脑白质病变患者脑结构网络小世界属性与认知功能障碍关系的弥散张量成像研究

doi:10.3969/j.issn.1006-9771.2021.07.007

《中国康复理论与实践》 ›› 2021, Vol. 27 ›› Issue (7): 780-784.doi: 10.3969/j.issn.1006-9771.2021.07.007

脑白质病变患者脑结构网络小世界属性与认知功能障碍关系的弥散张量成像研究

王金芳¹,石庆丽²,陈红燕²,王诗男²,姚静璠²,冯丽²,张玉梅²()

1.长江航运总医院·武汉脑科医院,湖北武汉市 430010
2.首都医科大学附属北京天坛医院,北京市 100070

收稿日期:2021-01-01 修回日期:2021-05-19 出版日期:2021-07-25 发布日期:2021-07-28
通讯作者: 张玉梅 E-mail:zhangyumei95@aliyun.com
作者简介:王金芳(1984-),女,汉族,河南新乡市人,博士,副主任医师,主要研究方向：脑血管病、血管性认知功能障碍。
基金资助:
1.国家自然科学基金面上项目(81972144);2.国家重点研发项目(2018YFC2002302);3.北京市医院管理重点医学专业发展项目(ZYLX201836);4.武汉市卫生计生科研资金项目(WX19Q19);5.湖北省卫生健康科研基金项目(WJ2019H366)

Relationship between Small-world Network and Cognitive Impairment for Patients with White Matter Lesions Based On Diffusion Tensor Imaging

WANG Jin-fang¹,SHI Qing-li²,CHEN Hong-yan²,WANG Shi-nan²,YAO Jing-fan²,FENG Li²,ZHANG Yu-mei²()

1. General Hospital of the Yangtze River Shipping, Wuhan Brain Hospital, Wuhan, Hubei 430010, China
2. Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China

Received:2021-01-01 Revised:2021-05-19 Published:2021-07-25 Online:2021-07-28
Contact: ZHANG Yu-mei E-mail:zhangyumei95@aliyun.com
Supported by:
National Natural Science Foundation of China(General)(81972144);National Key R & D Program(2018YFC2002302);Beijing Municipal Administration of Hospitals Special Funding for Clinical Medicine Development(ZYLX201836);Wuhan Health and Family Planning Committee Research Project(WX19Q19);Hubei Health Research Project(WJ2019H366)

摘要/Abstract

摘要：

目的明确脑白质病变(WMLs)患者脑结构网络小世界网络属性与认知功能障碍的关系。

方法收集北京天坛医院2016年1月至2017年12月门诊WMLs患者46例(患者组)和年龄、性别、受教育程度相匹配的志愿者36例(对照组)。根据认知功能评定量表结果将患者分为血管性认知功能障碍非痴呆(VCIND)组和血管性痴呆(VaD)组。所有被试接受弥散张量成像(DTI),构建脑结构网络,计算脑结构网络全脑小世界拓扑属性,探讨脑结构网络异常与认知障碍严重程度的相关性。

结果所有被试全脑结构网络均具有小世界属性。与对照组相比,患者组脑网络全局效率、局部效率、最短路径长度、聚集效率均发生改变(F > 3.252, P < 0.05),小世界属性值λ、γ和σ有高度显著性差异(F > 7.378, P < 0.01)。小世界属性值与蒙特利尔认知评定量表评分呈正相关(|r| > 0.402, P < 0.05)。

结论 WMLs患者脑结构网络具有小世界属性,网络的小世界属性降低与认知功能减低相关。

关键词: 脑白质病变, 认知, 弥散张量成像, 脑网络, 小世界网络

Abstract:

Objective To explore the relationship between small-worldness of brain network and cognitive impairment in patients with white matter lesions (WMLs) based on diffusion tensor imaging (DTI).

Methods From January, 2016 to December, 2017, 46 WMLs patients and 36 controls matched genders, ages and education levels from Beijing Tiantan Hospital were screened with DTI. The patients were divided into vascular cognitive impairment non-dementia (VCIND) and vascular dementia (VaD) groups according to the results of cognitive assessments. The brain structure network was created based on DTI data, and the topological properties of the whole-brain small-world network were calculated, and the correlation between the small-worldness and the severity of cognitive impairment was analyzed.

