Mahjoory Keyvan, Nikulin Vadim V, Botrel Loïc, Linkenkaer-Hansen Klaus, Fato Marco M, Haufe Stefan
Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Genova, Italy; Machine Learning Department, Technische Universität Berlin, Berlin, Germany.
Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Neurophysics Group, Charité University Medicine Berlin, Berlin, Germany; Center for Cognition and Decision Making, National Research University Higher School of Economics, Russian Federation.
Neuroimage. 2017 May 15;152:590-601. doi: 10.1016/j.neuroimage.2017.02.076. Epub 2017 Mar 12.
As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure.
由于脑电图逆问题没有唯一解,从脑电图重建的源及其连接特性取决于正向和逆向建模参数,如解剖模板和电模型的选择、对源的先验假设以及进一步的实现细节。为了将源连接性分析用作可靠的研究工具,需要在更广泛的标准估计程序中保持稳定性。我们使用两项研究中采集的N = 65名参与者的静息态脑电图记录,首次全面评估了脑电图源定位以及功能/有效连接性指标在两种解剖模板(ICBM152和Colin27)、三种电模型(边界元法、有限元法和球谐展开)、三种逆方法(加权最小范数估计、精确低分辨率脑电磁断层成像和线性约束最小方差波束形成)以及三种软件实现(Brainstorm、Fieldtrip和我们自己的工具箱)之间的一致性。结果发现,源定位在重建管道之间比随后的功能连接性估计更稳定,而有效连接性估计的一致性最差。所有结果相对不受电头部模型选择的影响,而逆方法和源成像软件包的选择则导致了相当大的变异性。特别是,一方面在线性约束最小方差波束形成器解与另一方面在精确低分辨率脑电磁断层成像/加权最小范数估计分布式逆解之间发现了相对较大的差异。我们还观察到,当在研究之间、个体参与者内部以及个体参与者之间比较结果时,一致性会逐渐降低。为了在面对观察到的变异性时提供可靠的结果,需要进行涉及相互作用脑源的额外模拟。同时,我们鼓励使用不止一种源成像程序来验证所获得的结果。