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]