Results The global efficiency, local efficiency, shortest path length and clustering coefficient were different between the patients and the controls (F > 3.252, P < 0.05), as well as the properties of the small-world network, λ, γ and σ (F > 7.378, P < 0.01). The λ, γ and σ were correlated with the total score of Montreal Cognitive Assessment (|r| > 0.402, P < 0.05).

Conclusion The brain structure network is small-world network for patients with WMLs, and the decrease of small-world properties may relate to the cognitive impairment.

Key words: white matter lesions, cognition, diffusion tensor imaging, brain network, small-world network

中图分类号:

R743

王金芳,石庆丽,陈红燕,王诗男,姚静璠,冯丽,张玉梅. 脑白质病变患者脑结构网络小世界属性与认知功能障碍关系的弥散张量成像研究[J]. 《中国康复理论与实践》, 2021, 27(7): 780-784.

WANG Jin-fang,SHI Qing-li,CHEN Hong-yan,WANG Shi-nan,YAO Jing-fan,FENG Li,ZHANG Yu-mei. Relationship between Small-world Network and Cognitive Impairment for Patients with White Matter Lesions Based On Diffusion Tensor Imaging[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(7): 780-784.

图/表 3

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表3

参考文献 37

[1]	AOKI S, MASUTANI Y, KUNIMATSU A, et al. Diffusion tensor tractography[J]. Nosotchu, 2004, 26(4):561-566. doi: 10.3995/jstroke.26.561
[2]	ASSAF Y, PASTERNAK O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review[J]. J Mol Neurosci, 2008, 34(1):51-61. doi: 10.1007/s12031-007-0029-0
[3]	WANG P N, CHOU K H, LIRNG J F, et al. Multiple diffusivities define white matter degeneration in amnestic mild cognitive impairment and Alzheimer's disease[J]. J Alzheimers Dis, 2012, 30(2):423. doi: 10.3233/JAD-2012-111304
[4]	PARK C H, KIM S H, JUNG H Y. Characteristics of the uncinate fasciculus and cingulum in patients with mild cognitive impairment: diffusion tensor tractography study[J]. Brain Sci, 2019, 9(12):377. doi: 10.3390/brainsci9120377
[5]	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. doi: 10.1523/JNEUROSCI.4136-10.2010
[6]	SONG T A, ROY CHOWDHURY S, YANG F, et al. Graph convolutional neural networks for Alzheimer's disease classification[J]. Proc IEEE Int Symp Biomed Imaging, 2019, 19(4):414-417.
[7]	REIJMER Y D, LEEMANS A, CAEYENBERGHS K, et al. Disruption of cerebral networks and cognitive impairment in Alzheimer disease[J]. Neurology, 2013, 80(15):1370-1377. doi: 10.1212/WNL.0b013e31828c2ee5
[8]	STAM C J. Modern network science of neurological disorders[J]. Nature Rev Neurosci, 2014, 15(10):683. doi: 10.1038/nrn3801
[9]	PORTER M. Small-world network[J]. Scholarpedia, 2012, 7(2):1739. doi: 10.4249/scholarpedia.1739
[10]	SANZ-ARIGITA E J, SCHOONHEIM M M, DAMOISEAUX J S, et al. Loss of 'small-world' networks in Alzheimer's disease: graph analysis of fMRI resting-state functional connectivity[J]. PLoS One, 2010, 5(11):e13788. doi: 10.1371/journal.pone.0013788
[11]	WANG Z, XIN J, WANG Z, et al. Brain functional network modeling and analysis based on fMRI: a systematic review[J]. Cogn Neurodyn, 2021, 15(3):389-403. doi: 10.1007/s11571-020-09630-5
[12]	TENG S, WANG P S, LIAO Y L, et al. Small-world network for investigating functional connectivity in bipolar disorder: a functional magnetic images (fMRI) study[J]. IFMBE Proc, 2009, 23(1):726-729.
[13]	O'SULLIVAN M. Leukoaraiosis[J]. Prac Neurol, 2008, 8(1):26-38.
[14]	WAHLUND L O, BARKHOF F, FAZEKAS F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT[J]. Stroke, 2001, 32(6):1318-1322. doi: 10.1161/01.STR.32.6.1318
[15]	WILLIAMS J B. A structured interview guide for the Hamilton Depression Rating Scale[J]. Arch Gen Psychiatry, 1988, 45(8):742. doi: 10.1001/archpsyc.1988.01800320058007
