[1]龙云玲.基于张量的形态学测量在轻度认知障碍患者大脑灰质萎缩诊断中的价值[J].第三军医大学学报,2017,39(24):2423-2429.
 LONG Yunling.Diagnostic value of tensor-based morphology for grey matter atrophy in mild cognitive impairment patients[J].J Third Mil Med Univ,2017,39(24):2423-2429.
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基于张量的形态学测量在轻度认知障碍患者大脑灰质萎缩诊断中的价值(/HTML )
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《第三军医大学学报》[ISSN:1000-5404/CN:51-1095/R]

卷:
39卷
期数:
2017年第24期
页码:
2423-2429
栏目:
临床医学
出版日期:
2017-12-30

文章信息/Info

Title:
Diagnostic value of tensor-based morphology for grey matter atrophy in mild cognitive impairment patients
作者:
龙云玲
首都医科大学附属北京天坛医院医学工程处
Author(s):
LONG Yunling

Department of Medical Engineering, Beijing Tiantan Hospital Capital Medical University, Beijing, 100050, China

关键词:
轻度认知障碍MRI基于张量的形态学测量灰质萎缩
Keywords:
mild cognitive impairment magnetic resonance imaging tensor-based morphological measurement grey matter atrophy
分类号:
R445.2;R749;R811
文献标志码:
A
摘要:

目的     探索轻度认知障碍患者的大脑灰质结构萎缩的规律,区分不同轻度认知障碍亚型的灰质结构萎缩模式的异同。方法    数据来自阿尔茨海默病的神经影像(Alzheimer’s disease neuroimaging initiative,ADNI)数据库,包括34例正常人、22例稳定性轻度认知障碍和20例进展性轻度认知障碍患者60个月纵向高分辨MRI 3D T1W结构像。基于张量的形态学(tensorbased morphology,TBM)测量方法进行数据分析,利用DARTEL配准过程中得到的雅克比矩阵计算出行列式的值和迹,并对得到的特征参数进行组内方差分析,然后采用多重比较的配对t检验,获取相对首次扫描时间点时不同跟踪时间点大脑灰质萎缩的规律及特征。结果     采用两种参数得到的结果相对一致,3组被试数据均表现为随着时间的推移,大脑灰质萎缩区域逐步扩大,萎缩程度逐步增加,尤其对于颞叶、额叶、海马和扣带回等与认知相关的区域萎缩最为明显。但是在相同时间点,相对于正常组来说,稳定型的轻度认知障碍患者组大脑灰质萎缩程度较为严重,进展型的轻度认知障碍患者组大脑灰质萎缩最为严重。结论    TBM 测量法得到的大脑灰质萎缩差异可以用于区分稳定性、进展性轻度认知障碍以及正常人大脑结构,可指导阿尔茨海默病的早期诊断和相关临床干预。

Abstract:

Objective      To investigate the atrophy pattern of grey matter in the patients with mild cognitive impairment (MCI), and to identify the atrophy differences in the different subtypes of MCI. MethodsThe longitudinal highresolution 3D T1W images in this study were all downloaded from Alzheimer’s disease neuroimaging initiative(ADNI), including images of 34 healthy individuals, 22 patients of stable MCI and 20 ones with progressive MCI during a period of 60 months. Tensor-based morphology (TBM) was adopted for image analysis. The Jacobian matrix was firstly obtained through DARTEL registration, and the determinant value and trace were calculated from the Jacobian matrix. Finally, ANOVA and Paired t test were performed on the new parameters to statistically analyze the characteristics of grey matter atrophy by comparing the MRI images at tracking time points with those at baseline. ResultsThe results showed that there was a corresponding atrophy pattern by using the 2 parameters. In the 3 groups, with the time elapsed, the atrophy regions were expanded, especially the brain regions related to cognitive function, including temporal lobe, frontal lobe, hippocampus and cingulate gyrus etc. Meanwhile, at the same tracking time point, the grey matter atrophy in the SMCI patients was more serious, and the atrophy in the PMCI patients was the most serious when compared to the normal control group. Conclusion     TBM obtained regions and patterns in grey matter atrophy can be used as radiographic markers to distinguish PMCI from SMCI, and the indicators are helpful for the early diagnosis of AD and clinical treatment.

