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中华介入放射学电子杂志 ›› 2017, Vol. 05 ›› Issue (03) : 189 -193. doi: 10.3877/cma.j.issn.2095-5782.2017.03.016

所属专题: 文献

医学影像

DTI检测阿尔茨海默病脑白质的改变
任津瑶1, 龙淼淼2, 祁吉3, 倪红艳3,()   
  1. 1. 300222 天津医学高等专科学校
    2. 300192 天津市第一中心医院放射科
  • 收稿日期:2017-06-01 出版日期:2017-08-01
  • 通信作者: 倪红艳

Diffusion tensor imaging detection of white matter changes in Alzheimer's disease

Jinyao Ren1, Miaomiao Long2, Ji Qi3, Hongyan Ni3,()   

  1. 1. Tianjin Medical College, Tianjin 300222, China
    3. Department of Radiology, Tianjin First Central Hospital, Tianjin 300192, China
  • Received:2017-06-01 Published:2017-08-01
  • Corresponding author: Hongyan Ni
  • About author:
    Corresponding author: Email:
引用本文:

任津瑶, 龙淼淼, 祁吉, 倪红艳. DTI检测阿尔茨海默病脑白质的改变[J]. 中华介入放射学电子杂志, 2017, 05(03): 189-193.

Jinyao Ren, Miaomiao Long, Ji Qi, Hongyan Ni. Diffusion tensor imaging detection of white matter changes in Alzheimer's disease[J]. Chinese Journal of Interventional Radiology(Electronic Edition), 2017, 05(03): 189-193.

目的:

应用磁共振弥散张量成像(DTI)技术评价阿尔茨海默病(AD)患者脑白质的改变,分析部分各向异性(FA)和局域各向异性(GA)参数对AD患者脑白质损伤的诊断效率。

方法:

选取2014年8月—2015年8月确诊的AD患者12例(AD组),16例认知正常的健康老年志愿者为对照组,进行MRI常规扫描和DTI检查,测量上纵束、扣带束、胼胝体、内囊、额叶、顶叶、颞叶、枕叶脑白质的FA、GA值。应用ROC曲线评价FA、GA参数检出AD患者脑白质损伤的诊断效率。

结果:

与对照组比较,AD组的扣带束后部FA、GA值降低;上纵束FA、GA值降低;顶叶白质FA、GA降低;额叶白质GA值降低;颞叶白质GA值降低;胼胝体压部GA降低,差异均有统计学意义。ROC曲线评价AD患者扣带束后部、上纵束、顶叶GA值曲线下面积较FA大,顶叶GA值对于AD患者脑白质损伤具有较高的诊断效率。

结论:

AD患者表现为选择性脑白质域性损害,扣带束后部、上纵束、胼胝体压部、额叶、颞叶、顶叶皮层下脑白质损伤,GA诊断AD病患者脑白质异常的效率比FA更佳。

Objective:

To evaluate the changes of white matter in Alzheimer's disease(AD) patients by Magnetic resonance diffusion tensor imaging (DTI) and to analyze the diagnostic value of Fractional anisotropy (FA) and Geodesic anisotropy (GA) in detecting white matter abnormalities in AD patients.

Methods:

MRI routine scan and DTI were performed in 12 AD patients who were diagnosed from August 2014 to August 2015. 16 cognitively normal (NC) healthy elderly volunteers were enrolled, as control group, to this study. The FA and GA of superior longitudinal fasciculus, Cingulate, corpus callosum, internal capsule, frontal lobe, parietal lobe, temporal lobe and occipital lobe white matter were measured. The ROC curve was applied to analyze Fractional anisotropy (FA) and Geodesic anisotropy (GA) parameters to determine the diagnostic value in detecting white matter abnormalities in patients with AD.

Results:

Compared to the control group, the FA and GA values in the posterior cingulate were decreased, the FA and GA values of the superior longitudinal fasciculus were decreased, the FA and GA of the white matter in the parietal lobe were decreased, the GA value of the white matter in the frontal lobe was decreased, the GA value of the white matter in temporal lobe was decreased and the GA value of the splenium of corpus callosum was decreased. The differences were statistically significant. The area under the ROC curve was more than 0.700, which was larger than that of FA.

Conclusion:

AD patients usuaug show selective and regional damage in the posterior cingulated and the damage in superior longitudinal fasciculus, splenium of corpus callosum, frontal lobe, temporal lobe, parietal cortex and subcortical white matter lesions. The diagnostic value of GA in detection of white matter abnormalities in patients with AD was superior to FA.

