SPM-8 has three options for source localisation as a distributed source model: minimum norm (Hämäläinen & Ilmoniemi, 1984), LORETA (Pascual-Marqui et al., 1994) and Multiple Sparse Priors (MSP; Greedy Search Mattoutt et al., 2005; Friston et al., 2008).
Attal, Y. & Schwartz, D. Assessment of subcortical source localization using deep brain activity imaging model with minimum norm operators: a MEG study.
A major advantage of beamformer analysis relative to alternative source localisation techniques such as equivalent current dipole modelling or minimum norm estimation (which take evoked-average data as input) is the ability to image changes in cortical oscillatory power that do not give rise to a strong signal in the evoked-average response [11].
Whereas the previous studies were in principle based on localizing single point sources in the brain that can explain the measured magnetic fields, the present study applied a distributed source model, the cortically-based minimum norm estimate, which is well suited to analyze sources in an extensive network of brain areas that are activated more or less simultaneously [70].
The simplest is the minimum model stabilizing functional (MM), which is based on the least-squares criterion and uses the minimum norm of the difference from the a priori model m apr.
The data were then subjected to a supplementary reanalysis in terms of a distributed source model using a least square minimum norm criterium.
Here, we estimate the location of these sources using the neuroanatomically constrained minimum norm estimate (MNE) procedure, based on distributed source modeling rather than equivalent current dipoles (Hämäläinen et al. 1993).
The standardized low resolution brain electromagnetic tomography Minimum Norm Least Squares approach (Pascual-Marqui 2002) was used for distributed source modeling.
Following coregistration of MEG fiducials with the SPM8 standard MRI template and the construction of a forward model (single sphere [ 59]), source localization used a Bayesian cortically constrained group minimum norm inversion (with multiple sparse priors (MSP) used for priors) [ 60, 61].
