def getvertices(hemi,freesurferdir):
labellist = [1, 5, 13, 14, 15, 16, 24, 31, 32, 39, 40, 53, 54, 55, 63, 64, 65, 71]
[vertices,colortable,names] = surf.read_annot(freesurferdir+'/fsaverage4/label/'+hemi[-2:]+'.aparc.a2009s.annot', orig_ids=True)
chosenvertices = list()
for j, value in enumerate(vertices) :
for i, index in enumerate(labellist) :
if colortable[index][4]==value :
chosenvertices.append(j)
return chosenvertices
def createSurface(inputFile, hemi):
[vertices, colortable, names] = surf.read_annot(
freesurferdir + "/fsaverage4/label/" + hemi + ".aparc.a2009s.annot", orig_ids=True
)
clusters = []
with open(inputFile, "rb") as openfile:
for line in openfile:
if line[: len(flag)] == flag:
clusters.append(line)
for index, cluster in enumerate(clusters):
surface = np.zeros_like(vertices)
startIndex = cluster.find(rowflag) + len(rowflag)
stopIndex = cluster.find("]", startIndex) - 1
for x in cluster[startIndex:stopIndex].split(","):
vertex = x.lstrip()
if vertex.isdigit():
surface[int(x) % 2562] = +1
savefile = inputFile.split(".")[0] + "_cluster" + str(index)
newImage = nb.nifti1.Nifti1Image(surface, None)
nb.save(newImage, savefile)
def createSurface(inputFile, hemi):
[vertices, colortable, names] = surf.read_annot(
freesurferdir + '/fsaverage4/label/' + hemi + '.aparc.a2009s.annot',
orig_ids=True)
clusters = []
with open(inputFile, 'rb') as openfile:
for line in openfile:
if line[:len(flag)] == flag:
clusters.append(line)
for index, cluster in enumerate(clusters):
surface = np.zeros_like(vertices)
startIndex = cluster.find(rowflag) + len(rowflag)
stopIndex = cluster.find(']', startIndex) - 1
for x in cluster[startIndex:stopIndex].split(','):
vertex = x.lstrip()
if vertex.isdigit():
surface[int(x) % 2562] = +1
savefile = inputFile.split('.')[0] + '_cluster' + str(index)
newImage = nb.nifti1.Nifti1Image(surface, None)
nb.save(newImage, savefile)
surface = "inflated"
"""
Bring up the visualization
"""
brain = Brain(subject_id, hemi, surface,
config_opts=dict(background="lightslategray",
cortex="high_contrast"))
"""
Read in the aparc annotation file
"""
aparc_file = op.join(os.environ["SUBJECTS_DIR"],
subject_id, "label",
hemi + ".aparc.a2009s.annot")
labels, ctab, names = io.read_annot(aparc_file)
"""
Make a random vector of scalar data corresponding to
a value for each region in the parcellation.
"""
roi_data = np.random.random(len(names))
"""
Make a vector containing the data point at each vertex.
"""
vtx_data = np.zeros(len(labels))
for i, data in enumerate(roi_data):
vtx_data[labels == i] = data
"""
""" brain.add_foci(coords, map_surface="white", color=rgb) """ You can also plot foci with a set of surface vertex ids. For instance, you might want to plot the peak activation within an ROI for each of your indivdiual subjects over the group activation map. Here, we will just demonstrate with a set of randomly choosen vertices from within the superior temporal sulcus. First, we load in the Destrieux parcellation annotation file. """ annot_path = op.join(subjects_dir, subject_id, "label/lh.aparc.a2009s.annot") ids, ctab, names = io.read_annot(annot_path) """ Then, find 10 random vertices within the STS. """ verts = arange(0, len(ids)) coords = permutation(verts[ids == 74])[:10] """ You can also control the size of the spheroids. We'll make these a little bit bigger than our other foci. """ scale_factor = 1.3 """
hemi = "lh"
surface = "inflated"
"""
Bring up the visualization
"""
brain = Brain(subject_id,
hemi,
surface,
config_opts=dict(background="lightslategray",
cortex="high_contrast"))
"""
Read in the aparc annotation file
"""
aparc_file = op.join(os.environ["SUBJECTS_DIR"], subject_id, "label",
hemi + ".aparc.a2009s.annot")
labels, ctab, names = io.read_annot(aparc_file)
"""
Make a random vector of scalar data corresponding to
a value for each region in the parcellation.
"""
roi_data = np.random.random(len(names))
"""
Make a vector containing the data point at each vertex.
"""
vtx_data = np.zeros(len(labels))
for i, data in enumerate(roi_data):
vtx_data[labels == i] = data
"""
Display these values on the brain.
