脑功能磁共振成像数据处理算法及应用研究
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摘要
高空间分辨率的功能磁共振成像(Functional magnetic resonance imaging, fMRI)技术是人们认识脑的一种重要的无损检测手段,并且在相关领域受到了极大关注。fMRI脑功能信息提取技术是将fMRI与大脑认知、神经科学和临床应用相结合的关键。目前该技术仍然不完善,需要进一步的发展。
论文以脑功能活动区域定位、聚类的fMRI数据处理算法和脑功能不对称性等问题为核心,提出了利用fMRI时间序列的频率信息、相位谱(Phase spectrum,PS)信息、卷积功率谱(Convolution power spectrum,CPS)信息和仿射聚类(Affinity propagation clustering,APC)等方法来探测脑功能活动,发展了相应的抗干扰能力强的时频fMRI信号分析法,并进一步提出了利用定量分析功率谱变化来研究运动皮层功能不对称性的思想。通过仿真与实际数据的分析证明了这些方法和技术创新的有效性和应用价值。分列如下:
时域fMRI数据处理方法常忽略时间序列的频率信息,本论文提出了一种利用时间序列在希尔伯特(Hilbert)空间的频率信息去探测功能活动区的新方法。它的主要思想是利用任务状态与非任务状态的频率熵信息(Frequency entropy information,FEI)的差作为判断脑激活的指标。通过仿真确定了建议方法的有效性。比较统计参数图(Statistical parametric mapping,SPM)、支持向量机(Support vector machine,SVM)和FEI等算法的接受者工作特性曲线(Receiver operating characteristic,ROC),频率信息方法显示出优良性能,实际的fMRI数据处理也证实该方法能有效探测脑功能活动。
提出了利用fMRI时间序列的相位谱信息来探测脑功能活动的新方法。基本思想是将信号特征频率上的相位用作脑激活指标,并且通过仿真和实际fMRI数据验证和确认了这种方法的有效性。
常规fMRI数据分析方法以构造体素的血氧水平依赖性(Blood oxygen level-dependent,BOLD)响应时间函数为目标,但是功率谱分析理论完全聚焦在理解作用系统的动力学能量变化。因此,针对fMRI数据处理,论文提出了一种新的基于先验镜像信号的卷积能谱分析模型。首先,抑制fMRI信号中的噪声,计算测量的fMRI数据与预先设计的实验模式之间的卷积;然后将卷积信号的功率谱密度(Power spectrum density, PSD)分析作为定量分析BOLD信号变化的指标。并且对比常规的功率谱、SPM和SVM方法,仿真和实际fMRI块实验数据分析结果揭示CPS方法能有效地探测脑功能活动。对于具有复杂特性的fMRI时间序列,论文的结果证明抗噪声能力强的CPS方法是揭示脑功能信息的有用的分析工具。
聚类分析是一种很好的的数据驱动方法。然而对于fMRI时间序列数据,庞大的计算量使它很难实用。基于此,论文提出了将有效、快速的新聚类算法—仿射聚类APC用于分析脑功能活动的fMRI大数据集。该算法不用随机选择初始的类代表点,它将所有的数据点都作为潜在的类代表点,通过最小化能量函数与信息传递架构(Message passing architecture),得到最优化的类代表点与它们对应的类。四个仿真数据集和三个真实fMRI数据集(包括块设计与事件相关(Event-related)实验)的分析结果揭示APC能有效地探测脑功能活动并能区分不同的响应模式。性能参数指标平均均方误差(Average squared error)揭示在块设计与事件相关实验中APC算法明显优于k中心聚类(k-centers clustering)。研究结果表明利用fMRI数据,APC算法能有效地探测和聚类脑功能活动区。
本论文提出了利用定量分析BOLD信号的功率变化来研究脑功能不对称性的思想。六个右利手被实验者参与了fMRI实验,论文分析了双手运动与单手运动的实验数据。功率谱方法表明在任务与休息状态之间,大脑右侧运动皮层相对左侧运动皮层,BOLD信号有更大的功率差,这说明其右侧运动区域有更多相关神经细胞被诱发。此外,通过对比BOLD信号幅度变化,对用功率谱方法测量功能不对称性进行了合理性分析。对于脑功能不对称性分析,功率谱方法确信是有效的定量分析方法。
The functional magnetic resonance imaging (fMRI), with high spatial resolution, is an important nondestructive examination tool of detecting human brain and has drawn great attention in related fields. The fMRI method of acquiring brain function information is pivotal in integrating fMRI with brain cognition, neuro-science, and clinical application. This technology still is not quite mature now and still needs improving.
