def calc_seed_corr(args):
args = con.read_cmd_args(dict(
function='calc_seed_corr',
subject=args.subject, atlas=args.atlas,
identifier='freesurfer', labels_regex='precentral-rh',#''post*cingulate*rh',
seed_label_name='post_cingulate_rh', seed_label_r=5, overwrite_seed_data=True,
n_jobs=args.n_jobs
))
con.call_main(args)
def load_connectivity_results(args):
# from src.utils import matlab_utils as matu
# mat_file = matu.load_mat_to_bag(op.join(MMVT_DIR, args.subject[0], 'connectivity', 'corr.mat'))
# conn = mat_file.mAdjMat
# labels = matu.matlab_cell_str_to_list(mat_file.mLabels)
args = con.read_cmd_args(dict(
function='calc_rois_matlab_connectivity',
subject=args.subject, atlas=args.atlas,
mat_fname=op.join(MMVT_DIR, args.subject[0], 'connectivity', 'corr.mat'),
mat_field='mAdjMat', sorted_labels_names_field='mLabelsCort',
n_jobs=args.n_jobs
))
con.call_main(args)
def calc_meg_connectivity(args):
args = connectivity.read_cmd_args(
utils.Bag(
subject=args.subject,
atlas='laus125',
function='calc_lables_connectivity',
connectivity_modality='meg',
connectivity_method='pli',
windows_num=1,
# windows_length=500,
# windows_shift=100,
recalc_connectivity=True,
n_jobs=args.n_jobs))
connectivity.call_main(args)
def calc_fmri_static_connectivity(args):
args = con.read_cmd_args(
dict(
subject=args.subject,
atlas='laus125', # 'yao17'
function='calc_lables_connectivity',
connectivity_modality='fmri',
connectivity_method='corr',
labels_extract_mode='mean',
identifier='', #'freesurfer',#'hesheng', #''linda',#
save_mmvt_connectivity=False,
calc_subs_connectivity=False,
recalc_connectivity=True,
n_jobs=args.n_jobs))
con.call_main(args)
def analyse_connectivity(subject,
connectivity_method,
graph_func,
file_name,
atlas,
n_jobs=4):
bands = dict(theta=[4, 8],
alpha=[8, 15],
beta=[15, 30],
gamma=[30, 55],
high_gamma=[65, 120])
conn_fol = op.join(MMVT_DIR, subject, 'connectivity')
output_fol = utils.make_dir(op.join(conn_fol, file_name))
for band_name, band_freqs in bands.items():
output_files = [
f.format(band_name) for f in [
'meg_{}_laus125_mi_mean_flip_mean.npy', 'meg_{}_mi.npy',
'meg_{}_mi.npz'
]
]
files_exist = all(
[op.isfile(op.join(output_fol, f)) for f in output_files])
if not files_exist:
print('calc_meg_connectivity: {}'.format(band_name))
con_args = connectivity.read_cmd_args(
utils.Bag(
subject=subject,
atlas=atlas,
function='calc_lables_connectivity',
connectivity_modality='meg',
connectivity_method=connectivity_method,
windows_length=500,
windows_shift=100,
identifier=band_name,
fmin=band_freqs[0],
fmax=band_freqs[1],
sfreq=2035, # clin MEG
recalc_connectivity=True,
n_jobs=n_jobs))
connectivity.call_main(con_args)
for output_file_name in output_files:
utils.move_file(op.join(conn_fol, output_file_name),
output_fol)
con_name = 'meg_{}_mi'.format(band_name)
analyze_graph(subject, file_name, con_name, graph_func, n_jobs)
plot_graph_values(subject, file_name, con_name, graph_func)
def calc_connectivity(subject,
atlas,
connectivity_method,
files,
bands,
modality,
overwrite=False,
n_jobs=4):
conn_fol = op.join(MMVT_DIR, subject, 'connectivity')
now = time.time()
for run, fif_fname in enumerate(files):
utils.time_to_go(now, run, len(files), runs_num_to_print=1)
file_name = utils.namebase(fif_fname)
labels_data_name = 'labels_data_{}-epilepsy-{}-{}-{}_{}_mean_flip_{}.npz'.format(