参考文献/References:

[1]WEINER M W,  VEITCH D P,  AISEN P S,  et al. The alzheimer’s disease neuroimaging initiative:  a review of paperspublished since its inception[J]. Alzheimer Dement,  2012,  8(1):  S1-S68. DOI: 10.1016/j.jalz.2011.09.172.
[2]SINCHAI T,  NIHARIKA G,  JIAYU Z,  et al.Multitemplate tensor-based morphometry: Application to analysis of Alzheimer's disease[J]. Neuroimage, 2011, 56(3): 1134-1144. DOI: 10.1016/j.neuroimage.2011.03.029.
[3]SHEN K,  FRIPP J,  M-RIAUDEAU F, et al. Detecting global and local hippocampal shape changes in alzheimer’s disease using statistical shape models[J]. NeuroImage. 2012,  59(3):  2155-2166.
[4]TESSA C,  LUCETTI C,  GIANNELLI M,  et al. Progressionof brain atrophy in the early stages of Parkinson’s disease:  alongitudinal tensor-based morphometry study in de novo patients without cognitive impairment[J]. Hum Brain Mapp,  2014,  35(8):  3932-3944. DOI: 10.1002/hbm.22449.
[5]SINCHAI T,  NIHARIKA G,  ZHOU J Y, et al. Feature selective temporal prediction of Alzheimer’s disease progression using hippocampus surface morphometry[J]. Brain  Behav,  2017,  7: 27-33. DOI: 10.1002/brb3.733.
[6]WEILER M,  AGOSTA F,  CANU E, et al. Following the spreading of brain structural changes in alzheimer’s disease: alongitudinal, multimodal MRI study[J]. J Alzheimers Dis,  2015,  47(4):  995-1007. DOI: 10.3233/JAD-150196.
[7]LIU Y,  LI M,  ZHANG H, et al. A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training[J]. J Neurosci Methods,  2014,  222:  238-249. DOI: 10.1016/j.jneumeth.2013.11.009.
[8]WANG Y,  YUAN L,  SHI J,  et al. Applying tensor-based morphometry to parametric surfaces can improve MRIbased disease diagnosis[J]. Neuro Image,  2013,  74:  209-230. DOI: 10.1016/j.neuroimage.2013.02.011.
[9]THOMPSON W K,  HOLLAND D. Bias in tensor based morphometry StatROI measures may result in unrealistic power estimates[J]. Neuroimage,  2011,  57(1):  1-4. DOI: 10.1016/j.neuroimage.2010.11.092.
[10]YOON B,  SHIM Y S,  HONG Y J,  et al. Comparison of diffusion tensor imaging and voxelbased morphometry to detect white matter damage in Alzheimer’s disease[J]. J Neurol Sci,  2011,  302(1):  89-95. DOI: 10.1016/j.jns.2010.11.012.
[11]RAJAGOPALAN V,  SCOTT J,  HABAS P A, et al. Mapping directionality specific volume changes using tensor based morphometry:  An application to the study of gyrogenesis and lateralization of the human fetal brain[J]. NeuroImage, 2012, 63(2):  947-958. DOI: 10.1016/j.neuroimage.2012.03.092.
[12]LIPOWCZAN M,  PIEKARSKA A,  ELSNER J, et al. The tensorbased model of plant growth applied to leaves of arabidopsis thaliana: a two-dimensional computer model[J]. C R Biol,  2013,  336(9): 425-432. DOI: 10.1016/j.crvi.2013.09.001.
[13]HUA X,  GUTMAN B,  BOYLE B P,  et al. Accurate measurement of brain changes in longitudinal MRI scans using tensorbased morphometry[J]. NeuroImage,  2011,  57(1):  5-14. DOI: 10.1016/j.neuroimage.2011.01.079.
[14]YANG J,  PAN P,  SONG W,  et al. Voxelwise meta-analysis of gray matter anomalies in alzheimer’s disease and mild cognitive impairment using anatomic likelihood estimation[J]. J Neurol Sci,  2012,  316(1/2):  21-29. DOI: 10.1016/j.jns.2012.02.010.
[15]MISRA C,   FAN Y,  DAVATZIKOS C. Baseline and longitudinal patterns of brain atrophy in MCI patients,  and theiruse in prediction of short-term conversion to AD:  results from ADNI[J]. NeuroImage,  2009,  44(4):  1415-1422. DOI: 10.1016/j.neuroimage.2008.10.031.
[16]RISACHER S L,  SHEN L,  WEST J D, et al. Longitudinal MRI atrophy biomarkers:  relationship to conversion in the ADNI cohort[J]. Neurobiol Aging,  2010,  31(8):  1401-1418. DOI: 10.1016/j.neurobiolaging.2010.04.029.
[17]GROTHE M,  HEINSEN H,  TEIPEL S. Longitudinal measures of cholinergic forebrain atrophy in the transition fromhealthy aging to Alzheimer’s disease[J]. Neurobiol Aging,  2013,  34(4):  1210-1220. DOI: 10.1016/j.neurobiolaging.2012.10.018.
[18]TONDELLI M,  WILCOCK G K,  NICHEL P, et al. Structural MRI changes detectable up to ten years before clinicalAlzheimer’s disease[J]. Neurobiol Aging,  2012,  33(4):  825-825. DOI: 10.1016/j.neurobiolaging.2011.05.018.

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更新日期/Last Update: 2017-12-27