def get_cammoun_schaefer(vers='fsaverage5', data_dir=None, networks='7'):
"""
Returns Cammoun 2012 and Schaefer 2018 atlases as dictionary
Parameters
----------
vers : str, optional
Which version of the atlases to get. Default: 'fsaverage5'
data_dir : str or os.PathLike, optional
Data directory where downloaded atlases should be stored. If not
specified will default to $NNT_DATA or ~/nnt-data
networks : {'7', '17'}, optional
Which networks to get for Schaefer 2018 atlas. Default: '7'
Returns
-------
atlases : dict
Where keys are 'atl-cammoun2012' and 'atl-schaefer2018'
"""
cammoun = nndata.fetch_cammoun2012(vers, data_dir=data_dir)
schaefer = nndata.fetch_schaefer2018('fsaverage5', data_dir=data_dir)
schaefer = {
k: schaefer.get(k)
for k in schaefer.keys() if 'Parcels7Networks' in k
}
return {'atl-cammoun2012': cammoun, 'atl-schaefer2018': schaefer}
def test_fetch_schaefer2018(tmpdir, version):
keys = [
'{}Parcels{}Networks'.format(p, n) for p in range(100, 1001, 100)
for n in [7, 17]
]
schaefer = datasets.fetch_schaefer2018(version, data_dir=tmpdir, verbose=0)
assert all(k in schaefer and len(schaefer[k]) == 2 and all(
os.path.isfile(hemi) for hemi in schaefer[k]) for k in keys)
def get_schaefer2018_yeo(scale, data_dir=None):
"""
Returns Yeo RSN affiliations for Schaefer parcellation
Parameters
----------
scale : str
Scale of Schaefer et al., 2018 to use
data_dir : str or os.PathLike
Directory where parcellation should be downloaded to (or exists, if
already downloaded). Default: None
Returns
-------
labels : (2,) namedtuple
Where the first entry ('vertices') is the vertex-level RSN affiliations
and the second entry ('parcels') is the parcel-level RSN affiliations
"""
# substring in schaefer parcel names indiciating yeo network affiliation
schaefer_yeo = dict(Vis='visual',
SomMot='somatomotor',
DorsAttn='dorsal attention',
SalVentAttn='ventral attention',
Limbic='limbic',
Cont='frontoparietal',
Default='default mode')
# get requested annotation files
schaefer = nndata.fetch_schaefer2018('fsaverage5',
data_dir=data_dir)[scale]
network_labels, parcel_labels = [], []
for hemi in ('lh', 'rh'):
# read in annotation file for given hemisphere
annot = getattr(schaefer, hemi)
labels, ctab, names = nib.freesurfer.read_annot(annot)
names = [m.decode() for m in names]
# create empty arrays for vertex- and parcel-level affiliations
networks = np.zeros_like(labels)
parcels = np.zeros(len(names), dtype=int)
for n, parcel in enumerate(names):
for abbrev, net in schaefer_yeo.items():
# check which network this parcel belongs to by looking for the
# network substring in the parcel name and assign accordingly
if abbrev in parcel:
parcels[n] = YEO_CODES[net]
networks[labels == n] = YEO_CODES[net]
# store network affiliations for this hemisphere
network_labels.append(networks)
parcel_labels.append(parcels)
return NETWORKS(np.hstack(network_labels), np.hstack(parcel_labels))
def _fetch_schaefer_parcellation(template: str, n_regions: int,
seven_networks: int,
data_dir: Path) -> List[np.ndarray]:
"""Fetches Schaefer parcellations."""
n_networks = 7 if seven_networks else 17
key = f"{n_regions}Parcels{n_networks}Networks"
bunch = nnt_datasets.fetch_schaefer2018(version=template,
data_dir=str(data_dir))
if template == "fslr32k":
cifti = nib_load(bunch[key])
parcellation_full = np.squeeze(cifti.get_fdata())
parcellations = [x for x in np.reshape(parcellation_full, (2, -1))]
else:
parcellations = [read_annot(file)[0] for file in bunch[key]]
parcellations[1][parcellations[1] != 0] += n_regions // 2
return parcellations
def get_schaefer2018_vek(scale, data_dir=None):
"""
Returns von Economo cytoarchitectonic classes for Schaefer parcellation
Parameters
----------
scale : str
Scale of Schaefer et al., 2018 to use
data_dir : str or os.PathLike
Directory where parcellation should be downloaded to (or exists, if
already downloaded). Default: None
Returns
-------
labels : (2,) namedtuple
Where the first entry ('vertices') is the vertex-level classes and the
second entry ('parcels') is the parcel-level classes
"""
schaefer = nndata.fetch_schaefer2018('fsaverage5',
data_dir=data_dir)[scale]
vek_annots = _apply_vek_prob(data_dir=data_dir)
return _parcellate_vek_classes(schaefer, vek_annots)
def plot_brain_surface(values,
network,
hemi=None,
cmap="viridis",
alpha=0.8,
colorbar=True,
centered=False,
vmin=None,
vmax=None,
representation='surface'):
'''
Function to plot data on the brain, on a surface parcellation.
PARAMETERS
----------
values : ndarray (n,)
Values to be plotted on the brain, where n is the number of nodes in
the parcellation.
network : dictionary
Dictionary storing the network on associated with the values (to be
used to identify the adequate surface parcellation)
'''
cortical_hemi_mask = network['hemi_mask'][network['subcortex_mask'] == 0]
n = len(cortical_hemi_mask)
if hemi is None:
hemi = network['info']['hemi']
if hemi == "L":
scores = np.zeros((n)) + np.mean(values)
scores[cortical_hemi_mask == 1] = values
values = scores
elif hemi == "R":
scores = np.zeros((n)) + np.mean(values)
scores[cortical_hemi_mask == 0] = values
values = scores
order = network["order"]
noplot = network["noplot"]
lh = network["lhannot"]
rh = network["rhannot"]
if os.path.isfile(lh) or os.path.isfile(rh) is False:
fetch_cammoun2012(version='fsaverage')
fetch_schaefer2018()
# Adjust colormap based on parameters
if centered is True:
m = max(abs(np.amin(values)), np.amax(values))
vmin = -m
vmax = m
else:
if vmin is None:
vmin = np.amin(values)
if vmax is None:
vmax = np.amax(values)
# Plot the brain surface
im = plot_fsaverage(values,
lhannot=lh,
rhannot=rh,
noplot=noplot,
order=order,
views=['lateral', 'm'],
vmin=vmin,
vmax=vmax,
colormap=cmap,
alpha=alpha,
colorbar=colorbar,
data_kws={'representation': representation},
show_toolbar=True)
return im