基于贝叶斯理论的EEG-fMRI融合技术研究
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摘要
脑电(electroencephalogram, EEG)和功能磁共振(functional magnetic resonance imaging, fMRI)作为无创的神经成像技术,在认知神经科学和心理学研究中得到广泛应用。它们的信号具有互补性:EEG时间分辨率高而fMRI空间分辨率高。在本文中,我们基于贝叶斯理论,对EEG-fMRI的融合技术及应用进行了比较系统的研究,提出了基于fMRI功能网络的EEG源定位技术,基于EEG信息的fMRI响应函数估计方法,从而建立了一个EEG-fMRI时空对称融合方案,并在癫痫病人的同步EEG-fMRI研究中进行了验证和展示。在此基础上,我们进一步借助阴阳互补平衡思想,提出一个新的框架,实现了从原始数据到网络信息,从模型驱动到数据驱动等多种方法的统一描述。本文完成的主要工作如下:
1.基于参数贝叶斯模型,提出了借助于fMRI功能网络的EEG源定位技术:网络源成像(network-based source imaging, NESOI)。该技术的核心思想是在EEG源定位的贝叶斯模型中,将fMRI的功能网络当成空间先验信息,并通过贝叶斯模型的参数来确定先验信息的有效性。NESOI在估计神经电活动分布的同时,可以确定与EEG信号相关的fMRI功能网络,为讨论fMRI静息网络相关的电生理特征提供了新的手段。
2.基于参数贝叶斯模型,发展了借助于EEG信息的fMRI响应函数估计方法。该方法利用EEG信号来提取神经响应的时间和幅度信息,进而解卷积出fMRI的血氧动力学响应函数,实现EEG特征时间相关的血氧代谢活动的成像。该方法与NESOI一起,共同构成了EEG-fMRI时空对称融合(Spatial-temporal EEG-fMRI fusion, STEFF)。STEFF将目前基于空间约束和时间预测的两种融合方法合并到一起,实现了EEG的高时间分辨率和fMRI的高空间分辨率的整合。通过仿真,我们对STEFF的有效性进行了验证。
3.借助STEFF对部分性癫痫同步EEG-fMRI进行了研究。重点探讨了发作间期放电相关的fMRI活动的时空特征。提出从血氧动力学响应的正负性,响应峰值延迟时间以及与EEG的空间对应关系等多个角度对癫痫相关的血氧代谢活动进行分类。结果表明,STEFF不但能给出时空信息更为丰富的定位结果,还能提高识别癫痫刺激灶的能力。使得从电生理和血氧代谢两方面描绘癫痫网络的动力学过程成为可能。
4.在STEFF技术的基础上,进一步借助阴阳互补平衡思想,提出了一个EEG-fMRI融合的系统框架,实现了从原始数据到网络信息,从模型驱动到数据驱动等多种方法的统一描述。该框架对现有的基于fMRI约束的EEG成像,基于EEG信息的fMRI分析和EEG-fMRI对称融合等方法进行了重新梳理,阐明它们之间内在的联系:融合的信息层次和时空互补性。在此基础上,讨论了时空对称融合和大尺度脑网络。同时,从该框架出发,我们预示了多种目前没有得到研究的新的融合方法,为理解EEG-fMRI融合提供了新的视角。
Electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are the mostly used two predominant techniques for their ability to reveal noninvasive brain mapping of the mental process. Simultaneous EEG-fMRI recording provides complementary information about the cerebral activity, and the EEG-fMRI fusion enables a better understanding of the brain with the enhanced spatiotemporal resolution. The work in this dissertation concentrates in the EEG-fMRI fusion based on Bayesian theory and its application in cognitive and clinic problemes. We start with an introduction of the fMRI-constrained EEG source imaging and its applications in multi-modal face study and epileptic discharges. We then develop an EEG-informed fMRI analysis and a novel spatial-temporal EEG-fMRI symmetric fusion. The proposed symmetric fusion is applied in the study of partial epilepsy. Finally, we introduce a systematic perspective of the EEG-fMRI fusion, which is inspired by the proposed symmetric fusion and the harmonic balance between yin and yang. The main contributions of this dissertation are as follows:
1. NEtwork-based SOurce Imaging (NESOI) is a new method to reconstruct neuroelectric sources based on empirical Bayesian model. In NESOI, multiple functional networks derived from fMRI are employed as constraints for EEG source imaging. In contrast with previous applications of empirical Bayesian model in source reconstruction with smoothness or sparseness priors, functional networks play a fundamental role among the priors employed by NESOI. Using synthetic and real data, we systemically compared the performance of NESOI with other source inversion methods when fMRI priors are used or not used. Our results indicate that NESOI is a potentially useful approach for understanding the electrophysiological signatures of fMRI resting state networks.
