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BMC Medical Imaging
Published in: BMC Medical Imaging 1/2022

01-12-2022 | Cerebral Small Vessel Disease | Research

Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease

Authors: Yifan Wang, Xiao Liu, Ying Hu, Zekuan Yu, Tianhao Wu, Junjie Wang, Jie Liu, Jun Liu

Published in: BMC Medical Imaging | Issue 1/2022

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Abstract

Background

White matter hyperintensity (WMH) is one of the typical neuroimaging manifestations of cerebral small vessel disease (CSVD), and the WMH correlates closely to cognitive impairment (CI). CSVD patients with WMH own altered topological properties of brain functional network, which is a possible mechanism that leads to CI. This study aims to identify differences in the characteristics of some brain functional network among patients with different grades of WMH and estimates the correlations between these different brain functional network characteristics and cognitive assessment scores.

Methods

110 CSVD patients underwent 3.0 T Magnetic resonance imaging scans and neuropsychological cognitive assessments. WMH of each participant was graded on the basis of Fazekas grade scale and was divided into two groups: (A) WMH score of 1–2 points (n = 64), (B) WMH score of 3–6 points (n = 46). Topological indexes of brain functional network were analyzed using graph-theoretical method. T-test and Mann–Whitney U test was used to compare the differences in topological properties of brain functional network between groups. Partial correlation analysis was applied to explore the relationship between different topological properties of brain functional networks and overall cognitive function.

Results

Patients with high WMH scores exhibited decreased clustering coefficient values, global and local network efficiency along with increased shortest path length on whole brain level as well as decreased nodal efficiency in some brain regions on nodal level (p < 0.05). Nodal efficiency in the left lingual gyrus was significantly positively correlated with patients' total Montreal Cognitive Assessment (MoCA) scores (p < 0.05). No significant difference was found between two groups on the aspect of total MoCA and Mini-mental State Examination (MMSE) scores (p > 0.05).