subject, 'dSPM', modality, file_name, atlas, '{hemi}')
if not utils.both_hemi_files_exist(
op.join(MMVT_DIR, subject, modality, labels_data_name)):
print('labels data does not exist for {}!'.format(file_name))
for band_name, band_freqs in bands.items():
output_fname = op.join(
conn_fol,
'{}_{}_{}_{}.npy'.format(modality, file_name, band_name,
connectivity_method))
if op.isfile(output_fname) and not overwrite:
print('{} {} connectivity for {} already exist'.format(
connectivity_method, band_name, file_name))
continue
print('calc_meg_connectivity: {}'.format(band_name))
con_args = connectivity.read_cmd_args(
utils.Bag(
subject=subject,
atlas=atlas,
function='calc_lables_connectivity',
connectivity_modality=modality,
connectivity_method=connectivity_method,
labels_data_name=labels_data_name,
windows_length=500,
windows_shift=100,
identifier='{}_{}'.format(file_name, band_name),
fmin=band_freqs[0],
fmax=band_freqs[1],
sfreq=2035, # clin MEG
recalc_connectivity=False,
save_mmvt_connectivity=True,
threshold_percentile=80,
n_jobs=n_jobs))
connectivity.call_main(con_args)
def calc_meg_gamma_connectivity(args):
args = con.read_cmd_args(utils.Bag(
subject=args.subject,
atlas='laus125',
function='calc_lables_connectivity',
connectivity_modality='meg',
connectivity_method='pli',
windows_length=100,
windows_shift=10,
# sfreq=1000.0,
fmin=30,
fmax=55,
recalc_connectivity=True,
# max_windows_num=100,
n_jobs=args.n_jobs
))
con.call_main(args)
def calc_fmri_connectivity(args):
'-s hc029 -a laus125 -f calc_lables_connectivity --connectivity_modality fmri connectivity_method=corr,cv--windows_length 20 --windows_shift 3'
args = con.read_cmd_args(
dict(
subject=args.subject,
atlas='laus125',
function='calc_lables_connectivity',
connectivity_modality='fmri',
# connectivity_method='mi_vec,cv',
connectivity_method='corr,cv',
# labels_extract_mode='pca_2,pca_4,pca_8', #mean,pca,
labels_extract_mode='mean',
windows_length=34, #20,
windows_shift=4, #3,
save_mmvt_connectivity=False,
calc_subs_connectivity=False,
recalc_connectivity=True,
n_jobs=args.n_jobs))
con.call_main(args)
def analyze_rest_fmri(args):
for subject in args.mri_subject:
remote_rest_fol = get_fMRI_rest_fol(subject, args.remote_fmri_dir)
local_rest_fname = convert_rest_dicoms_to_mgz(subject, remote_rest_fol)
if not op.isfile(local_rest_fname):
print('{}: Can\'t find {}!'.format(subject, local_rest_fname))
continue
args = fmri.read_cmd_args(
dict(
subject=subject,
atlas=args.atlas,
remote_subject_dir=args.remote_subject_dir,
function='clean_4d_data',
fmri_file_template=local_rest_fname,
))
fmri.call_main(args)
args = fmri.read_cmd_args(
dict(subject=args.subject,
atlas=args.atlas,
function='analyze_4d_data',
fmri_file_template='rest.sm6.{subject}.{hemi}.mgz',
labels_extract_mode='mean',
overwrite_labels_data=False))
fmri.call_main(args)
args = connectivity.read_cmd_args(
dict(subject=args.subject,
atlas=args.atlas,
function='calc_lables_connectivity',
connectivity_modality='fmri',
connectivity_method='corr',
labels_extract_mode='mean',
identifier='',
save_mmvt_connectivity=True,
calc_subs_connectivity=False,
recalc_connectivity=True,
n_jobs=args.n_jobs))
connectivity.call_main(args)
def calc_meg_connectivity(args):
