期刊
NEUROIMAGE
卷 156, 期 -, 页码 29-42出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2017.04.038
关键词
Brain connectivity; Magnetoencephalography (MEG); Minimum Norm Estimate (MNE); Linearly Constrained Minimum Variance (LCMV); Beamforming; Dynamic Imaging of Coherent Sources (DICS)
资金
- Comision Nacional de Ciencia y Tecnologia de Chile (CONICYT)
- Colfuturo - Colombia
- French ANR Project [ANR-DEFIS 09-EMER-002 CoAdapt]
- NSERC (Canada) Discovery Grant [RGPIN-2015-04854]
- LABEX CORTEX of Universite de Lyon [ANR-11-LABX-0042, ANR-11-IDEX-0007]
- Academy of Finland [257576]
- FONDECYT [1130758]
- Fund for Innovation and Competitiveness (FIC) of the Chilean Ministry of Economy, Development and Tourism, through the Millennium Scientific Initiative [IS130005]
- Discovery Grant - Natural Sciences and Engineering Research Council of Canada [RGPIN-2015-04854]
- Canada Research Chairs Program
- Academy of Finland (AKA) [257576, 257576] Funding Source: Academy of Finland (AKA)
Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter-and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据