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首页> 外文期刊>NeuroImage: Clinical >Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease
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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

机译:在预测中风后小血管疾病患者的早期认知障碍方面,结构性脑网络测量优于血管负荷评分

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ObjectivesIn 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.Method127 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.ResultsGroup 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?
机译:目的在本横断面研究中,我们旨在通过比较SVD得分与结构性脑网络指标来探讨中风后非痴呆后脑小血管疾病(SVD)队列中早期认知障碍的机制。方法连续招募127名SVD患者来自中风诊所,包括76名患有轻度认知障碍(MCI)的人和51名没有认知障碍(NCI)的人。进行了详细的神经心理学评估和多模式MRI。 SVD分数以标准量表计算,并通过扩散张量成像(DTI)分析结构性大脑网络测度。分析组间差异,并应用逻辑回归确定SVD的预测价值和认知状况的网络测度。使用结构方程模型(SEM)进行的调解分析可更好地理解SVD负担,脑网络和认知缺陷的相互作用。结果在所有全局脑网络测度中均发现了群体差异。在对年龄,性别,教育程度和抑郁进行调整后,发现全球脑网络测度与多种神经心理学测试之间存在显着相关性,包括TMT-B(r?=?0.209,p?

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