title: MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks.
abstract: We present an MEG source reconstruction method that simultaneously reconstructs source amplitudes and identifies source interactions across the whole brain. In the proposed method, a full multivariate autoregressive (MAR) model formulates directed interactions (i.e., effective connectivity) between sources. The MAR coefficients (the entries of the MAR matrix) are constrained by the prior knowledge of whole-brain anatomical networks inferred from diffusion MRI. Moreover, to increase the accuracy and robustness of our method, we apply an fMRI prior on the spatial activity patterns and a sparse prior on the MAR coefficients. The observation process of MEG data, the source dynamics, and a series of the priors are combined into a Bayesian framework using a state-space representation. The parameters, such as the source amplitudes and the MAR coefficients, are jointly estimated from a variational Bayesian learning algorithm. By formulating the source dynamics in the context of MEG source reconstruction, and unifying the estimations of source amplitudes and interactions, we can identify the effective connectivity without requiring the selection of regions of interest. Our method is quantitatively and qualitatively evaluated on simulated and experimental data, respectively. Compared with non-dynamic methods, in which the interactions are estimated after source reconstruction with no dynamic constraints, the proposed dynamic method improves most of the performance measures in simulations, and provides better physiological interpretation and inter-subject consistency in real data applications.
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan; ATR Neural Information Analysis Laboratories, Kyoto, Japan.
authors.forename: Makoto
authors.initials: M
authors.lastname: Fukushima
authors.affiliation: ATR Neural Information Analysis Laboratories, Kyoto, Japan; Brain Functional Imaging Technologies Group, CiNet, Osaka, Japan. Electronic address: oyamashi@atr.jp.
authors.forename: Okito
authors.initials: O
authors.lastname: Yamashita
authors.affiliation: Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Knösche
authors.affiliation: ATR Neural Information Analysis Laboratories, Kyoto, Japan.
authors.forename: Masa-aki
authors.initials: MA
authors.lastname: Sato
mesh_terms: D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography / Q000379:methods*; D009415:Nerve Net / Q000502:physiology*
keywords: Anatomical connectivity; Effective connectivity; MEG source reconstruction; Multivariate autoregressive model; Prior knowledge; Variational Bayes