Use the hot colormap and add an alpha channel
so the underlying anatomy is visible.
hemi,
"white",
subjects_dir=subjects_dir)
foci_surf.load_geometry()
foci_vtxs = utils.find_closest_vertices(foci_surf.coords, coords)
# Load the geometry
curv_file = op.join(surf_dir, "%s.curv" % hemi)
curv = io.read_morph_data(curv_file) # < 0 = gyrus & > 0 = sulcus
# Load the parcellations
aparc_file = op.join(label_dir, "%s.aparcDKT40JT.annot" % hemi)
aparc9_file = op.join(label_dir, "%s.aparc.a2009s.annot" % hemi)
ba_file = op.join(label_dir, "%s.PALS_B12_Brodmann.annot" % hemi)
yeo_file = op.join(label_dir, "%s.Yeo2011_7Networks_N1000.annot" % hemi)
aparc = io.read_annot(aparc_file)
ba = io.read_annot(ba_file)
yeo = io.read_annot(yeo_file)
aparc9 = io.read_annot(aparc9_file)
yeo_names = [
"Medial_Wall", "Visual", "Somatomotor", "Dorsal Attention",
"Ventral Attention", "Limbic", "Frontoparietal", "Default"
]
aparc_names = [
'Unknown', 'Banks Superior Temporal', 'Caudal Anterior Cingulate',
'Caudal Middle Frontal', 'Corpus Callosum', 'Cuneus', 'Entorhinal',
'Fusiform', 'Inferior Parietal', 'Inferior Temporal',
'Isthmus Cingulate', 'Lateral Occipital', 'Lateral Orbital Frontal',
'Lingual', 'Medial Orbital Frontal', 'Middle Temporal',
# Now we can take these peak coordinates and get the surface vertex
foci_surf = io.Surface("fsaverage_copy", hemi, "white", subjects_dir=subjects_dir)
foci_surf.load_geometry()
foci_vtxs = utils.find_closest_vertices(foci_surf.coords, coords)
# Load the geometry
curv_file = op.join(surf_dir, "%s.curv" % hemi)
curv = io.read_morph_data(curv_file) # < 0 = gyrus & > 0 = sulcus
# Load the parcellations
aparc_file = op.join(label_dir, "%s.aparcDKT40JT.annot" % hemi)
aparc9_file = op.join(label_dir, "%s.aparc.a2009s.annot" % hemi)
ba_file = op.join(label_dir, "%s.PALS_B12_Brodmann.annot" % hemi)
yeo_file = op.join(label_dir, "%s.Yeo2011_7Networks_N1000.annot" % hemi)
aparc = io.read_annot(aparc_file)
ba = io.read_annot(ba_file)
yeo = io.read_annot(yeo_file)
aparc9 = io.read_annot(aparc9_file)
yeo_names = ["Medial_Wall", "Visual", "Somatomotor", "Dorsal Attention",
"Ventral Attention", "Limbic", "Frontoparietal", "Default"]
aparc_names = ['Unknown',
'Banks Superior Temporal',
'Caudal Anterior Cingulate',
'Caudal Middle Frontal',
'Corpus Callosum',
'Cuneus',
'Entorhinal',
'Fusiform',
scan = "short"
easydir = easydirs[scan]
surf_files = {
"lh": op.join(easydir, "surf_lh_thresh_zstat_FSIQ.nii.gz"),
"rh": op.join(easydir, "surf_rh_thresh_zstat_FSIQ.nii.gz")
}
ba_files = {
"lh": "/home2/data/PublicProgram/freesurfer/fsaverage_copy/label/lh.PALS_B12_Brodmann.annot",
"rh": "/home2/data/PublicProgram/freesurfer/fsaverage_copy/label/rh.PALS_B12_Brodmann.annot",
}
hemi = "lh"
ba = io.read_annot(ba_files[hemi])
cwas = io.read_scalar_data(surf_files[hemi])
rois = ba[0]
urois = np.unique(rois); urois.sort()
labels = np.array(ba[2])[urois]
cols = ["index", "roi", "ba", "summary_wt", "summary_uwt", "meta_analysis"]
dict_df = { k : [] for k in cols }
for i,label in enumerate(labels):
if label.find("Brodmann") == -1:
continue
ba = int(label[9:])
roi = urois[i]
"""
brain.add_foci(coords, map_surface="white", color="gold")
"""
You can also plot foci with a set of surface vertex ids.
For instance, you might want to plot the peak activation
within an ROI for each of your indivdiual subjects over
the group activation map.
Here, we will just demonstrate with a set of randomly
choosen vertices from within the superior temporal sulcus.
First, we load in the Destrieux parcellation annotation file
and find 10 random vertices within the STS.
"""
annot_path = op.join(subjects_dir, subject_id, "label/lh.aparc.a2009s.annot")
ids, ctab, names = io.read_annot(annot_path)
verts = arange(0, len(ids))
coords = permutation(verts[ids == 74])[:10]
"""
You can also control the size of the focus glpyhs.
We'll make these a little bit smaller than our
other foci.
"""
scale_factor = 0.7
"""
Finally, plot the foci using the coords_as_verts option to
center each sphereoid at its vertex id.
"""
brain.add_foci(coords,
coords_as_verts=True,
scale_factor=scale_factor,
""" brain.add_foci(coords, map_surface="white", color=rgb) """ You can also plot foci with a set of surface vertex ids. For instance, you might want to plot the peak activation within an ROI for each of your indivdiual subjects over the group activation map. Here, we will just demonstrate with a set of randomly choosen vertices from within the superior temporal sulcus. First, we load in the Destrieux parcellation annotation file. """ annot_path = op.join(subjects_dir, subject_id, "label/lh.aparc.a2009s.annot") ids, ctab = io.read_annot(annot_path) """ Then, find 10 random vertices within the STS. """ verts = arange(0, len(ids)) coords = permutation(verts[ids == 74])[:10] """ You can also control the size of the spheroids. We'll make these a little bit bigger than our other foci. """ scale_factor = 1.3 """
