# labs.spatial_models.bayesian_structural_analysis

## 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

  Parameters:
    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

  Returns:
    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