AccScience Publishing / AN / Volume 1 / Issue 1 / DOI: 10.36922/an.v1i1.48
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RESEARCH ARTICLE

Decreased cortical thickness and normal regional homogeneity underlying cognitive impairment in cerebral small vessel disease

Yuting Mo1,2,3† Lili Huang1,2,3† Ruomeng Qin1,2,3 Kelei He4 Zhihong Ke1,2,3 Zheqi Hu1,2,3 Chenglu Mao1,2,3 Biyun Xu5 Ruowen Qi5 Xiaolei Zhu1,2,3* Qing Ye1,2,3*
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1 Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210008, China
2 Nanjing Neurology Clinic Medical Center, Nanjing 210008, China
3 Institute of Brain Science, Nanjing University, Nanjing 210008, China
4 National Institute of Healthcare Data Science at Nanjing University, China
5 Medical Statistics and Analysis Center, Nanjing Drum Tower Hospital, Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, China
Advanced Neurology 2022, 1(1), 48 https://doi.org/10.36922/an.v1i1.48
Submitted: 16 November 2021 | Accepted: 14 March 2022 | Published: 30 March 2022
© 2022 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Cerebral small vessel disease (CSVD) is the most common cause of vascular cognitive impairment (CI). However, the cognitive performance of individuals with CSVD varies considerably. Early identification of neuroimaging changes related to CI caused by CSVD is important for the prediction and management of CI. The present study aimed to explore the early alterations in cortical thickness and regional homogeneity (ReHo) related to CI in people with CSVD. A total of 106 participants with CSVD with CI, 77 participants with CSVD without CI, and 121 control participants underwent neuropsychological tests and multimodal magnetic resonance imaging scans. Cortical thickness was analyzed using FreeSurfer software, and ReHo patterns were explored using the DPARSF toolbox in brain regions that exhibited alterations in cortical thickness. The CSVD with CI group exhibited decreased cortical thickness but normal ReHo in the right anterior cingulate gyrus (ACG), right cuneus, bilateral insula, and right middle temporal gyrus compared with that in the other two groups. Specifically, both cortical thickness and ReHo in the right ACG were significantly associated with memory performance in CSVD patients with CI. In addition, impaired visuospatial function occurred earlier than the decline of global function in CSVD patients and was related to cortical thinning in the right middle temporal gyrus. In conclusion, decreased cortical thickness but normal ReHo in the right ACG, right cuneus, bilateral insula, and right middle temporal gyrus are characteristic alterations related to the development of CI in CSVD patients. These findings may contribute to the early prediction of CI in CSVD patients.

Keywords
Cerebral small vessel disease
Cognitive impairment
Cortical thickness
Regional homogeneity
Funding
National Natural Science Foundation of China
References
[1]

Huang L, Chen X, Sun W, et al., 2020, Early segmental white matter fascicle microstructural damage predicts the corresponding cognitive domain impairment in cerebral small vessel disease patients by automated fiber quantification. Front Aging Neurosci, 12: 598242. https://doi.org/10.3389/fnagi.2020.598242

[2]

Cannistraro RJ, Badi M, Eidelman BH, et al., 2019, CNS small vessel disease: A clinical review. Neurology, 92(24): 1146–1156.

[3]

Jokinen H, Melkas S, Madureira S, et al., 2016, Cognitive reserve moderates long-term cognitive and functional outcome in cerebral small vessel disease. J Neurol Neurosurg Psychiatry, 87: 1296–1302. https://doi.org/10.1136/jnnp-2016-313914 

[4]

Liu R, Wu W, Ye Q, et al., 2019, Distinctive and pervasive alterations of functional brain networks in cerebral small vessel disease with and without cognitive impairment. Dement Geriatr Cogn Disord, 47: 55–67. https://doi.org/10.1159/000496455

[5]

Chen H, Zhu H, Huang L, et al., 2022, The flexibility of cognitive reserve in regulating the frontoparietal control network and cognitive function in subjects with white matter hyperintensities. Behav Brain Res, 2022: 113831. https://doi.org/10.1016/j.bbr.2022.113831

[6]

Dey AK, Stamenova V, Turner G, et al., 2016, Pathoconnectomics of cognitive impairment in small vessel disease: A systematic review. Alzheimers Dement, 12: 831–845. https://doi.org/10.1016/j.jalz.2016.01.007

[7]

Chen H, Huang L, Yang D, et al., 2019, Nodal global efficiency in front-parietal lobe mediated periventricular white matter hyperintensity (PWMH)-related cognitive impairment. Front Aging Neurosci, 11: 347. https://doi.org/10.3389/fnagi.2019.00347

[8]

