[Application of weighted minimum-norm estimation with Tikhonov regularization for neuromagnetic source imaging].
In magnetoencepholography(MEG) inverse research, according to the point source model and distributed source model, the neuromagnetic source reconstruction methods are classified as parametric current dipole localization and nonparametric source imaging (or current density reconstruction). MEG source imaging technique can be formulated as an inherent ill-posed and highly underdetermined linear inverse problem. In order to yield a robust and plausible neural current distribution image, various approaches have been proposed. Among those, the weighted minimum-norm estimation with Tikhonov regularization is a popular technique. The authors present a relatively overall theoretical framework Followed by a discussion of the development, several regularized minimum-norm algorithms have been described in detail, including the depth normalization, low resolution electromagnetic tomography(LORETA), focal underdetermined system solver(FOCUSS), selective minimum-norm(SMN). In addition, some other imaging methods, e.g., maximum entropy method(MEM), the method incorporating other brain functional information such as fMRI data and maximum a posteriori(MAP) method using Markov random field model, are explained as well. From the generalized point of view based on minimum-norm estimation with Tikhonov regularization, all these algorithms are aiming to resolve the tradeoff between fidelity to the measured data and the constraints assumptions about the neural source configuration such as anatomical and physiological information. In conclusion, almost all the source imaging approaches can be consistent with the regularized minimum-norm estimation to some extent.
Hu
Jing
J
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310014.
Hu
Jie
J
Wang
Yuanmei
Y
D000465:Algorithms; D001499:Bayes Theorem; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015225:Magnetoencephalography; D015233:Models, Statistical; D017711:Nonlinear Dynamics