labs.spatial_models.hierarchical_parcellation¶
Module: labs.spatial_models.hierarchical_parcellation
¶
Computation of parcellations using a hierarchical approach. Author: Bertrand Thirion, 2008
Functions¶
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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 :
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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