def calc_prob_map(hemi='lh'):
import numpy as np
import nibabel as nib
from cxy_hcp_ffa.lib.predefine import roi2label
from magicbox.io.io import save2nifti
# inputs
n_roi = len(roi2label)
print(n_roi)
roi_file = pjoin(work_dir, f'rois_v3_{hemi}.nii.gz')
# outputs
out_file = pjoin(work_dir, f'prob_maps_v3_{hemi}.nii.gz')
# prepare
rois = nib.load(roi_file).get_fdata().squeeze().T
n_vtx = rois.shape[1]
# calculate
prob_maps = np.ones((n_roi, n_vtx)) * np.nan
for idx, roi in enumerate(roi2label.keys()):
label = roi2label[roi]
print(roi)
# get indices of subjects which contain the roi
indices = rois == label
subj_indices = np.any(indices, 1)
# calculate roi probability map among valid subjects
prob_map = np.mean(indices[subj_indices], 0)
prob_maps[idx] = prob_map
# save out
save2nifti(out_file, prob_maps.T[:, None, None, :])
def calc_meas_individual(hemi='lh'):
import nibabel as nib
import numpy as np
import pickle as pkl
from commontool.io.io import CiftiReader
from cxy_hcp_ffa.lib.predefine import hemi2stru, roi2label
# inputs
work_dir = pjoin(proj_dir,
'analysis/s2/1080_fROI/refined_with_Kevin/tfMRI')
rois_file = pjoin(proj_dir, 'analysis/s2/1080_fROI/'
f'refined_with_Kevin/rois_v3_{hemi}.nii.gz')
meas_file = pjoin(proj_dir, 'analysis/s2/activation.dscalar.nii')
# outputs
out_file = pjoin(work_dir, f'individual_activ_{hemi}.pkl')
rois = nib.load(rois_file).get_data().squeeze().T
n_roi = len(roi2label)
meas_reader = CiftiReader(meas_file)
meas = meas_reader.get_data(hemi2stru[hemi], True)
n_subj = meas.shape[0]
roi_meas = {'shape': 'n_roi x n_subj', 'roi': list(roi2label.keys()),
'meas': np.ones((n_roi, n_subj)) * np.nan}
for roi_idx, roi in enumerate(roi_meas['roi']):
lbl_idx_arr = rois == roi2label[roi]
for subj_idx in range(n_subj):
lbl_idx_vec = lbl_idx_arr[subj_idx]
if np.any(lbl_idx_vec):
roi_meas['meas'][roi_idx, subj_idx] = np.mean(
meas[subj_idx][lbl_idx_vec])
pkl.dump(roi_meas, open(out_file, 'wb'))
def calc_meas_individual(gid=1, hemi='lh', morph='thickness'):
"""
Calculate morphology using individual ROIs.
"""
import nibabel as nib
import numpy as np
import pickle as pkl
from cxy_hcp_ffa.lib.predefine import hemi2stru, roi2label
from commontool.io.io import CiftiReader
morph2file = {
'thickness': '/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.thickness_MSMAll.32k_fs_LR.dscalar.nii',
'myelin': '/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.MyelinMap_BC_MSMAll.32k_fs_LR.dscalar.nii',
'va': '/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.midthickness_MSMAll_va.32k_fs_LR.dscalar.nii'
}
meas_file = morph2file[morph]
gid_file = pjoin(proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
f'grouping/group_id_{hemi}.npy')
roi_file = pjoin(proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
f'rois_v3_{hemi}.nii.gz')
subj_file = pjoin(proj_dir, 'analysis/s2/subject_id')
meas_id_file = pjoin(proj_dir, 'data/HCP/subject_id_1096')
trg_file = pjoin(work_dir, f'individual_G{gid}_{morph}_{hemi}.pkl')
gid_idx_vec = np.load(gid_file) == gid
subj_ids = open(subj_file).read().splitlines()
subj_ids = [subj_ids[i] for i in np.where(gid_idx_vec)[0]]
n_subj = len(subj_ids)
roi_maps = nib.load(roi_file).get_data().squeeze().T[gid_idx_vec]
meas_reader = CiftiReader(meas_file)
meas_ids = open(meas_id_file).read().splitlines()
meas_indices = [meas_ids.index(i) for i in subj_ids]
meas = meas_reader.get_data(hemi2stru[hemi], True)[meas_indices]
out_dict = {'shape': 'n_roi x n_subj',
'roi': list(roi2label.keys()),
'subject': subj_ids,
'meas': np.ones((len(roi2label), n_subj)) * np.nan}