Conclusion

Therefore, we come to conclusions that patients with high WMH scores showed less optimized small-world networks compared to patients with low WMH scores. Global and local network efficiency on the whole-brain level, as well as nodal efficiency in certain brain regions on the nodal level, can be viewed as markers to reflect the course of WMH.
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Literature
1.
2.
3.
Brugulat-Serrat A, et al. White matter hyperintensities mediate gray matter volume and processing speed relationship in cognitively unimpaired participants. Hum Brain Mapp. 2020;41(5):1309–22.PubMedCrossRef
4.
5.
Lee WJ, et al. Progression of cerebral white matter hyperintensities and the associated sonographic index. Radiology. 2017;284(3):824–33.PubMedCrossRef
6.
Wang J, et al. Structural changes in white matter lesion patients and their correlation with cognitive impairment. Neuropsychiatr Dis Treat. 2019;15:1355–63.PubMedPubMedCentralCrossRef
7.
Wang Y, et al. Correlation between white matter hyperintensities related gray matter volume and cognition in cerebral small vessel disease. J Stroke Cerebrovasc Dis. 2020;29(12):105275.PubMedCrossRef
8.
Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341:c3666.PubMedPubMedCentralCrossRef
9.
Gorelick PB, et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the american heart association/american stroke association. Stroke. 2011;42(9):2672–713.PubMedPubMedCentralCrossRef
10.
Chen X, et al. Disrupted functional and structural connectivity within default mode network contribute to WMH-related cognitive impairment. Neuroimage Clin. 2019;24:102088.PubMedPubMedCentralCrossRef
11.
Hart MG, et al. Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery. J Neurosurg. 2016;124(6):1665–78.PubMedCrossRef
12.
13.
14.
15.
16.
van den Heuvel MP, et al. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp. 2009;30(10):3127–41.PubMedPubMedCentralCrossRef
17.
18.
Ter Telgte A, et al. Cerebral small vessel disease: from a focal to a global perspective. Nat Rev Neurol. 2018;14(7):387–98.PubMedCrossRef
19.
20.
21.
Du J, et al. Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease. NeuroImage Clin. 2019;22:101712.PubMedPubMedCentralCrossRef
22.
Fazekas F, et al. MR signal abnormalities at 15-T IN Alzheimers dementia and normal aging. Am J Neuroradiol. 1987;8(3):421–6.PubMedCentral
23.
Tzourio-Mazoyer N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–89.PubMedCrossRef
24.
Fox MD, et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102(27):9673–8.PubMedPubMedCentralCrossRef
25.
Bullmore ET, Bassett DS. Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol. 2011;7:113–40.PubMedCrossRef
26.
Ithapu V, et al. Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer’s disease risk and aging studies. Hum Brain Mapp. 2014;35(8):4219–35.PubMedPubMedCentralCrossRef
27.
Faskhodi MM, Einalou Z, Dadgostar M. Diagnosis of Alzheimer’s disease using resting-state fMRI and graph theory. Technol Health Care. 2018;26(6):921–31.PubMedCrossRef
28.
Yu Y, et al. Small-world brain network and dynamic functional distribution in patients with subcortical vascular cognitive impairment. PLoS One. 2015;10(7):e0131893.PubMedPubMedCentralCrossRef
29.
Wang J, et al. The role of disturbed small-world networks in patients with white matter lesions and cognitive impairment revealed by resting state function magnetic resonance images (rs-fMRI). Med Sci Monit. 2019;25:341–56.PubMedPubMedCentralCrossRef
30.
31.
Kaiser M, Hilgetag CC. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Comput Biol. 2006;2(7):e95.PubMedPubMedCentralCrossRef
32.
33.
Sang L, et al. Progressively disrupted brain functional connectivity network in subcortical ischemic vascular cognitive impairment patients. Front Neurol. 2018;9:94.PubMedPubMedCentralCrossRef
34.
Wang, Z., et al., Disrupted structural brain connectome underlying the cognitive deficits in remitted late-onset depression. Brain Imaging Behav, 2019.
35.
36.
Tuladhar AM, et al. Disruption of rich club organisation in cerebral small vessel disease. Hum Brain Mapp. 2017;38(4):1751–66.PubMedCrossRef
37.
Ding W, et al. Altered functional connectivity in patients with subcortical vascular cognitive impairment—a resting-state functional magnetic resonance imaging study. PLOS one. 2015;10(9):e0138180.PubMedPubMedCentralCrossRef
38.
Liu C, et al. Changes of white matter integrity and structural network connectivity in nondemented cerebral small-vessel disease. J Magn Reson Imaging. 2020;51(4):1162–9.PubMedCrossRef
39.
Tuladhar AM, et al. Structural network connectivity and cognition in cerebral small vessel disease. Hum Brain Mapp. 2016;37(1):300–10.PubMedCrossRef
40.
Cheng HL, et al. Impairments in cognitive function and brain connectivity in severe asymptomatic carotid stenosis. Stroke. 2012;43(10):2567–73.PubMedCrossRef
41.
Chen, H., et al., Nodal global efficiency in front-parietal lobe mediated periventricular white matter hyperintensity (PWMH)-related cognitive impairment. Front Aging Neurosci, 2019. 11.
42.
Barulli D, Stern Y. Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve. Trends Cogn Sci. 2013;17(10):502–9.PubMedCrossRef
43.
44.
45.
Wang Z, et al. Altered resting state networks in epileptic patients with generalized tonic-clonic seizures. Brain Res. 2011;1374:134–41.PubMedCrossRef
46.
Song M, et al. Impaired resting-state functional integrations within default mode network of generalized tonic-clonic seizures epilepsy. PLoS One. 2011;6(2):e17294.PubMedPubMedCentralCrossRef
47.
48.
49.
Cao B, et al. The global cognition, frontal lobe dysfunction and behavior changes in chinese patients with multiple system atrophy. PLoS One. 2015;10(10):e0139773.PubMedPubMedCentralCrossRef
50.
Henke K. A model for memory systems based on processing modes rather than consciousness. Nat Rev Neurosci. 2010;11(7):523–32.PubMedCrossRef
51.
Bergmann E, et al. The organization of mouse and human cortico-hippocampal networks estimated by intrinsic functional connectivity. Cereb Cortex. 2016;26(12):4497–512.PubMedPubMedCentralCrossRef
52.
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69.CrossRefPubMed
53.
Chen H, et al. Nodal global efficiency in front-parietal lobe mediated periventricular white matter hyperintensity (PWMH)-related cognitive impairment. Front Aging Neurosci. 2019;11:347.PubMedPubMedCentralCrossRef
54.
Qin Y, et al. Functional brain connectome and its relation to mild cognitive impairment in cerebral small vessel disease patients with thalamus lacunes. Medicine. 2019;98(40):e17127.PubMedPubMedCentralCrossRef
55.
56.
Wen DS, et al. Effects of 17 beta-estradiol on the adhesion, invasion and motility potential of salivary mucoepidermoid carcinoma Mc3 cells. Zhonghua Kou Qiang Yi Xue Za Zhi. 2005;40(5):390–3.PubMed
57.
Yi LY, et al. Disrupted topological organization of resting-state functional brain network in subcortical vascular mild cognitive impairment. CNS Neurosci Ther. 2015;21(10):846–54.PubMedPubMedCentralCrossRef
58.
Schroeter ML, et al. Neurovascular coupling is impaired in cerebral microangiopathy–An event-related Stroop study. Neuroimage. 2007;34(1):26–34.PubMedCrossRef
59.
Freeze WM, et al. White matter hyperintensities mediate the association between blood-brain barrier leakage and information processing speed. Neurobiol Aging. 2020;85:113–22.PubMedCrossRef
61.
Kapur N, et al. Activation of human hippocampal formation during memory for faces: a PET study. Cortex. 1995;31(1):99–108.PubMedCrossRef
61.
Lampe L, et al. Lesion location matters: The relationships between white matter hyperintensities on cognition in the healthy elderly. J Cereb Blood Flow Metab. 2019;39(1):36–43.PubMedCrossRef
Metadata
Title
Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease
Authors
Yifan Wang
Xiao Liu
Ying Hu
Zekuan Yu
Tianhao Wu
Junjie Wang
Jie Liu
Jun Liu
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2022
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-022-00769-7

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