静息态fMRI有效连接方法及其在双相障碍中的应用研究
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摘要
人类大脑是一个分工精细且相互协调的高级信息处理系统,通过多个脑区的共同协调工作,人脑能够完成对信息的接收、传递、加工、融合等过程。功能分离和功能整合是大脑功能两个最基本的组织原则,其中功能分离是功能定位假说的基础,功能整合指人的行为是由多个脑区共同协调完成,是研究脑连接和脑网络的理论基础。对于大脑功能整合研究,通常基于功能连接(functional connectivity)和有效连接(effective connectivity)概念。大脑的有效连接度量的是脑区之间是否存在相互作用以及其相互作用的方向性和强弱。目前有不同的方法用来研究有效连接,例如结构方程模型(Structural Equation Model, SEM)、动态因果模型(Dynamic Causal Modeling, DCM)和格兰杰因果分析(Granger Causality Analysis, GCA)模型。其中,格兰杰因果分析以其模型简单、不需要模型先验假设等优点被广泛应用在探索不同脑区之间的有效连接中。
人脑有效连接的研究通常需要借助于功能磁共振成像(functional Magnetic Resonance Imaging, fMRI)技术。fMRI以其无创性、较高的空间、时间分辨率和良好的可重复性等优势,已成为目前进行脑科学研究的重要技术手段。根据图像采集状态,功能磁共振成像分为任务态fMRI (Task-state fMRI)和静息态fMRI (Resting-state fMRI)。由于病重患者无法完成实验任务等因素,任务态功能磁共振成像在对一些疾病的研究上受到限制,然而,受试者在静息态功能磁共振扫描过程中不需做任何任务、只需保持安静等,静息态功能磁共振成像在双相障碍、精神分裂症、和抑郁症等神经性疾病研究中有广泛的应用。
格兰杰因果分析模型是静息态功能磁共振有效连接研究的重要方法,在对功能磁共振数据进行格兰杰因果分析建模时,如何选取模型的阶数是目前格兰杰因果分析中需要解决的关键问题之一。在利用格兰杰因果分析处理静息态fMRI数据时,研究人员通常选择一阶模型,但是,一阶模型是否能够正确反映脑区之间的因果关系还有待于进一步研究。本论文的主要研究内容是格兰杰因果分析中的模型阶数选取方法。由于人脑脑区之间真实的有效连接形式尚不清楚,我们使用模拟数据来研究模型阶数选取对格兰杰因果分析结果的影响,并给出科学选取格兰杰因果模型阶数的方法。在此基础上,将格兰杰因果分析应用于双相障碍患者静息态功能磁共振数据的有效连接分析中。
双相障碍是一种由神经系统功能整合不良引发的疾病,目前已有研究表明,杏仁核和前额叶在双相障碍的发病机制研究中具有关键作用,本研究分别对15例双相障碍患者和15例健康志愿者的静息状态功能磁共振数据,采用格兰杰因果分析方法,研究双相障碍患者杏仁核(Amygdala)和内侧前额叶(interMedial Prefrontal Cortex, mPFC)之间有效连接形式的改变。研究发现,与正常组相比,双相障碍患者右侧杏仁核与内侧前额叶之间的有效连接消失,左侧杏仁核与内侧前额叶之间虽然仍存在有效连接,但其连接形式出现异常,本研究对今后双相障碍的诊治具有重大的借鉴意义和推动作用。
The human brain is a delicate and harmonious superior system which can process information in the currently known universe, and it can receive, transfer, process, syncretize information to complete the high-level function of brain by the joint coordination of multiple brain regions. The two most fundamental organizational principles of the human brain functions are functional separation and functional integration are. Functional separation is the basis of the hypothesis of functional orientation, however, functional integration is that multiple brain areas coordinate to complete the human behavioral function, and it is the theoreyical basis of the brain connectivity. It is usually in terms of functional connectivity and effective connectivity to investigate human brain. Effective connectivity of the human brain regions had been a focused research which measures whether there was connectivity correlation between brain regions as well as the direction and strength of the connectivity. Many methods were used to investigate the effective connectivity, such as structural equation model (SEM), dynamic causal modeling (DCM), granger causality analysis (GCA). GCA model is simple and with no need for model as a priori assumptions, it is widely used to explore the effective connectivity between different brain regions.
