def compute_graph_lasso_covariance(subject_id,
group,
session='func1',
preprocessing_folder='pipeline_1',
plot=True,
save=True,
save_file=True,
msdl=False):
"""Returns graph lasso covariance for a subject_id
"""
# load timeseries
if msdl:
ts = load_dynacomp_msdl_timeseries(
subject_id,
session=session,
preprocessing_folder=preprocessing_folder)
roi_names, roi_coords = load_msdl_names_and_coords()
else:
ts = load_dynacomp_roi_timeseries(
subject_id,
session=session,
preprocessing_folder=preprocessing_folder)
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
# compute covariance
gl = covariance.GraphLassoCV(verbose=2)
gl.fit(ts)
if plot:
plot_connectivity_matrix(subject_id, group, gl.covariance_, roi_names,
'gl_covariance', session,
preprocessing_folder, save, msdl)
plot_connectivity_matrix(subject_id, group, gl.precision_, roi_names,
'gl_precision', session, preprocessing_folder,
save, msdl)
sparsity = (gl.precision_ == 0)
plot_connectivity_matrix(subject_id, group, sparsity, roi_names,
'gl_sparsity', session, preprocessing_folder,
save, msdl)
plot_connectivity_glassbrain(subject_id, group, gl.covariance_,
roi_coords, 'gl_covariance', session,
preprocessing_folder, save, msdl)
if save_file:
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
sparsity = (gl.precision_ == 0)
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(
CONN_DIR, subject_id, 'gl_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file,
covariance=gl.covariance_,
precision=gl.precision_,
sparsity=sparsity,
roi_names=roi_names,
roi_coords=roi_coords)
return gl, roi_names, roi_coords
def compute_graph_lasso_covariance(subject_id, group, session='func1',
preprocessing_folder='pipeline_1',
plot=True, save=True, save_file=True,
msdl=False):
"""Returns graph lasso covariance for a subject_id
"""
# load timeseries
if msdl:
ts = load_dynacomp_msdl_timeseries(subject_id, session=session,
preprocessing_folder=preprocessing_folder)
roi_names, roi_coords = load_msdl_names_and_coords()
else:
ts = load_dynacomp_roi_timeseries(subject_id, session=session,
preprocessing_folder=preprocessing_folder)
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
# compute covariance
gl = covariance.GraphLassoCV(verbose=2)
gl.fit(ts)
if plot:
plot_connectivity_matrix(subject_id, group, gl.covariance_,
roi_names, 'gl_covariance', session,
preprocessing_folder, save, msdl)
plot_connectivity_matrix(subject_id, group, gl.precision_,
roi_names, 'gl_precision', session,
preprocessing_folder, save, msdl)
sparsity = (gl.precision_ == 0)
plot_connectivity_matrix(subject_id, group, sparsity,
roi_names, 'gl_sparsity', session,
preprocessing_folder, save, msdl)
plot_connectivity_glassbrain(subject_id, group, gl.covariance_,
roi_coords, 'gl_covariance', session,
preprocessing_folder, save, msdl)
if save_file:
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
sparsity = (gl.precision_ == 0)
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(CONN_DIR, subject_id,
'gl_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file, covariance=gl.covariance_,
precision=gl.precision_, sparsity=sparsity,
roi_names=roi_names, roi_coords=roi_coords)
return gl, roi_names, roi_coords
def compute_pearson_connectivity(subject_id,
group,
session='func1',
preprocessing_folder='pipeline_1',
plot=True,
save=True,
save_file=True,
msdl=False):
"""Returns Pearson correlation coefficient for a subject_id
"""
# load timeseries
if msdl:
ts = load_dynacomp_msdl_timeseries(
subject_id,
session=session,
preprocessing_folder=preprocessing_folder)
roi_names, roi_coords = load_msdl_names_and_coords()
else:
ts = load_dynacomp_roi_timeseries(
subject_id,
session=session,
preprocessing_folder=preprocessing_folder)
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
# pearson correlation
pc = np.corrcoef(ts.T)
if plot:
print session
plot_connectivity_matrix(subject_id, group, pc, roi_names, 'pc',
session, preprocessing_folder, save, msdl)
plot_connectivity_glassbrain(subject_id, group, pc, roi_coords, 'pc',
session, preprocessing_folder, save, msdl)
if save_file:
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(
CONN_DIR, subject_id, 'pc_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file,
correlation=pc,
roi_names=roi_names,
roi_coords=roi_coords)
return pc, roi_names, roi_coords
def compute_pearson_connectivity(subject_id, group, session='func1',
preprocessing_folder='pipeline_1',
plot=True, save=True, save_file=True,
msdl=False):
"""Returns Pearson correlation coefficient for a subject_id
"""
# load timeseries
if msdl:
ts = load_dynacomp_msdl_timeseries(subject_id, session=session,
preprocessing_folder=preprocessing_folder)
roi_names, roi_coords = load_msdl_names_and_coords()
else:
ts = load_dynacomp_roi_timeseries(subject_id, session=session,
preprocessing_folder=preprocessing_folder)
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
# pearson correlation
pc = np.corrcoef(ts.T)
if plot:
print session
plot_connectivity_matrix(subject_id, group, pc,
roi_names, 'pc',
session,
preprocessing_folder, save, msdl)
plot_connectivity_glassbrain(subject_id, group, pc,
roi_coords, 'pc', session,
preprocessing_folder, save, msdl)
if save_file:
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(CONN_DIR, subject_id,