[16]	MAIER W, BULLER R, PHILIPP M, et al. The Hamilton Anxiety Scale: reliability, validity and sensitivity to change in anxiety and depressive disorders[J]. J Affect Disord, 1988, 14(1):61-68. doi: 10.1016/0165-0327(88)90072-9
[17]	PANTONI L, INZITARI D. Hachinski's Ischemic Score and the diagnosis of vascular dementia: a review[J]. Italian J Neurol Sci, 1993, 14(7):539-546. doi: 10.1007/BF02339212
[18]	王征宇, 张明园. 中文版简易智能状态检查(MMSE)的应用[J]. 上海精神医学, 1989, 7(3):108-111.
	WANG Z Y, ZHANG M Y. Shanghai Arch Psychiatry, 1989, 7(3):108-111.
[19]	甘露, 刘涛, 王淑华, 等. 中文版简明精神状态量表与蒙特利尔认知评估量表临床应用进展[J]. 中国康复医学杂志, 2017, 32(7):842-845.
	GAN L, LIU T, WANG S H, et al. Chin J Rehabil Med, 2017, 32(7):842-845.
[20]	JING E T, STRAUSS E, SHERMAN E. Clinical Dementia Rating[J]. Br J Psychiatry, 1994, 44(10):1983.
[21]	POHJASVAARA T, MANTYLA R, YLIKOSKI R, et al. Comparison of different clinical criteria (DSM-III, ADDTC, ICD-10, NINDS-AIREN, DSM-IV) for the diagnosis of vascular dementia[J]. Stroke, 2000, 31(12):2952-2957. doi: 10.1161/01.STR.31.12.2952
[22]	CUI Z, ZHONG S, XU P, et al. PANDA: a pipeline toolbox for analyzing brain diffusion images[J]. Front Hum Neurosci, 2013, 7:42.
[23]	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. doi: 10.3389/fpsyg.2018.00386
[24]	YONG L, MENG L, YUAN Z, et al. Disrupted small-world networks in schizophrenia[J]. Brain, 2008, 4:945-961.
[25]	WATTS D J, STROGATZ S H. Collective dynamics of 'small-world' networks[J]. Nature, 1998, 393(6684):440-442. doi: 10.1038/30918
[26]	王金芳, 陈红艳, 李越秀, 等. 脑白质疏松患者大脑功能网络小世界属性的静息态功能磁共振研究[J]. 中华行为医学与脑科学杂志, 2017, 26(11):977-982.
	WANG J F, CHEN H Y, LI Y X, et al. Small-world networks analysis of leukoaraiosis: a rest-state functional MRI study[J]. Chin J Behav Med Brain Sci, 2017, 26(11):977-982.
[27]	BASSETT D S, BULLMORE E. Small-world brain networks[J]. Neuroscientist, 2007, 12(6):512-523. doi: 10.1177/1073858406293182
[28]	BASSETT D S, BULLMORE E T. Small-world brain networks revisited[J]. Neuroscientist, 2016, 23(5):499-516. doi: 10.1177/1073858416667720
[29]	YI H, WANG J, XIN Y, et al. Small-world networks in mild cognitive impairment: graph analysis of resting-state brain functional connectivity[J]. Alzheimers Demen, 2011, 7(4S):S213.
[30]	DOUW L, SCHOONHEIM M M, LANDI D, et al. Cognition is related to resting-state small-world network topology: an magnetoencephalographic study[J]. Neuroscience, 2011, 175:169-177. doi: 10.1016/j.neuroscience.2010.11.039
[31]	YU Y, ZHOU X, WANG H, et al. Small-world brain network and dynamic functional distribution in patients with subcortical vascular cognitive impairment[J]. PLoS One, 2015, 10(7):e0131893. doi: 10.1371/journal.pone.0131893
[32]	SPORNS O, ZWI J D. The small world of the cerebral cortex[J]. Neuroinformatics, 2004, 2(2):145-162. doi: 10.1385/NI:2:2
[33]	HORWITZ B. The elusive concept of brain connectivity[J]. NeuroImage, 2003, 19(2 Pt 1):466-470. doi: 10.1016/S1053-8119(03)00112-5
[34]	DORICCHI F, THIEBAUT DE SCHOTTEN M, TOMAIUOLO F, et al. White matter (dis)connections and gray matter (dys)functions in visual neglect: gaining insights into the brain networks of spatial awareness[J]. Cortex, 2008, 44(8):983-995. doi: 10.1016/j.cortex.2008.03.006
[35]	SHAN Y, HUANG D, SINGER W, et al. A small world of neuronal synchrony[J]. Cereb Cortex, 2008, 18(12):2891-2901. doi: 10.1093/cercor/bhn047
[36]	LATORA V, MARCHIORI M. Efficient behavior of small-world networks[J]. Phys Rev Letters, 2001, 87(19):198701. doi: 10.1103/PhysRevLett.87.198701
[37]	HUANG Q, ZHANG R, HU X, et al. Disturbed small-world networks and neurocognitive function in frontal lobe low-grade glioma patients[J]. PLoS One, 2014, 9(4):e94095. doi: 10.1371/journal.pone.0094095

