def display_coactivation(brain,
niftis,
colormap=None,
reduce_alpha_step=0,
**kwargs):
args = {'thresh': 0.001, 'alpha': 0.85, 'colorbar': False, 'min': 0}
if kwargs != {}:
args.update(kwargs)
if colormap is None:
colormap = sns.color_palette('Set1', len(niftis))
for i, image in enumerate(niftis):
with tempfile.NamedTemporaryFile(suffix='.nii.gz') as f:
nib.save(image, f.name)
l_roi_surf = project_volume_data(f.name,
"lh",
subject_id="fsaverage",
smooth_fwhm=2)
r_roi_surf = project_volume_data(f.name,
"rh",
subject_id="fsaverage",
smooth_fwhm=2)
args['remove_existing'] = i == 0
color = sns.light_palette(colormap[i], n_colors=10)[5:]
if l_roi_surf.sum() > 0:
brain.add_data(l_roi_surf, hemi='lh', colormap=color, **args)
if r_roi_surf.sum() > 0:
brain.add_data(r_roi_surf, hemi='rh', colormap=color, **args)
args['alpha'] -= reduce_alpha_step
def project_surface(result_file, hemi):
'''
VERY helpfully taken from the Pysurfer example gallery
http://pysurfer.github.io/examples/plot_fmri_activation_volume.html
Parameters
----------
result_file : str
3D volume that has continuous values
eg: thresh_zstat1.nii.gz
hemi : {'lh', 'rh'}
string indicating left or right hemisphere
Returns
----------
zstat : pysurfer surface
Surface projection of result_file to fsaverage
brain
'''
import os
from surfer import project_volume_data
reg_file = os.path.join(os.environ["FREESURFER_HOME"],
"average/mni152.register.dat")
zstat = project_volume_data(result_file, hemi, reg_file)
return zstat
def project_surface(result_file, hemi):
'''
VERY helpfully taken from the Pysurfer example gallery
http://pysurfer.github.io/examples/plot_fmri_activation_volume.html
Parameters
----------
result_file : str
3D volume that has continuous values
eg: thresh_zstat1.nii.gz
hemi : {'lh', 'rh'}
string indicating left or right hemisphere
Returns
----------
zstat : pysurfer surface
Surface projection of result_file to fsaverage
brain
'''
import os
from surfer import project_volume_data
reg_file = os.path.join(os.environ["FREESURFER_HOME"],
"average/mni152.register.dat")
zstat = project_volume_data(result_file, hemi, reg_file)
return zstat
def make_surface_mask(img, thresh=None, hemi='lh', use_nans=True):
"""Convert ROI mask to surface array"""
surface_data = project_volume_data(img, 'lh', subject_id='fsaverage')
if use_nans:
mask = np.where(surface_data > thresh, 1, np.nan)
else:
mask = np.where(surface_data > thresh, 1, 0)
return mask
def vis_ers_comp(group=None,phase=None,surf=None,cmap=None,split='lh'):
mri_file = f'/mnt/c/Users/ACH/Desktop/ers_comps/{group}_{phase}/{group}_{phase}_ClusterEffEst.nii.gz' #find the file containing stats
surf_data_lh = project_volume_data(mri_file, "lh", reg_file, projarg=[0, 1, .01], smooth_fwhm=1) #project to lh
_max = .55 if phase == 'acquisition' else .3
for view in ['med','lat']: #lateral and medial views
brain = Brain('MNI2009c', split, surf, cortex='low_contrast',size=1000,
views=view, background='white', foreground=None) #initialize the brain object
brain.add_data(surf_data_lh, 0, _max, center=None, hemi='lh', thresh=None,
colorbar=False, colormap=cmap, transparent=True) #add lh data
for vert, color in zip([115262,135014],['white','black']): #add focal ROIs
brain.add_foci(vert,coords_as_verts=True,color=color,alpha=1)
fname = f'/mnt/c/Users/ACH/Documents/gPPI/paper/pysurfer/{group}_{phase}_{view}.png'
os.system(f'rm {fname}')
brain.save_image(fname,antialiased=True)
"""
import os
from surfer import Brain, project_volume_data
print(__doc__)
"""Bring up the visualization"""
brain = Brain("fsaverage", "split", "inflated",
views=['lat', 'med'], background="white")
"""Project the volume file and return as an array"""
mri_file = "example_data/resting_corr.nii.gz"
reg_file = os.path.join(os.environ["FREESURFER_HOME"],
"average/mni152.register.dat")
surf_data_lh = project_volume_data(mri_file, "lh", reg_file)
surf_data_rh = project_volume_data(mri_file, "rh", reg_file)
"""
You can pass this array to the add_overlay method for a typical activation
overlay (with thresholding, etc.).