The present study focuses on such issues in brain science as the fMRI data processing algorithms of the locating and clustering of the brain functional activation areas, the brain functional asymmetry and so on, and proposes adopting the frequency information of fMRI time series, phase spectrum (PS) information, convolution power spectrum (CPS), and affinity propagation clustering (APC) to detect brain functional activity, with improvement to the time-frequency fMRI signal analysis method which has strong anti-interference capability. The study further raises the idea of exploring functional asymmetry in the motor cortex through quantitative analysis of the changes of power spectrum. Analysis of the simulation and the real data demonstrate that these methods and technical innovations are effective and applicable. The details are shown as follows:
1) The fMRI data processing method for temporal information often neglects the frequency information of the time series. In this paper, a new method is proposed to detect the functional activation regions in brain by using the frequency information of fMRI time series in the Hilbert space. The main idea is that the frequency entropy information (FEI) difference of fMRI data between task and control states is specified as brain activation index. Simulation is conducted to confirm the validity of the proposed approach. The comparison of receiver operating characteristic (ROC) curves acquired respectively from the proposed scheme, the statistical parametric mapping (SPM), and the support vector machine (SVM) methods of fMRI data analysis indicates an obvious superiority of the frequency information method. The in vivo fMRI studies too reveal that this method can enable the effective detection of brain functional activation.
2) A phase spectrum method is presented to identify brain functional activation areas via using the phase information of fMRI time series. The basic idea is that the phase at the characteristic frequency of fMRI signal is specified as brain activation index. The developed phase approach is tested and confirmed by the result from both simulation and in vivo fMRI data.
3) The conventional fMRI data-processing method aims at modeling the blood oxygen level-dependent (BOLD) response of voxels as a function of time. But the theory of power spectrum analysis focuses completely on the understanding the dynamic energy change of interacting systems. This study therefore proposes a new CPS analysis of fMRI data, based on the theory of prior image signal, to detect brain functional activation for fMRI data. First, convolution signals are computed between the measured fMRI signals and the image signal of prior experimental pattern to suppress noise in the fMRI data. Then, the power spectrum density (PSD) analysis of the convolution signal is specified as the quantitative analysis energy index of BOLD signal change. The data from simulation studies and in vivo fMRI studies, including block-design experiments, reveal that the CPS method enables a more effective detection of some aspects of brain functional activation, as compared with the canonical power spectrum, SPM and SVM methods. Our results demonstrate that the CPS method with strong anti-noise capability is useful as a complementary analysis in revealing brain functional information regarding the complex nature of fMRI time series.
4) Clustering analysis is a quite good data-driven method for the analysis of fMRI time series. The huge computation load, however, makes it difficult for practical use. In view of this, proposal is made to use APC, an efficient and fast new clustering algorithm especially for large data sets to detect brain functional activation from fMRI. It considers all data points as possible exemplars through the minimization of an energy function and message passing architecture, and obtains the optimal set of exemplars and their corresponding clusters instead of randomly choosing initial exemplars. Four simulation studies and three in vivo fMRI data sets containing both block-design and event-related experiments reveal that brain functional activation can be effectively detected and that different response patterns can be distinguished using this method. The performance measures in the average squared error show that APC is clearly superior to the general k-centers cluster methods in block-design and event-related experiments. The results of research demonstrate that through fMRI data, APC algorithm can effectively detect and cluster brain function areas.
5) This paper proposes analyzing quantitatively power changes in BOLD signals to investigate functional asymmetry of cortical activity in motor areas. Six right-handed subjects are included in the fMRI experiments. Both bi-handed and single-handed movements are analyzed. The power spectrum method demonstrated that right-handed subjects exhibited a larger power difference in BOLD signals between task and rest states in the right motor area than in the left motor area. These results show that more nerve cells are evoked in the right motor area. In addition, in contrast with the signal magnitude analysis, reasnablness analysis is conducted of the power spectrum method involved in the detection of functional asymmetry. The power spectrum method is confirmed to be a valid quantitative-analysis method for brain asymmetry analyses.
