts
contrasts_ = {}
for contrast, val in contrasts.items():
if not contrast == "avg":
contrasts_["%s_minus_avg" % contrast] = val - contrasts["avg"]
contrasts = contrasts_
# fit GLM
from nilearn.image import smooth_img
func = smooth_img(func, fwhm=8.)
print('Fitting a GLM (this takes time)...')
fmri_glm = FMRILinearModel(func, [
check_design_matrix(design_matrix)[1]
for design_matrix in design_matrices
],
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
# save computed mask
mask_path = os.path.join(subject_output_dir, "mask.nii")
print("Saving mask image to %s ..." % mask_path)
nibabel.save(fmri_glm.mask, mask_path)
# compute contrast maps
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.items():
print("\tcontrast id: %s" % contrast_id)
z_map, t_map, effects_map, var_map = fmri_glm.contrast(
[contrast_val] * n_runs,
con_id=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True)
for map_type, out_map in zip(['z', 't', 'effects', 'variance'],
[z_map, t_map, effects_map, var_map]):
map_dir = os.path.join(subject_output_dir, '%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, '%s.nii.gz' % contrast_id)
print("\t\tWriting %s ..." % map_path)
nibabel.save(out_map, map_path)
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# # generate stats report
# stats_report_filename = os.path.join(subject_output_dir, "reports",
# "report_stats.html")
# generate_subject_stats_report(
# stats_report_filename, contrasts, z_maps, fmri_glm.mask, anat=anat,
# threshold=2.3, cluster_th=15, design_matrices=design_matrices, TR=tr,
# subject_id="sub001", n_scans=n_scans, hfcut=hfcut,
# paradigm=paradigm, frametimes=frametimes,
# drift_model=drift_model, hrf_model=hrf_model)
# ProgressReport().finish_dir(subject_output_dir)
return dict(subject_id=subject_id,
mask=mask_path,
effects_maps=effects_maps,
z_maps=z_maps,
contrasts=contrasts)
frametimes = np.linspace(0, (nscans - 1) * tr, nscans)
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
period_cut = 2 * 2 * epoch_duration
design_matrix = make_design_matrix(
frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model,
period_cut=period_cut)
# plot and save design matrix
ax = plot_design_matrix(design_matrix)
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
# specify contrasts
contrasts = {}
_, matrix, names = check_design_matrix(design_matrix)
contrast_matrix = np.eye(len(names))
for i in range(len(names)):
contrasts[names[i]] = contrast_matrix[i]
# Use a more interesting contrast
contrasts = {'active-rest': contrasts['active'] - contrasts['rest']}
# fit GLM
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FirstLevelGLM(noise_model='ar1', standardize=False).fit(
[subject_data.func], matrix)
# compute bg unto which activation will be projected
mean_img = mean_img(subject_data.func)
scrambled_onsets = timing['onsets'][1].ravel()
onsets = np.hstack((faces_onsets, scrambled_onsets))
onsets *= tr # because onsets were reporting in 'scans' units
conditions = ['faces'] * len(faces_onsets) + ['scrambled'] * len(
scrambled_onsets)
paradigm = DataFrame({'name': conditions, 'onset': onsets})
# build design matrix
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
design_matrix = make_design_matrix(
frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model,
period_cut=period_cut)
design_matrices.append(design_matrix)
# specify contrasts
_, matrix, names = check_design_matrix(design_matrix)
contrasts = {}
n_columns = len(names)
contrast_matrix = np.eye(n_columns)
for i in range(2):
contrasts[names[2 * i]] = contrast_matrix[2 * i]
# more interesting contrasts
contrasts['faces-scrambled'] = contrasts['faces'] - contrasts['scrambled']
contrasts['scrambled-faces'] = -contrasts['faces-scrambled']
contrasts['effects_of_interest'] = np.vstack((contrasts['faces'],
contrasts['scrambled']))
# fit GLM
print('Fitting a GLM (this takes time)...')
fmri_glm = FMRILinearModel(
def first_level(subject):
subject_id = subject['subject_id']
data_dir = subject['output_dir']
subject_session_output_dir = os.path.join(data_dir, 'res_stats')
if not os.path.exists(subject_session_output_dir):
os.makedirs(subject_session_output_dir)
design_matrices=[]
for e, i in enumerate(subject['func']) :
# Parameters
tr = subject['TR']
drift_model = None
hrf_model = 'spm' # hemodynamic reponse function
hfcut = 128.
