神经心理学
痴呆
认知
血管性痴呆
神经心理评估
神经影像学
听力学
心理学
情景记忆
记忆诊所
神经心理学测验
医学
内科学
精神科
疾病
作者
Rui Tian,Yanxin Zhang,Fang Liu,Xinran Xue,Yutong Zhang,Zhuo Tian,Tingting Fang,Ruxue Fan,Yuan Li,Nan Zhang
摘要
Abstract Objectives Cognitive and neuroimaging assessments are still the main clinical practice methods for screening and diagnosing vascular dementia (VaD) patients. This study aimed to establish the neuropsychological characteristics of mild‐to‐moderate subcortical ischaemic vascular dementia (SIVD) patients, find an optimal cognitive marker for differentiating them from Alzheimer's disease (AD) patients, and explore the correlation between cognitive function and total small vessel disease (SVD) burden. Methods SIVD ( n = 60) and AD ( n = 30) patients and cognitively unimpaired healthy controls (HCs; n = 30) were recruited from our longitudinal MRI AD and SIVD study (ChiCTR1900027943) and received a comprehensive neuropsychological assessment and a multimodal MRI scan. Cognitive performance and MRI SVD markers were compared between groups. Combined cognitive scores were established for differentiating between SIVD and AD patients. Correlations between cognitive function and total SVD scores were analysed in dementia patients. Results SIVD patients showed poorer performance in information processing speed and better performance in memory, language, and visuospatial function than AD patients, although all cognitive domains were impaired in both groups compared with HCs. Combined cognitive scores showed an area under the curve of 0.727 (95%CI 0.62–0.84, p < 0.001) for differentiating SIVD and AD patients. Auditory Verbal Learning Test recognition scores were negatively correlated with total SVD scores in SIVD patients. Conclusions Our results suggested that neuropsychological assessments, specifically combined tests including episodic memory, information processing speed, language and visuospatial ability, are useful in the clinical differentiation between SIVD and AD patients. Moreover, cognitive dysfunction was partly correlated with MRI SVD burden in SIVD patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI