title: Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm.
abstract: This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity.
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China; School of Chemical and Biomedical Engineering, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.
authors.forename: Junpeng
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Humanities and Information Management, Chengdu Medical College, Chengdu 610083, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China.
authors.forename: Lihua
authors.initials: L
authors.lastname: Deng
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China.
authors.forename: Ling
authors.initials: L
authors.lastname: He
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China.
authors.forename: Junran
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China.
authors.forename: Qun
authors.initials: Q
authors.lastname: Zhou
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.affiliation: School of Chemical and Biomedical Engineering, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.
authors.forename: Zhiguo
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D063132:Connectome; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*
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