def get_roi_idx_vec():
"""
Get index vector with bool values for each ROI.
The length of each index vector is matched with 1080 subjects.
True value means the ROI is delineated in the corresponding subject.
"""
import numpy as np
import pandas as pd
import nibabel as nib
from cxy_hcp_ffa.lib.predefine import roi2label
# inputs
hemis = ('lh', 'rh')
roi_file = pjoin(work_dir, 'rois_v3_{hemi}.nii.gz')
# outputs
out_file = pjoin(work_dir, 'rois_v3_idx_vec.csv')
out_dict = {}
for hemi in hemis:
roi_maps = nib.load(roi_file.format(hemi=hemi)).get_data().squeeze().T
for roi, lbl in roi2label.items():
out_dict[f'{hemi}_{roi}'] = np.any(roi_maps == lbl, axis=1)
out_df = pd.DataFrame(out_dict)
out_df.to_csv(out_file, index=False)
def roi_stats(gh_id=11, hemi='lh'):
import numpy as np
import nibabel as nib
import pickle as pkl
from cxy_hcp_ffa.lib.predefine import roi2label
from commontool.io.io import save2nifti
gh_id_file = pjoin(split_dir, f'half_id_{hemi}.npy')
roi_file = pjoin(
proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
f'rois_v3_{hemi}.nii.gz')
info_trg_file = pjoin(split_dir, f'rois_info_GH{gh_id}_{hemi}.pkl')
prob_trg_file = pjoin(split_dir, f'prob_maps_GH{gh_id}_{hemi}.nii.gz')
gh_id_idx_vec = np.load(gh_id_file) == gh_id
rois = nib.load(roi_file).get_data().squeeze().T[gh_id_idx_vec]
# prepare rois information dict
rois_info = dict()
for roi in roi2label.keys():
rois_info[roi] = dict()
prob_maps = []
for roi, label in roi2label.items():
# get indices of subjects which contain the roi
indices = rois == label
subj_indices = np.any(indices, 1)
# calculate the number of the valid subjects
n_subject = np.sum(subj_indices)
rois_info[roi]['n_subject'] = n_subject
# calculate roi sizes for each valid subject
sizes = np.sum(indices[subj_indices], 1)
rois_info[roi]['sizes'] = sizes
# calculate roi probability map among valid subjects
prob_map = np.mean(indices[subj_indices], 0)
prob_maps.append(prob_map)
prob_maps = np.array(prob_maps)
# save out
pkl.dump(rois_info, open(info_trg_file, 'wb'))
save2nifti(prob_trg_file, prob_maps.T[:, None, None, :])
def calc_TM(proj_name='HCPD', meas_name='thickness', atlas_name='MPM1'):
"""
Calculate thickness or myelination
"""
import time
import pandas as pd
import nibabel as nib
from cxy_hcp_ffa.lib.predefine import roi2label, hemi2stru
from commontool.io.io import CiftiReader
# inputs
hemis = ('lh', 'rh')
info_file = pjoin(work_dir, f'{proj_name}_SubjInfo.csv')
label2roi = {}
for k, v in roi2label.items():
label2roi[v] = k
atlas2file = {
'MPM1':
pjoin(
proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
'MPM_v3_{hemi}_0.25.nii.gz')
}
atlas_file = atlas2file[atlas_name]
proj2par = {
'HCPD': '/nfs/e1/HCPD/fmriresults01',
'HCPA': '/nfs/e1/HCPA/fmriresults01'
}
proj_par = proj2par[proj_name]
meas2file = {
'myelin':
pjoin(
proj_par, '{sid}_V1_MR/MNINonLinear/fsaverage_LR32k/'
'{sid}_V1_MR.MyelinMap_BC_MSMAll.32k_fs_LR.dscalar.nii'),
'thickness':
pjoin(
proj_par, '{sid}_V1_MR/MNINonLinear/fsaverage_LR32k/'
'{sid}_V1_MR.thickness_MSMAll.32k_fs_LR.dscalar.nii')
}
meas_file = meas2file[meas_name]
# outputs
out_file = pjoin(work_dir, f'{proj_name}_{meas_name}_{atlas_name}.csv')
# prepare
df = pd.read_csv(info_file)
n_subj = df.shape[0]
hemi2atlas = {}
hemi2atlas_labels = {}
for hemi in hemis:
atlas_map = nib.load(
atlas_file.format(hemi=hemi)).get_fdata().squeeze()
hemi2atlas[hemi] = atlas_map
atlas_labels = np.unique(atlas_map)
hemi2atlas_labels[hemi] = atlas_labels[atlas_labels != 0]
# calculate
for i, idx in enumerate(df.index, 1):
time1 = time.time()
sid = df.loc[idx, 'subID']
reader = CiftiReader(meas_file.format(sid=sid))
for hemi in hemis:
meas_map = reader.get_data(hemi2stru[hemi], True)[0]
atlas_map = hemi2atlas[hemi]
atlas_labels = hemi2atlas_labels[hemi]
for lbl in atlas_labels:
roi = label2roi[lbl]
col = f"{roi.split('-')[0]}_{hemi}"
meas = np.mean(meas_map[atlas_map == lbl])
df.loc[idx, col] = meas
print(f'Finished {i}/{n_subj}: cost {time.time() - time1}')
# save
df.to_csv(out_file, index=False)
