def perm_prfx(domain, graphs, features, nb_parcel, ldata, initial_mask=None, nb_perm=100, niter=5, dmax=10., lamb=100.0, chunksize=1.e5, verbose=1): """ caveat: assumes that the functional dimension is 1 """ from ..utils.reproducibility_measures import ttest # permutations for the assesment of the results prfx0 = [] adim = domain.coord.shape[1] nb_subj = len(ldata) for q in range(nb_perm): feature = [] sldata = [] for s in range(nb_subj): lf = features[s].copy() swap = (rand() > 0.5) * 2 - 1 lf[:, 0:-adim] = swap * lf[:, 0:-adim] sldata.append(swap * ldata[s]) feature.append(lf) # optimization part all_labels, proto_anat = _optim_hparcel(feature, domain, graphs, nb_parcel, lamb, dmax, niter, initial_mask, chunksize=chunksize) labels = -np.ones((domain.size, nb_subj)).astype(np.int) for s in range(nb_subj): labels[initial_mask[:, s] > -1, s] = all_labels[s] # compute the group-level labels template_labels = voronoi(domain.coord, proto_anat) # create the parcellation pcl = MultiSubjectParcellation(domain, individual_labels=labels, template_labels=template_labels) pdata = pcl.make_feature('functional', np.rollaxis(np.array(ldata), 1, 0)) prfx = ttest(np.squeeze(pdata)) if verbose: print q, prfx.max(0) prfx0.append(prfx.max(0)) return prfx0
def perm_prfx(domain, graphs, features, nb_parcel, ldata, initial_mask=None, nb_perm=100, niter=5, dmax=10., lamb=100.0, chunksize=1.e5, verbose=1): """ caveat: assumes that the functional dimension is 1 """ from ..utils.reproducibility_measures import ttest # permutations for the assesment of the results prfx0 = [] adim = domain.coord.shape[1] nb_subj = len(ldata) for q in range(nb_perm): feature = [] sldata = [] for s in range(nb_subj): lf = features[s].copy() swap = (rand() > 0.5) * 2 - 1 lf[:, 0:-adim] = swap * lf[:, 0:-adim] sldata.append(swap * ldata[s]) feature.append(lf) # optimization part all_labels, proto_anat = _optim_hparcel( feature, domain, graphs, nb_parcel, lamb, dmax, niter, initial_mask, chunksize=chunksize) labels = - np.ones((domain.size, nb_subj)).astype(np.int) for s in range(nb_subj): labels[initial_mask[:, s] > -1, s] = all_labels[s] # compute the group-level labels template_labels = voronoi(domain.coord, proto_anat) # create the parcellation pcl = MultiSubjectParcellation(domain, individual_labels=labels, template_labels=template_labels) pdata = pcl.make_feature('functional', np.rollaxis(np.array(ldata), 1, 0)) prfx = ttest(np.squeeze(pdata)) if verbose: print q, prfx.max(0) prfx0.append(prfx.max(0)) return prfx0
def hparcel(domain, ldata, nb_parcel, nb_perm=0, niter=5, mu=10., dmax=10., lamb=100.0, chunksize=1.e5, verbose=0, initial_mask=None): """ Function that performs the parcellation by optimizing the inter-subject similarity while retaining the connectedness within subject and some consistency across subjects. Parameters ---------- domain: discrete_domain.DiscreteDomain instance, yields all the spatial information on the parcelled domain ldata: list of (n_subj) arrays of shape (domain.size, dim) the feature data used to inform the parcellation nb_parcel: int, the number of parcels nb_perm: int, optional, the number of times the parcellation and prfx computation is performed on sign-swaped data niter: int, optional, number of iterations to obtain the convergence of the method information in the clustering algorithm mu: float, optional, relative weight of anatomical information dmax: float optional, radius of allowed deformations lamb: float optional parameter to control the relative importance of space vs function chunksize; int, optional number of points used in internal sub-sampling verbose: bool, optional, verbosity mode initial_mask: array of shape (domain.size, nb_subj), optional initial subject-depedent masking of the domain Results ------- Pa: the resulting parcellation structure appended with the labelling """ # a various parameters nbvox = domain.size nb_subj = len(ldata) if initial_mask