Effective connectivity of studies usually rely on functional magnetic resonance imaging (fMRI) technology. fMRI has become an important technical means for brain connectivity research because of its non-invasive, high spatial and temporal resolution and good repeatedly. According to the collection status, fMRI is divided into Task-state fMRI and Resting-state fMRI. Due to the seriously patients unable to complete the experimental task, Task-state fMRI in the study of a number of diseases is limited, However, the subjects in the Resting-state fMRI scanning process does not need to do any task, just keep quiet, Resting-state fMRI is widely used in bipolar disorder, schizophrenia, depression and other neurological diseases.
Granger causality analysis is one of the important approaches to study the effective connectivity in the Resting-state fMRI. How to select the modeling order in granger causality analysis of fMRI is one of the key problems need to solve. In the use of granger causality analysis in the Resting-state fMRI, researchers usually use the order one, however, the first order model can correctly reflect the causality between brain areas has yet to be further research. Main research content of this paper is the granger causality analysis model order selection method. In this paper, the main research content is the model order selection methods in granger causality analysis. Real effective connectivity form between the human brain regions is not yet clear, so we use simulation data to study the impact of the model order in granger causality analysis results and scientific granger causality model order selection method is given. On this basis, granger causality analysis was applied to resting state fMRI data of bipolar disorder in order to investigate effective connectivity.
Bipolar disorder is one of the most debilitating illnesses, it is reported that the amygdala and prefrontal has a key role in the pathogenesis of bipolar disorder. In this paper,15patients with bipolar disorder and15healthy control subjects participated to complete the functional magnetic resonance imaging scans in the resting state, granger causality analysis was applied to explore the effective connectivity between amygdala and intermedial prefrontal cortex (mPFC) of the two groups. The results indicated that compared with healthy controls, there is no connectivity between the right amygdala and the intermedial prefrontal cortex, while there are still effective connectivity between the left amygdala and the intermedial prefrontal cortex, but its connectivity form is abnormal in bipolar disorder. This study maybe of great significance to the diagnosis and treatment of bipolar disorder in the future.
人脑有效连接的研究通常需要借助于功能磁共振成像(functional Magnetic Resonance Imaging, fMRI)技术。fMRI以其无创性、较高的空间、时间分辨率和良好的可重复性等优势,已成为目前进行脑科学研究的重要技术手段。根据图像采集状态,功能磁共振成像分为任务态fMRI (Task-state fMRI)和静息态fMRI (Resting-state fMRI)。由于病重患者无法完成实验任务等因素,任务态功能磁共振成像在对一些疾病的研究上受到限制,然而,受试者在静息态功能磁共振扫描过程中不需做任何任务、只需保持安静等,静息态功能磁共振成像在双相障碍、精神分裂症、和抑郁症等神经性疾病研究中有广泛的应用。
格兰杰因果分析模型是静息态功能磁共振有效连接研究的重要方法,在对功能磁共振数据进行格兰杰因果分析建模时,如何选取模型的阶数是目前格兰杰因果分析中需要解决的关键问题之一。在利用格兰杰因果分析处理静息态fMRI数据时,研究人员通常选择一阶模型,但是,一阶模型是否能够正确反映脑区之间的因果关系还有待于进一步研究。本论文的主要研究内容是格兰杰因果分析中的模型阶数选取方法。由于人脑脑区之间真实的有效连接形式尚不清楚,我们使用模拟数据来研究模型阶数选取对格兰杰因果分析结果的影响,并给出科学选取格兰杰因果模型阶数的方法。在此基础上,将格兰杰因果分析应用于双相障碍患者静息态功能磁共振数据的有效连接分析中。
双相障碍是一种由神经系统功能整合不良引发的疾病,目前已有研究表明,杏仁核和前额叶在双相障碍的发病机制研究中具有关键作用,本研究分别对15例双相障碍患者和15例健康志愿者的静息状态功能磁共振数据,采用格兰杰因果分析方法,研究双相障碍患者杏仁核(Amygdala)和内侧前额叶(interMedial Prefrontal Cortex, mPFC)之间有效连接形式的改变。研究发现,与正常组相比,双相障碍患者右侧杏仁核与内侧前额叶之间的有效连接消失,左侧杏仁核与内侧前额叶之间虽然仍存在有效连接,但其连接形式出现异常,本研究对今后双相障碍的诊治具有重大的借鉴意义和推动作用。