"""
brain.add_overlay(surf_data_lh, min=.3, max=.7, name="ang_corr_lh", hemi='lh')
brain.add_overlay(surf_data_rh, min=.3, max=.7, name="ang_corr_rh", hemi='rh')
"""
You can also pass it to add_data for more control
over the visualization. Here we'll plot the whole
range of correlations
"""
for overlay in brain.overlays_dict["ang_corr_lh"]:
def project_volume(work_dir, subject, do_bbr=True):
# first find the session where T1w and T2w files could be
ref_file = sorted(
glob.glob(
os.path.join(work_dir, subject, 'ses-*', 'anat',
'*-highres_T1w.nii*')))[-1]
# session = ref_file.split('/')[-3]
anat_dir = os.path.dirname(ref_file)
write_dir = os.path.join(anat_dir, 'analysis')
if not os.path.exists(write_dir):
os.mkdir(write_dir)
os.environ['SUBJECTS_DIR'] = anat_dir
data = {}
for modality in ['T1w', 'T2w']:
if modality == ['T1w']:
image = ref_file
else:
image = sorted(
glob.glob(
os.path.join(work_dir, subject, 'ses-*', 'anat',
'*-highres_T2w.nii*')))[-1]
image_ = closing(image)
# --------------------------------------------------------------------
# run the projection using freesurfer
print("image", image)
basename = os.path.basename(image).split('.')[0]
if modality == 'T1w':
bbreg = BBRegister(subject_id=subject,
source_file=image,
init='header',
contrast_type='t1')
else:
# use BBR registration to finesse the coregistration
bbreg = BBRegister(subject_id=subject,
source_file=image,
init='header',
contrast_type='t2')
regheader = os.path.join(anat_dir,
basename + '_bbreg_%s.dat' % subject)
bbreg.run()
if 1:
# output names
# the .gii files will be put in the same directory as the input
left_tex = os.path.join(write_dir, basename + '_lh.gii')
right_tex = os.path.join(write_dir, basename + '_rh.gii')
# run freesrufer command for projection
os.system(
'$FREESURFER_HOME/bin/mri_vol2surf --src %s --o %s '
'--out_type gii --srcreg %s --hemi lh --projfrac-avg 0 1 0.1' %
(image_, left_tex, regheader))
os.system(
'$FREESURFER_HOME/bin/mri_vol2surf --src %s --o %s '
'--out_type gii --srcreg %s --hemi rh --projfrac-avg 0 1 0.1' %
(image_, right_tex, regheader))
# resample to fsaverage
left_smooth_tex = os.path.join(write_dir,
basename + '_fsaverage_lh.gii')
right_smooth_tex = os.path.join(write_dir,
basename + '_fsaverage_rh.gii')
os.system('$FREESURFER_HOME/bin/mri_surf2surf --srcsubject %s '
'--srcsurfval %s --trgsurfval %s --trgsubject ico '
'--trgicoorder 7 --hemi lh' %
(subject, left_tex, left_smooth_tex))
os.system('$FREESURFER_HOME/bin/mri_surf2surf --srcsubject %s '
'--srcsurfval %s --trgsubject ico --trgicoorder 7 '
'--trgsurfval %s --hemi rh' %
(subject, right_tex, right_smooth_tex))
data[modality] = {
'lh': nib.load(left_smooth_tex).darrays[0].data,
'rh': nib.load(right_smooth_tex).darrays[0].data
}
else:
from surfer import project_volume_data
data[modality] = {}
for hemi in ['lh', 'rh']:
data_ = project_volume_data(image_,
hemi,
regheader,
projarg=[0, 1., .1],
smooth_fwhm=0)
data[modality][hemi] = data_
# reset subject_dir to set fsaverage
os.environ['SUBJECTS_DIR'] = os.path.join(work_dir, subject, 'ses-00',
'anat')
for hemi in ['lh', 'rh']:
ratio = data['T1w'][hemi] / data['T2w'][hemi]
from nibabel.gifti import write, GiftiImage, GiftiDataArray as gda
file_ratio = os.path.join(write_dir, 't1_t2_ratio_%s.gii' % hemi)
write(GiftiImage(darrays=[gda(data=ratio.astype('float32'))]),
file_ratio)
"""
#os.environ['SUBJECTS_DIR'] = expanduser('/Volumes/rmasis/MemPal/data/BIDS/derivatives/freesurfer/')
