# 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¶

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.

__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)
centers()

returns the average of the coordinates for each region

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 kde: array of shape(n) the density sampled at the coords
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 label: array of shape (n): the posterior labelling
roi_prevalence()

Return a confidence index over the different rois

Returns: confid: array of shape self.k the population_prevalence
show()

function to print basic information on self

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 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)