基于结构方程的脑卒中运动功能神经机制建模研究
摘要
大脑是我们目前所知的最复杂、最完善的动态信息处理系统。在某种意义上,大脑可以被看作一个多层次的动态分布式网络,为实现一系列复杂功能,它能够不断组织和重塑其功能连接。
本文使用组块实验范式设计的方法采集了健康人和脑卒中病人的功能磁共振成像数据,并运用SPM对fMRI数据进行预处理,介绍了数据驱动和模型驱动的数据处理方法。详细介绍了独立成分分析方法,包括它的原理、实现过程。采用组独立成分分析方法对手部运动的fMRI数据进行处理,选取与运动相关的功能感兴趣区,得到对应感兴趣区的时间序列。对结构方程建模进行了深入研究,并采用结构方程建模对得到的时间序列进行建模,得到了脑卒中病人和健康人手部运动时相关运动皮层的网络模型。通过健康人和病人模型的比较,分析了脑卒中后运动功能神经机制的变化,并对左右手以及复杂运动和随意运动神经网络的变化进行了研究。通过健康人左右手运动的比较,发现除了对侧运动皮层和同侧小脑的激活,可能还伴有同侧大脑皮层激活,常常局限在同侧的M1、SMA区域,这可能就是左右利手的非对称性激活;病人患侧运动时,没有相关辅助运动区(SMA)的明显激活,因此可以推断SMA区域的活性对于人的自主运动的能力具有很大的影响;病人患侧运动时除了对侧M1区域的明显激活,还有很多同侧相关皮层区域(如M1,感觉皮层)激活。这些结果表明大脑中枢代表区发生改变,运动皮层有效连接网络发生重构,实现了大脑功能重组和自修复,这就是大脑神经可塑性的充分体现。
It’s well known that the brain is the most complicated and perfect dynamic information processing system. In a sense, the brain can be considered as a multi-layered dynamic distributed network. In order to execute a series of complex functions, it can establish and reestablish its functional connections incessantly.
This paper uses block design method as experimental method to obtain functional magnetic resonance imaging data from healthy individuals and stroke patients, and uses SPM for fMRI data pre-processing. Describes the data-driven and model-driven data processing method, and also describes the independent component analysis in detail, including its principle and its realization process. Using group ICA to analysis of the fMRI data of hand movement, and select the movement-related regions of interest, obtain the corresponding time series of region of interest. This thesis studies structural equation modeling in depth, and modeling model for the selected time series with SEM, obtaining the related motor cortex network model about the department of stroke patients and health staff hand movement. By comparing healthy people and patients models, analyzes the neural mechanisms changes of motor function after stroke, and studies the neural network changes about the left/right hand movements and between the complex movement and voluntary movement. By comparing healthy people left and right hand movements, it found that in addition to the contralateral motor cortex and ipsilateral cerebellum activation, may also be associated with activation of the ipsilateral cerebral cortex, often confined to the ipsilateral M1, SMA region, which may be left-handed non- symmetry activation; Ipsilateral movement of patients, no significant activation of the relevant supplementary motor area (SMA), it can be inferred that the activity of SMA regional is very important for the people’s autonomy exercise ability; Ipsilateral movement of patients, except that the contralateral M1 area has obvious activated, there are a lot of ipsilateral related cortical areas activation (such as M1 or sensory cortex). The results indicated that the district on behalf of the brain function has changed, the motor cortex effective network is reconstructed to realize functional reorganization and self-repair, which is a full expression of brain plasticity.
本文使用组块实验范式设计的方法采集了健康人和脑卒中病人的功能磁共振成像数据,并运用SPM对fMRI数据进行预处理,介绍了数据驱动和模型驱动的数据处理方法。详细介绍了独立成分分析方法,包括它的原理、实现过程。采用组独立成分分析方法对手部运动的fMRI数据进行处理,选取与运动相关的功能感兴趣区,得到对应感兴趣区的时间序列。对结构方程建模进行了深入研究,并采用结构方程建模对得到的时间序列进行建模,得到了脑卒中病人和健康人手部运动时相关运动皮层的网络模型。通过健康人和病人模型的比较,分析了脑卒中后运动功能神经机制的变化,并对左右手以及复杂运动和随意运动神经网络的变化进行了研究。通过健康人左右手运动的比较,发现除了对侧运动皮层和同侧小脑的激活,可能还伴有同侧大脑皮层激活,常常局限在同侧的M1、SMA区域,这可能就是左右利手的非对称性激活;病人患侧运动时,没有相关辅助运动区(SMA)的明显激活,因此可以推断SMA区域的活性对于人的自主运动的能力具有很大的影响;病人患侧运动时除了对侧M1区域的明显激活,还有很多同侧相关皮层区域(如M1,感觉皮层)激活。这些结果表明大脑中枢代表区发生改变,运动皮层有效连接网络发生重构,实现了大脑功能重组和自修复,这就是大脑神经可塑性的充分体现。
It’s well known that the brain is the most complicated and perfect dynamic information processing system. In a sense, the brain can be considered as a multi-layered dynamic distributed network. In order to execute a series of complex functions, it can establish and reestablish its functional connections incessantly.
