Module: labs.spatial_models.bayesian_structural_analysis

The main routine of this package that aims at performing the extraction of ROIs from multisubject dataset using the localization and activation strength of extracted regions.

This has been published in: - Thirion et al. High level group analysis of FMRI data based on Dirichlet process mixture models, IPMI 2007 - Thirion et al. Accurate Definition of Brain Regions Position Through the Functional Landmark Approach, MICCAI 2010

Author : Bertrand Thirion, 2006-2013

nipy.labs.spatial_models.bayesian_structural_analysis.compute_landmarks(domain, stats, sigma, prevalence_pval=0.5, prevalence_threshold=0, threshold=3.0, smin=5, method='prior', algorithm='density', n_iter=1000, burnin=100)

Compute the Bayesian Structural Activation patterns


domain: StructuredDomain instance,

Description of the spatial context of the data

stats: array of shape (nbnodes, subjects):

the multi-subject statistical maps

sigma: float > 0:

expected cluster std in the common space in units of coord

prevalence_pval: float in the [0,1] interval, optional

posterior significance threshold

prevalence_threshold: float, optional,

reference threshold for the prevalence value

threshold: float, optional,

first level threshold

smin: int, optional,

minimal size of the regions to validate them

method: {‘gauss_mixture’, ‘emp_null’, ‘gam_gauss’, ‘prior’}, optional,

‘gauss_mixture’ A Gaussian Mixture Model is used ‘emp_null’ a null mode is fitted to test ‘gam_gauss’ a Gamma-Gaussian mixture is used ‘prior’ a hard-coded function is used

algorithm: string, one of [‘density’, ‘co-occurrence’], optional

method used to compute the landmarks

niter: int, optional,

number of iterations of the DPMM

burnin: int, optional,

number of iterations of the DPMM


landmarks: Instance of sbf.LandmarkRegions or None,

Describes the ROIs found in inter-subject inference None if nothing can be defined

hrois: list of nipy.labs.spatial_models.hroi.Nroi instances

representing individual ROIs