def resample_to_average(tex_path, subject, side, verbose=False):
"""Resample a given texture to fsaverage
Parameters
==========
tex_path: string, path of the input texture
subject: string, subject id in the freesurfer database
side: string, on of ['left', 'right']
verbose: boolean, the verbosity mode
Returns
=======
resmapled: string, path of the result
"""
resampled = op.join(op.dirname(tex_path),
op.basename(tex_path)[:-4] + '_resampled.gii')
# convert the input to .mgz format
mgz = tex_path[:-4] + '.mgz'
tex = load_texture(tex_path)[np.newaxis, np.newaxis].T
mghformat.save(Nifti1Image(tex, np.eye(4)), mgz)
# run the resmapling using freesurfer tools
fs_comment = commands.getoutput(
'mri_surf2surf --srcsubject %s --srcsurfval %s --trgsubject ico --trgsurfval %s --hemi %sh --trgicoorder 7' % (
subject, mgz, resampled, side[0]))
if verbose:
print fs_comment
return resampled
def resample_to_subject(tex_path, subject, side, output_path, verbose=False):
"""Resample a given texture from fsaverage to subject space
Parameters
==========
tex_path: string, path of the input texture
subject: string, subject id in the freesurfer database
side: string, on of ['left', 'right']
outoput_path: string
verbose: boolean, the verbosity mode
"""
# convert the input to .mgz format
mgz = tex_path[:-4] + '.mgz'
tex = load_texture(tex_path)[np.newaxis, np.newaxis].T
mghformat.save(Nifti1Image(tex, np.eye(4)), mgz)
fs_comment = commands.getoutput(
'mri_surf2surf --trgsubject %s --trgsurfval %s --srcsubject ico --srcsurfval %s --hemi %sh --srcicoorder 7' % (
subject, output_path, mgz, side[0]))
if verbose:
print fs_comment
return output_path
'sin_wedge_neg', 'cos_wedge_neg', 'sin_wedge_pos', 'cos_wedge_pos'
]
offset_ring, offset_wedge = np.pi, -np.pi / 2
do_phase_unwrapping = True
do_plot = False
for subject in subjects:
print subject
tex_dir = op.join(paths[subject]['base'], paths[subject]['acquisition'],
'analysis')
for side in ['right', 'left']:
# First compute a mask of the active regions, by thresholding the
# so-called "average stat" map
mesh = paths[subject]['%s_inflated' % side]
mask = load_texture(op.join(tex_dir, side + '_mask.gii')) > 0
# Then, given the mask, extract the beta data from each subject
data = dict([
(r,
load_texture(op.join(tex_dir,
'%s_%s_con.gii' % (side, r))).ravel()[mask])
for r in all_reg
])
# Compute the phase value of the data
phase_wedge, phase_ring, hemo = phase_maps(
data,
offset_ring,
offset_wedge,
do_ring=True,
all_reg = ['sin_ring_pos', 'cos_ring_pos', 'sin_ring_neg', 'cos_ring_neg',
'sin_wedge_neg', 'cos_wedge_neg', 'sin_wedge_pos', 'cos_wedge_pos']
offset_ring, offset_wedge = np.pi, - np.pi / 2
do_phase_unwrapping = True
do_plot = False
for subject in subjects:
print subject
tex_dir = op.join(paths[subject]['base'],
paths[subject]['acquisition'], 'analysis')
for side in ['right', 'left']:
# First compute a mask of the active regions, by thresholding the
# so-called "average stat" map
mesh = paths[subject]['%s_inflated' % side]
mask = load_texture(op.join(tex_dir, side + '_mask.gii')) > 0
# Then, given the mask, extract the beta data from each subject
data = dict([ (r, load_texture(
op.join(tex_dir, '%s_%s_con.gii' % (side, r))).ravel()
[mask]) for r in all_reg])
# Compute the phase value of the data
phase_wedge, phase_ring, hemo = phase_maps(
data, offset_ring, offset_wedge, do_ring=True, do_wedge=True,
do_phase_unwrapping=do_phase_unwrapping, mesh=mesh, mask=mask)
# get planar coordinates on the surface
planar_coord = isomap_patch(mesh, mask)
# template-based visual area delineation
do_phase_unwrapping = True
for side in ['left', 'right']:
# First compute a mask of the active regions, by thresholding the
# so-called "average stat" map
avg_stat = None
mesh = op.join(op.dirname(paths[subjects[0]]['base']), 'fsaverage',
'%sh.inflated.gii' % side[0])
for subject in subjects:
tex_dir = op.join(paths[subject]['base'],
paths[subject]['acquisition'], 'analysis')
stat_map = op.join(tex_dir,
'%s_effects_of_interest_z_map.gii' % side)
r_stat_map = resample_to_average(stat_map, subject, side)
if avg_stat == None:
avg_stat = load_texture(r_stat_map)
else:
avg_stat += load_texture(r_stat_map)
mask = avg_stat / np.sqrt(len(subjects)) > threshold
mask = main_cc(mesh, mask)
# Then, given the mask, extract the beta data from each subject
data = dict([ (r, []) for r in all_reg])
for subject in subjects:
tex_dir = op.join(paths[subject]['base'],
paths[subject]['acquisition'], 'analysis')
for r in all_reg:
contrast_file = op.join(tex_dir, '%s_%s_con.gii' % (side, r))
r_cfile = resample_to_average(contrast_file, subject, side)
datar = load_texture(r_cfile)[mask]
data[r].append(datar)
