def compute_network_connectivity_subject(conn, func, masker, rois):
""" Returns connectivity of one fMRI for a given atlas
"""
ts = masker.fit_transform(func)
ts = np.asarray(ts)[:, rois]
if conn == 'gl':
fc = GraphLassoCV(max_iter=1000)
elif conn == 'lw':
fc = LedoitWolf()
elif conn == 'oas':
fc = OAS()
elif conn == 'scov':
fc = ShrunkCovariance()
fc = Bunch(covariance_=0, precision_=0)
if conn == 'corr' or conn == 'pcorr':
fc = Bunch(covariance_=0, precision_=0)
fc.covariance_ = np.corrcoef(ts)
fc.precision_ = partial_corr(ts)
else:
fc.fit(ts)
ind = np.tril_indices(ts.shape[1], k=-1)
return fc.covariance_[ind], fc.precision_[ind]
def compute_connectivity_subject(conn, masker, func, confound=None):
""" Returns connectivity of one fMRI for a given atlas
"""
ts = do_mask_img(masker, func, confound)
if conn == 'gl':
fc = GraphLassoCV(max_iter=1000)
elif conn == 'lw':
fc = LedoitWolf()
elif conn == 'oas':
fc = OAS()
elif conn == 'scov':
fc = ShrunkCovariance()
fc = Bunch(covariance_=0, precision_=0)
if conn == 'corr' or conn == 'pcorr':
fc = Bunch(covariance_=0, precision_=0)
fc.covariance_ = np.corrcoef(ts)
fc.precision_ = partial_corr(ts)
else:
fc.fit(ts)
ind = np.tril_indices(ts.shape[1], k=-1)
return fc.covariance_[ind], fc.precision_[ind]