论文以脑功能活动区域定位、聚类的fMRI数据处理算法和脑功能不对称性等问题为核心,提出了利用fMRI时间序列的频率信息、相位谱(Phase spectrum,PS)信息、卷积功率谱(Convolution power spectrum,CPS)信息和仿射聚类(Affinity propagation clustering,APC)等方法来探测脑功能活动,发展了相应的抗干扰能力强的时频fMRI信号分析法,并进一步提出了利用定量分析功率谱变化来研究运动皮层功能不对称性的思想。通过仿真与实际数据的分析证明了这些方法和技术创新的有效性和应用价值。分列如下:
时域fMRI数据处理方法常忽略时间序列的频率信息,本论文提出了一种利用时间序列在希尔伯特(Hilbert)空间的频率信息去探测功能活动区的新方法。它的主要思想是利用任务状态与非任务状态的频率熵信息(Frequency entropy information,FEI)的差作为判断脑激活的指标。通过仿真确定了建议方法的有效性。比较统计参数图(Statistical parametric mapping,SPM)、支持向量机(Support vector machine,SVM)和FEI等算法的接受者工作特性曲线(Receiver operating characteristic,ROC),频率信息方法显示出优良性能,实际的fMRI数据处理也证实该方法能有效探测脑功能活动。
提出了利用fMRI时间序列的相位谱信息来探测脑功能活动的新方法。基本思想是将信号特征频率上的相位用作脑激活指标,并且通过仿真和实际fMRI数据验证和确认了这种方法的有效性。
常规fMRI数据分析方法以构造体素的血氧水平依赖性(Blood oxygen level-dependent,BOLD)响应时间函数为目标,但是功率谱分析理论完全聚焦在理解作用系统的动力学能量变化。因此,针对fMRI数据处理,论文提出了一种新的基于先验镜像信号的卷积能谱分析模型。首先,抑制fMRI信号中的噪声,计算测量的fMRI数据与预先设计的实验模式之间的卷积;然后将卷积信号的功率谱密度(Power spectrum density, PSD)分析作为定量分析BOLD信号变化的指标。并且对比常规的功率谱、SPM和SVM方法,仿真和实际fMRI块实验数据分析结果揭示CPS方法能有效地探测脑功能活动。对于具有复杂特性的fMRI时间序列,论文的结果证明抗噪声能力强的CPS方法是揭示脑功能信息的有用的分析工具。
聚类分析是一种很好的的数据驱动方法。然而对于fMRI时间序列数据,庞大的计算量使它很难实用。基于此,论文提出了将有效、快速的新聚类算法—仿射聚类APC用于分析脑功能活动的fMRI大数据集。该算法不用随机选择初始的类代表点,它将所有的数据点都作为潜在的类代表点,通过最小化能量函数与信息传递架构(Message passing architecture),得到最优化的类代表点与它们对应的类。四个仿真数据集和三个真实fMRI数据集(包括块设计与事件相关(Event-related)实验)的分析结果揭示APC能有效地探测脑功能活动并能区分不同的响应模式。性能参数指标平均均方误差(Average squared error)揭示在块设计与事件相关实验中APC算法明显优于k中心聚类(k-centers clustering)。研究结果表明利用fMRI数据,APC算法能有效地探测和聚类脑功能活动区。
本论文提出了利用定量分析BOLD信号的功率变化来研究脑功能不对称性的思想。六个右利手被实验者参与了fMRI实验,论文分析了双手运动与单手运动的实验数据。功率谱方法表明在任务与休息状态之间,大脑右侧运动皮层相对左侧运动皮层,BOLD信号有更大的功率差,这说明其右侧运动区域有更多相关神经细胞被诱发。此外,通过对比BOLD信号幅度变化,对用功率谱方法测量功能不对称性进行了合理性分析。对于脑功能不对称性分析,功率谱方法确信是有效的定量分析方法。
The functional magnetic resonance imaging (fMRI), with high spatial resolution, is an important nondestructive examination tool of detecting human brain and has drawn great attention in related fields. The fMRI method of acquiring brain function information is pivotal in integrating fMRI with brain cognition, neuro-science, and clinical application. This technology still is not quite mature now and still needs improving.
