def extract_net_parcel_info():
"""
把每个网络的编号，名字以及所包含的MMP parcel的编号和名字列出来
"""
from magicbox.io.io import CiftiReader
# inputs
net_file = pjoin(work_dir, 'networks.dlabel.nii')
# outputs
out_file = pjoin(work_dir, 'net_parcel_info.txt')
# prepare
reader = CiftiReader(net_file)
net_names = reader.map_names()
lbl_tables = reader.label_tables()
# calculate & save
wf = open(out_file, 'w')
for net_idx, net_name in enumerate(net_names):
wf.write(f'>>>{net_idx+1}-{net_name}\n')
lbl_tab = lbl_tables[net_idx]
for lbl_k, lbl_v in lbl_tab.items():
if lbl_k == 0:
continue
wf.write(f'{lbl_k}-{lbl_v.label[:-4]}\n')
wf.write('<<<\n')
wf.close()
def make_non_outlier_map(fpath,
thr,
roi_name,
out_file_mask=None,
out_file_prob=None):
"""
将同时在thr%以上的被试中被认定为outlier的顶点判定为跨被试的outlier
If out_file_mask is not None, save mask map. (.dlabel.nii or .npy)
If out_file_prob is not None, save prob map. (.dscalar.nii)
"""
# prepare
data = np.load(fpath)
n_subj, n_vtx = data.shape
atlas1 = Atlas('Cole_visual_LR')
atlas2 = Atlas('Cole_visual_ROI')
assert atlas1.maps.shape == (1, LR_count_32k)
assert atlas2.maps.shape == (1, LR_count_32k)
roi_idx_map = atlas1.maps[0] == atlas1.roi2label[roi_name]
if roi_name == 'R_cole_visual':
prefix = 'R_'
elif roi_name == 'L_cole_visual':
prefix = 'L_'
else:
raise ValueError("error roi_name:", roi_name)
mmp_reader = CiftiReader(mmp_map_file)
mmp_lbl_tab = mmp_reader.label_tables()[0]
# calculate
if out_file_mask is not None:
data_tmp = np.sum(data, axis=0)
outlier_vec = data_tmp > thr / 100 * n_subj
print(f'#outliers/total: {np.sum(outlier_vec)}/{n_vtx}')
mask_npy = np.zeros(LR_count_32k, bool)
mask_npy[roi_idx_map] = ~outlier_vec
if out_file_mask.endswith('.npy'):
np.save(out_file_mask, mask_npy)
elif out_file_mask.endswith('.dlabel.nii'):
mask_cii = atlas2.maps.copy()
mask_cii[0, ~mask_npy] = np.nan
lbl_tab = nib.cifti2.cifti2.Cifti2LabelTable()
for roi, lbl in atlas2.roi2label.items():
if roi.startswith(prefix):
lbl_tab[lbl] = mmp_lbl_tab[lbl]
save2cifti(out_file_mask,
mask_cii,
mmp_reader.brain_models(),
label_tables=[lbl_tab])
else:
raise ValueError("Not supported file name:", out_file_mask)
if out_file_prob is not None:
data_tmp = np.mean(data, axis=0)
prob_map = np.ones((1, LR_count_32k), dtype=np.float64) * np.nan
prob_map[0, roi_idx_map] = data_tmp
assert out_file_prob.endswith('.dscalar.nii')
save2cifti(out_file_prob, prob_map, mmp_reader.brain_models())
def separate_networks():
"""
把ColeNetwork的12个网络分到单独的map里。
每个map的MMP parcel的label保留原来的样子。
需要注意的是multimodal_glasser的MMP_mpmLR32k.dlabel.nii中，
ROI label的编号1~180是右脑，181~360是左脑。0对应的是???
而cortex_parcel_network_assignments.mat中0~359的index是左脑在先
"""
import numpy as np
import nibabel as nib
from scipy.io import loadmat
from cxy_visual_dev.lib.ColeNet import get_name_label_of_ColeNetwork
from magicbox.io.io import CiftiReader, save2cifti
# inputs
mmp_file = '/nfs/p1/atlases/multimodal_glasser/surface/'\
'MMP_mpmLR32k.dlabel.nii'
roi2net_file = pjoin(cole_dir, 'cortex_parcel_network_assignments.mat')
# outputs
out_file = pjoin(work_dir, 'networks.dlabel.nii')
# load
mmp_reader = CiftiReader(mmp_file)
mmp_map = mmp_reader.get_data()[0]
lbl_tab_raw = mmp_reader.label_tables()[0]
roi2net = loadmat(roi2net_file)['netassignments'][:, 0]
roi2net = np.r_[roi2net[180:], roi2net[:180]]
net_labels = np.unique(roi2net)
# prepare
data = np.zeros((len(net_labels), len(mmp_map)), dtype=np.uint16)
map_names = []
label_tables = []
net_lbl2name = {}
for name, lbl in zip(*get_name_label_of_ColeNetwork()):
net_lbl2name[lbl] = name
# calculate
for net_idx, net_lbl in enumerate(net_labels):
roi_labels = np.where(roi2net == net_lbl)[0] + 1
lbl_tab = nib.cifti2.cifti2.Cifti2LabelTable()
lbl_tab[0] = lbl_tab_raw[0]
for roi_lbl in roi_labels:
data[net_idx, mmp_map == roi_lbl] = roi_lbl
lbl_tab[roi_lbl] = lbl_tab_raw[roi_lbl]
map_names.append(net_lbl2name[net_lbl])
label_tables.append(lbl_tab)
# save
save2cifti(out_file,
data,
mmp_reader.brain_models(),
map_names,
label_tables=label_tables)
def separate_networks():
"""
把ColeNetwork的12个网络分到单独的map里。
每个map的MMP parcel的label保留原来的样子。
需要注意的是multimodal_glasser的MMP_mpmLR32k.dlabel.nii中，
ROI label的编号1~180是右脑，181~360是左脑。0对应的是???
而cortex_parcel_network_assignments.mat中0~359的index是左脑在先
"""
# outputs
out_file = pjoin(work_dir, 'networks.dlabel.nii')
# load
mmp_reader = CiftiReader(mmp_map_file)
mmp_map = mmp_reader.get_data()[0]
lbl_tab_raw = mmp_reader.label_tables()[0]
roi2net = loadmat(cole_net_assignment_file)['netassignments'][:, 0]
roi2net = np.r_[roi2net[180:], roi2net[:180]]
net_labels = np.unique(roi2net)
# prepare
data = np.zeros((len(net_labels), len(mmp_map)), dtype=np.uint16)
map_names = []
label_tables = []
# calculate
for net_idx, net_lbl in enumerate(net_labels):
roi_labels = np.where(roi2net == net_lbl)[0] + 1
lbl_tab = nib.cifti2.cifti2.Cifti2LabelTable()
lbl_tab[0] = lbl_tab_raw[0]
for roi_lbl in roi_labels:
data[net_idx, mmp_map == roi_lbl] = roi_lbl
lbl_tab[roi_lbl] = lbl_tab_raw[roi_lbl]
map_names.append(cole_label2name[net_lbl])
label_tables.append(lbl_tab)
# save
save2cifti(out_file,
data,
mmp_reader.brain_models(),
map_names,
label_tables=label_tables)
