Dual-Core Beamformer for obtaining highly correlated neuronal networks in MEG

被引:49
作者
Diwakar, Mithun [2 ]
Huang, Ming-Xiong [1 ,3 ,4 ]
Srinivasan, Ramesh [5 ,6 ]
Harrington, Deborah L. [3 ,4 ]
Robb, Ashley [3 ,4 ]
Angeles, Annemarie [3 ,4 ]
Muzzatti, Laura
Pakdaman, Reza
Song, Tao
Theilmann, Rebecca J.
Lee, Roland R. [3 ,4 ]
机构
[1] Univ Calif San Diego, Radiol Imaging Lab, Dept Radiol, San Diego, CA 92121 USA
[2] Univ Calif San Diego, Dept Bioengn, San Diego, CA 92121 USA
[3] VA San Diego Healthcare Syst, Res Serv, San Diego, CA USA
[4] VA San Diego Healthcare Syst, Serv Radiol, San Diego, CA USA
[5] Univ Calif Irvine, Dept Cognit Sci, Irvine, CA 92717 USA
[6] Univ Calif Irvine, Dept Biomed Engn, Irvine, CA USA
关键词
SIGNAL SPACE SEPARATION; SOMATOSENSORY-EVOKED RESPONSES; SPATIOTEMPORAL DYNAMICS; BRAIN; INTERFERENCE; ARTIFACTS;
D O I
10.1016/j.neuroimage.2010.07.023
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The "Dual-Core Beamformer" (DCBF) is a new lead-field based MEG inverse-modeling technique designed for localizing highly correlated networks from noisy MEG data. Conventional beamformer techniques are successful in localizing neuronal sources that are uncorrelated under poor signal-to-noise ratio (SNR) conditions. However, they fail to reconstruct multiple highly correlated sources. Though previously published dual-beamformer techniques can successfully localize multiple correlated sources, they are computationally expensive and impractical, requiring a priori information. The DCBF is able to automatically calculate optimal amplitude-weighting and dipole orientation for reconstruction, greatly reducing the computational cost of the dual-beamformer technique. Paired with a modified Powell algorithm, the DCBF can quickly identify multiple sets of correlated sources contributing to the MEG signal. Through computer simulations, we show that the DCBF quickly and accurately reconstructs source locations and their time-courses under widely varying SNR, degrees of correlation, and source strengths. Simulations also show that the DCBF identifies multiple simultaneously active correlated networks. Additionally, DCBF performance was tested using MEG data in humans. In an auditory task, the DCBF localized and reconstructed highly correlated left and right auditory responses. In a median-nerve stimulation task, the DCBF identified multiple meaningful networks of activation without any a priori information. Altogether, our results indicate that the DCBF is an effective and valuable tool for reconstructing correlated networks of neural activity from MEG recordings. Published by Elsevier Inc.
引用
收藏
页码:253 / 263
页数:11
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