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Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease
OBJECTIVES: In this cross-sectional study, we aimed to explore the mechanisms of early cognitive impairment in a post stroke non-dementia cerebral small vessel disease (SVD) cohort by comparing the SVD score with the structural brain network measures. METHOD: 127 SVD patients were recruited consecut...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6378318/ https://www.ncbi.nlm.nih.gov/pubmed/30772684 http://dx.doi.org/10.1016/j.nicl.2019.101712 |
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author | Du, Jing Wang, Yao Zhi, Nan Geng, Jieli Cao, Wenwei Yu, Ling Mi, Jianhua Zhou, Yan Xu, Qun Wen, Wei Sachdev, Perminder |
author_facet | Du, Jing Wang, Yao Zhi, Nan Geng, Jieli Cao, Wenwei Yu, Ling Mi, Jianhua Zhou, Yan Xu, Qun Wen, Wei Sachdev, Perminder |
author_sort | Du, Jing |
collection | PubMed |
description | OBJECTIVES: In this cross-sectional study, we aimed to explore the mechanisms of early cognitive impairment in a post stroke non-dementia cerebral small vessel disease (SVD) cohort by comparing the SVD score with the structural brain network measures. METHOD: 127 SVD patients were recruited consecutively from a stroke clinic, comprising 76 individuals with mild cognitive impairment (MCI) and 51 with no cognitive impairment (NCI). Detailed neuropsychological assessments and multimodal MRI were performed. SVD scores were calculated on a standard scale, and structural brain network measures were analyzed by diffusion tensor imaging (DTI). Between-group differences were analyzed, and logistic regression was applied to determine the predictive value of SVD and network measures for cognitive status. Mediation analysis with structural equation modeling (SEM) was used to better understand the interactions of SVD burden, brain networks and cognitive deficits. RESULTS: Group difference was found on all global brain network measures. After adjustment for age, gender, education and depression, significant correlations were found between global brain network measures and diverse neuropsychological tests, including TMT-B (r = −0.209, p < .05), DSST (r = 0.206, p < .05), AVLT short term free recall (r = 0.233, p < .05), AVLT long term free recall (r = 0.264, p < .05) and Rey-O copy (r = 0.272, p < .05). SVD score showed no group difference and was not correlated with cognition tests. Network global efficiency (E(Global)) was significantly related to cognitive state (p < .01) but not the SVD score. Mediation analysis showed that the standardized total effect (p = .013) and the standardized indirect effect (p = .016) of SVD score on cognition was significant, but the direct effect was not. CONCLUSIONS: Brain network measures, but not the SVD score, are significantly correlated with cognition in post-stroke SVD patients. Mediation analysis showed that the cerebral vascular lesions produce cognitive dysfunction by interfering with the structural brain network in SVD patients. The brain network measures may be regarded as direct and independent surrogate markers of cognitive impairment in SVD. |
format | Online Article Text |
id | pubmed-6378318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-63783182019-02-27 Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease Du, Jing Wang, Yao Zhi, Nan Geng, Jieli Cao, Wenwei Yu, Ling Mi, Jianhua Zhou, Yan Xu, Qun Wen, Wei Sachdev, Perminder Neuroimage Clin Regular Article OBJECTIVES: In this cross-sectional study, we aimed to explore the mechanisms of early cognitive impairment in a post stroke non-dementia cerebral small vessel disease (SVD) cohort by comparing the SVD score with the structural brain network measures. METHOD: 127 SVD patients were recruited consecutively from a stroke clinic, comprising 76 individuals with mild cognitive impairment (MCI) and 51 with no cognitive impairment (NCI). Detailed neuropsychological assessments and multimodal MRI were performed. SVD scores were calculated on a standard scale, and structural brain network measures were analyzed by diffusion tensor imaging (DTI). Between-group differences were analyzed, and logistic regression was applied to determine the predictive value of SVD and network measures for cognitive status. Mediation analysis with structural equation modeling (SEM) was used to better understand the interactions of SVD burden, brain networks and cognitive deficits. RESULTS: Group difference was found on all global brain network measures. After adjustment for age, gender, education and depression, significant correlations were found between global brain network measures and diverse neuropsychological tests, including TMT-B (r = −0.209, p < .05), DSST (r = 0.206, p < .05), AVLT short term free recall (r = 0.233, p < .05), AVLT long term free recall (r = 0.264, p < .05) and Rey-O copy (r = 0.272, p < .05). SVD score showed no group difference and was not correlated with cognition tests. Network global efficiency (E(Global)) was significantly related to cognitive state (p < .01) but not the SVD score. Mediation analysis showed that the standardized total effect (p = .013) and the standardized indirect effect (p = .016) of SVD score on cognition was significant, but the direct effect was not. CONCLUSIONS: Brain network measures, but not the SVD score, are significantly correlated with cognition in post-stroke SVD patients. Mediation analysis showed that the cerebral vascular lesions produce cognitive dysfunction by interfering with the structural brain network in SVD patients. The brain network measures may be regarded as direct and independent surrogate markers of cognitive impairment in SVD. Elsevier 2019-02-05 /pmc/articles/PMC6378318/ /pubmed/30772684 http://dx.doi.org/10.1016/j.nicl.2019.101712 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Du, Jing Wang, Yao Zhi, Nan Geng, Jieli Cao, Wenwei Yu, Ling Mi, Jianhua Zhou, Yan Xu, Qun Wen, Wei Sachdev, Perminder Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease |
title | Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease |
title_full | Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease |
title_fullStr | Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease |
title_full_unstemmed | Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease |
title_short | Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease |
title_sort | structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6378318/ https://www.ncbi.nlm.nih.gov/pubmed/30772684 http://dx.doi.org/10.1016/j.nicl.2019.101712 |
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