def runfeat(scanlist_file):
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
# dir config
doc_dir = os.path.abspath(
os.path.join(scan_info['pardir'], '../doc/feat_emo'))
nii_dir = scan_info['sessdir']
# template config
template_fsf = os.path.join(doc_dir, 'trial.fsf')
for subj in subj_list:
# get run infor for emo task
sid = subj.sess_ID
subj_dir = os.path.join(nii_dir, sid, 'emo')
# get par index for each emo run
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
for i in range(len(run_idx)):
run_dir = os.path.join(subj_dir, '00' + run_idx[i])
lss_dir = os.path.join(run_dir, 'lss')
for j in range(80):
t_fsf = os.path.join(lss_dir, 't%s' % (j + 1), 'design.fsf')
with open(template_fsf, 'r') as fin:
with open(t_fsf, 'w') as fout:
for line in fin:
line - line.replace('AAA', sid[:3] + '1')
line = line.replace('SSS', sid)
line = line.replace('RRR', '00' + run_idx[i])
line = line.replace('TTT', 't%s' % (j + 1))
fout.write(line)
os.system('feat %s' % t_fsf)
def clcfeat(scanlist_file):
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
# dir config
doc_dir = os.path.abspath(
os.path.join(scan_info['pardir'], '../doc/feat_emo'))
nii_dir = scan_info['sessdir']
# template config
template_fsf = os.path.join(doc_dir, 'trial.fsf')
for subj in subj_list:
# get run infor for emo task
sid = subj.sess_ID
subj_dir = os.path.join(nii_dir, sid, 'emo')
# get par index for each emo run
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
for i in range(len(run_idx)):
run_dir = os.path.join(subj_dir, '00' + run_idx[i])
lss_dir = os.path.join(run_dir, 'lss')
for j in range(80):
t_fsf = os.path.join(lss_dir, 't%s' % (j + 1), 'design.fsf')
feat_dir = os.path.join(lss_dir, 't%s' % (j + 1), 'func.feat')
if os.path.exists(t_fsf):
os.system('rm %s' % t_fsf)
else:
print 'No fsf file found - %s' % (t_fsf)
if os.path.exists(feat_dir):
os.system('rm -rf %s' % feat_dir)
else:
print 'No feat dir found - %s' % (feat_dir)
def copypar(scanlist_file):
"""Copy par file for each separate calculation."""
# get data info from scanlist file
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
# dir config
par_dir = os.path.join(scan_info['pardir'], 'emo')
nii_dir = scan_info['sessdir']
for subj in subj_list:
# get run infor for emo task
sid = subj.sess_ID
subj_dir = os.path.join(nii_dir, sid, 'emo')
# get par index for each emo run
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
for i in range(len(run_idx)):
run_dir = os.path.join(subj_dir, '00' + run_idx[i])
lss_dir = os.path.join(run_dir, 'lss')
os.system('mkdir ' + lss_dir)
pair_file = os.path.join(par_dir,
'pair_run_' + par_idx[i] + '.txt')
os.system('cp %s %s' % (pair_file, lss_dir))
for j in range(80):
t_dir = os.path.join(lss_dir, 't%s' % (j + 1))
os.system('mkdir %s' % (t_dir))
par_file = os.path.join(par_dir,
'run_%s_%s.par' % (par_idx[i], j + 1))
os.system('cp %s %s' %
(par_file, os.path.join(t_dir, 'trial.par')))
par2ev(os.path.join(t_dir, 'trial.par'))
def mergecope(scanlist_file):
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
# dir config
par_dir = os.path.join(scan_info['pardir'], 'emo')
nii_dir = scan_info['sessdir']
seq_prefix = r'trial_seq'
seq_type = ['train', 'test']
for subj in subj_list:
# get run infor for emo task
sid = subj.sess_ID
subj_dir = os.path.join(nii_dir, sid, 'emo')
# get par index for each emo run
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
for i in range(len(run_idx)):
run_dir = os.path.join(subj_dir, '00' + run_idx[i])
lss_dir = os.path.join(run_dir, 'lss')
for st in seq_type:
seq_file = os.path.join(
par_dir, seq_prefix + '_' + str(i + 1) + '_' + st + '.txt')
seq = open(seq_file).readlines()
seq = [line.strip().split(',') for line in seq]
merged_file = os.path.join(run_dir, st + '_merged_cope.nii.gz')
strcmd = 'fslmerge -t %s' % (merged_file)
for line in seq:
tmp_file = os.path.join(lss_dir, line[0], 'func.feat',
'reg_standard', 'stats',
'cope1.nii.gz')
strcmd = strcmd + ' ' + tmp_file
subprocess.call(strcmd, shell=True)
def get_run_idx(scanlist_file, sid, par_idx):
"""Get run index from one subject's info based on par index."""
