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)

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

nipy.labs.spatial_models.hierarchical_parcellation.perm_prfx(domain, graphs, features, nb_parcel, ldata, initial_mask=None, nb_perm=100, niter=5, dmax=10.0, lamb=100.0, chunksize=100000.0, verbose=1)

caveat: assumes that the functional dimension is 1