def test_fetch_spm_multimodal(request_mocker, tmp_path):
data_dir = str(tmp_path / 'spm_multimodal_fmri')
os.mkdir(data_dir)
subject_dir = os.path.join(data_dir, 'sub001')
os.mkdir(subject_dir)
os.mkdir(os.path.join(subject_dir, 'fMRI'))
os.mkdir(os.path.join(subject_dir, 'sMRI'))
open(os.path.join(subject_dir, 'sMRI', 'smri.img'), 'a').close()
for session in [0, 1]:
open(
os.path.join(subject_dir, 'fMRI',
'trials_ses%i.mat' % (session + 1)), 'a').close()
dir_ = os.path.join(subject_dir, 'fMRI', 'Session%d' % (session + 1))
os.mkdir(dir_)
for i in range(390):
open(
os.path.join(dir_,
'fMETHODS-000%i-%i-01.img' % (session + 5, i)),
'a').close()
dataset = func.fetch_spm_multimodal_fmri(data_dir=tmp_path)
assert isinstance(dataset.anat, str)
assert isinstance(dataset.func1[0], str)
assert len(dataset.func1) == 390
assert isinstance(dataset.func2[0], str)
assert len(dataset.func2) == 390
assert dataset.slice_order == 'descending'
assert isinstance(dataset.trials_ses1, str)
assert isinstance(dataset.trials_ses2, str)
Henson, R.N., Goshen-Gottstein, Y., Ganel, T., Otten, L.J., Quayle, A., Rugg, M.D. Electrophysiological and haemodynamic correlates of face perception, recognition and priming. Cereb Cortex. 2003 Jul;13(7):793-805. http://www.dx.doi.org/10.1093/cercor/13.7.793 This example takes a lot of time because the input are lists of 3D images sampled in different positions (encoded by different affine functions). """ print(__doc__) ######################################################################### # Fetch the SPM multimodal_faces data. from nilearn.datasets.func import fetch_spm_multimodal_fmri subject_data = fetch_spm_multimodal_fmri() ######################################################################### # Specfiy timing and design matrix parameters. tr = 2. # repetition time, in seconds slice_time_ref = 0. # we will sample the design matrix at the beggining of each acquisition drift_model = 'Cosine' # We use a discrete cosine transform to model signal drifts. high_pass = .01 # The cutoff for the drift model is 0.01 Hz. hrf_model = 'spm + derivative' # The hemodynamic response function is the SPM canonical one. ######################################################################### # Resample the images. # # This is achieved by the concat_imgs function of Nilearn. from nilearn.image import concat_imgs, resample_img, mean_img fmri_img = [