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
for subj in subj_list:
if (subj.sess_ID[:2]==sid) and ('emo' in subj.run_info):
[run_list, par_list] = subj.getruninfo('emo')
if str(par_idx) in par_list:
return subj.sess_ID, '00'+run_list[par_list.index(str(par_idx))]
return None, None
def get_mvp_group_roi(root_dir):
"""Get multivoxel activity pattern for each srimulus from each ROI."""
# directory config
nii_dir = os.path.join(root_dir, 'nii')
ppi_dir = os.path.join(root_dir, 'ppi')
# load rois
#mask_data = nib.load(os.path.join(ppi_dir, 'cube_rois.nii.gz')).get_data()
mask_data = nib.load(
os.path.join(root_dir, 'group-level', 'rois', 'neurosynth',
'cube_rois.nii.gz')).get_data()
roi_num = int(mask_data.max())
# get scan info from scanlist
scanlist_file = os.path.join(root_dir, 'doc', 'scanlist.csv')
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
for subj in subj_list:
# get run infor for emo task
sid = subj.sess_ID
subj_dir = os.path.join(nii_dir, sid, 'emo')
# get run index
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
# var for MVP
mvp_dict = {}
for r in range(roi_num):
mvp_dict['roi_%s' % (r + 1)] = []
for i in range(10):
if str(i + 1) in par_idx:
print 'Run %s' % (i + 1)
# load cope data
ipar = par_idx.index(str(i + 1))
run_dir = os.path.join(subj_dir, '00' + run_idx[ipar])
print run_dir
trn_file = os.path.join(run_dir, 'train_merged_cope.nii.gz')
test_file = os.path.join(run_dir, 'test_merged_cope.nii.gz')
trn_cope = nib.load(trn_file).get_data()
test_cope = nib.load(test_file).get_data()
run_cope = np.concatenate((trn_cope, test_cope), axis=3)
# XXX: remove mean cope from each trial
mean_cope = np.mean(run_cope, axis=3, keepdims=True)
run_cope = run_cope - mean_cope
# get MVP for each ROI
for r in range(roi_num):
roi_mask = mask_data.copy()
roi_mask[roi_mask != (r + 1)] = 0
roi_mask[roi_mask == (r + 1)] = 1
roi_coord = niroi.get_roi_coord(roi_mask)
for j in range(run_cope.shape[3]):
vtr = niroi.get_voxel_value(roi_coord, run_cope[...,
j])
mvp_dict['roi_%s' % (r + 1)].append(vtr.tolist())
for roi in mvp_dict:
mvp_dict[roi] = np.array(mvp_dict[roi])
outfile = r'%s_roi_mvp.mat' % (sid)
sio.savemat(outfile, mvp_dict)
def get_emo_ts(root_dir, seq):
"""Get neural activity time course of each roi on each emotion condition."""
nii_dir = os.path.join(root_dir, 'nii')
ppi_dir = os.path.join(root_dir, 'ppi')
# load roi
rois = nib.load(
os.path.join(root_dir, 'group-level', 'rois', 'neurosynth',
'cube_rois_r2.nii.gz')).get_data()
roi_num = int(rois.max())
# get run info from scanlist
scanlist_file = os.path.join(root_dir, 'doc', 'scanlist.csv')
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
for subj in subj_list:
sid = subj.sess_ID
print sid
subj_dir = os.path.join(nii_dir, sid, 'emo')
# get par index for each emo run
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
for i in range(10):
if str(i + 1) in par_idx:
print 'Run %s' % (i + 1)
# load cope data
ipar = par_idx.index(str(i + 1))
run_dir = os.path.join(subj_dir, '00' + run_idx[ipar])
print run_dir
train_cope_f = os.path.join(run_dir,
'train_merged_cope.nii.gz')
test_cope_f = os.path.join(run_dir, 'test_merged_cope.nii.gz')
train_cope = nib.load(train_cope_f).get_data()
test_cope = nib.load(test_cope_f).get_data()
# get trial sequence for each emotion
for j in range(4):
train_seq = [
line[0] for line in seq[i + 1]['train']
if line[1] == (j + 1)
]
test_seq = [
line[0] for line in seq[i + 1]['test']
if line[1] == (j + 1)
]
emo_data = np.zeros(
(91, 109, 91, len(train_seq) + len(test_seq)))
emo_data[..., :len(train_seq)] = train_cope[..., train_seq]
emo_data[..., len(train_seq):] = test_cope[..., test_seq]
# get time course for each roi
roi_ts = np.zeros((emo_data.shape[3], roi_num))
for k in range(roi_num):
roi_ts[:, k] = niroi.extract_mean_ts(
emo_data, rois == (k + 1))
outfile = '%s_roi_ts_run%s_emo%s.npy' % (sid[:2], i + 1,
j + 1)
outfile = os.path.join(ppi_dir, 'decovPPI', outfile)
np.save(outfile, roi_ts)
def get_trial_data(root_dir, seq):
"""Get neural activity time course of each roi on each emotion condition."""