os.environ['FREESURFER_HOME'] = expanduser('/Applications/freesurfer/')
# #reg_file = os.environ['FREESURFER_HOME']
os.environ['SUBJECTS_DIR'] = expanduser('/Applications/freesurfer/subjects')
#mri_file = '/Volumes/norman/rmasis/MemPal/analysis/PythonData/' + 'testMNIpathvideoNii.nii.gz'
mri_file = '/Volumes/norman/rmasis/MemPal/analysis/PythonData/troubleshoot/' + 'PV1_isc' + '.nii.gz'
reg_file = '/Applications/freesurfer/average/mni152.register.dat'
data = nib.load(mri_file)
brain = Brain(subject_id, hemi, surf, background="white")
minval = 0.0 #np.min(data.get_data())
maxval = np.max(data.get_data())
surf_data_lh = project_volume_data(mri_file,"lh", reg_file) #uses freesurfers mri_vol2surf
surf_data_rh = project_volume_data(mri_file,"rh", reg_file)
brain.add_overlay(surf_data_lh,min=minval,max=maxval,name="thirty_sec_lh", hemi='lh',sign="pos")
brain.add_overlay(surf_data_rh,min=minval,max=maxval,name="thirty_sec_rh", hemi='rh',sign="pos")
mlab.show()
time=str(time), subid=subid))
############################################
# Visualize accuracy (for given subject)
############################################
from surfer import Brain, project_volume_data
tr_shift_test_list = [0, 2, 4, 6, 8, 10,
12] # seconds to shift onset forward by
# for subid in subj_info.subid:
subid = 'ap101'
for time in tr_shift_test_list:
brain = Brain(subid,
"split",
"inflated",
views=['lat', 'med', 'ven'],
background="white")
volume_file = '/Volumes/group/awagner/sgagnon/AP/analysis/mvpa_raw/searchlight_test/sourcehit_time{time}_acc_{subid}.nii.gz'.format(
time=str(time), subid=subid)
for hemi in ['lh', 'rh']:
zstat = project_volume_data(volume_file,
hemi,
subject_id=subid,
smooth_fwhm=0.5)
brain.add_overlay(zstat, hemi=hemi, min=.333)
brain.save_image(
'/Volumes/group/awagner/sgagnon/AP/analysis/mvpa_raw/searchlight_test/sourcehit_time{time}_acc_{subid}.png'
.format(time=str(time), subid=subid))
"""
volume_file = "example_data/zstat.nii.gz"
"""
There are two options for specifying the registration between the volume and
the surface you want to plot on. The first is to give a path to a
Freesurfer-style linear transformation matrix that will align the statistical
volume with the Freesurfer anatomy.
Most of the time you will be plotting data that are in MNI152 space on the
fsaverage brain. For this case, Freesurfer actually ships a registration matrix
file to align your data with the surface.
"""
reg_file = os.path.join(os.environ["FREESURFER_HOME"],
"average/mni152.register.dat")
zstat = project_volume_data(volume_file, "lh", reg_file)
"""
Note that the contours of the fsaverage surface don't perfectly match the
MNI brain, so this will only approximate the location of your activation
(although it generally does a pretty good job). A more accurate way to
visualize data would be to run the MNI152 brain through the recon-all pipeline.
Alternatively, if your data are already in register with the Freesurfer
anatomy, you can provide project_volume_data with the subject ID, avoiding the
need to specify a registration file.
By default, 3mm of smoothing is applied on the surface to clean up the overlay a
bit, although the extent of smoothing can be controlled.
"""
zstat = project_volume_data(volume_file, "lh",
# cb2 = mpl.colorbar.ColorbarBase(ax[1], cmap=mpl.cm.Greens,
# norm=norm, orientation='horizontal')
# cb1.set_label('CS+ - CS- reinstatement')
# fig.show()
"""
This overlay represents resting-state correlations with a
seed in left angular gyrus. Let's plot that seed.
