Automatic relevance determination based hierarchical Bayesian MEG inversion in practice.
In recent simulation studies, a hierarchical Variational Bayesian (VB) method, which can be seen as a generalisation of the traditional minimum-norm estimate (MNE), was introduced for reconstructing distributed MEG sources. Here, we studied how nonlinearities in the estimation process and hyperparameter selection affect the inverse solutions, the feasibility of a full Bayesian treatment of the hyperparameters, and multimodality of the true posterior, in an empirical dataset wherein a male subject was presented with pure tone and checkerboard reversal stimuli, alone and in combination. An MRI-based cortical surface model was employed. Our results show, with a comparison to the basic MNE, that the hierarchical VB approach yields robust and physiologically plausible estimates of distributed sources underlying MEG measurements, in a rather automated fashion.
Nummenmaa
Aapo
A
Laboratory of Computational Engineering, Helsinki University of Technology, Espoo, Finland; Advanced Magnetic Imaging Centre, Helsinki University of Technology, Espoo, Finland. Aapo.Nummenmaa@hut.fi
Auranen
Toni
T
Hämäläinen
Matti S
MS
Jääskeläinen
Iiro P
IP
Sams
Mikko
M
Vehtari
Aki
A
Lampinen
Jouko
J
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*