图1 胼胝体膝部、双侧扣带束前部FA、GA图及ROI
图2 胼胝体体部、双侧上纵束FA、GA图及ROI
图3 胼胝体压部、双侧扣带束后部FA、GA图及ROI
图4 双侧内囊前、后肢FA、GA图及ROI
图5 双侧额叶白质FA、GA图及ROI
图6 双侧颞叶白质FA、GA图及ROI
图7 双侧顶叶白质FA、GA图及ROI
图8 双侧枕叶白质FA、GA图及ROI
表1 AD组与NC组的FA值比较(±s
表2 AD组与NC组的GA值比较(±s
表3 各部位FA、GA值对AD患者脑白质变化的诊断效率
1
Bennett DA, Schneider JA, Arvanitakis Z, et al. Neuropathology of older persons without cognitive impairment from two community-based studies[J]. Neurology, 2006,66(12):1837-1844.
2
Jack CR, Petersen RC, Xu YC, et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment[J]. Neurology, 1999,52(7):1397-1403.
3
Sjöbeck M, Englund E. Glial levels determine severity of white matter disease in Alzheimer's disease: a neuropathological study of glial changes[J]. Neuropathol Appl Neurobiol, 2003,29(2):159-169.
4
Sjöbeck M, Haglund M, Englund E. Decreasing myelin density reflected increasing white matter pathology in azheimer’s disease—a neuropathological study[J]. Int J Geriatr Psychiatry 2005 20(10):919-926.
5
Bartzokis G, Cummings JL, Sultzer D, et al. White matter structural integrity in healthy aging adults and patients with Alzheimer disease: a magnetic resonance imaging study[J]. Archives of Neurology 2003 60(3):393-398.
6
Thompson PM, Hayashi KM, Dutton RA, et al. Tracking Alzheimer's disease[J].Ann NY Acad Sci, 2007, 1097:183-214.
7
Fellgiebel A, Müller MJ, Wille P, et al. Color-coded diffusion-tensor-Imaging of posterior cingulate fiber tracts in mild cognitive impairment[J].Neurobiol Aging, 2005, 26(8):1193-1198.
8
Braak E, Griffing K, Arai K, et al. Neuropathology of Alzheimer’s disease: what is new since A[J]. Eur Arch Psychiatry Clin Neurosci, 1999, 249(suppl 3):14-22.
9
Mirra SS, Heyman A, McKeel D, et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease[J]. Neurology, 1991, 41:479-486.
10
Hauw JJ, Duyckaerts C, Delaere P, et al. Alzheimer’s disease: neuropathological and etiological data[J]. Biomed Pharmacother, 1989, 43:468-482.
11
Pearson RC, Esiri MM, Hiorns RW, et al. Anatomical correlates of the distribution of the pathological changes in the neocortex in Alzheimer’s disease[J]. Proc Natl Acad Sci USA, 1985, 82:4531-4534.
12
Lee PH, Oh SH, Bang OY, et al. Pathogenesis of deep white matter medullary infarcts: a diffusion weighted magnetic resonance imaging study[J]. J Neurol Neurosurg Psychiatry, 2005, 76:1659-1663.
13
Bucur B, Madden DJ, Spaniol J, et al. Age-related slowing of memory retrieval: contributions of perceptual speed and cerebral white matter integrity[J]. Neurobiol Aging, 2008, 29(7):1070-1079.
14
Jeurissen B, Leemans A, Tournier JD, et al. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging[J]. Hum Brain Mapp, 2013,34(11):2747-2766. doi: 10.1002/hbm.22099.
15
Petersen RC, Doody R, Kurz A, et al. Currentconcepts in mild cognitive impairment[J].Arch Neurol, 2001, 58(12):1985-1992.
16
Rose SE, McMahon KL, Janke AL, et al. MRI diffusion indices and neuropsychological performance in amnestic mild cognitive impairment[J]. J Neurol Neurosurg Psychiatry, 2006, 77(10):1122-1128.
17
Stěpán-Buksakowska I, Keller J, Laczó J, et al. Diffusion tensor imaging in Alzheimer disease and mild cognitive impairment[J]. Neurol Neurochir Pol, 2012, 46(5):464-471.
18
Wegrzyn M, Teipel SJ, Oltmann I, et al. Structural and functional cortical disconnection in Alzheimer's disease: a combined study using diffusion tensor imaging and transcranial magnetic stimulation[J]. Psychiatry Res, 2013, 212(3):192-200. doi: 10.1016/j.pscychresns.2012.04.008.
19
孟京志,皇丽丽,郭李炜, 等.阿尔茨海默病患者联络纤维的DTI诊断价值[J].临床放射学杂志, 2012, (2):158-162.
20
Henley SM, Downey LE, Nicholas JM, et al. Degradation of cognitive timing mechanisms in behavioural variant frontotemporal dementia[J]. Neuropsychologia 2014, 65:88-101. doi: 10.1016/j.neuropsychologia.2014.10.009.
21
Lim JS, Park YH, Jang JW, et al. Differential white matter connectivity in early mild cognitive impairment according to CSF biomarkers[J]. PloS one, 2014, 9(3):e91400. doi: 10.1371/journal.pone.0091400.
22
Fletcher PT, Joshi S. Principal geodesic analysis on symmetric spaces: statistics of diffusion tensors[J].Proceedings of ECCV 200 Workshop on Computer Vision Approaches to Medical Image Analysis (CVAMIA)LNCS, 2004, 3117:87-98.
23
Corouge I, Fletcher PT, Joshi S, et al. Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis[J].Med Image Anal, 2006, 10(5):786-798.
24
Nir TM, Jahanshad N, Villalon-Reina JE, et al. Thompson PM. Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging[J]. Neuroimage Clin, 2013, 3:180-195.doi: 10.1016/j.nicl.2013.07.006.
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