def test_matrix():
m = ci.Cifti2Matrix()
assert_raises(TypeError, m, setattr, 'metadata', ci.Cifti2Parcel())
assert_raises(TypeError, m.__setitem__, 0, ci.Cifti2Parcel())
assert_raises(TypeError, m.insert, 0, ci.Cifti2Parcel())
mim_none = ci.Cifti2MatrixIndicesMap(None, 'CIFTI_INDEX_TYPE_LABELS')
mim_0 = ci.Cifti2MatrixIndicesMap(0, 'CIFTI_INDEX_TYPE_LABELS')
mim_1 = ci.Cifti2MatrixIndicesMap(1, 'CIFTI_INDEX_TYPE_LABELS')
mim_01 = ci.Cifti2MatrixIndicesMap([0, 1], 'CIFTI_INDEX_TYPE_LABELS')
assert_raises(ci.Cifti2HeaderError, m.insert, 0, mim_none)
assert_equal(m.mapped_indices, [])
h = ci.Cifti2Header(matrix=m)
assert_equal(m.mapped_indices, [])
m.insert(0, mim_0)
assert_equal(h.mapped_indices, [0])
assert_equal(h.number_of_mapped_indices, 1)
assert_raises(ci.Cifti2HeaderError, m.insert, 0, mim_0)
assert_raises(ci.Cifti2HeaderError, m.insert, 0, mim_01)
m[0] = mim_1
assert_equal(list(m.mapped_indices), [1])
m.insert(0, mim_0)
assert_equal(list(sorted(m.mapped_indices)), [0, 1])
assert_equal(h.number_of_mapped_indices, 2)
assert_equal(h.get_index_map(0), mim_0)
assert_equal(h.get_index_map(1), mim_1)
assert_raises(ci.Cifti2HeaderError, h.get_index_map, 2)
def yeo_to_91k(dlabel, medial_wall, reference, out):
"""Convert Yeo-style dlabels (Yeo and Schaefer parcellations) to 91k
grayordinate space
The Yeo lab generates dlabel's inclusive of medial wall vertices and only
for the cortical surfaces. This is different from how typical dlabels are
formatted, which exclude medial wall vertices and include voxels from all
subcortical and cerebellar structures (i.e. the full 91k grayordinate
space). This function corrects Yeo dlabels to proper 91k grayordinates.
Parameters
----------
dlabel : str
A Yeo-style .dlabel.nii atlas
medial_wall : str
HCP medial wall mask (.dlabel.nii)
reference : str
A reference .dlabel.nii file with 91k grayordinates and all brain
models included
out : str
Output 91k grayordinate .dlabel.nii file
"""
dlabel = nib.load(dlabel)
medial_wall = nib.load(medial_wall)
ref = nib.load(reference)
# remove medial wall vertices
array = dlabel.get_fdata()
corrected_array = array[np.logical_not(medial_wall.get_fdata())]
# expand to 91k
grayordinates = np.zeros(ref.shape)
grayordinates[0, :corrected_array.shape[0]] = corrected_array
# make header
labels = dlabel.header.get_axis(index=0).label[0]
label_table = ci.Cifti2LabelTable()
for key, (tag, rgba) in labels.items():
label_table[key] = ci.Cifti2Label(key, tag, *rgba)
maps = [ci.Cifti2NamedMap('labels', ci.Cifti2MetaData({}), label_table)]
label_map = ci.Cifti2MatrixIndicesMap(
applies_to_matrix_dimension=(0, ),
indices_map_to_data_type='CIFTI_INDEX_TYPE_LABELS',
maps=maps)
model_map = ci.Cifti2MatrixIndicesMap(
applies_to_matrix_dimension=(1, ),
indices_map_to_data_type='CIFTI_INDEX_TYPE_BRAIN_MODELS',
maps=list(ref.header.get_index_map(1).brain_models))
model_map.volume = ref.header.get_index_map(1).volume
matrix = ci.Cifti2Matrix()
matrix.append(label_map)
matrix.append(model_map)
hdr = ci.Cifti2Header(matrix)
out_dtseries = ci.Cifti2Image(grayordinates, hdr)
out_dtseries.to_filename(out)
return out
def dlabel_to_dtseries(dlabel, out, n=10):
"""Create a mock .dtseries.nii from an .dlabel file
All timepoints (rows) in .dtseries.nii are duplicates of the .dlabel array.
This enables a way to verify that expected data is correctly extracted
(e.g., label 1 should extract a timeseries of all 1's, etc).
Parameters
----------
dlabel : str
File name of a .dlabel.nii file
out : str
File name of output .dtseries.nii
n : int, optional
Number of timepoints to generate, by default 100
"""
dlabel = nib.load(dlabel)
# imitate data with TR=2
label_array = dlabel.get_fdata().ravel()
tseries = np.tile(label_array, (n, 1))
data_map = ci.Cifti2MatrixIndicesMap(
applies_to_matrix_dimension=(0, ),
indices_map_to_data_type='CIFTI_INDEX_TYPE_SERIES',
number_of_series_points=tseries.shape[0],
series_start=0,
series_step=2,
series_exponent=0,
series_unit='SECOND')
# take brain models from dlabel
model_map = ci.Cifti2MatrixIndicesMap(
applies_to_matrix_dimension=(1, ),
indices_map_to_data_type='CIFTI_INDEX_TYPE_BRAIN_MODELS',
maps=list(dlabel.header.get_index_map(1).brain_models))
volume = dlabel.header.get_index_map(1).volume
if volume is not None:
model_map.volume = dlabel.header.get_index_map(1).volume
# make header
matrix = ci.Cifti2Matrix()
matrix.append(data_map)
matrix.append(model_map)
hdr = ci.Cifti2Header(matrix)
out_dtseries = ci.Cifti2Image(tseries, hdr)
out_dtseries.to_filename(out)
return out
def create_geometry_map(applies_to_matrix_dimension):
voxels = ci.Cifti2VoxelIndicesIJK(brain_models[0][1])
left_thalamus = ci.Cifti2BrainModel(index_offset=0,
index_count=4,
model_type='CIFTI_MODEL_TYPE_VOXELS',
brain_structure=brain_models[0][0],
voxel_indices_ijk=voxels)
vertices = ci.Cifti2VertexIndices(np.array(brain_models[1][1]))
left_cortex = ci.Cifti2BrainModel(index_offset=4,
index_count=5,
model_type='CIFTI_MODEL_TYPE_SURFACE',
brain_structure=brain_models[1][0],
vertex_indices=vertices)
left_cortex.surface_number_of_vertices = number_of_vertices
vertices = ci.Cifti2VertexIndices(np.array(brain_models[2][1]))
right_cortex = ci.Cifti2BrainModel(index_offset=9,
index_count=1,
model_type='CIFTI_MODEL_TYPE_SURFACE',
brain_structure=brain_models[2][0],
vertex_indices=vertices)
right_cortex.surface_number_of_vertices = number_of_vertices
volume = ci.Cifti2Volume(
dimensions,
ci.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(-3, affine))
return ci.Cifti2MatrixIndicesMap(
applies_to_matrix_dimension,
'CIFTI_INDEX_TYPE_BRAIN_MODELS',
maps=[left_thalamus, left_cortex, right_cortex, volume])
def to_mapping(self, dim):
"""
Converts the parcels to a MatrixIndicesMap for storage in CIFTI format
Parameters
----------
dim : int
which dimension of the CIFTI vector/matrix is described by this dataset (zero-based)
Returns
-------
cifti2.Cifti2MatrixIndicesMap
"""
mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_PARCELS')
if self.affine is not None:
affine = cifti2.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(-3, matrix=self.affine)
mim.volume = cifti2.Cifti2Volume(self.volume_shape, affine)
for name, nvertex in self.nvertices.items():
mim.append(cifti2.Cifti2Surface(name, nvertex))
for name, voxels, vertices in self.arr:
cifti_voxels = cifti2.Cifti2VoxelIndicesIJK(voxels)
element = cifti2.Cifti2Parcel(name, cifti_voxels)
for name, idx_vertices in vertices.items():
element.vertices.append(cifti2.Cifti2Vertices(name, idx_vertices))
mim.append(element)
return mim
def to_mapping(self, dim):
"""
Converts the brain model axis to a MatrixIndicesMap for storage in CIFTI format
Parameters
----------
dim : int
which dimension of the CIFTI vector/matrix is described by this dataset (zero-based)
Returns
-------
cifti2.Cifti2MatrixIndicesMap
"""
mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_BRAIN_MODELS')
for name, to_slice, bm in self.iter_structures():
is_surface = name in self.nvertices.keys()
if is_surface:
voxels = None
vertices = cifti2.Cifti2VertexIndices(bm.vertex)
nvertex = self.nvertices[name]
else:
voxels = cifti2.Cifti2VoxelIndicesIJK(bm.voxel)
vertices = None
nvertex = None
if mim.volume is None:
affine = cifti2.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(-3, matrix=self.affine)
mim.volume = cifti2.Cifti2Volume(self.volume_shape, affine)
cifti_bm = cifti2.Cifti2BrainModel(to_slice.start, len(bm),
'CIFTI_MODEL_TYPE_SURFACE' if is_surface else 'CIFTI_MODEL_TYPE_VOXELS',
name, nvertex, voxels, vertices)
mim.append(cifti_bm)
return mim
def test_matrixindicesmap():
mim = ci.Cifti2MatrixIndicesMap(0, 'CIFTI_INDEX_TYPE_LABELS')
volume = ci.Cifti2Volume()
volume2 = ci.Cifti2Volume()
parcel = ci.Cifti2Parcel()
assert mim.volume is None
mim.append(volume)
mim.append(parcel)
assert mim.volume == volume
with pytest.raises(ci.Cifti2HeaderError):
mim.insert(0, volume)
with pytest.raises(ci.Cifti2HeaderError):
mim[1] = volume
mim[0] = volume2
assert mim.volume == volume2
del mim.volume
assert mim.volume is None
with pytest.raises(ValueError):
del mim.volume
mim.volume = volume
assert mim.volume == volume
mim.volume = volume2
assert mim.volume == volume2
with pytest.raises(ValueError):
mim.volume = parcel
def test_matrixindicesmap():
mim = ci.Cifti2MatrixIndicesMap(0, 'CIFTI_INDEX_TYPE_LABELS')
volume = ci.Cifti2Volume()
volume2 = ci.Cifti2Volume()
parcel = ci.Cifti2Parcel()
assert_is_none(mim.volume)
mim.append(volume)
mim.append(parcel)
assert_equal(mim.volume, volume)
assert_raises(ci.Cifti2HeaderError, mim.insert, 0, volume)
assert_raises(ci.Cifti2HeaderError, mim.__setitem__, 1, volume)
mim[0] = volume2
assert_equal(mim.volume, volume2)
del mim.volume
assert_is_none(mim.volume)
assert_raises(ValueError, delattr, mim, 'volume')
mim.volume = volume
assert_equal(mim.volume, volume)
mim.volume = volume2
assert_equal(mim.volume, volume2)
assert_raises(ValueError, setattr, mim, 'volume', parcel)
def create_series_map():
return ci.Cifti2MatrixIndicesMap((0, ),
'CIFTI_INDEX_TYPE_SERIES',
number_of_series_points=timepoints,
series_exponent=0,
series_start=0,
series_step=1,
series_unit='SECOND')
def create_series_map(applies_to_matrix_dimension):
return ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
'CIFTI_INDEX_TYPE_SERIES',
number_of_series_points=13,
series_exponent=-3,
series_start=18.2,
series_step=10.5,
series_unit='SECOND')
def create_scalar_map(applies_to_matrix_dimension):
maps = [
ci.Cifti2NamedMap(name, ci.Cifti2MetaData(meta))
for name, meta in scalars
]
return ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
'CIFTI_INDEX_TYPE_SCALARS',
maps=maps)
def make_imaker(self, arr, header=None, ni_header=None):
for idx, sz in enumerate(arr.shape):
maps = [ci.Cifti2NamedMap(str(value)) for value in range(sz)]
mim = ci.Cifti2MatrixIndicesMap((idx, ),
'CIFTI_INDEX_TYPE_SCALARS',
maps=maps)
header.matrix.append(mim)
return lambda: self.image_maker(arr.copy(), header, ni_header)
def test_matrix():
m = ci.Cifti2Matrix()
with pytest.raises(ValueError):
m.metadata = ci.Cifti2Parcel()
with pytest.raises(TypeError):
m[0] = ci.Cifti2Parcel()
with pytest.raises(TypeError):
m.insert(0, ci.Cifti2Parcel())
mim_none = ci.Cifti2MatrixIndicesMap(None, 'CIFTI_INDEX_TYPE_LABELS')
mim_0 = ci.Cifti2MatrixIndicesMap(0, 'CIFTI_INDEX_TYPE_LABELS')
mim_1 = ci.Cifti2MatrixIndicesMap(1, 'CIFTI_INDEX_TYPE_LABELS')
mim_01 = ci.Cifti2MatrixIndicesMap([0, 1], 'CIFTI_INDEX_TYPE_LABELS')
with pytest.raises(ci.Cifti2HeaderError):
m.insert(0, mim_none)
assert m.mapped_indices == []
h = ci.Cifti2Header(matrix=m)
assert m.mapped_indices == []
m.insert(0, mim_0)
assert h.mapped_indices == [0]
assert h.number_of_mapped_indices == 1
with pytest.raises(ci.Cifti2HeaderError):
m.insert(0, mim_0)
with pytest.raises(ci.Cifti2HeaderError):
m.insert(0, mim_01)
m[0] = mim_1
assert list(m.mapped_indices) == [1]
m.insert(0, mim_0)
assert list(sorted(m.mapped_indices)) == [0, 1]
assert h.number_of_mapped_indices == 2
assert h.get_index_map(0) == mim_0
assert h.get_index_map(1) == mim_1
with pytest.raises(ci.Cifti2HeaderError):
h.get_index_map(2)
def create_label_map(applies_to_matrix_dimension):
maps = []
for name, meta, label in labels:
label_table = ci.Cifti2LabelTable()
for key, (tag, rgba) in label.items():
label_table[key] = ci.Cifti2Label(key, tag, *rgba)
maps.append(
ci.Cifti2NamedMap(name, ci.Cifti2MetaData(meta), label_table))
return ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
'CIFTI_INDEX_TYPE_LABELS',
maps=maps)
def create_parcel_map(applies_to_matrix_dimension, surfaces, volume, pinfo):
"""Creaters a pracel map
PARAMETERS
---------
applies_to_matrix_dimension : tuple
surfaces : list of Cifti2Surface
volume : Cifti2Volume
pinfo : list of ParcelInfo
"""
mapping = ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
Map.PARCELS)
for p in pinfo:
mapping.append(create_parcel(p))
mapping.extend(surfaces)
mapping.volume = volume
return mapping
def to_mapping(self, dim):
"""
Converts the hcp_labels to a MatrixIndicesMap for storage in CIFTI format
Parameters
----------
dim : int
which dimension of the CIFTI vector/matrix is described by this dataset (zero-based)
Returns
-------
cifti2.Cifti2MatrixIndicesMap
"""
mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_SCALARS')
for elem in self.arr:
meta = None if len(elem['meta']) == 0 else elem['meta']
named_map = cifti2.Cifti2NamedMap(elem['name'], cifti2.Cifti2MetaData(meta))
mim.append(named_map)
return mim
def to_mapping(self, dim):
"""
Converts the series to a MatrixIndicesMap for storage in CIFTI format
Parameters
----------
dim : int
which dimension of the CIFTI vector/matrix is described by this dataset (zero-based)
Returns
-------
cifti2.Cifti2MatrixIndicesMap
"""
mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_SERIES')
mim.series_exponent = 0
mim.series_start = self.start
mim.series_step = self.step
mim.number_of_series_points = self.size
return mim
def create_parcel_map(applies_to_matrix_dimension):
mapping = ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
'CIFTI_INDEX_TYPE_PARCELS')
for name, elements in parcels:
surfaces = []
volume = None
for element in elements:
if isinstance(element[0], str):
surfaces.append(ci.Cifti2Vertices(element[0], element[1]))
else:
volume = ci.Cifti2VoxelIndicesIJK(element)
mapping.append(ci.Cifti2Parcel(name, volume, surfaces))
mapping.extend([
ci.Cifti2Surface('CIFTI_STRUCTURE_CORTEX_%s' % orientation,
number_of_vertices)
for orientation in ['LEFT', 'RIGHT']
])
mapping.volume = ci.Cifti2Volume(
dimensions,
ci.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(-3, affine))
return mapping
def to_mapping(self, dim):