for roi_idx, roi in enumerate(out_dict['roi']):
lbl_idx_arr = roi_maps == roi2label[roi]
for subj_idx in range(n_subj):
lbl_idx_vec = lbl_idx_arr[subj_idx]
if np.any(lbl_idx_vec):
if morph == 'va':
out_dict['meas'][roi_idx, subj_idx] = np.sum(
meas[subj_idx][lbl_idx_vec])
else:
out_dict['meas'][roi_idx, subj_idx] = np.mean(
meas[subj_idx][lbl_idx_vec])
pkl.dump(out_dict, open(trg_file, 'wb'))
def calc_morphology_individual(hemi='lh', morph='thickness'):
import nibabel as nib
import numpy as np
import pickle as pkl
from commontool.io.io import CiftiReader
from cxy_hcp_ffa.lib.predefine import hemi2stru, roi2label
rois_file = pjoin(
proj_dir, 'analysis/s2/1080_fROI/'
f'refined_with_Kevin/rois_v3_{hemi}.nii.gz')
subj_file = pjoin(proj_dir, 'analysis/s2/subject_id')
meas_id_file = pjoin(proj_dir, 'data/HCP/subject_id_1096')
morph2meas = {
'thickness':
'/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.thickness_MSMAll.32k_fs_LR.dscalar.nii',
'myelin':
'/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.MyelinMap_BC_MSMAll.32k_fs_LR.dscalar.nii',
'va':
'/nfs/p1/public_dataset/datasets/hcp/DATA/'
'HCP_S1200_GroupAvg_v1/HCP_S1200_GroupAvg_v1/'
'S1200.All.midthickness_MSMAll_va.32k_fs_LR.dscalar.nii'
}
meas_file = morph2meas[morph]
trg_file = pjoin(work_dir, f'individual_{morph}_{hemi}.pkl')
rois = nib.load(rois_file).get_data().squeeze().T
n_roi = len(roi2label)
subj_ids = open(subj_file).read().splitlines()
n_subj = len(subj_ids)
meas_reader = CiftiReader(meas_file)
meas_ids = open(meas_id_file).read().splitlines()
meas_indices = [meas_ids.index(i) for i in subj_ids]
meas = meas_reader.get_data(hemi2stru[hemi], True)[meas_indices]
roi_meas = {
'shape': 'n_roi x n_subj',
'roi': list(roi2label.keys()),
'meas': np.ones((n_roi, n_subj)) * np.nan
}
for roi_idx, roi in enumerate(roi_meas['roi']):
lbl_idx_arr = rois == roi2label[roi]
for subj_idx in range(n_subj):
lbl_idx_vec = lbl_idx_arr[subj_idx]
if np.any(lbl_idx_vec):
if morph == 'va':
tmp = np.sum(meas[subj_idx][lbl_idx_vec])
else:
tmp = np.mean(meas[subj_idx][lbl_idx_vec])
roi_meas['meas'][roi_idx, subj_idx] = tmp
pkl.dump(roi_meas, open(trg_file, 'wb'))
def calc_meas(gid=1, hemi='lh'):
"""
用一半被试的MPM去提取另一半被试的激活值
如果某个被试没有某个ROI，就不提取该被试该ROI的信号
Args:
gid (int, optional): group ID. Defaults to '1'.
hemi (str, optional): hemisphere. Defaults to 'lh'.
"""
import numpy as np
import pickle as pkl
import nibabel as nib
from cxy_hcp_ffa.lib.predefine import hemi2stru, roi2label
from commontool.io.io import CiftiReader
gh_ids = (gid * 10 + 1, gid * 10 + 2)
gh_id_file = pjoin(split_dir, f'half_id_{hemi}.npy')
mpm_file = pjoin(split_dir, 'MPM_GH{gh_id}_{hemi}.nii.gz')
roi_file = pjoin(
proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
f'rois_v3_{hemi}.nii.gz')
subj_file = pjoin(proj_dir, 'analysis/s2/subject_id')
src_file = pjoin(proj_dir, 'analysis/s2/activation.dscalar.nii')
trg_file = pjoin(work_dir, f'G{gid}_activ_{hemi}.pkl')
gh_id_vec = np.load(gh_id_file)
gid_idx_vec = np.logical_or(gh_id_vec == gh_ids[0], gh_id_vec == gh_ids[1])
hid_vec = gh_id_vec[gid_idx_vec]
subj_ids = open(subj_file).read().splitlines()
subj_ids = [subj_ids[i] for i in np.where(gid_idx_vec)[0]]
n_subj = len(subj_ids)
roi_maps = nib.load(roi_file).get_data().squeeze().T[gid_idx_vec]
meas_reader = CiftiReader(src_file)
meas = meas_reader.get_data(hemi2stru[hemi], True)[gid_idx_vec]
out_dict = {
'shape': 'n_roi x n_subj',
'roi': list(roi2label.keys()),
'subject': subj_ids,
'meas': np.ones((len(roi2label), n_subj)) * np.nan
}
for gh_id in gh_ids:
hid_idx_vec = hid_vec == gh_id
