labs.spatial_models.hierarchical_parcellation¶
Module: labs.spatial_models.hierarchical_parcellation¶
Computation of parcellations using a hierarchical approach. Author: Bertrand Thirion, 2008
Functions¶
-
nipy.labs.spatial_models.hierarchical_parcellation.hparcel(domain, ldata, nb_parcel, nb_perm=0, niter=5, mu=10.0, dmax=10.0, lamb=100.0, chunksize=100000.0, verbose=0, initial_mask=None)[source]¶ 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