def test_split_label():
aparc = labels_from_parc('fsaverage', 'aparc', 'lh', regexp='lingual',
subjects_dir=subjects_dir)[0]
lingual = aparc[0]
# split with names
parts = ('lingual_post', 'lingual_ant')
post, ant = split_label(lingual, parts, subjects_dir=subjects_dir)
# check output names
assert_equal(post.name, parts[0])
assert_equal(ant.name, parts[1])
# check vertices add up
lingual_reconst = post + ant
lingual_reconst.name = lingual.name
lingual_reconst.comment = lingual.comment
assert_labels_equal(lingual_reconst, lingual)
# compare output of Label.split() method
post1, ant1 = lingual.split(parts, subjects_dir=subjects_dir)
assert_labels_equal(post1, post)
assert_labels_equal(ant1, ant)
# compare fs_like split with freesurfer split
antmost = split_label(lingual, 40, None, subjects_dir, True)[-1]
fs_vert = [210, 4401, 7405, 12079, 16276, 18956, 26356, 32713, 32716,
32719, 36047, 36050, 42797, 42798, 42799, 59281, 59282, 59283,
71864, 71865, 71866, 71874, 71883, 79901, 79903, 79910, 103024,
107849, 107850, 122928, 139356, 139357, 139373, 139374, 139375,
139376, 139377, 139378, 139381, 149117, 149118, 149120, 149127]
assert_array_equal(antmost.vertices, fs_vert)
# check default label name
assert_equal(antmost.name, "lingual_div40-lh")
def test_split_label():
"""Test splitting labels"""
aparc = read_labels_from_annot('fsaverage',
'aparc',
'lh',
regexp='lingual',
subjects_dir=subjects_dir)
lingual = aparc[0]
# Test input error
assert_raises(ValueError, lingual.split, 'bad_input_string')
# split with names
parts = ('lingual_post', 'lingual_ant')
post, ant = split_label(lingual, parts, subjects_dir=subjects_dir)
# check output names
assert_equal(post.name, parts[0])
assert_equal(ant.name, parts[1])
# check vertices add up
lingual_reconst = post + ant
lingual_reconst.name = lingual.name
lingual_reconst.comment = lingual.comment
lingual_reconst.color = lingual.color
assert_labels_equal(lingual_reconst, lingual)
# compare output of Label.split() method
post1, ant1 = lingual.split(parts, subjects_dir=subjects_dir)
assert_labels_equal(post1, post)
assert_labels_equal(ant1, ant)
# compare fs_like split with freesurfer split
antmost = split_label(lingual, 40, None, subjects_dir, True)[-1]
fs_vert = [
210, 4401, 7405, 12079, 16276, 18956, 26356, 32713, 32716, 32719,
36047, 36050, 42797, 42798, 42799, 59281, 59282, 59283, 71864, 71865,
71866, 71874, 71883, 79901, 79903, 79910, 103024, 107849, 107850,
122928, 139356, 139357, 139373, 139374, 139375, 139376, 139377, 139378,
139381, 149117, 149118, 149120, 149127
]
assert_array_equal(antmost.vertices, fs_vert)
# check default label name
assert_equal(antmost.name, "lingual_div40-lh")
# Apply contiguous splitting to DMN label from parcellation in Yeo, 2011
label_default_mode = read_label(
op.join(subjects_dir, 'fsaverage', 'label', 'lh.7Networks_7.label'))
DMN_sublabels = label_default_mode.split(parts='contiguous',
subject='fsaverage',
subjects_dir=subjects_dir)
assert_equal([len(label.vertices) for label in DMN_sublabels],
[16181, 7022, 5965, 5300, 823] + [1] * 23)
def test_split_label():
"""Test splitting labels"""
aparc = read_labels_from_annot('fsaverage', 'aparc', 'lh',
regexp='lingual', subjects_dir=subjects_dir)
lingual = aparc[0]
# Test input error
assert_raises(ValueError, lingual.split, 'bad_input_string')
# split with names
parts = ('lingual_post', 'lingual_ant')
