Dong Li, Gong Diankun, Valdes-Sosa Pedro A, Xia Yang, Luo Cheng, Xu Peng, Yao Dezhong
The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; Cuban Neuroscience Center, School of Life Science and Technology, Havana, Cuba.
Neuroimage. 2014 Oct 1;99:28-41. doi: 10.1016/j.neuroimage.2014.05.029. Epub 2014 May 20.
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been pursued in an effort to integrate complementary noninvasive information on brain activity. The primary goal involves better information discovery of the event-related neural activations at a spatial region of the BOLD fluctuation with the temporal resolution of the electrical signal. Many techniques and algorithms have been developed to integrate EEGs and fMRIs; however, the relative reliability of the integrated information is unclear. In this work, we propose a hierarchical framework to ensure the relative reliability of the integrated results and attempt to understand brain activation using this hierarchical ideal. First, spatial Independent Component Analysis (ICA) of fMRI and temporal ICA of EEG were performed to extract features at the trial level. Second, the maximal information coefficient (MIC) was adopted to temporally match them across the modalities for both linear and non-linear associations. Third, fMRI-constrained EEG source imaging was utilized to spatially match components across modalities. The simultaneously occurring events in the above two match steps provided EEG-fMRI spatial-temporal reliable integrated information, resulting in the most reliable components with high spatial and temporal resolution information. The other components discovered in the second or third steps provided second-level complementary information for flexible and cautious explanations. This paper contains two simulations and an example of real data, and the results indicate that the framework is a feasible approach to reveal cognitive processing in the human brain.
同步脑电图(EEG)和功能磁共振成像(fMRI)技术已被用于整合关于大脑活动的互补性非侵入性信息。其主要目标是在具有电信号时间分辨率的情况下,更好地发现与血氧水平依赖(BOLD)波动空间区域相关的事件相关神经激活信息。人们已经开发了许多技术和算法来整合脑电图和功能磁共振成像;然而,整合信息的相对可靠性尚不清楚。在这项工作中,我们提出了一个分层框架,以确保整合结果的相对可靠性,并尝试利用这个分层理念来理解大脑激活情况。首先,对功能磁共振成像进行空间独立成分分析(ICA),对脑电图进行时间独立成分分析,以在试验层面提取特征。其次,采用最大信息系数(MIC)在不同模态间对它们进行时间匹配,以建立线性和非线性关联。第三,利用功能磁共振成像约束的脑电图源成像技术在不同模态间进行成分的空间匹配。上述两个匹配步骤中同时出现的事件提供了脑电图 - 功能磁共振成像的时空可靠整合信息,从而得到具有高空间和时间分辨率信息的最可靠成分。在第二步或第三步中发现的其他成分提供了二级补充信息,以便进行灵活且谨慎的解释。本文包含两个模拟和一个真实数据示例,结果表明该框架是揭示人类大脑认知过程的一种可行方法。