Multiple sparse priors for the M/EEG inverse problem.
This paper describes an application of hierarchical or empirical Bayes to the distributed source reconstruction problem in electro- and magnetoencephalography (EEG and MEG). The key contribution is the automatic selection of multiple cortical sources with compact spatial support that are specified in terms of empirical priors. This obviates the need to use priors with a specific form (e.g., smoothness or minimum norm) or with spatial structure (e.g., priors based on depth constraints or functional magnetic resonance imaging results). Furthermore, the inversion scheme allows for a sparse solution for distributed sources, of the sort enforced by equivalent current dipole (ECD) models. This means the approach automatically selects either a sparse or a distributed model, depending on the data. The scheme is compared with conventional applications of Bayesian solutions to quantify the improvement in performance.
Friston
Karl
K
The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, 12 Queen Square, London, WC1N 3BG, UK. k.friston@fil.ion.ucl.ac.uk
Harrison
Lee
L
Daunizeau
Jean
J
Kiebel
Stefan
S
Phillips
Christophe
C
Trujillo-Barreto
Nelson
N
Henson
Richard
R
Flandin
Guillaume
G
Mattout
Jérémie
J
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005071:Evoked Potentials; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016013:Likelihood Functions; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012984:Software