变量	对照组 (n = 36)	VCIND组 (n = 30)	VaD组 (n = 16)	χ²/F值	P值
性别(男/, n)	19/17	16/14	10/6	1.682	0.431
年龄(年)	66.20±7.18	65.59±7.59	62.83±6.97	1.828	0.167
受教育程度(小学及以下/初中及以上, n)	19/17	21/9	13/3	1.682	0.431
高血压(n)	13	8	10	5.265	0.068
吸烟(n)	6	5	4	0.514	0.773
糖尿病(n)	4	2	4	1.364	0.505
高脂血症(n)	5	3	6	1.094	0.579
体质量指数(kg/m²)	23.87±1.18	24.27±1.12	24.13±1.01	0.772	0.466
MMSE	29.53±0.69	27.000±2.28	23.75±4.30	33.400	< 0.001
MoCA	27.69±1.4	22.500±2.40	17.87±4.17	91.310	< 0.001

网络参数	对照组 (n = 36)	VCIND组 (n = 30)	VaD组 (n = 16)	F值	P值
度值	7.37±1.05	7.30±1.29	6.05±1.80	6.020	0.004
全局效率	0.40±0.05	0.41±0.04	0.36±0.07	5.550	0.006
局部效率	0.56±0.07	0.55±0.08	0.48±0.11	4.709	0.012
Lp	2.58±0.72	2.48±0.25	2.93±0.66	3.252	0.044
Cp	0.39±0.04	0.38±0.05	0.35±0.05	3.308	0.042
λ	1.10±0.02	1.10±0.02	1.14±0.06	10.153	< 0.001
γ	3.92±0.68	3.85±0.51	4.91±1.59	8.273	< 0.001
σ	3.55±0.57	3.51±0.43	4.26±1.16	7.378	0.001

小世界属性		MMSE	MoCA
γ	r值	0.209	0.402
γ	P值	0.306	0.042
λ	r值	-0.176	-0.419
λ	P值	0.390	0.033
σ	r值	0.220	0.435
σ	P值	0.280	0.027

脑白质病变患者脑结构网络小世界属性与认知功能障碍关系的弥散张量成像研究

Relationship between Small-world Network and Cognitive Impairment for Patients with White Matter Lesions Based On Diffusion Tensor Imaging

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