title: Estimation of hyper-parameters for a hierarchical model of combined cortical and extra-brain current sources in the MEG inverse problem.
abstract: One of the major obstacles in estimating cortical currents from MEG signals is the disturbance caused by magnetic artifacts derived from extra-cortical current sources such as heartbeats and eye movements. To remove the effect of such extra-brain sources, we improved the hybrid hierarchical variational Bayesian method (hyVBED) proposed by Fujiwara et al. (NeuroImage, 2009). hyVBED simultaneously estimates cortical and extra-brain source currents by placing dipoles on cortical surfaces as well as extra-brain sources. This method requires EOG data for an EOG forward model that describes the relationship between eye dipoles and electric potentials. In contrast, our improved approach requires no EOG and less a priori knowledge about the current variance of extra-brain sources. We propose a new method, "extra-dipole," that optimally selects hyper-parameter values regarding current variances of the cortical surface and extra-brain source dipoles. With the selected parameter values, the cortical and extra-brain dipole currents were accurately estimated from the simulated MEG data. The performance of this method was demonstrated to be better than conventional approaches, such as principal component analysis and independent component analysis, which use only statistical properties of MEG signals. Furthermore, we applied our proposed method to measured MEG data during covert pursuit of a smoothly moving target and confirmed its effectiveness.
authors.affiliation: Department of Intelligent Systems Design Engineering, Toyama Prefectural University, 5180 Kurokawa, Imizu-shi, Toyama 939-0398, Japan; Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan. Electronic address: kmorishi@pu-toyama.ac.jp.
authors.forename: Ken-ichi
authors.initials: K
authors.lastname: Morishige
authors.affiliation: Chinou Jouhou Shisutemu Inc., 134 Chudoji-Minami-cho, Shimogyo-ku, Kyoto-shi, Kyoto 600-8813, Japan.
authors.forename: Taku
authors.initials: T
authors.lastname: Yoshioka
authors.affiliation: Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.
authors.forename: Dai
authors.initials: D
authors.lastname: Kawawaki
authors.affiliation: Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.
authors.forename: Nobuo
authors.initials: N
authors.lastname: Hiroe
authors.affiliation: Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.
authors.forename: Masa-aki
authors.initials: MA
authors.lastname: Sato
authors.affiliation: Department of Intelligent Systems Design Engineering, Toyama Prefectural University, 5180 Kurokawa, Imizu-shi, Toyama 939-0398, Japan; Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.
authors.forename: Mitsuo
authors.initials: M
authors.lastname: Kawato
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D055724:Electrophysiological Phenomena / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D008959:Models, Neurological
keywords: Covert pursuit; Denoising; Hierarchical Bayesian method; MEG