"""
Converts the hcp_labels to a MatrixIndicesMap for storage in CIFTI format
Parameters
----------
dim : int
which dimension of the CIFTI vector/matrix is described by this dataset (zero-based)
Returns
-------
cifti2.Cifti2MatrixIndicesMap
"""
mim = cifti2.Cifti2MatrixIndicesMap([dim], 'CIFTI_INDEX_TYPE_LABELS')
for elem in self.arr:
label_table = cifti2.Cifti2LabelTable()
for key, value in elem['label'].items():
label_table[key] = (value[0],) + tuple(value[1])
meta = None if len(elem['meta']) == 0 else elem['meta']
named_map = cifti2.Cifti2NamedMap(elem['name'], cifti2.Cifti2MetaData(meta),
label_table)
mim.append(named_map)
return mim
def create_geometry_map():
index_offset = 0
brain_models = []
timeseries = np.zeros((timepoints, 0))
for name, data in models:
if "CORTEX" in name:
model_type = "CIFTI_MODEL_TYPE_SURFACE"
attr = "vertex_indices"
indices = ci.Cifti2VertexIndices(np.arange(len(data)))
else:
model_type = "CIFTI_MODEL_TYPE_VOXELS"
attr = "voxel_indices_ijk"
indices = ci.Cifti2VoxelIndicesIJK(np.arange(len(data)))
bm = ci.Cifti2BrainModel(
index_offset=index_offset,
index_count=len(data),
model_type=model_type,
brain_structure=name,
)
setattr(bm, attr, indices)
index_offset += len(data)
brain_models.append(bm)
timeseries = np.column_stack((timeseries, data.T))
brain_models.append(
ci.Cifti2Volume(
(4, 4, 4),
ci.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(
-3, np.eye(4)),
))
return ci.Cifti2MatrixIndicesMap(
(1, ),
"CIFTI_INDEX_TYPE_BRAIN_MODELS",
maps=brain_models,
), timeseries
def create_geometry_map(applies_to_matrix_dimension, brain_models):
"""Creates a geometry map"""
return ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
Map.BRAIN_MODELS,
maps=brain_models)
def create_scalar_map(applies_to_matrix_dimension, info):
"""Creates a scalar map form a list of NamedMapInfo"""
maps = [ci.Cifti2NamedMap(i.name, ci.Cifti2MetaData(i.meta)) for i in info]
return ci.Cifti2MatrixIndicesMap(applies_to_matrix_dimension,
Map.SCALARS,
maps=maps)
indices=cortex_left_indices, model_type=ch.ModelType.SURFACE,
surface_number_of_vertices=ch.HCP_32K_FS_L)
right_thalamus = ch.MapInfo(brain_structure=ch.Structure.THALAMUS_LEFT,
indices=voxels, model_type=ch.ModelType.VOXEL,
surface_number_of_vertices=None)
volume = ch.create_volume([50, 50, 50], affine, -3)
# create scalar maps
wrong_scalar_map = ch.create_scalar_map((0,),
[ch.NamedMapInfo(name="wrong", meta={})])
correct_scalar_map = ch.create_scalar_map((0,),
[ch.NamedMapInfo(name="right", meta={})])
# create series maps
series_map = ci.Cifti2MatrixIndicesMap((0, ), ch.Map.SERIES,
number_of_series_points=40, series_exponent=0, series_start=0,
series_step=0.5, series_unit="SECOND")
# create brain models
wrong_brain_models = ch.create_brain_models([wrong_right_cortex, left_cortex,
right_thalamus])
wrong_brain_models.append(volume)
correct_brain_models = ch.create_brain_models([right_cortex, left_cortex,
right_thalamus])
correct_brain_models.append(volume)
no_volume_brain_models = ch.create_brain_models([right_cortex, left_cortex])
# create geometry maps
wrong_geometry_map = ch.create_geometry_map((1,), wrong_brain_models)
correct_geometry_map = ch.create_geometry_map((1, ), correct_brain_models)
no_volume_geometry_map = ch.create_geometry_map((1, ), no_volume_brain_models)
def _create_cifti_image(bold_file, label_file, bold_surfs, annotation_files,
tr, targets):
"""
Generate CIFTI image in target space.
