def coord_bsa(domain, betas, theta=3.0, dmax=5.0, ths=0, thq=0.5, smin=0,
    afname=None):
    """ main function for  performing bsa on a dataset
    where bsa =  nipy.labs.spatial_models.bayesian_structural_analysis

    Parameters
    ----------
    domain: image instance,
          referential- and domain-defining image
    betas: array of shape (nbnodes, subjects),
           the multi-subject statistical maps
    theta: float, optional
           first level threshold
    dmax: float>0, optional
          expected cluster std in the common space in units of coord
    ths: int, >=0), optional
         representatitivity threshold
    thq: float, optional,
         posterior significance threshold should be in [0,1]
    smin: int, optional,
          minimal size of the regions to validate them
    afname: string, optional
            path where intermediate results cam be pickled

    Returns
    -------
    afcoord array of shape(number_of_regions,3):
            coordinate of the found landmark regions
    """
    from ..spatial_models.bayesian_structural_analysis import compute_BSA_quick
    crmap, AF, BF, p = compute_BSA_quick(domain, betas, dmax, thq, smin,
        ths, theta, verbose=0)
    if AF is None:
        return None
    if afname is not None:
        import pickle
        pickle.dump(AF, afname)
    afcoord = AF.discrete_to_roi_features('position')
    return afcoord
