|本期目录/Table of Contents|

[1]吕 旻,丁 皓,张雪君,等.基于KL散度的轻度认知功能障碍患者的个体脑结构网络研究[J].天津医科大学学报,2020,26(01):13-17.
 LV Min,DING Hao,ZHANG XUE-jun,et al.Research on the individual brain structrual network of mild cognitive impairment using KL divergence[J].Journal of Tianjin Medical University,2020,26(01):13-17.
点击复制

基于KL散度的轻度认知功能障碍患者的个体脑结构网络研究(PDF)
分享到:

《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:
26卷
期数:
2020年01期
页码:
13-17
栏目:
临床医学
出版日期:
2020-04-06

文章信息/Info

Title:
Research on the individual brain structrual network of mild cognitive impairment using KL divergence
文章编号:
1006-8147(2020)01-0013-05
作者:
吕 旻1 丁 皓1张雪君1张 敬2
(1.天津医科大学医学影像学院,天津300070;2.天津医科大学总医院医学影像学科,天津300052)
Author(s):
LV Min1DING Hao1ZHANG XUE-jun1ZHANG Jing2
(1.School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China;2.Department of Radiology, General Hospital,Tianjin Medical University, Tianjin 300052, China)
关键词:
基于体素的形态学分析KL散度脑结构网络轻度认知功能障碍阿尔茨海默神经影像倡议
Keywords:
VBM Kullback-Leibler divergence brain structural network MCI ADNI
分类号:
R742.8+2
DOI:
-
文献标志码:
A
摘要:
目的:探究正常老龄化人群和易转化为阿尔茨海默症(AD)的轻度认知功能障碍(MCI)患者个体化脑结构网络指标的差异。方法:本实验选取ADNI公共数据库中178例正常老龄者和170例易转化为AD的MCI患者(随访9年内)的脑结构影像数据,运用基于KL散度的个体化脑形态相似度网络并结合图论的方法,分别比较脑网络指标差异。结果:本研究结果表明:(1)与随机网络相比,易转化为AD的MCI患者组和正常老龄化对照组的脑结构网络在所有稀疏度阈值范围内都具有较高的聚类系数(γ>> 1)和近似相等的特征路径长度(λ ≈ 1),并且两组都满足典型的小世界属性(σ>> 1);(2)通过双样本t检验分析,与正常老龄对照组相比,小世界属性在稀疏度阈值0.3~0.5的范围内,易转化为AD的MCI患者组的小世界性σ显著减低(P<0.006,多重比较校正),而在0.1~0.25的范围内无明显差异;患者组标准化的聚类系数γ和特征路径长度λ在全部稀疏度阈值的范围内均呈现显著减低(P<0.006,多重比较校正);聚类系数Cp和特征路径长度Lp相比正常对照组呈现显著减低(P<0.006,多重比较校正)。(3)通过双样本t检验分析,与正常老龄对照组相比,在全部稀疏度阈值范围内,易转化AD的MCI患者组的全局效率Eg和局部效率Eloc均呈现显著减低(P<0.006,多重比较校正)。结论:(1)本方法能够呈现个体化脑结构的 “小世界”拓扑属性,这为个体化精准诊疗提供新思路;(2)在全局水平,患者组存在小世界性、聚类系数、特征路径长度、全局效率和局部效率的显著减低,提示该疾病患者的脑结构网络向随机化转变。基于KL散度的个体化脑形态相似度网络的小世界性和网络效率有可能作为潜在的生物学标记用来监测MCI转化的疾病过程。
Abstract:
Objective: The paper aimed to investigate the index differences of individual brain structural networks between MCI-to-AD subjects (cMCI) and normal aging controls (NC). Methods:The paper pre-processed the structural imaging data of 170 patients with cMCI within nine years and 178 NC from ADNI. The whole-brain structural network was constructed and analyzed using the morphometric similarity method based on Kullback-Leibler divergence, as well as graph theoretical method. The global topological properties were compared between groups. Results:The paper suggested that (1)Compared with the random network, NC and cMCIexhibitedsignificantclustering coefficient (γ>> 1), approximatecharacteristic path length (λ≈1) and significant small-worldness (σ>> 1); (2) Compared with NC, patients with cMCI showed significantly decreased small-worldness in the sparsity ranging from 0.3 to 0.5, while no differences in the sparsity from 0.1 to 0.25; The normalizedclustering coefficient and normalized characteristic path length were also found the significant decrease in the patients with cMCI (P<0.006,FWE-corrected); (3)The global efficiency and local efficiency were found significant decrease in the patients with cMCI (P<0.006,FWE-corrected). Conclusion:These results suggested that (1) NC and cMCI exhibited common small-world architecture of individual brain structural networks; (2) At the global level, cMCI showed significantly reduce of small-worldness, clustering coefficient, characteristic path length, global efficiency and local efficiency, indicating a randomization shift of their brain networks. These results also suggested that cMCI had segregated disruptions in the topological organization of the individual brain structural network, which maybe contribute to the clinical guidance.