2. Base on empirical Bayesian model, an EEG-informed hemodynamic response function (HRF) estimation is proposed to reconstruct the hemodynamic fluctuation related to EEG features. This estimation and NESOI combine into a parallel fusion, termed Spatial-Temporal Eeg-Fmri Fusion (STEFF), to symmetrically integrate the simultaneous EEG-fMRI recordings. STEFF enables information of one modality to be utilized as priors for the other and hence improves the spatial (for EEG) or temporal (for fMRI) resolution of the other modality. Simulations under realistic noise conditions indicated that STEFF is a feasible and physiologically reasonable hybrid approach for spatiotemporal mapping of cognitive processing in the human brain.
3. STEFF is applied in simultaneous EEG-fMRI recording for the partial epilepsy study. As interictal epileptiform discharges related components are widespread, STEFF classifies the fMRI component as a function of response sign (positive or negative), peak delay of HRF and consistence of the spatial pattern. Our results indicate that the EEG-fMRI spatial consistent components with early HRF peaks would be the indictors of the epileptogenic focus. STEFF make possible the discripting of the dynamic responses of epileptic networks with bioelectric and hemodanimic information.
4. Inspired by STEFF and the harmonic balance between yin and yang, a systematic framework is proposed to break the boundary between data level fusion and feature leve fusion. Based on this framework, we discuss many newly emerging fusion methods, including fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and EEG-fMRI symmetric fusion. Our systematic perspective is helpful in explaining the relationship between different fusion schemes: the levels of signal abstraction and the complementary natures of EEG and fMRI. Moreover, some schemes that are little investigated but have great potential are also revealed in this framework.
1.基于参数贝叶斯模型,提出了借助于fMRI功能网络的EEG源定位技术:网络源成像(network-based source imaging, NESOI)。该技术的核心思想是在EEG源定位的贝叶斯模型中,将fMRI的功能网络当成空间先验信息,并通过贝叶斯模型的参数来确定先验信息的有效性。NESOI在估计神经电活动分布的同时,可以确定与EEG信号相关的fMRI功能网络,为讨论fMRI静息网络相关的电生理特征提供了新的手段。
2.基于参数贝叶斯模型,发展了借助于EEG信息的fMRI响应函数估计方法。该方法利用EEG信号来提取神经响应的时间和幅度信息,进而解卷积出fMRI的血氧动力学响应函数,实现EEG特征时间相关的血氧代谢活动的成像。该方法与NESOI一起,共同构成了EEG-fMRI时空对称融合(Spatial-temporal EEG-fMRI fusion, STEFF)。STEFF将目前基于空间约束和时间预测的两种融合方法合并到一起,实现了EEG的高时间分辨率和fMRI的高空间分辨率的整合。通过仿真,我们对STEFF的有效性进行了验证。
3.借助STEFF对部分性癫痫同步EEG-fMRI进行了研究。重点探讨了发作间期放电相关的fMRI活动的时空特征。提出从血氧动力学响应的正负性,响应峰值延迟时间以及与EEG的空间对应关系等多个角度对癫痫相关的血氧代谢活动进行分类。结果表明,STEFF不但能给出时空信息更为丰富的定位结果,还能提高识别癫痫刺激灶的能力。使得从电生理和血氧代谢两方面描绘癫痫网络的动力学过程成为可能。
4.在STEFF技术的基础上,进一步借助阴阳互补平衡思想,提出了一个EEG-fMRI融合的系统框架,实现了从原始数据到网络信息,从模型驱动到数据驱动等多种方法的统一描述。该框架对现有的基于fMRI约束的EEG成像,基于EEG信息的fMRI分析和EEG-fMRI对称融合等方法进行了重新梳理,阐明它们之间内在的联系:融合的信息层次和时空互补性。在此基础上,讨论了时空对称融合和大尺度脑网络。同时,从该框架出发,我们预示了多种目前没有得到研究的新的融合方法,为理解EEG-fMRI融合提供了新的视角。
Electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are the mostly used two predominant techniques for their ability to reveal noninvasive brain mapping of the mental process. Simultaneous EEG-fMRI recording provides complementary information about the cerebral activity, and the EEG-fMRI fusion enables a better understanding of the brain with the enhanced spatiotemporal resolution. The work in this dissertation concentrates in the EEG-fMRI fusion based on Bayesian theory and its application in cognitive and clinic problemes. We start with an introduction of the fMRI-constrained EEG source imaging and its applications in multi-modal face study and epileptic discharges. We then develop an EEG-informed fMRI analysis and a novel spatial-temporal EEG-fMRI symmetric fusion. The proposed symmetric fusion is applied in the study of partial epilepsy. Finally, we introduce a systematic perspective of the EEG-fMRI fusion, which is inspired by the proposed symmetric fusion and the harmonic balance between yin and yang. The main contributions of this dissertation are as follows:
1. NEtwork-based SOurce Imaging (NESOI) is a new method to reconstruct neuroelectric sources based on empirical Bayesian model. In NESOI, multiple functional networks derived from fMRI are employed as constraints for EEG source imaging. In contrast with previous applications of empirical Bayesian model in source reconstruction with smoothness or sparseness priors, functional networks play a fundamental role among the priors employed by NESOI. Using synthetic and real data, we systemically compared the performance of NESOI with other source inversion methods when fMRI priors are used or not used. Our results indicate that NESOI is a potentially useful approach for understanding the electrophysiological signatures of fMRI resting state networks.
2. Base on empirical Bayesian model, an EEG-informed hemodynamic response function (HRF) estimation is proposed to reconstruct the hemodynamic fluctuation related to EEG features. This estimation and NESOI combine into a parallel fusion, termed Spatial-Temporal Eeg-Fmri Fusion (STEFF), to symmetrically integrate the simultaneous EEG-fMRI recordings. STEFF enables information of one modality to be utilized as priors for the other and hence improves the spatial (for EEG) or temporal (for fMRI) resolution of the other modality. Simulations under realistic noise conditions indicated that STEFF is a feasible and physiologically reasonable hybrid approach for spatiotemporal mapping of cognitive processing in the human brain.
3. STEFF is applied in simultaneous EEG-fMRI recording for the partial epilepsy study. As interictal epileptiform discharges related components are widespread, STEFF classifies the fMRI component as a function of response sign (positive or negative), peak delay of HRF and consistence of the spatial pattern. Our results indicate that the EEG-fMRI spatial consistent components with early HRF peaks would be the indictors of the epileptogenic focus. STEFF make possible the discripting of the dynamic responses of epileptic networks with bioelectric and hemodanimic information.
4. Inspired by STEFF and the harmonic balance between yin and yang, a systematic framework is proposed to break the boundary between data level fusion and feature leve fusion. Based on this framework, we discuss many newly emerging fusion methods, including fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and EEG-fMRI symmetric fusion. Our systematic perspective is helpful in explaining the relationship between different fusion schemes: the levels of signal abstraction and the complementary natures of EEG and fMRI. Moreover, some schemes that are little investigated but have great potential are also revealed in this framework.
引文
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