The human brain is a delicate and harmonious superior system which can process information in the currently known universe, and it can receive, transfer, process, syncretize information to complete the high-level function of brain by the joint coordination of multiple brain regions. The two most fundamental organizational principles of the human brain functions are functional separation and functional integration are. Functional separation is the basis of the hypothesis of functional orientation, however, functional integration is that multiple brain areas coordinate to complete the human behavioral function, and it is the theoreyical basis of the brain connectivity. It is usually in terms of functional connectivity and effective connectivity to investigate human brain. Effective connectivity of the human brain regions had been a focused research which measures whether there was connectivity correlation between brain regions as well as the direction and strength of the connectivity. Many methods were used to investigate the effective connectivity, such as structural equation model (SEM), dynamic causal modeling (DCM), granger causality analysis (GCA). GCA model is simple and with no need for model as a priori assumptions, it is widely used to explore the effective connectivity between different brain regions.
Effective connectivity of studies usually rely on functional magnetic resonance imaging (fMRI) technology. fMRI has become an important technical means for brain connectivity research because of its non-invasive, high spatial and temporal resolution and good repeatedly. According to the collection status, fMRI is divided into Task-state fMRI and Resting-state fMRI. Due to the seriously patients unable to complete the experimental task, Task-state fMRI in the study of a number of diseases is limited, However, the subjects in the Resting-state fMRI scanning process does not need to do any task, just keep quiet, Resting-state fMRI is widely used in bipolar disorder, schizophrenia, depression and other neurological diseases.
Granger causality analysis is one of the important approaches to study the effective connectivity in the Resting-state fMRI. How to select the modeling order in granger causality analysis of fMRI is one of the key problems need to solve. In the use of granger causality analysis in the Resting-state fMRI, researchers usually use the order one, however, the first order model can correctly reflect the causality between brain areas has yet to be further research. Main research content of this paper is the granger causality analysis model order selection method. In this paper, the main research content is the model order selection methods in granger causality analysis. Real effective connectivity form between the human brain regions is not yet clear, so we use simulation data to study the impact of the model order in granger causality analysis results and scientific granger causality model order selection method is given. On this basis, granger causality analysis was applied to resting state fMRI data of bipolar disorder in order to investigate effective connectivity.
Bipolar disorder is one of the most debilitating illnesses, it is reported that the amygdala and prefrontal has a key role in the pathogenesis of bipolar disorder. In this paper,15patients with bipolar disorder and15healthy control subjects participated to complete the functional magnetic resonance imaging scans in the resting state, granger causality analysis was applied to explore the effective connectivity between amygdala and intermedial prefrontal cortex (mPFC) of the two groups. The results indicated that compared with healthy controls, there is no connectivity between the right amygdala and the intermedial prefrontal cortex, while there are still effective connectivity between the left amygdala and the intermedial prefrontal cortex, but its connectivity form is abnormal in bipolar disorder. This study maybe of great significance to the diagnosis and treatment of bipolar disorder in the future.
