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

Abstract

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

CLC Number:

R743

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.

Figures/Tables 3

References 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

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 3

References 37

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LIN Na, GAO Hanlu, LU Huiping, CHEN Yanqing, ZHENG Junfan, CHEN Shurong. Effect of virtual reality on upper limb function after stroke: a study of diffusion tensor imaging [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2024, 30(1): 61-67.
[2]	WANG Haoyi, SHI Yawei, LU Jun, XU Guangxu. Impact of subjective vertical perception impairment on function in stroke patients: a retrospective study [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2024, 30(1): 68-73.
[3]	JIANG Changhao, HUANG Chen, GAO Xiaoyan, DAI Yuanfu, ZHAO Guoming. Effect of neurofeedback training on cognitive function in the elderly: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(8): 903-909.
[4]	JIANG Changhao, GAO Xiaoyan. Effect of acute physical activity on cognitive function in children: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(6): 667-672.
[5]	ZHU Xiaomin, LIU Huilin, LIU Yuanmin, YAN Zhiyu, DU Xuejing, WANG Ya'nan, ZHANG Tong. Relationship among spontaneous turning direction, balance and fall risk in stroke patients during walking [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(5): 510-515.
[6]	KANG Xiaoyu, LIU Lixu, WANG Wenzhu, WANG Yunlei. Effects of pramipexole combined with levodopa on cognitive and mitochondrial function of rats after global cerebral ischemia-reperfusion injury [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(5): 533-540.
[7]	ZHU Xu,LIU Jing,DONG Zeping,QIU Dawei. Gesture action intent recognition based on surface electromyography: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2022, 28(9): 1032-1038.
[8]	ZHANG Xiaoyu,YANG Fan,WEN Jianzhong,YU Weiyong. Application of resting-state functional magnetic resonance imaging in acute mild traumatic brain injury [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2022, 28(9): 1084-1088.
[9]	XUAN Wenru,SHEN Yuqing,ZHOU Miao,FENG Shiwen. Bilingual training for cognition of older adults: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2022, 28(5): 578-584.
[10]	ZHONG Xiaoke,ZHANG Ji,WANG Zhipeng,JIANG Changhao. Effect of physical activity on neurocognitive function of overweight children: a systematic review [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2022, 28(4): 421-428.
[11]	LU Jiamin,YAN Sinian,CHEN Yihao,LU Rongrong,WU Yi. Characteristics of resting brain network for patients with cognitive impairment after stroke using functional near-infrared spectroscopy [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2022, 28(4): 447-452.
[12]	LIN Ai-jin,WANG Jie-qiong,AO Li-juan,CHEN Mo-xian. Long-term Behavioral Disorder after Hypoxic Ischemic Brain Damage in Newborn Mice [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(8): 908-912.
[13]	Xin LIU,Zhi-ke YIN,Tao FENG,Yong-mei DENG,Yue-ying ZHAO,Ke DONG,Chun-xue WANG,Hui-zi MA. Risk Factors of Apathy in Parkinson's Disease [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(6): 719-723.
[14]	Xiao-qian YING,Yi GAO,Li-min LIAO. Small-world Network Features of Brain Functional Network as Strong Void Perception for Healthy Female [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(5): 510-515.
[15]	Meng-lin CAO,Yu-hao CHEN,Jue WANG,Tian LIU. Advance in Human Motion Intention Recognition Based on Surface Electromyography (review) [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(5): 595-603.