"""
seed_coords = (-45, -67, 36)
brain.add_foci(seed_coords, map_surface="white", hemi='lh')
'''
a priori rois for paper
'''
mri_file = f'{HOME}/Desktop/standard/vmPFC_mask.nii.gz'
surf_data_lh = project_volume_data(mri_file, "lh", reg_file, projarg=[0, 1, .5], smooth_fwhm=1)
surf_data_rh = project_volume_data(mri_file, "rh", reg_file, projarg=[0, 1, .5], smooth_fwhm=1)
minval = np.min([np.min(surf_data_lh[np.nonzero(surf_data_lh)]),np.min(surf_data_rh[np.nonzero(surf_data_rh)])])
for view in ['med','lat']:
brain = Brain('fsaverage', 'split', surf, cortex='low_contrast',size=(400,400),
views=view, background='white', foreground=None)
brain.add_data(surf_data_lh, 0, .5, center=None, hemi='lh', thresh=minval, colorbar=False, colormap=cmap)
brain.add_data(surf_data_rh, 0, .5, center=None, hemi='rh',thresh=minval, colorbar=False, colormap=cmap)
dACC_coords = (1, 21, 27)
brain.add_foci(dACC_coords, map_surface='pial', hemi='rh',color='orange')
def display_bilateral(brain,
nifti,
colormap=None,
spatial_mask=None,
level_mask=None,
discrete=True,
**kwargs):
args = {
'thresh': 0.001,
'alpha': 0.33,
'colorbar': False,
'remove_existing': True,
'min': 1
}
if kwargs != {}:
args.update(kwargs)
if spatial_mask is None:
args['alpha'] = .8
if colormap is None:
n_clusters = int(nifti.get_data().max())
colormap = sns.color_palette('husl', n_clusters)
if level_mask is not None:
nifti = deepcopy(nifti)
data = nifti.get_data()
unique = np.unique(data[data.nonzero()])
for val in unique:
if not val in level_mask:
data[data == val] = float(0)
unique = np.unique(data[data.nonzero()])
colormap = [v for i, v in enumerate(colormap) if i + 1 in unique]
compress_values(nifti.get_data())
with tempfile.NamedTemporaryFile(suffix='.nii.gz') as f:
nib.save(nifti, f.name)
l_roi_surf = project_volume_data(f.name,
"lh",
subject_id="fsaverage",
projsum='max',
smooth_fwhm=0)
r_roi_surf = project_volume_data(f.name,
"rh",
subject_id="fsaverage",
projsum='max',
smooth_fwhm=0)
if discrete == True:
l_cols = [colormap[int(c - 1)] for c in np.unique(l_roi_surf)[1:]]
if len(l_cols) < 2:
l_cols = l_cols + [(0, 0, 0)]
r_cols = [colormap[int(c - 1)] for c in np.unique(r_roi_surf)[1:]]
if len(r_cols) < 2:
r_cols = r_cols + [(0, 0, 0)]
else:
l_cols = colormap
r_cols = colormap
brain.add_data(l_roi_surf, hemi='lh', colormap=colormap, **args)
brain.add_data(r_roi_surf, hemi='rh', colormap=r_cols, **args)
if spatial_mask is not None:
spatial_masked_nifti = mask_nifti(nifti, spatial_mask)
with tempfile.NamedTemporaryFile(suffix='.nii.gz') as f:
nib.save(spatial_masked_nifti, f.name)
args['remove_existing'] = False
args['alpha'] = .7
l_roi_surf = project_volume_data(f.name,
"lh",
subject_id="fsaverage",
projsum='max',
smooth_fwhm=0)
r_roi_surf = project_volume_data(f.name,
"rh",
subject_id="fsaverage",
projsum='max',
smooth_fwhm=0)
l_cols = [colormap[int(c - 1)] for c in np.unique(l_roi_surf)[1:]]
if len(l_cols) < 2:
l_cols = l_cols + [(0, 0, 0)]
r_cols = [colormap[int(c - 1)] for c in np.unique(r_roi_surf)[1:]]
if len(r_cols) < 2:
r_cols = r_cols + [(0, 0, 0)]
brain.add_data(l_roi_surf, hemi='lh', colormap=l_cols, **args)
brain.add_data(r_roi_surf, hemi='rh', colormap=r_cols, **args)
# load DMN ROI and plot with pysurfer
import os
from surfer import Brain, project_volume_data
print(__doc__)
brain = Brain("fsaverage", "lh", "inflated")
volume_file = '/Volumes/norman/amennen/prettymouth_fmriprep2/ROI/TOM_large_resampled_maskedbybrain.nii.gz'
reg_file = os.path.join(os.environ["FREESURFER_HOME"],
"average/mni152.register.dat")
zstat = project_volume_data(volume_file, "lh", reg_file).astype(bool)
brain.add_data(zstat, min=0, max=1,colormap="bone", alpha=.6, colorbar=False)
brain.show_view("medial")
from coma.datasets import sample
data_path = sample.data_path()
subjects_dir = op.join(data_path, "subjects")
os.environ['SUBJECTS_DIR'] = subjects_dir
subject_id = "Bend1"
hemi = "lh"
surf = "pial"
bgcolor = 'w'
brain = Brain(subject_id, hemi, surf, config_opts={'background': bgcolor},
subjects_dir=subjects_dir)
volume_file = op.abspath("example_fspet/corrected_pet_to_t1/_subject_id_Bend1/r_volume_MGRousset_flirt.nii")
pet = project_volume_data(volume_file, hemi,
subject_id=subject_id)
brain.add_data(pet, min=250, max=12000,
colormap="jet", alpha=.6, colorbar=True)
image = brain.save_montage("Example_FDG-PET.png", ['l', 'd', 'm'], orientation='v')
brain.close()
###############################################################################
# View created image
import pylab as pl
fig = pl.figure(figsize=(5, 3), facecolor=bgcolor)
ax = pl.axes(frameon=False)
ax.imshow(image, origin='upper')
pl.draw()
Get a path to the volume file.
