基于弥散张量白质网络的阿尔茨海默病研究
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摘要
目的:利用弥散张量成像(DTI)技术构建大脑白质结构网络,通过网络拓扑参数研究阿尔茨海默病(AD)患者大脑微观结构的异常。方法:重建19例正常人和20例AD患者的结构脑网络,采用双样本t检验,从全脑和特定脑区两个水平对AD患者与正常对照组的网络拓扑参数进行差异性分析。结果:在全脑水平上,AD患者的加权特征路径长度(L_p)值上升,全局效率(E_g)、网络强度(S_p)、局部效率(E_(local))值下降,并且进一步研究发现:AD患者左半球L_p、加权簇系数(C_p)值上升,E_g值下降比右半球明显。在特定脑区水平上,选取楔前叶为感兴趣脑区,发现两侧楔前叶脑区的L_p值上升、E_g值下降,而C_p、E_(local)值并没有明显差异。结论:网络拓扑参数可以作为评估AD患者微观结构异常的指标,对早期诊断AD具有重要的指向作用。
Objective To construct white matter structural network with diffusion tensor imaging,and research the abnormalities of brain microstructure in patients with Alzheimer's disease(AD).Methods The structural brain networks of 19 normal controls and 20 AD patients were reconstructed.Two-sample t-test was used to compare the differences in the network topological parameters of the whole brain and the specific brain region between AD patients and normal controls.Results For the whole brain,the weighted path length(L_p)was increased,while the global efficiency(E_g),network strength(S_p)and local efficiency(E_(local)) were decreased in AD patients.The further studies showed that the increases of L_p and weighted clustering coefficient(C_p)and the decrease of E_g were more obviously in the left hemisphere of AD patient as compared with the right hemisphere.Precuneus was taken as the region of interest for analyzing the network topological parameters of the specific brain region,and the results revealed that L_p was increased and E_g was decreased in bilateral precuneus,and that no significant difference was found in C_p and E_(local).Conclusion The network topological parameters can be used as indexes to evaluate the abnormalities of brian microstructure in AD patients,which is of significance in the early diagnosis of AD.
Objective To construct white matter structural network with diffusion tensor imaging,and research the abnormalities of brain microstructure in patients with Alzheimer's disease(AD).Methods The structural brain networks of 19 normal controls and 20 AD patients were reconstructed.Two-sample t-test was used to compare the differences in the network topological parameters of the whole brain and the specific brain region between AD patients and normal controls.Results For the whole brain,the weighted path length(L_p)was increased,while the global efficiency(E_g),network strength(S_p)and local efficiency(E_(local)) were decreased in AD patients.The further studies showed that the increases of L_p and weighted clustering coefficient(C_p)and the decrease of E_g were more obviously in the left hemisphere of AD patient as compared with the right hemisphere.Precuneus was taken as the region of interest for analyzing the network topological parameters of the specific brain region,and the results revealed that L_p was increased and E_g was decreased in bilateral precuneus,and that no significant difference was found in C_p and E_(local).Conclusion The network topological parameters can be used as indexes to evaluate the abnormalities of brian microstructure in AD patients,which is of significance in the early diagnosis of AD.
引文
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[3]WILSON R S,SEGAWA E,BOYLE P A,et al.The natural history of cognitive decline in Alzheimer's disease[J].Psychol Aging,2012,27(4):1008-1017.
[4]STIELTJES B,BRUNNER R M,FRITZSCHE K,et al.Diffusion tensor imaging[J].Methods Mol Biol,2011,711:127-144.
[5]MEARS D,POLLARD H B.Network science and the human brain:using graph theory to understand the brain and one of its hubs,the amygdala,in health and disease[J].J Neurosci Res,2016,94(6):590-605.
[6]LO C Y,WANG P N,CHOU K H,et al.Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease[J].J Neurosci,2010,30(50):16876-16885.
[7]SUN Y,YIN Q,FANG R,et al.Disrupted functional brain connectivity and its association to structural connectivity in amnestic mild cognitive impairment and Alzheimer's disease[J].PLoS One,2014,9(5):e96505.
[8]CUI Z,ZHONG S,XU P,et al.PANDA:a pipeline toolbox for analyzing brain diffusion images[J].Front Hum Neurosci,2013,7(42):1-16.
[9]TZOURIOMAZOYER N,LANDEAU B,PAPATHANASSIOU D,et al.Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J].Neuroimage,2002,15(1):273-289.
[10]KLEIN J,GR?TSCH A,BETZ D,et al.Qualitative and quantitative analysis of probabilistic and deterministic fiber tracking[J].Proc SPIEInt Soc Opt Eng,2010,7623:1-8.
[11]WANG J,WANG X,XIA M,et al.GRETNA:a graph theoretical network analysis toolbox for imaging connectomics[J].Front Hum Neurosci,2015,9(386):386-458.
[12]ZALESKY A,FORNITO A,HARDING I H,et al.Whole-brain anatomical networks:does the choice of nodes matter?[J].Neuroimage,2010,50(3):970-983.
[13]HUMPHRIES M D,GURNEY K,PRESCOTT T J.The brainstem reticular formation is a small-world,not scale-free,network[J].Proc R Soc Lond B Biol Sci,2006,273(1585):503-511.
[14]XIA M,WANG J,HE Y.Brain Net viewer:a network visualization tool for human brain connectomics[J].PLoS One,2013,8(7):e68910.
[15]ZHANG S,LI C S.Functional connectivity mapping of the human precuneus by resting state fMRI[J].Neuroimage,2012,59(4):3548-3562.
[16]DAIANU M,JAHANSHAD N,NIR T M,et al.Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network[J].Hum Brain Mapp,2015,36(8):3087-3103.
[17]LIU H,ZHANG L,XI Q,et al.Changes in brain lateralization in patients with mild cognitive impairment and Alzheimer's disease:a resting-state functional magnetic resonance study from Alzheimer's disease neuroimaging initiative[J].Front Neurol,2018,9(3):1-10.
[18]NELLESSEN N,ROTTSCHY C,EICKHOFF S B,et al.Specific and disease stage-dependent episodic memory-related brain activation patterns in Alzheimer's disease:a coordinate-based meta-analysis[J].Brain Struct Funct,2015,220(3):1555-1571.
[19]TULADHAR A M,VAN-UDEN I W,RUTTEN-JACOBS L C,et al.Structural network efficiency predicts conversion to dementia[J].Neurology,2016,86(12):1112-1119.