flattening, and a surface-based coordinate system. Neuroimage 9.
http://dx.doi.org/10.1006/nimg.1998.0396
Destrieux et al, (2010). Automatic parcellation of human cortical gyri and
sulci using standard anatomical nomenclature. NeuroImage, 53, 1.
URL http://dx.doi.org/10.1016/j.neuroimage.2010.06.010.
"""
###############################################################################
# Retrieving the data
# -------------------
# NKI resting state data from nilearn
from nilearn import datasets
nki_dataset = datasets.fetch_surf_nki_enhanced(n_subjects=1)
# The nki dictionary contains file names for the data
# of all downloaded subjects.
print(('Resting state data of the first subjects on the '
'fsaverag5 surface left hemisphere is at: %s' %
nki_dataset['func_left'][0]))
# Destrieux parcellation for left hemisphere in fsaverage5 space
destrieux_atlas = datasets.fetch_atlas_surf_destrieux()
parcellation = destrieux_atlas['map_left']
labels = destrieux_atlas['labels']
# Fsaverage5 surface template
fsaverage = datasets.fetch_surf_fsaverage()
"""
try:
from nilearn import datasets
from nilearn import surface
except ImportError:
raise ImportError(
"You must have nilearn installed to run this example."
)
import numpy as np
import napari
# Fetch datasets - this will download dataset if datasets are not found
nki_dataset = datasets.fetch_surf_nki_enhanced(n_subjects=1)
fsaverage = datasets.fetch_surf_fsaverage()
# Load surface data and resting state time series from nilearn
brain_vertices, brain_faces = surface.load_surf_data(fsaverage['pial_left'])
brain_vertex_depth = surface.load_surf_data(fsaverage['sulc_left'])
timeseries = surface.load_surf_data(nki_dataset['func_left'][0])
# nilearn provides data as n_vertices x n_timepoints, but napari requires the
# vertices axis to be placed last to match NumPy broadcasting rules
timeseries = timeseries.transpose((1, 0))
with napari.gui_qt():
# create an empty viewer
viewer = napari.Viewer(ndisplay=3)
# add the mri