"""
volume_file = "example_data/zstat.nii.gz"
"""
There are two options for specifying the registration between the volume and
the surface you want to plot on. The first is to give a path to a
Freesurfer-style linear transformation matrix that will align the statistical
volume with the Freesurfer anatomy.
Most of the time you will be plotting data that are in MNI152 space on the
fsaverage brain. For this case, Freesurfer actually ships a registration matrix
file to align your data with the surface.
"""
reg_file = os.path.join(os.environ["FREESURFER_HOME"],
"average/mni152.register.dat")
zstat = project_volume_data(volume_file, "lh", reg_file)
"""
Note that the contours of the fsaverage surface don't perfectly match the
MNI brain, so this will only approximate the location of your activation
(although it generally does a pretty good job). A more accurate way to
visualize data would be to run the MNI152 brain through the recon-all pipeline.
Alternatively, if your data are already in register with the Freesurfer
anatomy, you can provide project_volume_data with the subject ID, avoiding the
need to specify a registration file.
By default, 3mm of smoothing is applied on the surface to clean up the overlay
a bit, although the extent of smoothing can be controlled.
"""
zstat = project_volume_data(volume_file,
"lh",
title='Z=' + str(z),
cortex='low_contrast',
views=['lat', 'med'],
background="white")
"""
Get a path to the overlay file.
"""
mri_file = '/Users/jamalw/Desktop/PNI/music_event_structures/data/best_k_map_z' + str(
z) + '.nii.gz'
reg_file = '/Applications/freesurfer/average/mni152.register.dat'
data = nib.load(mri_file)
minval = 0
maxval = np.max(data.get_data())
surf_data_lh = project_volume_data(mri_file, "lh", reg_file)
surf_data_rh = project_volume_data(mri_file, "rh", reg_file)
brain.add_overlay(surf_data_lh,
min=minval,
max=maxval,
name="thirty_sec_lh",
hemi='lh')
brain.add_overlay(surf_data_rh,
min=minval,
max=maxval,
name="thirty_sec_rh",
hemi='rh')
for overlay in brain.overlays_dict["thirty_sec_lh"]:
overlay.remove()
## script to visualizee volume results in surcace, using pysurfer
import os
from surfer import Brain, project_volume_data
import numpy as np
### script to visualize cognitive components
subject_id = "fsaverage"
subjects_dir = os.environ["SUBJECTS_DIR"]
brain = Brain("fsaverage", "lh", "inflated", views=['lat'], background="white")
overlay_file = "/Volumes/neuro/Rest/ROIs/Yeo_12.nii.gz"
reg_file = os.path.join(os.environ["FREESURFER_HOME"], "average/mni152.register.dat")
zstat = project_volume_data(overlay_file, "lh", reg_file)
zstat = project_volume_data(overlay_file, "lh", subject_id="fsaverage", smooth_fwhm=4)
brain.add_data(zstat, min=1, max=5, thresh=1, colormap="hot", colorbar=False)
brain.show_view("medial")
#load data to visualize
#mask_file = "Cortical_CI.nii.gz"
### to visualize ROI's CI assignment
#load coordinates
subject_id = "fsaverage"
subjects_dir = os.environ["SUBJECTS_DIR"]
brain = Brain("fsaverage", "rh", "inflated", views=['lat', 'med'], background="white")
#brain = Brain("fsaverage", "rh", "inflated", background="white")
coords = np.loadtxt('/Volumes/neuro/Rest/ROIs/Gordon_coordinates')
CIs = np.loadtxt('/Volumes/neuro/Rest/ROIs/Gordon_consensus_CI')
colors = {1:'red', 2:'purple', 3:'green', 4:'yellow', 5:'cyan', 6:'blue', 7:'brown', 8:'pink', 9:'teal', 12:'pink'}