nii_dir = os.path.join(root_dir, 'nii')
ppi_dir = os.path.join(root_dir, 'ppi')
# load roi
rois = nib.load(
os.path.join(root_dir, 'group-level', 'rois', 'neurosynth',
'cube_rois_r2.nii.gz')).get_data()
roi_num = int(rois.max())
# get run info from scanlist
scanlist_file = os.path.join(root_dir, 'doc', 'scanlist.csv')
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
for subj in subj_list:
sid = subj.sess_ID
print sid
subj_dir = os.path.join(nii_dir, sid, 'emo')
# get par index for each emo run
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
for i in range(10):
if str(i + 1) in par_idx:
print 'Run %s' % (i + 1)
# load cope data
ipar = par_idx.index(str(i + 1))
run_dir = os.path.join(subj_dir, '00' + run_idx[ipar])
print run_dir
train_cope_f = os.path.join(run_dir,
'train_merged_cope.nii.gz')
test_cope_f = os.path.join(run_dir, 'test_merged_cope.nii.gz')
train_cope = nib.load(train_cope_f).get_data()
test_cope = nib.load(test_cope_f).get_data()
# get time course for each roi
train_x = np.zeros((train_cope.shape[3], roi_num))
test_x = np.zeros((test_cope.shape[3], roi_num))
for k in range(roi_num):
train_x[:,
k] = niroi.extract_mean_ts(train_cope,
rois == (k + 1))
test_x[:,
k] = niroi.extract_mean_ts(test_cope,
rois == (k + 1))
train_y = [line[1] for line in seq[i + 1]['train']]
test_y = [line[1] for line in seq[i + 1]['test']]
# save dataset
outfile = '%s_run%s_roi_data' % (sid[:2], i + 1)
outfile = os.path.join(ppi_dir, 'decovPPI', outfile)
np.savez(outfile,
train_x=train_x,
train_y=train_y,
test_x=test_x,
test_y=test_y)
def get_vxl_trial_rsp(root_dir):
"""Get multivoxel activity pattern for each srimulus
from whole brain mask.
"""
# directory config
nii_dir = os.path.join(root_dir, 'prepro')
rsp_dir = os.path.join(root_dir, 'workshop', 'trial_rsp', 'whole_brain')
# load rois
mask_data = nib.load(
os.path.join(root_dir, 'group-level', 'rois', 'neurosynth',
'cube_rois_r2.nii.gz')).get_data()
mask_data = mask_data > 0
# get scan info from scanlist
scanlist_file = os.path.join(root_dir, 'doc', 'scanlist.csv')
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
for subj in subj_list:
# get run infor for emo task
sid = subj.sess_ID
print sid
subj_dir = os.path.join(nii_dir, sid)
# get run index
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
# var for MVP
for i in range(10):
if not str(i + 1) in par_idx:
continue
print 'Run %s' % (i + 1)
mvp_data = []
# load cope data
ipar = par_idx.index(str(i + 1))
run_dir = os.path.join(subj_dir, '00' + run_idx[ipar])
print run_dir
rsp_file = os.path.join(run_dir, 'mni_sfunc_data_mcf_hp.nii.gz')
rsp = nib.load(rsp_file).get_data()
# derive trial-wise response
trsp = np.zeros((91, 109, 91, 88))
for t in range(88):
trsp[..., t] = (rsp[..., 4 * t + 5] + rsp[..., 4 * t + 6]) / 2
# get MVP of mask
vxl_coord = niroi.get_roi_coord(mask_data)
for j in range(trsp.shape[3]):
vtr = niroi.get_voxel_value(vxl_coord, trsp[..., j])
mvp_data.append(vtr.tolist())
outfile = os.path.join(rsp_dir,
'%s_r%s_mvp.npy' % (sid[:2], i + 1))
np.save(outfile, np.array(mvp_data))
def get_mvp_group_roi(scanlist_file, mask_file):
"""Get multivoxel activity pattern for each srimulus from each ROI."""