参考文献/References:

[1] Benetti S, VanAckeren M J, Rabini G A, et al. Functional selectivity for face processing in the temporal voice area of early deaf individuals[J]. Proc Natl Acad Sci U S A, 2017, 114(31):E6437
[2] Du A T, Schuff N, Amend D, et al. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease[J]. J Neurol Neurosurg Psychiatry, 2001, 71(4):441
[3] Xu Y, Jack C R, O’brien P C, et al. Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD[J]. Neurology, 2000, 54(9):1760
[4] He Y J, Evans A C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI[J]. Cereb Cortex, 2007, 17(10):2407
[5] He Y C, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease[J].J Neurosci, 2008, 28(18):4756
[6] Zhang Y. Abnormal topological organization of structural brain networks in schizophrenia[J]. Schizophr Res, 2012, 141(2/3):109
[7] Bernhardt B C, Rozen D A, Worsley K J, et al. Thalamo-cortical network pathology in idiopathic generalized epilepsy: Insights from MRI-based morphometric correlation analysis[J]. Neuroimage, 2009, 46(2):373
[8] Jack J, Bernstein M A, Fox N C, et al. Thealzheimer’s disease neuroimaging initiative(ADNI): MRI methods[J]. J Magn Reson Imaging, 2008, 27(4):685
[9] Kong Z X. Measuring individual morphological relationship of cortical regions[Z]. 2014:103
[10] Duncan J, WattsSteven H. Strogatz Collective dynamics of‘small-world’ networks[J]. Nature, 1998, 393(6684):440
[11] Achard S, Bullmore E. Efficiency and cost of economical brain functional networks[J]. PloS Comput Biol, 2007,3(2):e17
[12] Bullmore E T, Sporns O. The economy of brain network organization[J]. Nat Rev Neurosci, 2012,13(5):336
[13] Chen J Z. Revealing modular architecture of human brain structural networks by using cortical thickness from MRI[J]. Cereb Cortex, 2008,18(10):2374
[14] Essen V D C. A tension-based theory of morphogenesis and compact wiring in the central nervous system[J]. Nature, 1997, 385(6614):313
[15] Alexander-Bloch A, Raznahan A, Bullmore E T, et al. The convergence of maturational change and structural covariance in human cortical networks[J]. J Neurosci, 2013, 33(7):2889
[16] Lerch J P, Worsley K, Shaw W P, et al. Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI[J]. Neuroimage, 2006, 31(3):993
[17] Draganski B, Gaser C, Busch V, et al. Neuroplasticity: changes in grey matter induced by training[J]. Nature, 2004, 427(6972):311
[18] Hawrylycz J M. An anatomically comprehensive Atlas of the adult human brain transcriptome[J]. Nature, 2012, 489(7416):391
[19] He Y, Chen Z, Gong G L, et al. Neuronal networks in alzheimer'sdisease[J]. Neuroscientist, 2009, 15(4):333

相似文献/References:

备注/Memo

备注/Memo:
基金项目 国家自然科学基金青年项目(81601473);天津市自然科学基金资助项目(17JCYBJC29200)
作者简介 吕旻(1984-),女,实验师,硕士,研究方向:影像医学与核医学;通信作者:张敬,E-mail:ZhangJing1970@163.com。
更新日期/Last Update: 2020-04-16