The present study focuses on such issues in brain science as the fMRI data processing algorithms of the locating and clustering of the brain functional activation areas, the brain functional asymmetry and so on, and proposes adopting the frequency information of fMRI time series, phase spectrum (PS) information, convolution power spectrum (CPS), and affinity propagation clustering (APC) to detect brain functional activity, with improvement to the time-frequency fMRI signal analysis method which has strong anti-interference capability. The study further raises the idea of exploring functional asymmetry in the motor cortex through quantitative analysis of the changes of power spectrum. Analysis of the simulation and the real data demonstrate that these methods and technical innovations are effective and applicable. The details are shown as follows:
1) The fMRI data processing method for temporal information often neglects the frequency information of the time series. In this paper, a new method is proposed to detect the functional activation regions in brain by using the frequency information of fMRI time series in the Hilbert space. The main idea is that the frequency entropy information (FEI) difference of fMRI data between task and control states is specified as brain activation index. Simulation is conducted to confirm the validity of the proposed approach. The comparison of receiver operating characteristic (ROC) curves acquired respectively from the proposed scheme, the statistical parametric mapping (SPM), and the support vector machine (SVM) methods of fMRI data analysis indicates an obvious superiority of the frequency information method. The in vivo fMRI studies too reveal that this method can enable the effective detection of brain functional activation.
2) A phase spectrum method is presented to identify brain functional activation areas via using the phase information of fMRI time series. The basic idea is that the phase at the characteristic frequency of fMRI signal is specified as brain activation index. The developed phase approach is tested and confirmed by the result from both simulation and in vivo fMRI data.
3) The conventional fMRI data-processing method aims at modeling the blood oxygen level-dependent (BOLD) response of voxels as a function of time. But the theory of power spectrum analysis focuses completely on the understanding the dynamic energy change of interacting systems. This study therefore proposes a new CPS analysis of fMRI data, based on the theory of prior image signal, to detect brain functional activation for fMRI data. First, convolution signals are computed between the measured fMRI signals and the image signal of prior experimental pattern to suppress noise in the fMRI data. Then, the power spectrum density (PSD) analysis of the convolution signal is specified as the quantitative analysis energy index of BOLD signal change. The data from simulation studies and in vivo fMRI studies, including block-design experiments, reveal that the CPS method enables a more effective detection of some aspects of brain functional activation, as compared with the canonical power spectrum, SPM and SVM methods. Our results demonstrate that the CPS method with strong anti-noise capability is useful as a complementary analysis in revealing brain functional information regarding the complex nature of fMRI time series.
4) Clustering analysis is a quite good data-driven method for the analysis of fMRI time series. The huge computation load, however, makes it difficult for practical use. In view of this, proposal is made to use APC, an efficient and fast new clustering algorithm especially for large data sets to detect brain functional activation from fMRI. It considers all data points as possible exemplars through the minimization of an energy function and message passing architecture, and obtains the optimal set of exemplars and their corresponding clusters instead of randomly choosing initial exemplars. Four simulation studies and three in vivo fMRI data sets containing both block-design and event-related experiments reveal that brain functional activation can be effectively detected and that different response patterns can be distinguished using this method. The performance measures in the average squared error show that APC is clearly superior to the general k-centers cluster methods in block-design and event-related experiments. The results of research demonstrate that through fMRI data, APC algorithm can effectively detect and cluster brain function areas.
5) This paper proposes analyzing quantitatively power changes in BOLD signals to investigate functional asymmetry of cortical activity in motor areas. Six right-handed subjects are included in the fMRI experiments. Both bi-handed and single-handed movements are analyzed. The power spectrum method demonstrated that right-handed subjects exhibited a larger power difference in BOLD signals between task and rest states in the right motor area than in the left motor area. These results show that more nerve cells are evoked in the right motor area. In addition, in contrast with the signal magnitude analysis, reasnablness analysis is conducted of the power spectrum method involved in the detection of functional asymmetry. The power spectrum method is confirmed to be a valid quantitative-analysis method for brain asymmetry analyses.
引文
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