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An fMRI-EEG Integrative Method with Model Selection Procedures for Reconstruction of Multiple Cortical Activities

机译：具有模型选择程序的FMRI-EEG集成方法，用于重建多种皮质活动

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Background: Neuroimaging techniques with high spatio-temporal resolution would are crucial for the advancement in brain research, improvement of clinical diagnosis and management of neuropsychiatric disorders. Functional MRI (fMRI) is characterized by its high spatial resolution. Onthe other hand, the techniques measuring electromagnetic features of neurons, such as electroencephalography (EEG), provide millisecond order temporal resolution. Therefore, integrative analyses of the fMRI and EEG are expected to provide information with high spatio-temporal resolution enablingto clarify dynamic multiple cortical activities Objective: We propose a novel fMRI-EEG integrative reconstruction method for multiple cortical activities using EEG data, and we validate the accuracy of our method by comparing it with other popular reconstruction approaches that are assumedto have obtained prior information from fMRI. Methods: We determined the first model via fMRI data, and we obtained the final model which contained the source that the fMRI could not capture through iterative model selection procedures based on the Akaike information criterion (AIC).We then used a linearly constrained generalized least-squares (LCGLS) filter to suppress unconscious activities. We carried out numerical simulations to validate the proposed method and compared it to two commonly used representative reconstructions method, sLORETA and the LCMV beamformermethods, using the residual sum of the squares. Results: The proposed method gave a good estimation of the multiple cortical activities by suppressing other fMRI-visible and fMRI-invisible sources. Conclusion: These results demonstrate that the proposed method can reconstruct corticalactivities more accurately than either sLORETA or the LCMV beamformer methods.
机译：背景：具有高时空分辨率的神经影像学技术对于大脑研究的进步至关重要，改善神经精神疾病的临床诊断和管理。功能性MRI（FMRI）的特点是其高空间分辨率。另一方面，测量神经元的电磁特征的技术，例如脑电图（EEG），提供毫秒的时间分辨率。因此，预计FMRI和EEG的综合分析将提供具有高时空分辨率的信息，该信息阐明了动态多种皮质活动目标：我们提出了一种使用EEG数据的多种皮质活动的新型FMRI-EEG综合重建方法，我们验证了通过将其与其他受欢迎的重建方法进行比较来精确方法，该方法已经从FMRI获得了先前信息。方法：我们通过FMRI数据确定了第一个模型，我们获得了最终模型，该模型包含通过基于Akaike信息标准（AIC）通过迭代模型选择过程无法捕获FMRI的源.WE然后使用线性约束的广义最少-Squares（LCGLS）过滤器抑制无意识的活动。我们进行了数值模拟以验证所提出的方法，并将其与两个常用的代表性重建方法，斜面和LCMV BeamformerMethod进行比较，使用正方形的残余和。结果：所提出的方法通过抑制其他FMRI可见和FMRI隐形来源，良好地估计了多种皮质活动。结论：这些结果表明，所提出的方法可以比斜面或LCMV波形形成器方法更精确地重建皮质活动。

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