Parameters
----------
bold_file : str
BOLD volumetric timeseries
label_file : str
Subcortical label file
bold_surfs : list
BOLD surface timeseries [L,R]
annotation_files : list
Surface label files used to remove medial wall
tr : float
BOLD repetition time
targets : tuple or list
Surface and volumetric output spaces
Returns
-------
out :
BOLD data saved as CIFTI dtseries
"""
bold_img = nb.load(bold_file)
label_img = nb.load(label_file)
if label_img.shape != bold_img.shape[:3]:
warnings.warn("Resampling bold volume to match label dimensions")
bold_img = resample_to_img(bold_img, label_img)
bold_data = bold_img.get_fdata(dtype='float32')
timepoints = bold_img.shape[3]
label_data = np.asanyarray(label_img.dataobj).astype('int16')
# Create brain models
idx_offset = 0
brainmodels = []
bm_ts = np.empty((timepoints, 0))
for structure, labels in CIFTI_STRUCT_WITH_LABELS.items():
if labels is None: # surface model
model_type = "CIFTI_MODEL_TYPE_SURFACE"
# use the corresponding annotation
hemi = structure.split('_')[-1]
# currently only supports L/R cortex
surf = nb.load(bold_surfs[hemi == "RIGHT"])
surf_verts = len(surf.darrays[0].data)
if annotation_files[0].endswith('.annot'):
annot = nb.freesurfer.read_annot(
annotation_files[hemi == "RIGHT"])
# remove medial wall
medial = np.nonzero(annot[0] != annot[2].index(b'unknown'))[0]
else:
annot = nb.load(annotation_files[hemi == "RIGHT"])
medial = np.nonzero(annot.darrays[0].data)[0]
# extract values across volumes
ts = np.array([tsarr.data[medial] for tsarr in surf.darrays])
vert_idx = ci.Cifti2VertexIndices(medial)
bm = ci.Cifti2BrainModel(index_offset=idx_offset,
index_count=len(vert_idx),
model_type=model_type,
brain_structure=structure,
vertex_indices=vert_idx,
n_surface_vertices=surf_verts)
idx_offset += len(vert_idx)
bm_ts = np.column_stack((bm_ts, ts))
else:
model_type = "CIFTI_MODEL_TYPE_VOXELS"
vox = []
ts = None
for label in labels:
ijk = np.nonzero(label_data == label)
if ijk[0].size == 0: # skip label if nothing matches
continue
ts = (bold_data[ijk] if ts is None else np.concatenate(
(ts, bold_data[ijk])))
vox += [[ijk[0][ix], ijk[1][ix], ijk[2][ix]]
for ix, row in enumerate(ts)]
vox = ci.Cifti2VoxelIndicesIJK(vox)
bm = ci.Cifti2BrainModel(index_offset=idx_offset,
index_count=len(vox),
model_type=model_type,
brain_structure=structure,
voxel_indices_ijk=vox)
idx_offset += len(vox)
bm_ts = np.column_stack((bm_ts, ts.T))
# add each brain structure to list
brainmodels.append(bm)
# add volume information
brainmodels.append(
ci.Cifti2Volume(
bold_img.shape[:3],
ci.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(
-3, bold_img.affine)))
# generate Matrix information
series_map = ci.Cifti2MatrixIndicesMap((0, ),
'CIFTI_INDEX_TYPE_SERIES',
number_of_series_points=timepoints,
series_exponent=0,
series_start=0.0,
series_step=tr,
series_unit='SECOND')
geometry_map = ci.Cifti2MatrixIndicesMap((1, ),
'CIFTI_INDEX_TYPE_BRAIN_MODELS',
maps=brainmodels)
# provide some metadata to CIFTI matrix
meta = {
"surface": targets[0],
"volume": targets[1],
}
# generate and save CIFTI image
matrix = ci.Cifti2Matrix()
matrix.append(series_map)
matrix.append(geometry_map)
matrix.metadata = ci.Cifti2MetaData(meta)
hdr = ci.Cifti2Header(matrix)
img = ci.Cifti2Image(bm_ts, hdr)
img.nifti_header.set_intent('NIFTI_INTENT_CONNECTIVITY_DENSE_SERIES')
out_file = "{}.dtseries.nii".format(split_filename(bold_file)[1])
