drift_model=drift_model, hfcut=hf_cut)
# plot the design matrix
ax = design_matrix.show()
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
pylab.savefig(os.path.join(write_dir, 'design_matrix_%s.png') %\
session)
# get the data
if side == False:
Y, _ = data_scaling(np.array([load(f).get_data()[mask_array]
for f in fmri_data]))
affine = load(fmri_data[0]).get_affine()
else:
Y, _ = data_scaling(np.array(
[load_texture(f) for f in fmri_data]))
mask_array = np.var(Y, 0) > 0
Y = Y[:, mask_array]
# fit the glm
print 'Fitting a GLM (this takes time)...'
result = GeneralLinearModel(design_matrix.matrix)
result.fit(Y, model='ar1', steps=100)
for contrast_id, contrast_val in contrasts.iteritems():
if (contrast_val[session] == 0).all():
continue
if contrast_id in contrast_obj.keys():
contrast_obj[contrast_id] = contrast_obj[contrast_id] +\
result.contrast(contrast_val[session])
right_fmri_tex = fmri_session[:-4] + '_rh.gii'
# run freesrufer command
commands.getoutput(
'$FREESURFER_HOME/bin/mri_vol2surf --src %s --o %s '\
'--out_type gii --regheader %s --hemi lh --projfrac 0.5'
% (fmri_vol, left_fmri_tex, fs_dir))
plop = commands.getoutput(
'$FREESURFER_HOME/bin/mri_vol2surf --src %s --o %s '\
'--out_type gii --regheader %s --hemi rh --projfrac 0.5'
% (fmri_vol, right_fmri_tex, fs_dir))
# smooth the data
for subject in subject_info.keys():
fmri_dir = os.path.join(main_dir, subject, 'fmri')
fs_dir = os.path.join(main_dir, subject, 't1', subject)
for session in subject_info[subject]['session_keys']:
for hemi in ['left', 'right']:
wild_card = '%sh.gii' % hemi[0]
fmri_series = os.path.join(fmri_dir, '%s_series_%s_%s' %
(subject, session, wild_card))
tex = load_texture(fmri_series)
smooth_tex = []
output = fmri_series[:-4] + '_smooth5.gii'
plop = commands.getoutput(
'mri_surf2surf --srcsubject %s --cortex --fwhm %s '\
'--hemi %sh --sval %s --tval %s --s %s' % (
fs_dir, FWHM, hemi[0], fmri_series, output, fs_dir))
if side == False:
# in the volume, compute a mask o fthe brain
mask_array = compute_mask_files(fmri_series[0], epi_mask, True,
inf_threshold, sup_threshold)[0]
for (sess, (session, fmri_data)) in enumerate(zip(
sessions, fmri_series)):
# get the data
if side == False:
Y, _ = data_scaling(np.array([load(f).get_data()[mask_array]
for f in fmri_data]))
affine = load(fmri_data[0]).get_affine()
else:
Y, _ = data_scaling(np.array(
[load_texture(f) for f in fmri_data]))
mask_array = np.var(Y, 0) > 0
Y = Y[:, mask_array]
best_z, best_d = - np.ones(Y.shape[1]), - np.ones(Y.shape[1])
# create design matrices
for i_delay, delay in enumerate(
np.linspace(0, 30, n_models + 1)[:-1]):
paradigm = make_paradigm(
delay, duration=duration, length=length,
odd=(subject in ['sujet10', 'sujet12']))
design_matrix = make_dmtx(
frametimes, paradigm, drift_model=drift_model, hfcut=hf_cut)
# plot the design matrix
left_pos, right_pos = [], []
for subject in paths.keys():
print subject
func_path = op.join(paths[subject]['base'], paths[subject]['acquisition'],
'analysis')
# set all the paths
ltex_path = op.join(func_path, 'left_phase_ring.gii')
rtex_path = op.join(func_path, 'right_phase_ring.gii')
for side in ['left', 'right']:
#resample on tyhe average
tex_path = ltex_path if side == 'left' else rtex_path
resampled = resample_to_average(tex_path, paths[subject]['fs_subj'],
side)
# mask the values that are small enough
rring = load_texture(resampled).ravel()
mask = rring < rring.min() + 1.
if side == 'left':
left_pos.append(left_vertices[mask].mean(0))
else:
right_pos.append(right_vertices[mask].mean(0))
left_pos = np.array(left_pos)
right_pos = np.array(right_pos)
print left_pos.std(0), right_pos.std(0)
if True:
left_fovea_, right_fovea_ = np.median(left_pos, 0), np.median(right_pos, 0)
else:
left_fovea_, right_fovea_ = np.mean(left_pos, 0), np.mean(right_pos, 0)
# the .gii files will be put in the same directory as the input fMRI
left_fmri_tex = fmri_session[:-4] + "_lh.gii"
right_fmri_tex = fmri_session[:-4] + "_rh.gii"
# run freesrufer command
commands.getoutput(
"$FREESURFER_HOME/bin/mri_vol2surf --src %s --o %s "
"--out_type gii --regheader %s --hemi lh --projfrac 0.5" % (fmri_vol, left_fmri_tex, fs_dir)
)
plop = commands.getoutput(
"$FREESURFER_HOME/bin/mri_vol2surf --src %s --o %s "
"--out_type gii --regheader %s --hemi rh --projfrac 0.5" % (fmri_vol, right_fmri_tex, fs_dir)
)
# smooth the data
for subject in subject_info.keys():
fmri_dir = os.path.join(main_dir, subject, "fmri")
fs_dir = os.path.join(main_dir, subject, "t1", subject)
for session in subject_info[subject]["session_keys"]:
for hemi in ["left", "right"]:
wild_card = "%sh.gii" % hemi[0]
fmri_series = os.path.join(fmri_dir, "%s_series_%s_%s" % (subject, session, wild_card))
tex = load_texture(fmri_series)
smooth_tex = []
output = fmri_series[:-4] + "_smooth5.gii"
plop = commands.getoutput(
"mri_surf2surf --srcsubject %s --cortex --fwhm %s "
"--hemi %sh --sval %s --tval %s --s %s" % (fs_dir, FWHM, hemi[0], fmri_series, output, fs_dir)
)
left_pos, right_pos = [], []
for subject in paths.keys():
print subject
func_path = op.join(paths[subject]['base'], paths[subject]['acquisition'],
'analysis')
# set all the paths
ltex_path = op.join(func_path, 'left_phase_ring.gii')
rtex_path = op.join(func_path, 'right_phase_ring.gii')
for side in ['left', 'right']:
#resample on tyhe average
tex_path = ltex_path if side == 'left' else rtex_path
resampled = resample_to_average(tex_path, paths[subject]['fs_subj'],
side)
# mask the values that are small enough
rring = load_texture(resampled).ravel()
mask = rring < rring.min() + 1.
if side == 'left':
left_pos.append(left_vertices[mask].mean(0))
else:
right_pos.append(right_vertices[mask].mean(0))
left_pos = np.array(left_pos)
right_pos = np.array(right_pos)
print left_pos.std(0), right_pos.std(0)
if True:
left_fovea_, right_fovea_ = np.median(left_pos, 0), np.median(right_pos, 0)
else:
left_fovea_, right_fovea_ = np.mean(left_pos, 0), np.mean(right_pos, 0)