fwhm = [5, 5, 5]
n_scans = nibabel.load(subject['func'][e]).shape[3]
# Preparation of paradigm
events_file = subject['onset'][e]
paradigm = paradigm_contrasts.localizer_paradigm(events_file)
# Motion parameter
motion_path = subject['realignment_parameters'][e]
motion_names = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz']
motion = np.loadtxt(motion_path)
# Build design matrix
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
design_matrix = make_first_level_design_matrix(
frametimes, paradigm, hrf_model=hrf_model, drift_model=drift_model,
high_pass=hfcut, add_regs=motion,
add_reg_names=motion_names)
_, dmtx, names = check_design_matrix(design_matrix)
design_matrices.append(design_matrix)
#print(names)
# Specify contrasts
contrasts = paradigm_contrasts.localizer_contrasts(design_matrix)
# GLM Analysis
print('Fitting a GLM (this takes time)...')
#for mask_img; use the False or the mask of t1 mni template
#the computed mask by default on fmri seems not always correct.
# For a specific mask, try this:
#mask_path = os.path.join(subject_session_output_dir, "mask.nii.gz")
#mask = compute_epi_mask(fmri_f)
#nibabel.save(mask , mask_path)
#mask_images.append(compute_epi_mask(mask))
fmri_glm = FirstLevelModel(mask_img=False, t_r=tr,
smoothing_fwhm=fwhm).fit(subject['func'], design_matrices=design_matrices)
# compute contrasts
z_maps = {}
for contrast_id, contrast_val in contrasts.items():
print("\tcontrast id: %s" % contrast_id)
# store stat maps to disk
for map_type in ['z_score', 'stat', 'effect_size', 'effect_variance']:
stat_map = fmri_glm.compute_contrast(
contrast_val, output_type=map_type)
map_dir = os.path.join(
subject_session_output_dir, '%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, '%s.nii.gz' % contrast_id)
print("\t\tWriting %s ..." % map_path)
stat_map.to_filename(map_path)
# collect zmaps for contrasts we're interested in
if map_type == 'z_score':
z_maps[contrast_id] = map_path
anat_img = glob.glob(os.path.join(data_dir, 'anat/wsub*T1w.nii.gz'))[0]
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" % subject_id,
)
report.save_as_html(stats_report_filename)
return z_maps
scrambled_onsets = timing['onsets'][1].ravel()
onsets = np.hstack((faces_onsets, scrambled_onsets))
onsets *= tr # because onsets were reporting in 'scans' units
conditions = (['faces'] * len(faces_onsets) +
['scrambled'] * len(scrambled_onsets))
paradigm = DataFrame({'name': conditions, 'onset': onsets})
# build design matrix
frame_times = np.arange(n_scans) * tr
design_matrix = make_design_matrix(
frame_times, paradigm, hrf_model=hrf_model, drift_model=drift_model,
period_cut=period_cut)
design_matrices.append(design_matrix)
# specify contrasts
_, matrix, names = check_design_matrix(design_matrix)
contrasts = {}
n_columns = len(names)
contrast_matrix = np.eye(n_columns)
for i in range(2):
contrasts[names[2 * i]] = contrast_matrix[2 * i]
# more interesting contrasts
contrasts['faces-scrambled'] = contrasts['faces'] - contrasts['scrambled']
contrasts['scrambled-faces'] = -contrasts['faces-scrambled']
contrasts['effects_of_interest'] = np.vstack((contrasts['faces'],
contrasts['scrambled']))
# fit GLM
print('Fitting a GLM')
X = [check_design_matrix(design_)[1] for design_ in design_matrices]
def do_subject_glm(subject_id):
subject_output_dir = os.path.join(output_dir, subject_id)
# make design matrices
design_matrices = []
func = []
anat = os.path.join(subject_output_dir, "anatomy", "whighres001_brain.nii")
for run_path in sorted(glob.glob(os.path.join(
data_dir, subject_id, "model/model001/onsets/task*"))):
run_id = os.path.basename(run_path)
run_func = glob.glob(os.path.join(subject_output_dir, "BOLD", run_id,
"wrbold*.nii"))
assert len(run_func) == 1
run_func = run_func[0]
run_onset_paths = sorted(glob.glob(os.path.join(
data_dir, subject_id, "model/model001/onsets/%s/*" % run_id)))