ci.save(img, out_file)
return os.path.join(os.getcwd(), out_file)
def _create_cifti_image(bold_file, label_file, annotation_files, gii_files,
volume_target, surface_target, tr):
"""
Generate CIFTI image in target space
Parameters
bold_file : 4D BOLD timeseries
label_file : label atlas
annotation_files : FreeSurfer annotations
gii_files : 4D BOLD surface timeseries in GIFTI format
volume_target : label atlas space
surface_target : gii_files space
tr : repetition timeseries
Returns
out_file : BOLD data as CIFTI dtseries
"""
label_img = nb.load(label_file)
bold_img = resample_to_img(bold_file, label_img)
bold_data = bold_img.get_data()
timepoints = bold_img.shape[3]
label_data = label_img.get_data()
# set up CIFTI information
series_map = ci.Cifti2MatrixIndicesMap(
(0, ),
'CIFTI_INDEX_TYPE_SERIES',
number_of_series_points=timepoints,
series_exponent=0,
series_start=0.0,
series_step=tr,
series_unit='SECOND')
# Create CIFTI brain models
idx_offset = 0
brainmodels = []
bm_ts = np.empty((timepoints, 0))
for structure, labels in CIFTI_STRUCT_WITH_LABELS.items():
if labels is None: # surface model
model_type = "CIFTI_MODEL_TYPE_SURFACE"
# use the corresponding annotation
hemi = structure.split('_')[-1]
annot = nb.freesurfer.read_annot(
annotation_files[hemi == "RIGHT"])
# currently only supports L/R cortex
gii = nb.load(gii_files[hemi == "RIGHT"])
# calculate total number of vertices
surf_verts = len(annot[0])
# remove medial wall for CIFTI format
vert_idx = np.nonzero(
annot[0] != annot[2].index(b'unknown'))[0]
# extract values across volumes
ts = np.array([tsarr.data[vert_idx] for tsarr in gii.darrays])
vert_idx = ci.Cifti2VertexIndices(vert_idx)
bm = ci.Cifti2BrainModel(index_offset=idx_offset,
index_count=len(vert_idx),
model_type=model_type,
brain_structure=structure,
vertex_indices=vert_idx,
n_surface_vertices=surf_verts)
bm_ts = np.column_stack((bm_ts, ts))
idx_offset += len(vert_idx)
brainmodels.append(bm)
else:
model_type = "CIFTI_MODEL_TYPE_VOXELS"
vox = []
ts = None
for label in labels:
ijk = np.nonzero(label_data == label)
ts = (bold_data[ijk] if ts is None else np.concatenate(
(ts, bold_data[ijk])))
vox += [[ijk[0][ix], ijk[1][ix], ijk[2][ix]]
for ix, row in enumerate(ts)]
bm_ts = np.column_stack((bm_ts, ts.T))
vox = ci.Cifti2VoxelIndicesIJK(vox)
bm = ci.Cifti2BrainModel(index_offset=idx_offset,
index_count=len(vox),
model_type=model_type,
brain_structure=structure,
voxel_indices_ijk=vox)
idx_offset += len(vox)
brainmodels.append(bm)
volume = ci.Cifti2Volume(
bold_img.shape[:3],
ci.Cifti2TransformationMatrixVoxelIndicesIJKtoXYZ(
-3, bold_img.affine))
brainmodels.append(volume)
# create CIFTI geometry based on brainmodels
geometry_map = ci.Cifti2MatrixIndicesMap(
(1, ), 'CIFTI_INDEX_TYPE_BRAIN_MODELS', maps=brainmodels)
# provide some metadata to CIFTI matrix
meta = {
"target_surface": surface_target,
"target_volume": volume_target,
}
# generate and save CIFTI image
matrix = ci.Cifti2Matrix()
matrix.append(series_map)
matrix.append(geometry_map)
matrix.metadata = ci.Cifti2MetaData(meta)
hdr = ci.Cifti2Header(matrix)
img = ci.Cifti2Image(bm_ts, hdr)
img.nifti_header.set_intent('NIFTI_INTENT_CONNECTIVITY_DENSE_SERIES')
_, out_base, _ = split_filename(bold_file)
out_file = "{}.dtseries.nii".format(out_base)
ci.save(img, out_file)
return os.path.join(os.getcwd(), out_file)