Banerjee G, Wilson D, Jager HR, et al., 2016, Novel imaging techniques in cerebral small vessel diseases and vascular cognitive impairment. Biochim Biophys Acta, 1862: 926–938. https://doi.org/10.1016/j.bbadis.2015.12.010

[9]

Lambert C, Narean JS, Benjamin P, et al., 2015, Characterising the grey matter correlates of leukoaraiosis in cerebral small vessel disease. Neuroimage Clin, 9: 194–205. https://doi.org/10.1016/j.nicl.2015.07.002

[10]

Li H, Jia X, Li Y, et al., 2021, Aberrant amplitude of low-frequency fluctuation and degree centrality within the default mode network in patients with vascular mild cognitive impairment. Brain Sci, 11: 1534. https://doi.org/10.3390/brainsci11111534

[11]

Wang Y, Yang Y, Wang T, et al., 2020, Correlation between white matter hyperintensities related gray matter volume and cognition in cerebral small vessel disease. J Stroke Cerebrovasc Dis, 29: 105275. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105275

[12]

Tang J, Shi L, Zhao Q, et al., 2017, Coexisting cortical atrophy plays a crucial role in cognitive impairment in moderate to severe cerebral small vessel disease patients. Discov Med, 23: 175–182.

[13]

Zang Y, Jiang T, Lu Y, et al., 2004, Regional homogeneity approach to fMRI data analysis. Neuroimage, 22: 394–400. https://doi.org/10.1016/j.neuroimage.2003.12.030

[14]

Ye Q, Chen X, Qin R, et al., 2019, Enhanced regional homogeneity and functional connectivity in subjects with white matter hyperintensities and cognitive impairment. Front Neurosci, 13: 695. https://doi.org/10.3389/fnins.2019.00695

[15]

Prins ND, Scheltens P. White matter hyperintensities, cognitive impairment and dementia: An update. Nat Rev Neurol, 11: 157–165. https://doi.org/10.1038/nrneurol.2015.10

[16]

Wardlaw JM, Smith EE, Biessels GJ, et al., 2013, Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol, 12:822–838. https://doi.org/10.1016/S1474-4422(13)70124-8

[17]

Lawrence AJ, Chung AW, Morris RG, et al., 2014, Structural network efficiency is associated with cognitive impairment in small-vessel disease. Neurology, 83: 304–311. https://doi.org/10.1212/WNL.0000000000000612

[18]

Lu J, Li D, Li F, et al., 2011, Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: A population-based study. J Geriatr Psychiatry Neurol, 24: 184–190. https://doi.org/10.1177/0891988711422528

[19]

Fischl B, 2012, FreeSurfer. Neuroimage, 62: 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021

[20]

Sled JG, Zijdenbos AP, Evans AC, 1998, A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging, 17: 87–97. https://doi.org/10.1109/42.668698

[21]

Dale AM, Fischl B, Sereno MI, 1999, Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage, 9: 179–194. https://doi.org/10.1006/nimg.1998.0395

[22]

Fischl B, Liu A, Dale AM, 2011, Automated manifold surgery: Constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans Med Imaging, 20: 70–80. https://doi.org/10.1109/42.906426

[23]

Fischl B, Sereno MI, Dale AM, 1999, Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage, 9(2): 195–207. https://doi.org/10.1006/nimg.1998.0396

[24]

Fischl B, Sereno MI, Tootell RB, et al., 1999, High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp, 8: 272–284. https://doi.org/10.1002/(sici)1097-0193(1999)8:4<272::aid-hbm10>3.0.co;2-4

[25]

Fischl B, van der Kouwe A, Destrieux C, et al., 2004, Automatically parcellating the human cerebral cortex. Cereb Cortex, 14: 11–22. https://doi.org/10.1093/cercor/bhg087

[26]

Desikan RS, Segonne F, Fischl B, et al., 2006, An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31: 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021

[27]

Fischl B, Dale AM, 2000, Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A, 97: 11050–11055. https://doi.org/10.1073/pnas.200033797

[28]

Klein A, Tourville J, 2012, 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci, 6: 171. https://doi.org/10.3389/fnins.2012.00171

[29]

Kloppenborg RP, Nederkoorn PJ, Geerlings MI, et al., 2014, Presence and progression of white matter hyperintensities and cognition: A meta-analysis. Neurology, 82: 2127–38. https://doi.org/10.1212/WNL.0000000000000505

[30]

Alber J, Alladi S, Bae HJ, et al., 2019, White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities. Alzheimers Dement (NY) 5: 107–117. https://doi.org/10.1016/j.trci.2019.02.001

[31]

Brundel M, Kwa VI, Bouvy WH, et al., 2014, Cerebral microbleeds are not associated with long-term cognitive outcome in patients with transient ischemic attack or minor stroke. Cerebrovasc Dis, 37: 195–202. https://doi.org/10.1159/000358119