post, ant = split_label(lingual, parts, subjects_dir=subjects_dir)
# check output names
assert_equal(post.name, parts[0])
assert_equal(ant.name, parts[1])
# check vertices add up
lingual_reconst = post + ant
lingual_reconst.name = lingual.name
lingual_reconst.comment = lingual.comment
lingual_reconst.color = lingual.color
assert_labels_equal(lingual_reconst, lingual)
# compare output of Label.split() method
post1, ant1 = lingual.split(parts, subjects_dir=subjects_dir)
assert_labels_equal(post1, post)
assert_labels_equal(ant1, ant)
# compare fs_like split with freesurfer split
antmost = split_label(lingual, 40, None, subjects_dir, True)[-1]
fs_vert = [210, 4401, 7405, 12079, 16276, 18956, 26356, 32713, 32716,
32719, 36047, 36050, 42797, 42798, 42799, 59281, 59282, 59283,
71864, 71865, 71866, 71874, 71883, 79901, 79903, 79910, 103024,
107849, 107850, 122928, 139356, 139357, 139373, 139374, 139375,
139376, 139377, 139378, 139381, 149117, 149118, 149120, 149127]
assert_array_equal(antmost.vertices, fs_vert)
# check default label name
assert_equal(antmost.name, "lingual_div40-lh")
# Apply contiguous splitting to DMN label from parcellation in Yeo, 2011
label_default_mode = read_label(op.join(subjects_dir, 'fsaverage', 'label',
'lh.7Networks_7.label'))
DMN_sublabels = label_default_mode.split(parts='contiguous',
subject='fsaverage',
subjects_dir=subjects_dir)
assert_equal([len(label.vertices) for label in DMN_sublabels],
[16181, 7022, 5965, 5300, 823] + [1] * 23)
def test_split_label():
"""Test splitting labels"""
aparc = read_labels_from_annot('fsaverage',
'aparc',
'lh',
regexp='lingual',
subjects_dir=subjects_dir)
lingual = aparc[0]
# split with names
parts = ('lingual_post', 'lingual_ant')
post, ant = split_label(lingual, parts, subjects_dir=subjects_dir)
# check output names
assert_equal(post.name, parts[0])
assert_equal(ant.name, parts[1])
# check vertices add up
lingual_reconst = post + ant
lingual_reconst.name = lingual.name
lingual_reconst.comment = lingual.comment
lingual_reconst.color = lingual.color
assert_labels_equal(lingual_reconst, lingual)
# compare output of Label.split() method
post1, ant1 = lingual.split(parts, subjects_dir=subjects_dir)
assert_labels_equal(post1, post)
assert_labels_equal(ant1, ant)
# compare fs_like split with freesurfer split
antmost = split_label(lingual, 40, None, subjects_dir, True)[-1]
fs_vert = [
210, 4401, 7405, 12079, 16276, 18956, 26356, 32713, 32716, 32719,
36047, 36050, 42797, 42798, 42799, 59281, 59282, 59283, 71864, 71865,
71866, 71874, 71883, 79901, 79903, 79910, 103024, 107849, 107850,
122928, 139356, 139357, 139373, 139374, 139375, 139376, 139377, 139378,
139381, 149117, 149118, 149120, 149127
]
assert_array_equal(antmost.vertices, fs_vert)
# check default label name
assert_equal(antmost.name, "lingual_div40-lh")
def split(label, parts=2):
return mne.split_label(label, parts, subjects_dir=subjects_dir)
def SN_semantic_ROIs():
# Loading Human Connectom Project parcellation
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=C.data_path,verbose=True)
labels = mne.read_labels_from_annot('fsaverage', 'HCPMMP1', 'both',\
subjects_dir=C.data_path)
##............................. Control Regions ............................##
# Temporal area - Splitting STSvp
label_STSvp = ['L_STSvp_ROI-lh']
my_STSvp=[]
for j in np.arange(0,len(label_STSvp )):
my_STSvp.append([label for label in labels if label.name == \