bands = dict(theta=[4, 8], alpha=[8, 15], beta=[15, 30], gamma=[30, 55], high_gamma=[65, 120])
for band_name, band_freqs in bands.items():
print('calc_meg_connectivity: {}'.format(band_name))
con_args = con.read_cmd_args(utils.Bag(
subject=args.subject,
atlas='laus125',
function='calc_lables_connectivity',
connectivity_modality='meg',
connectivity_method='mi',
windows_length=500,
windows_shift=100,
identifier=band_name,
fmin=band_freqs[0],
fmax=band_freqs[1],
sfreq=2035, # clin MEG
# recalc_connectivity=True,
# max_windows_num=100,
recalc_connectivity=True,
n_jobs=args.n_jobs
))
con.call_main(con_args)
def analyze_rest_fmri(gargs):
good_subjects = []
for subject in gargs.mri_subject:
# remote_rest_fol = get_fMRI_rest_fol(subject, gargs.remote_fmri_dir)
# print('{}: {}'.format(subject, remote_rest_fol))
# if remote_rest_fol == '':
# continue
# local_rest_fname = convert_rest_dicoms_to_mgz(subject, remote_rest_fol, overwrite=True)
# if local_rest_fname == '':
# continue
# if not op.isfile(local_rest_fname):
# print('{}: Can\'t find {}!'.format(subject, local_rest_fname))
# continue
# args = fmri.read_cmd_args(dict(
# subject=subject,
# atlas=gargs.atlas,
# remote_subject_dir=gargs.remote_subject_dir,
# function='clean_4d_data',
# fmri_file_template=local_rest_fname,
# overwrite_4d_preproc=True
# ))
# flags = fmri.call_main(args)
# if subject not in flags or not flags[subject]['clean_4d_data']:
# continue
output_fname = op.join(MMVT_DIR, subject, 'connectivity',
'fmri_corr.npz')
if op.isfile(output_fname):
print('{} already exist!'.format(output_fname))
good_subjects.append(subject)
continue
remote_fname = op.join(
gargs.remote_fmri_rest_dir, subject, 'rest_001', '001',
'fmcpr.siemens.sm6.{}.{}.nii.gz'.format(subject, '{hemi}'))
if not utils.both_hemi_files_exist(remote_fname):
print('Couldn\t find fMRI rest data for {} ({})'.format(
subject, remote_fname))
continue
local_fmri_fol = utils.make_dir(op.join(FMRI_DIR, subject))
local_fmri_template = op.join(local_fmri_fol,
utils.namebase_with_ext(remote_fname))
if utils.both_hemi_files_exist(local_fmri_template):
print('{} al')
for hemi in utils.HEMIS:
local_fmri_fname = op.join(local_fmri_fol,
local_fmri_template.format(hemi=hemi))
if op.isfile(local_fmri_fname):
os.remove(local_fmri_fname)
utils.make_link(remote_fname.format(hemi=hemi), local_fmri_fname)
args = fmri.read_cmd_args(
dict(subject=subject,
atlas=gargs.atlas,
function='analyze_4d_data',
fmri_file_template=local_fmri_template,
labels_extract_mode='mean',
overwrite_labels_data=True))
flags = fmri.call_main(args)
if subject not in flags or not flags[subject]['analyze_4d_data']:
continue
args = connectivity.read_cmd_args(
dict(
subject=subject,
atlas=gargs.atlas,
function='calc_lables_connectivity',
connectivity_modality='fmri',
connectivity_method='corr',
labels_extract_mode='mean',
windows_length=34, # tr = 3 -> 100s
windows_shift=4, # 12s
save_mmvt_connectivity=True,
calc_subs_connectivity=False,
recalc_connectivity=True,
n_jobs=gargs.n_jobs))
flags = connectivity.call_main(args)
if subject in flags and flags[subject]['calc_lables_connectivity']:
good_subjects.append(subject)
print('{}/{} good subjects'.format(len(good_subjects),
len(gargs.mri_subject)))
print('Good subject: ', good_subjects)
print('Bad subjects: ', set(gargs.mri_subject) - set(good_subjects))