[32]

Doi H, Inamizu S, Saito BY, et al., 2015, Analysis of cerebral lobar microbleeds and a decreased cerebral blood flow in a memory clinic setting. Intern Med, 54: 1027–1033. https://doi.org/10.2169/internalmedicine.54.3747

[33]

Makin SD, Turpin S, Dennis MS, et al., 2013, Cognitive impairment after lacunar stroke: Systematic review and meta-analysis of incidence, prevalence and comparison with other stroke subtypes. J Neurol Neurosurg Psychiatry, 84: 893–900. https://doi.org/10.1136/jnnp-2012-303645

[34]

Chen L, Song J, Cheng R, et al., 2020, Cortical thinning in the medial temporal lobe and precuneus is related to cognitive deficits in patients with subcortical ischemic vascular disease. Front Aging Neurosci, 12: 614833. https://doi.org/10.3389/fnagi.2020.614833

[35]

Ni L, Liu R, Yin Z, et al., 2016, Aberrant spontaneous brain activity in patients with mild cognitive impairment and concomitant lacunar infarction: A resting-state functional MRI study. J Alzheimers Dis, 50: 1243–1254. https://doi.org/10.3233/JAD-150622

[36]

Gasquoine PG, 2013, Localization of function in anterior cingulate cortex: from psychosurgery to functional neuroimaging. Neurosci Biobehav Rev, 37: 340–348. https://doi.org/10.1016/j.neubiorev.2013.01.002

[37]

Chen Y, Wang J, Zhang J, et al., 2014, Aberrant functional networks connectivity and structural atrophy in silent lacunar infarcts: Relationship with cognitive impairments. J Alzheimers Dis, 42: 841–850. https://doi.org/10.3233/JAD-140948 

[38]

Cabeza R, Nyberg L, 2000, Imaging cognition II: An empirical review of 275 PET and fMRI studies. J Cogn Neurosci, 12: 1–47. https://doi.org/10.1162/08989290051137585

[39]

Kobayashi Y, Morizumi T, Nagamatsu K, et al., 2021, Persistent working memory impairment associated with cerebral infarction in the anterior cingulate cortex: A case report and a literature review. Intern Med, 60: 3473–3476. https://doi.org/10.2169/internalmedicine.6927-20

[40]

Abd Razak MA, Ahmad NA, Chan YY, et al., 2019, Validity of screening tools for dementia and mild cognitive impairment among the elderly in primary health care: A systematic review. Public Health, 169: 84–92. https://doi.org/10.1016/j.puhe.2019.01.001

[41]

Zhuang L, Yang Y, Gao J, 2021, Cognitive assessment tools for mild cognitive impairment screening. J Neurol, 268: 1615–1622. https://doi.org/10.1007/s00415-019-09506-7

[42]

Tallarita GM, Parente A, Giovagnoli AR, 2019, The visuospatial pattern of temporal lobe epilepsy. Epilepsy Behav, 101: 106582. https://doi.org/10.1016/j.yebeh.2019.106582 

[43]

Nie S, Shen C, Guo Y, et al., 2021, Preliminary findings on visual event-related potential P3 in asymptomatic patients with cerebral small vessel disease. Neuropsychiatr Dis Treat, 17: 3379–3394. https://doi.org/10.2147/NDT.S338717

[44]

Farivar R, 2009, Dorsal-ventral integration in object recognition. Brain Res Rev, 61:144–153. https://doi.org/10.1016/j.brainresrev.2009.05.006

[45]

Uithol S, Bryant KL, Toni I, et al., 2021, The anticipatory and task-driven nature of visual perception. Cereb Cortex, 31: 5354–5362. 

[46]

Ibarretxe-Bilbao N, Junque C, Marti MJ, et al., 2011, Brain structural MRI correlates of cognitive dysfunctions in Parkinson’s disease. J Neurol Sci, 310: 70–74. https://doi.org/10.1016/j.jns.2011.07.054

[47]

Andrade K, Kas A, Valabrègue R, et al., 2012, Visuospatial deficits in posterior cortical atrophy: Structural and functional correlates. J Neurol Neurosurg Psychiatry, 83: 860–863. https://doi.org/10.1136/jnnp-2012-302278

[48]

Schulz M, Malherbe C, Cheng B, et al., 2021, Functional connectivity changes in cerebral small vessel disease a systematic review of the resting-state MRI literature. BMC Med, 19: 103. https://doi.org/10.1186/s12916-021-01962-1

Conflict of interest
The authors declare that they have no known conflicts of interest that could influence the work reported in this paper.
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Advanced Neurology, Electronic ISSN: 2810-9619 Published by AccScience Publishing