This paper uses block design method as experimental method to obtain functional magnetic resonance imaging data from healthy individuals and stroke patients, and uses SPM for fMRI data pre-processing. Describes the data-driven and model-driven data processing method, and also describes the independent component analysis in detail, including its principle and its realization process. Using group ICA to analysis of the fMRI data of hand movement, and select the movement-related regions of interest, obtain the corresponding time series of region of interest. This thesis studies structural equation modeling in depth, and modeling model for the selected time series with SEM, obtaining the related motor cortex network model about the department of stroke patients and health staff hand movement. By comparing healthy people and patients models, analyzes the neural mechanisms changes of motor function after stroke, and studies the neural network changes about the left/right hand movements and between the complex movement and voluntary movement. By comparing healthy people left and right hand movements, it found that in addition to the contralateral motor cortex and ipsilateral cerebellum activation, may also be associated with activation of the ipsilateral cerebral cortex, often confined to the ipsilateral M1, SMA region, which may be left-handed non- symmetry activation; Ipsilateral movement of patients, no significant activation of the relevant supplementary motor area (SMA), it can be inferred that the activity of SMA regional is very important for the people’s autonomy exercise ability; Ipsilateral movement of patients, except that the contralateral M1 area has obvious activated, there are a lot of ipsilateral related cortical areas activation (such as M1 or sensory cortex). The results indicated that the district on behalf of the brain function has changed, the motor cortex effective network is reconstructed to realize functional reorganization and self-repair, which is a full expression of brain plasticity.
引文
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[2] Carey J R, Kimberley T J, Lewis S M, et al. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain, 2002, 125:773~788.
[3] D. A. Nowak, C. Grefkes, M. Dafotakis, S. Eickhoff, J. Kust, H. Karbe, and G. R. Fink, Effects of low-frequency repetitive transcranial magnetic stimulation of the contralesional primary motor cortex on movement kinematics and neural activity in subcortical stroke,Arch Neurol, 2008, 65(6): 741~747.
[4] Larry W. Forrester, Lewis A. Wheaton, Andreas R. Luft, Exercise-mediated locomotor recovery and lower-limb neuroplasticity after stroke, Journal of Rehabilitation Research & Development, 2008,45(2): 205~220.
[5] Rachel P Allred, Theresa A Jones, Experience: a double-edged sword for restorative neural plasticity after brain damage, Future Neurology, 2008, 3(2):189-198.
[6] Ichiro Miyai, Hajime Yagura, Megumi Hatakenaka, Ichiro Oda, Longitudinal Optical Imaging Study for Locomotor Recovery After Stroke,Stroke.2003, 34:2866~2870.
[7] Dobkin B H, Firestine A, West M, Saremi K, Ankle dorsiflexion as an fMRI paradigm to assay motor control for walking during rehabilitation. Neuroimage.2004.23(1): 370~376.
[8] T. Askim, B. Indredavik, T. Vangberg, and A. Haberg,Motor Network Changes Associated With Successful Motor Skill Relearning After Acute Ischemic Stroke: A Longitudinal Functional Magnetic Resonance Imaging Study,Neurorehabil Neural Repair, 2009, 23(3): 295~304.
[9]孙学军,刘买利,叶朝辉.脑功能磁共振成像研究进展.中国神经科学杂志. 2001, 17(3):270~272.
[10]袁宏. fMRI数据分析方法研究(M).电子科技大学生物医学工程, 2005年3月.
[11] P M van Vliet, N B Lincoln, A Foxall, Comparison of Bobath based and movement science based treatment for stroke: a randomised controlled trial, Journal of Neurology Neurosurgery and Psychiatry 2005,76:503~508.
[12]孙华平.臂丛损伤神经移位术后运动皮层重组的功能磁共振研究(M).复旦大学博士研究生学位毕业论文, 2005.