onsets = map(np.loadtxt, run_onset_paths)
conditions = np.hstack(
[[condition_keys["cond%03i" % (c + 1)]] * len(onsets[c])
for c in range(len(run_onset_paths))])
onsets = np.vstack((onsets))
onsets *= tr
run_func = nibabel.load(run_func)
func.append(run_func)
n_scans = run_func.shape[-1]
onset, duration, modulation = onsets.T
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
paradigm = pd.DataFrame(dict(name=conditions, onset=onset,
duration=duration, modulation=modulation))
design_matrix = make_design_matrix(frametimes, paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut)
design_matrices.append(design_matrix)
n_runs = len(func)
# specify contrasts
_, _, names = check_design_matrix(design_matrix)
n_columns = len(names)
contrast_matrix = np.eye(n_columns)
contrasts = {}
for c in range(len(condition_keys)):
contrasts[names[2 * c]] = contrast_matrix[2 * c]
contrasts["avg"] = np.mean(contrasts.values(), axis=0)
# more interesting contrasts
contrasts_ = {}
for contrast, val in contrasts.items():
if not contrast == "avg":
contrasts_["%s_minus_avg" % contrast] = val - contrasts["avg"]
contrasts = contrasts_
# fit GLM
from nilearn.image import smooth_img
func = smooth_img(func, fwhm=8.)
print 'Fitting a GLM (this takes time)...'
fmri_glm = FMRILinearModel(func, [check_design_matrix(design_matrix)[1]
for design_matrix in design_matrices],
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
# save computed mask
mask_path = os.path.join(subject_output_dir, "mask.nii")
print "Saving mask image to %s ..." % mask_path
nibabel.save(fmri_glm.mask, mask_path)
# compute contrast maps
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.items():
print "\tcontrast id: %s" % contrast_id
z_map, t_map, effects_map, var_map = fmri_glm.contrast(
[contrast_val] * n_runs, con_id=contrast_id, output_z=True,
output_stat=True, output_effects=True, output_variance=True)
for map_type, out_map in zip(['z', 't', 'effects', 'variance'],
[z_map, t_map, effects_map, var_map]):
map_dir = os.path.join(subject_output_dir, '%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(
map_dir, '%s.nii.gz' % contrast_id)
print "\t\tWriting %s ..." % map_path
nibabel.save(out_map, map_path)
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# # generate stats report
# stats_report_filename = os.path.join(subject_output_dir, "reports",
# "report_stats.html")
# generate_subject_stats_report(
# stats_report_filename, contrasts, z_maps, fmri_glm.mask, anat=anat,
# threshold=2.3, cluster_th=15, design_matrices=design_matrices, TR=tr,
# subject_id="sub001", n_scans=n_scans, hfcut=hfcut,
# paradigm=paradigm, frametimes=frametimes,
# drift_model=drift_model, hrf_model=hrf_model)
# ProgressReport().finish_dir(subject_output_dir)
return dict(subject_id=subject_id, mask=mask_path,
effects_maps=effects_maps, z_maps=z_maps, contrasts=contrasts)
data = datasets.fetch_localizer_first_level()
paradigm_file = data.paradigm
epi_img = data.epi_img
########################################
# Design matrix
########################################
paradigm = DataFrame.from_csv(paradigm_file, sep=" ", header=None, index_col=None)
paradigm.columns = ["session", "name", "onset"]
n_conditions = len(paradigm.name.unique())
design_matrix = make_design_matrix(
frame_times, paradigm, hrf_model="canonical with derivative", drift_model="cosine", period_cut=128
)
_, matrix, column_names = check_design_matrix(design_matrix)
# Plot the design matrix
ax = plot_design_matrix(design_matrix)
ax.set_position([0.05, 0.25, 0.9, 0.65])
ax.set_title("Design matrix")
plt.savefig(path.join(write_dir, "design_matrix.png"))
########################################
# Perform a GLM analysis
########################################
fmri_glm = FirstLevelGLM().fit(epi_img, matrix)
#########################################
# Estimate contrasts