引文
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[2]Friston KJ, Functional and effective connectivity:a review [J]. Brain Connectivity, 2011,1(1):13～36
[3]Goncalves MS, Hall DA. Connectivity analysis with structural equation modelling:an example of the effects of voxel selection [J]. Neuroimage,2003,20(3):1455～1467
[4]Zhuang J, LaConte S, Peltier S, et al. Connectivity exploration with structural equation modeling:an fMRI study of bimanual motor coordination [J]. Neuroimage,2005, 25(2):462～470
[5]Ramnani N, Behrens TE, Penny W, et al. New approaches for exploring anatomical and functional connectivity in the human brain [J]. Biological Psychiatry,2004,56(9):613～619
[6]Friston KJ, Harrison L, Penny W. Dynamic causal modelling [J]. Neuroimage,2003, 19(4):1273-1302
[7]Schuyler B, Ollinger JM, Oakes TR, et al. Dynamic causal modeling applied to fMRI data shows high reliability [J]. Neuroimage,2010,49(1):603～611
[8]Desseilles M, Schwartz S, Dang-Vu TT, et al. Depression alters "top-down" visual attention: a dynamic causal modeling comparison between depressed and healthy subjects [J]. Neuroimage,2011,54(2):1662～1668
[9]David O, Maess B, Eckstein K, et al. Dynamic causal modeling of subcortical connectivity of language [J]. The Journal of Neuroscience,2011,31(7):2712～2717
[10]Ma L, Steinberg JL, Hasan KM, et al. Working memory load modulation of parieto-frontal connections:Evidence from dynamic causal modeling [J]. Human Brain Mapping,2011
[11]Curcic-Blake B, Swart M, Aleman A. Bidirectional information flow in frontoamygdalar circuits in humans:a dynamic causal modeling study of emotional associative learning [J]. Cerebral Cortex,2012,22(2):436～445
[12]Wiener N. The theory of prediction [J]. Modern Mathematics for Engineers New York: McGraw-Hill,1956:165～190
[13]Granger CW. Investigating causal relations by econometric models and cross-spectral methods [J]. Econometrica:Journal of the Econometric Society,1969:424～438
[14]Goebel R, Roebroeck A, Kim D-S, et al. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping [J]. Magnetic Resonance Imaging,2003,21(10):1251～1261
[15]Roebroeck A, Formisano E, Goebel R. The identification of interacting networks in the brain using fMRI:model selection, causality and deconvolution[J]. Neuroimage,2011, 58(2):296～302
[16]Harrison L, Penny WD, Friston K. Multivariate autoregressive modeling of fMRI time series [J]. Neuroimage,2003,19(4):1477～1491
[17]Deshpande G, Santhanam P, Hu X. Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data[J]. Neuroimage,2011,54(2):1043～1052
[18]Jiao Q, Lu G, Zhang Z, et al. Granger causal influence predicts BOLD activity levels in the default mode network [J]. Human Brain Mapping,2010,32(1):154～161
[19]Liao W, Qiu C, Gentili C, et al, Altered effective connectivity network of the amygdala in social anxiety disorder:a resting-state FMRI study[J]. Plos One,2010,5(12):e15238
[20]Wilke M, Lidzba K, Krageloh-Mann I. Combined functional and causal connectivity analyses of language networks in children:a feasibility study [J]. Brain Lang,2009,108(1):22～29
[21]Roebroeck A, Formisano E, Goebel R. Mapping directed influence over the brain using Granger causality and fMRI [J]. Neuroimage,2005,25(1):230～242
[22]Zhou Z, Wang X, Klahr NJ, et al. A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging [J]. Magnetic Resonance Imaging,2011, 29(3):418-433
[23]Zhou Z, Jiao Y, Tang T, et al. Detecting effective connectivity in human brain using granger causality,2008, IEEE:394-398
[24]Deshpande G, LaConte S, James GA, et al. Multivariate Granger causality analysis of fMRI data[J]. Human Brain Mapping,2008,30(4):1361～1373
[25]Chen H, Yang Q, Liao W, et al. Evaluation of the effective connectivity of supplementary motor areas during motor imagery using Granger causality mapping [J]. Neuroimage,2009, 47(4):1844～1853
[26]Bandettini PA. Twenty years of functional MRI:The science and the stories [J]. Neuroimage,2012
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