mpm = nib.load(mpm_file.format(gh_id=gh_id,
hemi=hemi)).get_data().squeeze()
for roi_idx, roi in enumerate(out_dict['roi']):
roi_idx_vec = np.any(roi_maps == roi2label[roi], 1)
valid_idx_vec = np.logical_and(~hid_idx_vec, roi_idx_vec)
mpm_idx_vec = mpm == roi2label[roi]
meas_masked = meas[valid_idx_vec][:, mpm_idx_vec]
out_dict['meas'][roi_idx][valid_idx_vec] = np.mean(meas_masked, 1)
pkl.dump(out_dict, open(trg_file, 'wb'))
def calc_meas_emotion(hemi='lh'):
"""
用个体ROI去提取emotion任务中 faces-shapes的信号
"""
import nibabel as nib
import numpy as np
import pickle as pkl
from cxy_hcp_ffa.lib.predefine import roi2label
# inputs
work_dir = pjoin(proj_dir,
'analysis/s2/1080_fROI/refined_with_Kevin/tfMRI')
rois_file = pjoin(proj_dir, 'analysis/s2/1080_fROI/'
f'refined_with_Kevin/rois_v3_{hemi}.nii.gz')
subj_file_wm = pjoin(proj_dir, 'analysis/s2/subject_id')
meas_file = pjoin(proj_dir, 'data/HCP/emotion/analysis_s2/'
f'cope3_face-shape_zstat_{hemi}.nii.gz')
subj_file_emo = pjoin(proj_dir, 'data/HCP/emotion/analysis_s2/subject_id')
# outputs
out_file = pjoin(work_dir, f'individual_activ_{hemi}_emo.pkl')
# prepare
rois = nib.load(rois_file).get_fdata().squeeze().T
n_roi = len(roi2label)
subj_ids_wm = open(subj_file_wm).read().splitlines()
meas = nib.load(meas_file).get_fdata().squeeze().T
subj_ids_emo = open(subj_file_emo).read().splitlines()
# find index in EMOTION subject IDs for WM subject IDs
subj_indices = []
for subj_id in subj_ids_wm:
if subj_id in subj_ids_emo:
subj_indices.append(subj_ids_emo.index(subj_id))
else:
subj_indices.append(None)
n_subj = len(subj_indices)
roi_meas = {'shape': 'n_roi x n_subj', 'roi': list(roi2label.keys()),
'meas': np.ones((n_roi, n_subj)) * np.nan}
for roi_idx, roi in enumerate(roi_meas['roi']):
lbl_idx_arr = rois == roi2label[roi]
for subj_idx in range(n_subj):
lbl_idx_vec = lbl_idx_arr[subj_idx]
subj_idx_emo = subj_indices[subj_idx]
if np.any(lbl_idx_vec) and subj_idx_emo is not None:
roi_meas['meas'][roi_idx, subj_idx] = np.mean(
meas[subj_idx_emo][lbl_idx_vec])
pkl.dump(roi_meas, open(out_file, 'wb'))
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_meas_MPM(hemi='lh'):
"""
用一半被试的MPM去提取另一半被试的激活值
如果某个被试没有某个ROI，就不提取该被试该ROI的信号
Args:
hemi (str, optional): hemisphere. Defaults to 'lh'.
"""
import numpy as np
import pickle as pkl
import nibabel as nib
from cxy_hcp_ffa.lib.predefine import hemi2stru, roi2label
from commontool.io.io import CiftiReader
hids = (1, 2)
hid_file = pjoin(split_dir, 'half_id.npy')
mpm_file = pjoin(split_dir, 'MPM_half{hid}_{hemi}.nii.gz')
roi_file = pjoin(proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
f'rois_v3_{hemi}.nii.gz')
src_file = pjoin(proj_dir, 'analysis/s2/activation.dscalar.nii')
trg_file = pjoin(work_dir, f'activ_{hemi}.pkl')
hid_vec = np.load(hid_file)
n_subj = len(hid_vec)
roi_maps = nib.load(roi_file).get_data().squeeze().T
n_roi = len(roi2label)
meas_reader = CiftiReader(src_file)
meas = meas_reader.get_data(hemi2stru[hemi], True)
out_dict = {'shape': 'n_roi x n_subj',
'roi': list(roi2label.keys()),
'meas': np.ones((n_roi, n_subj)) * np.nan}
for hid in hids:
hid_idx_vec = hid_vec == hid
mpm = nib.load(mpm_file.format(hid=hid, hemi=hemi)
).get_data().squeeze()
for roi_idx, roi in enumerate(out_dict['roi']):
roi_idx_vec = np.any(roi_maps == roi2label[roi], 1)
valid_idx_vec = np.logical_and(~hid_idx_vec, roi_idx_vec)
mpm_idx_vec = mpm == roi2label[roi]
meas_masked = meas[valid_idx_vec][:, mpm_idx_vec]
out_dict['meas'][roi_idx][valid_idx_vec] = np.mean(meas_masked, 1)
pkl.dump(out_dict, open(trg_file, 'wb'))
