基于磁共振影像的阿尔茨海默病脑皮层厚度分析的研究
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摘要
阿尔茨海默病是一种进行性神经退化痴呆症,其特征是大脑皮层厚度萎缩和认知功能逐步下降。通过分析阿尔茨海默病患者及其早期症状轻度认知障碍患者的大脑皮层厚度变化,我们可以了解该疾病的发生发展机理,为临床早期诊断和预防提供帮助。核磁共振成像技术的出现为我们对该疾病大脑皮层变化模式的研究提供了很好的手段。本文的研究工作是从图像处理的角度分析了阿尔茨海默病和轻度认知障碍患者大脑磁共振影像结构。
首先,基于组群的大脑皮层厚度纵向研究,分析阿尔茨海默病和轻度认知障碍患者大脑皮层厚度随着病程发展的变化模式和发生病变的大脑皮层的区域。我们发现随着病程的发展,大脑皮层厚度是逐渐萎缩变薄,首先发生病变而变薄的脑区主要集中在默认网络的核心区域,而这些区域也就是跟认知和记忆及其相关的区域,而后向默认网络的其他脑区扩散,最后发展到全脑萎缩。
其次,将阿尔茨海默病和轻度认知障碍患者的大脑皮层与脑脊液中的生物学指标和临床精神状态的评分作关联研究。我们发现大脑皮层的厚度与生物学指标的在脑脊液中的含量水平和精神状态量表有显著的相关性,相关性最为显著的区域也是主要集中在默认网络的核心区域。这个发现为用大脑磁共振成像技术研究该疾病的发生发展机理提供证据,同时也印证了大脑皮层厚度萎缩的发生是从默认网络的核心区域开始,而后向其他区域扩展最后发展到全脑。
最后,我们利用计算机图论的原理构建大脑皮层的结构网络。通过计算大脑皮层厚度网络的属性,寻找正常对照组,轻度认知障碍和阿尔茨海默病患者在网络属性上的差异。从而为将三者有效的分类提供可靠的影像特征,使得以大脑磁共振结构影像为特征的模式分类方法研究该疾病成为可能,同时也为临床诊断提供帮助。
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive impairment of cognitive function and atrophy of cerebral cortex. Doing investigation on the structure changes of cerebral cortex in AD and its prodromal mild cognitive impairment (MCI) could provides good insights into the mechanism of onset and progression of this disease and be of great help for its early clinical diagnosis. Magnetic resonance imaging (MRI) gives the chance to do such research on pattern of changes in cerebral cortical thickness. The purpose of this study is to analyze the brain structure of AD and MCI images on MRI in the image domain.
First, based on the population, this study investigated longitudinal changes of the cortical thickness of AD and MCI patients. The regions that have atrophies are mainly those in default mode network (DMN) which is considered to be associated with the cognitive function and memory.
Second, the association study of cortical thickness with the cerebrospinal fluid (CSF) biomarkers, involving Abl-42, Tau and P-Tau protein and the Mini-Mental State Exam (MMSE) scores was performed. The cortical thickness has significant correlation with the biomarker level and the MMSE scores, which suggests that the cortical thickness could be imaging biomarker for the diagnosis of the AD.
Finally, the graph theory was applied to the analysis of the brain structure and the structure network of the cortical thickness was constructed. The properties of the cortical thickness network have great discrepancies among AD, MCI and Normal Control. This findings could make the analysis of AD brain images using pattern classification methods be possible.
首先,基于组群的大脑皮层厚度纵向研究,分析阿尔茨海默病和轻度认知障碍患者大脑皮层厚度随着病程发展的变化模式和发生病变的大脑皮层的区域。我们发现随着病程的发展,大脑皮层厚度是逐渐萎缩变薄,首先发生病变而变薄的脑区主要集中在默认网络的核心区域,而这些区域也就是跟认知和记忆及其相关的区域,而后向默认网络的其他脑区扩散,最后发展到全脑萎缩。
其次,将阿尔茨海默病和轻度认知障碍患者的大脑皮层与脑脊液中的生物学指标和临床精神状态的评分作关联研究。我们发现大脑皮层的厚度与生物学指标的在脑脊液中的含量水平和精神状态量表有显著的相关性,相关性最为显著的区域也是主要集中在默认网络的核心区域。这个发现为用大脑磁共振成像技术研究该疾病的发生发展机理提供证据,同时也印证了大脑皮层厚度萎缩的发生是从默认网络的核心区域开始,而后向其他区域扩展最后发展到全脑。
最后,我们利用计算机图论的原理构建大脑皮层的结构网络。通过计算大脑皮层厚度网络的属性,寻找正常对照组,轻度认知障碍和阿尔茨海默病患者在网络属性上的差异。从而为将三者有效的分类提供可靠的影像特征,使得以大脑磁共振结构影像为特征的模式分类方法研究该疾病成为可能,同时也为临床诊断提供帮助。
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive impairment of cognitive function and atrophy of cerebral cortex. Doing investigation on the structure changes of cerebral cortex in AD and its prodromal mild cognitive impairment (MCI) could provides good insights into the mechanism of onset and progression of this disease and be of great help for its early clinical diagnosis. Magnetic resonance imaging (MRI) gives the chance to do such research on pattern of changes in cerebral cortical thickness. The purpose of this study is to analyze the brain structure of AD and MCI images on MRI in the image domain.