# get scan info from scanlist
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
# directory config
nii_dir = scan_info['sessdir']
# load rois
mask_data = nib.load(mask_file).get_data()
roi_dict = {'rOFA': 1, 'lOFA': 2, 'rFFA': 3, 'lFFA': 4}
#output_file = os.path.join(roi_dir, 'neo_group_roi_mvpa.csv')
#f = open(output_file, 'wb')
#f.write('SID,rOFA,lOFA,rFFA,lFFA,rpcSTS,lpcSTS\n')
for subj in subj_list:
# get run infor for emo task
sid = subj.sess_ID
subj_dir = os.path.join(nii_dir, sid, 'emo')
# get par index for each emo run
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
# var for MVP
mvp_dict = {}
for roi in roi_dict:
mvp_dict[roi] = []
for i in range(len(run_idx)):
run_dir = os.path.join(subj_dir, '00'+run_idx[i])
trn_file = os.path.join(run_dir, 'train_merged_cope.nii.gz')
test_file = os.path.join(run_dir, 'test_merged_cope.nii.gz')
trn_cope = nib.load(trn_file).get_data()
test_cope = nib.load(test_file).get_data()
run_cope = np.concatenate((trn_cope, test_cope), axis=3)
# XXX: remove mean cope from each trial
#mean_cope = np.mean(run_cope, axis=3, keepdims=True)
#run_cope = run_cope - mean_cope
# get MVP for each ROI
for roi in roi_dict:
roi_mask = mask_data.copy()
roi_mask[roi_mask!=roi_dict[roi]] = 0
roi_mask[roi_mask==roi_dict[roi]] = 1
roi_coord = niroi.get_roi_coord(roi_mask)
for j in range(run_cope.shape[3]):
trl_vtr = niroi.get_voxel_value(roi_coord, run_cope[..., j])
f.write(','.join(temp)+'\n')
print 'cost %s s'%(time.time() - start_time)
def standardizecope(scanlist_file, stage):
"""For stage 1: `updatefeatreg`;
For stage 2: Apply nonlinear warp for cope files.
"""
[scan_info, subj_list] = pyunpack.readscanlist(scanlist_file)
# dir config
nii_dir = scan_info['sessdir']
# warp source for nonlinear registration
srcfiles = ['highres2standard_2mm.mat', 'highres2standard_warp_2mm.nii.gz']
fsl_dir = os.getenv('FSL_DIR')
mnistd = os.path.join(fsl_dir, 'data', 'standard',
'MNI152_T1_2mm_brain.nii.gz')
# parameter estimates needed to be standardized
peimgs = ['cope', 'tstat', 'zstat', 'varcope']
for subj in subj_list:
# get run infor for emo task
sid = subj.sess_ID
anat_dir = os.path.join(nii_dir, sid[:3] + '1', '3danat', 'reg_fsl')
emo_dir = os.path.join(nii_dir, sid, 'emo')
# get par index for each emo run
if not 'emo' in subj.run_info:
continue
[run_idx, par_idx] = subj.getruninfo('emo')
for i in range(len(run_idx)):
run_dir = os.path.join(emo_dir, '00' + run_idx[i])
lss_dir = os.path.join(run_dir, 'lss')
for j in range(80):
feat_dir = os.path.join(lss_dir, 't%s' % (j + 1), 'func.feat')
print feat_dir
funcreg = os.path.join(feat_dir, 'reg')
if stage == 1:
shutil.copy(mnistd, os.path.join(funcreg,
'standard.nii.gz'))
for f in srcfiles:
shutil.copy(
os.path.join(anat_dir, f),
os.path.join(funcreg, f.replace('_2mm', '')))
subprocess.call(' '.join([
'fsl_sub', '-q', 'veryshort.q', 'updatefeatreg',
feat_dir
]),
shell=True)
else:
refvol = os.path.join(funcreg, 'standard.nii.gz')
warpvol = os.path.join(funcreg,
'highres2standard_warp.nii.gz')
premat = os.path.join(funcreg, 'example_func2highres.mat')
targdir = os.path.join(feat_dir, 'reg_standard', 'stats')
if not os.path.exists(targdir):
print targdir + 'not exist, create it automatically.'
os.makedirs(targdir)
statsdir = os.path.join(feat_dir, 'stats')
fl = os.listdir(statsdir)
file_num = len(
[item for item in fl if item[0:4] == 'cope'])
for idx in range(1, file_num + 1):
for img in peimgs:
infile = os.path.join(statsdir, img + str(idx))
outfile = os.path.join(targdir, img + str(idx))
subprocess.call(' '.join([
'fsl_sub', '-q', 'veryshort.q', 'applywarp',
'--ref=' + refvol, '--in=' + infile,
'--out=' + outfile, '--warp=' + warpvol,
'--premat=' + premat, '--interp=trilinear'
]),
shell=True)