Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm
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...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2016/4890497 |
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| author | Junpeng Zhang Yuan Cui Lihua Deng Ling He Junran Zhang Jing Zhang Qun Zhou Qi Liu Zhiguo Zhang |
| author_facet | Junpeng Zhang Yuan Cui Lihua Deng Ling He Junran Zhang Jing Zhang Qun Zhou Qi Liu Zhiguo Zhang |
| author_sort | Junpeng Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-efd41e5397df42d4ad05a5e230d4f098 |
| institution | Kabale University |
| issn | 2090-5904 1687-5443 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-efd41e5397df42d4ad05a5e230d4f0982025-02-03T00:59:45ZengWileyNeural Plasticity2090-59041687-54432016-01-01201610.1155/2016/48904974890497Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space AlgorithmJunpeng Zhang0Yuan Cui1Lihua Deng2Ling He3Junran Zhang4Jing Zhang5Qun Zhou6Qi Liu7Zhiguo Zhang8Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, ChinaSchool of Humanities and Information Management, Chengdu Medical College, Chengdu 610083, ChinaDepartment of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, ChinaDepartment of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, ChinaDepartment of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, ChinaDepartment of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, ChinaDepartment of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, ChinaDepartment of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, ChinaSchool of Chemical and Biomedical Engineering, School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, SingaporeThis 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.http://dx.doi.org/10.1155/2016/4890497 |
| spellingShingle | Junpeng Zhang Yuan Cui Lihua Deng Ling He Junran Zhang Jing Zhang Qun Zhou Qi Liu Zhiguo Zhang Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm Neural Plasticity |
| title | Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm |
| title_full | Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm |
| title_fullStr | Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm |
| title_full_unstemmed | Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm |
| title_short | Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm |
| title_sort | closely spaced meg source localization and functional connectivity analysis using a new prewhitening invariance of noise space algorithm |
| url | http://dx.doi.org/10.1155/2016/4890497 |
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