Controlled Support MEG imaging.
In this paper, we present a novel approach to imaging sparse and focal neural current sources from MEG (magnetoencephalography) data. Using the framework of Tikhonov regularization theory, we introduce a new stabilizer that uses the concept of controlled support to incorporate a priori assumptions about the area occupied by focal sources. The paper discusses the underlying Tikhonov theory and its relationship to a Bayesian formulation which in turn allows us to interpret and better understand other related algorithms.
Nagarajan
Srikantan S
SS
Biomagnetic Imaging Laboratory, Department of Radiology, University of California at San Francisco, San Francisco, CA 94122, USA.
Portniaguine
Oleg
O
Hwang
Dosik
D
Johnson
Chris
C
Sekihara
Kensuke
K
D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D015225:Magnetoencephalography / Q000379:methods* / Q000706:statistics & numerical data*; D009010:Monte Carlo Method; D015203:Reproducibility of Results