[13] Raymond C.K. Chan, Jia Huang and Xin Dic,Dexterous movement complexity and cerebellar activation: A meta-analysis,Brain Research Reviews, 2009, 59(2):316~323.
[14]金真,张磊,曾亚伟,王彦,纽竹,张通.偏瘫康复治疗中不同拇指运动方式的脑机制研究.中国临床康复, 2003, 37(7): 4238~4239.
[15] Dale AM. Optimal experimental design for event-related fMRI. Hum Brain Mapp 1999, 8:109-114.
[16] P.A.Bandettini, E.C.Wong, R.S.Hinks, R.S.Tikofsky, and J.S.Hyde,“Time course EPI of human brain function during task activation,”Magn.Reson.Med.,1992, 25:390-397.
[17] P.A.Bandettini, A.Jesmanowicz, E.C.Wong, and J.S.Hyde,“Processing strategies for time-course data sets in functional MRI of the human brain”, Magn.Reson.Med., 1993, 30:161-173.
[18] J.C.Bezdek, Pattern Recognition With Fuzzy Objective Functions Agorithms.Plenum, New York.1981.
[19] C.Coutte, P.Toft, E.Rostrup, F.A.Nielsen, L.Hansen,“On clustering fMRI time series,”NeruoImage, 1999, 9:298-310.
[20] Evgenia Dimitriadou,Markus Barch,Christian Windischberger et al.A quantitative comparison of functional MRI cluster analysis.Artificial Intelligence in Medicine ,2004, 31:57-71.
[21] Stuart C Ramsay, Andrew F McLaughlin, Robert Greenough, et al. Comparis on of Independent Aura Ictal and Interictal Cerebral Perfusion[ J ] . J Nucl Medi, 1991, 33(3): 438-440.
[22] InYoung Hyun, Jae Sung Lee, J oung Ho Rha, et al . Different up take of 99m Tc-ECD and 99m Tc-HMPAO in the same brains: analysis by statistical parametric mapping[ J ] . Euro J Nucl Med, 2001, 28(2): 191-197.
[23] Jong Doo Lee, Hee Joung Kim, Byung In Lee, et al . Evaluation of ictal brain SPET using statistical parametric mapping in temporal lobe epilepsy[ J] . Euro J Nucl Med, 2000, 27(11): 1658-1665.
[24] McKewon MJ, Makeig S, Brown GG, et al., Analysis of fMRI data by blind separation into independent spatial components,Humam Brain Mapping 1998,6(3):160~188.
[25] Aapo Hyvarinen, Juha Karhunen, Erkki Oja, Independent Component Analysis, New YorK:John Wiley&Sons,2001.
[26] Anthony J Bell, Terrence J Sejnowski, An information-maximization approach to blind deconvolution, Neural Computation,1995,7(6):1129~1159.
[27] Calhoun V.D., Adali T., Pealson G.D., et al., A method for making group inference from functional MRI data using independent component analysis,Human Brain Mapping,2001,14:140~151.
[28] Markus Svensen,Frithjof Kruggel,Habib Benali,ICA of fMRI Group Study Data, NeuroImage,2002,16(3):551~563.
[29] Lee,L.,Harrison,L.M.,and Mechelli,A., The Functional Brain Connectivity Workshop: report and commentary.Network: Comput.Neural Syst.,2003,14:R1-R15.
[30] Friston,K.J., Frith,C.D., Liddle,P.F.,et al.,Functional connectivity:the principal component analysis of large(PET)data sets.J.CerebBlood Flow Metab.,1993, 13:5-14.
[31] Friston,K.J.,Frith,C.D.,and Frackowiak,R.S.,Time-dependent changes in effective connectivity measured with PET.Hum.Brain Mapp.,1993,1:69-80.
[32] Friston K J, Harrison L, Penny W. Dynamic causal modeling [J]. NeuroImage (S1053-8119), 2003, 19 (4): l273-1302.
[33] Zhuang J-C, LaConte S, Peltier S, et al. Connectivity exploration with structural equation modeling: an fMRI study of bimanual motor coordination [J]. NeuroImage (S1053-8119), 2005, 25(2): 462-470.
[34] Jiancheng Zhuang, Scott Peltierb, Sheng Hec, Stephen LaConteb, and Xiaoping Hub, Mapping the connectivity with structural equation modeling in an fMRI study of shape-from-motion task, NeuroImage, 2008, 42(2): 799~806.
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