label_STSvp[j]][0])
for m in np.arange(0,len(my_STSvp)):
if m==0:
STSvp = my_STSvp[m]
else:
STSvp = STSvp + my_STSvp[m]
[STSvp1,STSvp2,STSvp3,STSvp4,STSvp5,STSvp6]=mne.split_label(label=STSvp,parts\
=('L_STSvp1_ROI-lh','L_STSvp2_ROI-lh','L_STSvp3_ROI-lh','L_STSvp4_ROI-lh',
'L_STSvp5_ROI-lh','L_STSvp6_ROI-lh',),subject='fsaverage',subjects_dir=\
C.data_path)
# Temporal area - Splitting PH
label_PH = ['L_PH_ROI-lh']
my_PH=[]
for j in np.arange(0,len(label_PH )):
my_PH.append([label for label in labels if label.name == label_PH[j]][0])
for m in np.arange(0,len(my_PH)):
if m==0:
PH = my_PH[m]
else:
PH = PH + my_PH[m]
[PH1,PH2]=mne.split_label(label=PH,parts=('L_PH1_ROI-lh','L_PH2_ROI-lh')\
,subject='fsaverage',subjects_dir=C.data_path)
[PH21,PH22,PH23,PH24]=mne.split_label(label=PH2,parts=\
('L_PH21_ROI-lh','L_PH22_ROI-lh','L_PH23_ROI-lh','L_PH24_ROI-lh'),\
subject='fsaverage',subjects_dir=C.data_path)
# Temporal area - Splitting TE2p
label_TE2p = ['L_TE2p_ROI-lh']
my_TE2p=[]
for j in np.arange(0,len(label_TE2p )):
my_TE2p.append([label for label in labels if label.name == label_TE2p[j]][0])
for m in np.arange(0,len(my_TE2p)):
if m==0:
TE2p = my_TE2p[m]
else:
TE2p = TE2p + my_TE2p[m]
[TE2p1,TE2p2]=mne.split_label(label=TE2p,parts=('L_TE2p1_ROI-lh',\
'L_TE2p2_ROI-lh'),subject='fsaverage',subjects_dir=C.data_path)
# Temporal area
label_TE1p = ['L_TE1p_ROI-lh']
my_TE1p=[]
for j in np.arange(0,len(label_TE1p )):
my_TE1p.append([label for label in labels if label.name == label_TE1p[j]][0])
for m in np.arange(0,len(my_TE1p)):
if m==0:
TE1p = my_TE1p[m]
else:
TE1p = TE1p + my_TE1p[m]
TG= STSvp1 + STSvp2 + STSvp3 + STSvp4 + TE2p1 + PH24 +TE1p
##.......................... Representation Regions .........................##
# Left ATL area - splitting TE2a
label_TE2a = ['L_TE2a_ROI-lh']
my_TE2a=[]
for j in np.arange(0,len(label_TE2a )):
my_TE2a.append([label for label in labels if label.name == label_TE2a[j]][0])
for m in np.arange(0,len(my_TE2a)):
if m==0:
l_TE2a = my_TE2a[m]
else:
l_TE2a = l_TE2a + my_TE2a[m]
[l_TE2a1,l_TE2a2,l_TE2a3]=mne.split_label(label=l_TE2a,parts=\
('L_TE2a1_ROI-lh','L_TE2a2_ROI-lh','L_TE2a3_ROI-lh'),subject='fsaverage',\
subjects_dir=C.data_path)
# Left ATL area - splitting TE1m
label_TE1m = ['L_TE1m_ROI-lh']
my_TE1m=[]
for j in np.arange(0,len(label_TE1m )):
my_TE1m.append([label for label in labels if label.name == label_TE1m[j]][0])
for m in np.arange(0,len(my_TE1m)):
if m==0:
l_TE1m = my_TE1m[m]
else:
l_TE1m = l_TE1m + my_TE1m[m]
[l_TE1m1,l_TE1m2,l_TE1m3]=mne.split_label(label=l_TE1m,parts=\
('L_TE1m1_ROI-lh','L_TE1m2_ROI-lh','L_TE1m3_ROI-lh'),subject='fsaverage',\
subjects_dir=C.data_path)
[l_TE1m11,l_TE1m12,l_TE1m13]=mne.split_label(label=l_TE1m1,parts=\
('L_TE1m11_ROI-lh','L_TE1m12_ROI-lh','L_TE1m13_ROI-lh'),subject='fsaverage',\
subjects_dir=C.data_path)
[l_TE1m21,l_TE1m22,l_TE1m23]=mne.split_label(label=l_TE1m2,parts=\
('L_TE1m21_ROI-lh','L_TE1m22_ROI-lh','L_TE1m23_ROI-lh'),subject='fsaverage',\
subjects_dir=C.data_path)
# Left ATL area
label_ATL = ['L_TGd_ROI-lh','L_TGv_ROI-lh','L_TE1a_ROI-lh']
my_ATL=[]
for j in np.arange(0,len(label_ATL )):
my_ATL.append([label for label in labels if label.name == label_ATL[j]][0])
for m in np.arange(0,len(my_ATL)):
if m==0:
l_ATL = my_ATL[m]
else:
l_ATL = l_ATL + my_ATL[m]
l_ATL = l_ATL + l_TE2a2 + l_TE2a3 + l_TE1m13 + l_TE1m23
# Right ATL area - splitting TE2a
label_TE2a = ['R_TE2a_ROI-rh']
