labs.spatial_models.structural_bfls¶
Module: labs.spatial_models.structural_bfls
¶
Inheritance diagram for nipy.labs.spatial_models.structural_bfls
:

The main routine of this module implement the LandmarkRegions class, that is used to represent Regions of interest at the population level (in a template space).
This has been used in Thirion et al. Structural Analysis of fMRI Data Revisited: Improving the Sensitivity and Reliability of fMRI Group Studies. IEEE TMI 2007
Author : Bertrand Thirion, 2006-2013
LandmarkRegions
¶
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class
nipy.labs.spatial_models.structural_bfls.
LandmarkRegions
(domain, k, indiv_coord, subjects, confidence)¶ Bases:
object
This class is intended to represent a set of inter-subject regions It should inherit from some abstract multiple ROI class, not implemented yet.
Methods
centers
()returns the average of the coordinates for each region kernel_density
([k, coord, sigma])Compute the density of a component as a kde map_label
([coord, pval, sigma])Sample the set of landmark regions roi_prevalence
()Return a confidence index over the different rois show
()function to print basic information on self -
__init__
(domain, k, indiv_coord, subjects, confidence)¶ Building the landmark_region
Parameters: domain: ROI instance :
defines the spatial context of the SubDomains
k: int, :
the number of landmark regions considered
indiv_coord: k-length list of arrays, :
coordinates of the nodes in some embedding space.
subjects: k-length list of integers :
these correspond to an ROI feature: the subject index of individual regions
confidence: k-length list of arrays, :
confidence values for the regions (0 is low, 1 is high)
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centers
()¶ returns the average of the coordinates for each region
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kernel_density
(k=None, coord=None, sigma=1.0)¶ Compute the density of a component as a kde
Parameters: k: int (<= self.k) or None :
component upon which the density is computed if None, the sum is taken over k
coord: array of shape(n, self.dom.em_dim), optional :
a set of input coordinates
sigma: float, optional :
kernel size
Returns: kde: array of shape(n) :
the density sampled at the coords
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map_label
(coord=None, pval=1.0, sigma=1.0)¶ Sample the set of landmark regions on the proposed coordiante set cs, assuming a Gaussian shape
Parameters: coord: array of shape(n,dim), optional, :
a set of input coordinates
pval: float in [0,1]), optional :
cutoff for the CR, i.e. highest posterior density threshold
sigma: float, positive, optional :
spatial scale of the spatial model
Returns: label: array of shape (n): the posterior labelling :
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roi_prevalence
()¶ Return a confidence index over the different rois
Returns: confid: array of shape self.k :
the population_prevalence
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show
()¶ function to print basic information on self
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nipy.labs.spatial_models.structural_bfls.
build_landmarks
(domain, coords, subjects, labels, confidence=None, prevalence_pval=0.95, prevalence_threshold=0, sigma=1.0)¶ Given a list of hierarchical ROIs, and an associated labelling, this creates an Amer structure wuch groups ROIs with the same label.
Parameters: domain: discrete_domain.DiscreteDomain instance, :
description of the spatial context of the landmarks
coords: array of shape(n, 3) :
Sets of coordinates for the different objects
subjects: array of shape (n), dtype = np.int :
indicators of the dataset the objects come from
labels: array of shape (n), dtype = np.int :
index of the landmark the object is associated with
confidence: array of shape (n), :
measure of the significance of the regions
prevalence_pval: float, optional :
prevalence_threshold: float, optional, :
(c) A label should be present in prevalence_threshold subjects with a probability>prevalence_pval in order to be valid
sigma: float optional, :
regularizing constant that defines a prior on the region extent
Returns: LR : None or structural_bfls.LR instance
describing a cross-subject set of ROIs. If inference yields a null result, LR is set to None
newlabel: array of shape (n) :
a relabelling of the individual ROIs, similar to u, that discards labels that do not fulfill the condition (c)