'pc_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file, correlation=pc,
roi_names=roi_names, roi_coords=roi_coords)
return pc, roi_names, roi_coords
def compute_group_sparse_covariance(dataset, session='func1',
preprocessing_folder='pipeline_1',
plot=True, save=True, save_file=True,
msdl=False):
"""Returns Group sparse covariance for all subjects
"""
ts = []
# load timeseries
if msdl:
for subject_id in dataset.subjects:
ts.append(load_dynacomp_msdl_timeseries(subject_id,\
session=session, preprocessing_folder=preprocessing_folder))
roi_names, roi_coords = load_msdl_names_and_coords()
else:
for subject_id in dataset.subjects:
ts.append(load_dynacomp_roi_timeseries(subject_id, session=session,\
preprocessing_folder=preprocessing_folder))
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
gsc = GroupSparseCovarianceCV(verbose=2)
gsc.fit(ts)
if plot:
for i in range(len(dataset.subjects)):
if not msdl:
# load rois
roi_names,\
roi_coords = load_roi_names_and_coords(dataset.subjects[i])
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
gsc.covariances_[..., i],
roi_names, 'gsc_covariance', session,
preprocessing_folder, save, msdl)
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
gsc.precisions_[..., i],
roi_names, 'gsc_precision', session,
preprocessing_folder, save, msdl)
sparsity = (gsc.precisions_[..., i] == 0)
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
sparsity,
roi_names, 'gsc_sparsity', session,
preprocessing_folder, save, msdl)
plot_connectivity_glassbrain(dataset.subjects[i], dataset.group[i],
gsc.covariances_[..., i],
roi_coords, 'gsc_covariance', session,
preprocessing_folder, save, msdl)
for i in range(len(dataset.subjects)):
if not msdl:
# load rois
roi_names,\
roi_coords = load_roi_names_and_coords(dataset.subjects[i])
sparsity = (gsc.precisions_[..., i] == 0)
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
subject_id = dataset.subjects[i]
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(CONN_DIR, subject_id,
'gsc_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file, covariance=gsc.covariances_[..., i],
precision=gsc.precisions_[..., i], sparsity=sparsity,
roi_names=roi_names, roi_coords=roi_coords)
return gsc, roi_names, roi_coords
preprocs.append({'preprocessing_folder': 'pipeline_2',
'prefix': 'resampled_wr'})
preprocs.append({'preprocessing_folder': 'pipeline_1',
'prefix': 'swr'})
for pr in preprocs:
preprocessing_folder = pr['preprocessing_folder']
prefix = pr['prefix']
dataset = load_dynacomp(preprocessing_folder, prefix)
for session in ['avg', 'func1', 'func2']:
for msdl in [False, True]:
print preprocessing_folder, prefix, session, msdl
# Roi names and coords
if msdl:
roi_names, roi_coords = load_msdl_names_and_coords()
msdl_str='msdl'
else:
roi_names, roi_coords = load_roi_names_and_coords(dataset.subjects[0])
msdl_str = ''
# Take only the lower diagonal values
ind = np.tril_indices(len(roi_names), k=-1)
# Label vector y
groups = ['avn', 'v', 'av']
y = np.zeros(len(dataset.subjects))
yn = np.zeros(len(dataset.subjects))
yv = np.ones(len(dataset.subjects))
yv[dataset.group_indices['v']] = 0
for i, group in enumerate(['v', 'av']):
def compute_group_sparse_covariance(dataset,
session='func1',
preprocessing_folder='pipeline_1',
plot=True,
save=True,
save_file=True,
msdl=False):
"""Returns Group sparse covariance for all subjects
"""
ts = []
# load timeseries
if msdl:
for subject_id in dataset.subjects:
ts.append(load_dynacomp_msdl_timeseries(subject_id,\
session=session, preprocessing_folder=preprocessing_folder))
roi_names, roi_coords = load_msdl_names_and_coords()
else:
for subject_id in dataset.subjects:
ts.append(load_dynacomp_roi_timeseries(subject_id, session=session,\
preprocessing_folder=preprocessing_folder))
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
gsc = GroupSparseCovarianceCV(verbose=2)
gsc.fit(ts)
if plot:
for i in range(len(dataset.subjects)):
if not msdl:
# load rois
roi_names,\
roi_coords = load_roi_names_and_coords(dataset.subjects[i])
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
gsc.covariances_[..., i], roi_names,
'gsc_covariance', session,
preprocessing_folder, save, msdl)
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
gsc.precisions_[..., i], roi_names,
'gsc_precision', session,
preprocessing_folder, save, msdl)
sparsity = (gsc.precisions_[..., i] == 0)
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
sparsity, roi_names, 'gsc_sparsity',
session, preprocessing_folder, save, msdl)
plot_connectivity_glassbrain(dataset.subjects[i], dataset.group[i],
gsc.covariances_[..., i], roi_coords,
'gsc_covariance', session,
preprocessing_folder, save, msdl)
for i in range(len(dataset.subjects)):
if not msdl:
# load rois
roi_names,\
roi_coords = load_roi_names_and_coords(dataset.subjects[i])
sparsity = (gsc.precisions_[..., i] == 0)
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
subject_id = dataset.subjects[i]
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(
CONN_DIR, subject_id,
'gsc_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file,
covariance=gsc.covariances_[..., i],
precision=gsc.precisions_[..., i],
sparsity=sparsity,
roi_names=roi_names,
roi_coords=roi_coords)
return gsc, roi_names, roi_coords