is None: initial_mask = np.ones((nbvox, nb_subj), np.int) graphs = [] feature = [] for s in range(nb_subj): # build subject-specific models of the data lnvox = np.sum(initial_mask[:, s] > - 1) lac = domain.coord[initial_mask[:, s] > - 1] beta = np.reshape(ldata[s], (lnvox, ldata[s].shape[1])) lf = np.hstack((beta, mu * lac / (1.e-15 + np.std(domain.coord, 0)))) feature.append(lf) g = wgraph_from_coo_matrix(domain.topology) g.remove_trivial_edges() graphs.append(g) # main function all_labels, proto_anat = _optim_hparcel( feature, domain, graphs, nb_parcel, lamb, dmax, niter, initial_mask, chunksize=chunksize, verbose=verbose) # write the individual labelling labels = - np.ones((nbvox, nb_subj)).astype(np.int) for s in range(nb_subj): labels[initial_mask[:, s] > -1, s] = all_labels[s] # compute the group-level labels template_labels = voronoi(domain.coord, proto_anat) # create the parcellation pcl = MultiSubjectParcellation(domain, individual_labels=labels, template_labels=template_labels, nb_parcel=nb_parcel) pcl.make_feature('functional', np.rollaxis(np.array(ldata), 1, 0)) if nb_perm > 0: prfx0 = perm_prfx(domain, graphs, feature, nb_parcel, ldata, initial_mask, nb_perm, niter, dmax, lamb, chunksize) return pcl, prfx0 else: return pcl
def hparcel(domain, ldata, nb_parcel, nb_perm=0, niter=5, mu=10., dmax=10., lamb=100.0, chunksize=1.e5, verbose=0, initial_mask=None): """ Function that performs the parcellation by optimizing the inter-subject similarity while retaining the connectedness within subject and some consistency across subjects. Parameters ---------- domain: discrete_domain.DiscreteDomain instance, yields all the spatial information on the parcelled domain ldata: list of (n_subj) arrays of shape (domain.size, dim) the feature data used to inform the parcellation nb_parcel: int, the number of parcels nb_perm: int, optional, the number of times the parcellation and prfx computation is performed on sign-swaped data niter: int, optional, number of iterations to obtain the convergence of the method information in the clustering algorithm mu: float, optional, relative weight of anatomical information dmax: float optional, radius of allowed deformations lamb: float optional parameter to control the relative importance of space vs function chunksize; int, optional number of points used in internal sub-sampling verbose: bool, optional, verbosity mode initial_mask: array of shape (domain.size, nb_subj), optional initial subject-depedent masking of the domain Returns ------- Pa: the resulting parcellation structure appended with the labelling """ # a various parameters nbvox = domain.size nb_subj = len(ldata) if initial_mask is None: initial_mask = np.ones((nbvox, nb_subj), np.int) graphs = [] feature = [] for s in range(nb_subj): # build subject-specific models of the data lnvox = np.sum(initial_mask[:, s] > -1) lac = domain.coord[initial_mask[:, s] > -1] beta = np.reshape(ldata[s], (lnvox, ldata[s].shape[1])) lf = np.hstack((beta, mu * lac / (1.e-15 + np.std(domain.coord, 0)))) feature.append(lf) g = wgraph_from_coo_matrix(domain.topology) g.remove_trivial_edges() graphs.append(g) # main function all_labels, proto_anat = _optim_hparcel(feature, domain, graphs, nb_parcel, lamb, dmax, niter, initial_mask, chunksize=chunksize, verbose=verbose) # write the individual labelling labels = -np.ones((nbvox, nb_subj)).astype(np.int) for s in range(nb_subj): labels[initial_mask[:, s] > -1, s] = all_labels[s] # compute the group-level labels template_labels = voronoi(domain.coord, proto_anat) # create the parcellation pcl = MultiSubjectParcellation(domain, individual_labels=labels, template_labels=template_labels, nb_parcel=nb_parcel) pcl.make_feature('functional', np.rollaxis(np.array(ldata), 1, 0)) if nb_perm > 0: prfx0 = perm_prfx(domain, graphs, feature, nb_parcel, ldata, initial_mask, nb_perm, niter, dmax, lamb, chunksize) return pcl, prfx0 else: return pcl