First, based on the population, this study investigated longitudinal changes of the cortical thickness of AD and MCI patients. The regions that have atrophies are mainly those in default mode network (DMN) which is considered to be associated with the cognitive function and memory.
Second, the association study of cortical thickness with the cerebrospinal fluid (CSF) biomarkers, involving Abl-42, Tau and P-Tau protein and the Mini-Mental State Exam (MMSE) scores was performed. The cortical thickness has significant correlation with the biomarker level and the MMSE scores, which suggests that the cortical thickness could be imaging biomarker for the diagnosis of the AD.
Finally, the graph theory was applied to the analysis of the brain structure and the structure network of the cortical thickness was constructed. The properties of the cortical thickness network have great discrepancies among AD, MCI and Normal Control. This findings could make the analysis of AD brain images using pattern classification methods be possible.
引文
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[2]Wimo A, Winblad B, Aguero-Torres H, von Strauss E. The magnitude of dementia occurrence in the world. Alzheimer Dis Assoc Disord 2003; 17:63-67.
[3]Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999; 56:303-308.
[4]Li X, Jiang J, Zhu W, Yu C, Sui M, Wang Y et al. Asymmetry of prefrontal cortical convolution complexity in males with attention-deficit/hyperactivity disorder using fractal information dimension. Brain Dev 2007; 29:649-655.
[5]Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A. Neuroplasticity:changes in grey matter induced by training. Nature 2004; 427:311-312.
[6]Thompson PM, Lee AD, Dutton RA, Geaga JA, Hayashi KM, Eckert MA et al. Abnormal cortical complexity and thickness profiles mapped in Williams syndrome. JNeurosci 2005; 25:4146-4158.
[7]Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. Ⅰ. Segmentation and surface reconstruction. Neuroimage 1999; 9:179-194.
[8]Narr KL, Bilder RM, Toga AW, Woods RP, Rex DE, Szeszko PR et al. Mapping cortical thickness and gray matter concentration in first episode schizophrenia. Cereb Cortex 2005; 15:708-719.
[9]Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP, Greenstein D et al. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci USA 2007; 104:19649-19654.
[10]Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. Ⅱ:Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999; 9:195-207.
[11]Gomolin IH, Chapron DJ. Elucidating the relationship between acetazolamide plasma protein binding and renal clearance using an albumin infusion. J Clin Pharmacol 1992; 32:1028-1032.
[12]Talairach JaPT. Co-Planar Stereotaxic Atlas of the Human Brain. Thieme Medical Publishers 1988.
[13]Hutton C, De Vita E, Ashburner J, Deichmann R, Turner R. Voxel-based cortical thickness measurements in MRI. Neuroimage 2008; 40:1701-1710.
[14]Kabani N, Le Goualher G, MacDonald D, Evans AC. Measurement of cortical thickness using an automated 3-D algorithm:a validation study. Neuroimage 2001; 13:375-380.
[15]Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 2000; 97:11050-11055.
[16]Chung MK, Robbins SM, Dalton KM, Davidson RJ, Alexander AL, Evans AC. Cortical thickness analysis in autism with heat kernel smoothing. Neuroimage 2005; 25:1256-1265.
[17]He Y, Chen ZJ, Evans AC. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex 2007; 17:2407-2419.
[18]Ashburner JaKJF. Voxel-based morphometry--the methods. Neuroimage 2000:805-821.
[19]Shen D, Davatzikos C. Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration. Neuroimage 2003; 48:28-41.
[20]Fan Y, Batmanghelich N, Clark CM, Davatzikos C. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 2008; 39:1731-1743.
[21]Lenroot RK, Giedd JN. Brain development in children and adolescents:insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev 2006; 30:718-729.
[22]Anstey KJ, Maller JJ. The role of volumetric MRI in understanding mild cognitive impairment and similar classifications. Aging Ment Health 2003; 7:238-250.
[23]Luders E, Thompson PM, Narr KL, Toga AW, Jancke L, Gaser C. A curvature-based approach to estimate local gyrification on the cortical surface. Neuroimage 2006; 29:1224-1230.
[24]Wiegand LC, Warfield SK, Levitt JJ, Hirayasu Y, Salisbury DF, Heckers S et al. An in vivo MRI study of prefrontal cortical complexity in first-episode psychosis. Am J Psychiatry 2005; 162:65-70.
[25]Chung MK, Dalton KM, Shen L, Evans AC, Davidson RJ. Weighted fourier series representation and its application to quantifying the amount of gray matter. IEEE Trans Med Imaging 2007; 26:566-581.
[26]Cachia A, Mangin JF, Riviere D, Kherif F, Boddaert N, Andrade A et al. A primal sketch of the cortex mean curvature:a morphogenesis based approach to study the variability of the folding patterns. IEEE Trans Med Imaging 2003; 22:754-765.
[27]Dale AM, Fischl B, Sereno MI. Cortical Surface-Based Analysis Ⅰ:Segmentation and Surface Reconstruction. NeuroImage 1999; 9:179-194
[28]Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. Ⅱ:Inflation, flattening, and a surface-based coordinate system. NeuroImage 1999 9:195-207.
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