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
Compute the Bayesian Structural Activation patterns
Parameters : | domain: StructuredDomain instance, :
stats: array of shape (nbnodes, subjects): :
sigma: float > 0: :
prevalence_pval: float in the [0,1] interval, optional :
prevalence_threshold: float, optional, :
threshold: float, optional, :
smin: int, optional, :
method: {‘gauss_mixture’, ‘emp_null’, ‘gam_gauss’, ‘prior’}, optional, :
algorithm: string, one of [‘density’, ‘co-occurrence’], optional :
niter: int, optional, :
burnin: int, optional, :
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Returns : | landmarks: Instance of sbf.LandmarkRegions or None, :
hrois: list of nipy.labs.spatial_models.hroi.Nroi instances :
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