  A recently proposed IAS MEG inverse solver algorithm, based on the coupling
of a hierarchical Bayesian model with computationally efficient Krylov subspace
linear solver, has been shown to perform well for both superficial and deep
brain sources. However, a systematic study of its sensitivity and specificity
as a function of the activity location is still missing. We propose novel
statistical protocols to quantify the performance of MEG inverse solvers,
focusing in particular on their sensitivity and specificity in identifying
active brain regions. We use these protocols for a systematic study of the
sensitivity and specificity of the IAS MEG inverse solver, comparing the
performance with three standard inversion methods, wMNE, dSPM, and sLORETA. To
avoid the bias of anecdotal tests towards a particular algorithm, the proposed
protocols are Monte Carlo sampling based, generating an ensemble of activity
patches in each brain region identified in a given atlas. The sensitivity is
measured by how much, on average, the reconstructed activity is concentrated in
the brain region of the simulated active patch. The specificity analysis is
based on Bayes factors, interpreting the estimated current activity as data for
testing the hypothesis that the active brain region is correctly identified,
vs. the hypothesis of any erroneous attribution. The methodology allows the
presence of a single or several simultaneous activity regions, without assuming
the knowledge of the number of active regions. The testing protocols suggest
that the IAS solver performs well in terms of sensitivity and specificity both
with cortical and subcortical activity estimation.