my_TE2a=[]
for j in np.arange(0,len(label_TE2a )):
my_TE2a.append([label for label in labels if label.name == label_TE2a[j]][0])
for m in np.arange(0,len(my_TE2a)):
if m==0:
r_TE2a = my_TE2a[m]
else:
r_TE2a = r_TE2a + my_TE2a[m]
[r_TE2a1,r_TE2a2,r_TE2a3]=mne.split_label(label=r_TE2a,parts=\
('R_TE2a1_ROI-rh','R_TE2a2_ROI-rh','R_TE2a3_ROI-rh'),subject='fsaverage',\
subjects_dir=C.data_path)
# Right ATL area - splitting TE1m
label_TE1m = ['R_TE1m_ROI-rh']
my_TE1m=[]
for j in np.arange(0,len(label_TE1m )):
my_TE1m.append([label for label in labels if label.name == label_TE1m[j]][0])
for m in np.arange(0,len(my_TE1m)):
if m==0:
r_TE1m = my_TE1m[m]
else:
r_TE1m = r_TE1m + my_TE1m[m]
[r_TE1m1,r_TE1m2,r_TE1m3]=mne.split_label(label=r_TE1m,parts=\
('R_TE1m1_ROI-rh','R_TE1m2_ROI-rh','R_TE1m3_ROI-rh'),subject='fsaverage',\
subjects_dir=C.data_path)
[r_TE1m31,r_TE1m32,r_TE1m33]=mne.split_label(label=r_TE1m3,parts=\
('R_TE1m31_ROI-rh','R_TE1m32_ROI-rh','R_TE1m33_ROI-rh'),subject='fsaverage',\
subjects_dir=C.data_path)
# Right ATL area
label_ATL = ['R_TGd_ROI-rh','R_TGv_ROI-rh','R_TE1a_ROI-rh']
my_ATL=[]
for j in np.arange(0,len(label_ATL )):
my_ATL.append([label for label in labels if label.name == label_ATL[j]][0])
for m in np.arange(0,len(my_ATL)):
if m==0:
r_ATL = my_ATL[m]
else:
r_ATL = r_ATL + my_ATL[m]
r_ATL = r_ATL + r_TE2a2 + r_TE2a3 + r_TE1m33
## ............................ Angular Gyrus .............................. ##
label_AG = ['L_PGi_ROI-lh','L_PGp_ROI-lh','L_PGs_ROI-lh']
my_AG=[]
for j in np.arange(0,len(label_AG)):
my_AG.append([label for label in labels if label.name == label_AG[j]][0])
for m in np.arange(0,len(my_AG )):
if m==0:
AG = my_AG[m]
else:
AG = AG + my_AG[m]
## ....................... Inferior Frontal Gyrus ......................... ##
label_IFG = ['L_44_ROI-lh','L_45_ROI-lh','L_47l_ROI-lh','L_p47r_ROI-lh']
my_IFG=[]
for j in np.arange(0,len(label_IFG )):
my_IFG.append([label for label in labels if label.name == label_IFG[j]][0])
for m in np.arange(0,len(my_IFG)):
if m==0:
IFG = my_IFG[m]
else:
IFG = IFG + my_IFG[m]
## ............................, Visual Area ............................... ##
label_V1 = ['L_V1_ROI-lh','L_V2_ROI-lh','L_V3_ROI-lh','L_V4_ROI-lh']
my_V1 =[]
for j in np.arange(0,len(label_V1 )):
my_V1.append([label for label in labels if label.name == label_V1[j]][0])
V1= my_V1[0]
my_labels =[l_ATL,r_ATL,TG,IFG,AG,V1]
return my_labels
def split(label, parts=2):
return mne.split_label(label, parts, subjects_dir=subjects_dir)
# Temporal area - Splitting STSvp
label_STSvp = ['L_STSvp_ROI-lh']
my_STSvp = []
for j in np.arange(0, len(label_STSvp)):
my_STSvp.append([label for label in labels if label.name == \
label_STSvp[j]][0])
for m in np.arange(0, len(my_STSvp)):
if m == 0:
STSvp = my_STSvp[m]
else:
STSvp = STSvp + my_STSvp[m]
[STSvp1,STSvp2,STSvp3,STSvp4,STSvp5,STSvp6]=mne.split_label(label=STSvp,parts\
=('L_STSvp1_ROI-lh','L_STSvp2_ROI-lh','L_STSvp3_ROI-lh','L_STSvp4_ROI-lh',
'L_STSvp5_ROI-lh','L_STSvp6_ROI-lh',),subject='fsaverage',subjects_dir=\
C.data_path)
# Temporal area - Splitting PH
label_PH = ['L_PH_ROI-lh']
my_PH = []
for j in np.arange(0, len(label_PH)):
my_PH.append([label for label in labels if label.name == label_PH[j]][0])
for m in np.arange(0, len(my_PH)):
if m == 0:
PH = my_PH[m]
else:
PH = PH + my_PH[m]
[PH1,PH2]=mne.split_label(label=PH,parts=('L_PH1_ROI-lh','L_PH2_ROI-lh')\
