ABSTRACT

Introduction: The Brief Visual Memory Test-Revised (BVMT-R) is a frequently used visuospatial declarative memory test, but normative data in the Czech population are lacking. Moreover, the BVMT-R includes promising learning indexes that can be used to detect learning deficits in Parkinson’s disease (PD). However, its clinical usefulness has not yet been thoroughly examined. Early detection of memory impairment in PD is essential for effective treatment. Therefore, this study aimed to provide BVMT-R Czech normative data for clinical use and to find the detection potential of the principal BVMT-R scores, including new learning indices, to capture the cognitive deficit in PD.

Method: The BVMT-R were administered to a normative sample of 920 participants aged 17 to 95 years and to a clinical sample of 60 PD patients; 25 with mild cognitive impairment (PD-MCI) and 35 with normal cognition (PD-NC). In order to provide normative values, multiple regression analyses were employed, and to compare the clinical and control sample, Bayesian Hierarchical Linear Models were used.

Results: The best model for regression-based norms showed to be with age + age² + education + sex as predictors. From all learning indexes, L6 (sum of trials 1–3), followed by, L4 (sum of trials 1–3 multiplied by the difference between the highest and the lowest score) best differentiated between controls or PD-NC and PD-MCI.

Conclusions: We provide regression-based normative values for BVMT-R that could be used in clinical settings and meta-analytic efforts. Furthermore, we revealed visuospatial learning and memory deficit in PD-MCI. We have also identified the most discriminative learning index adapted to BVMT-R.

摘要

简介：简短的修订版视觉记忆测试（BVMT-R）是一种经常使用的视觉空间声明性记忆测试，但捷克人口中缺乏规范数据。此外，BVMT-R包括有前途的学习指标，可用于检测帕金森氏病（PD）中的学习缺陷。但是，其临床用途尚未得到彻底检查。早期发现PD的记忆障碍对于有效治疗至关重要。因此，本研究旨在为临床使用BVMT-R捷克标准数据，并发现BVMT-R主要评分的检测潜力，包括新的学习指标，以捕捉PD的认知缺陷。

方法：对920名17至95岁的参与者和60名PD患者的临床样本进行BVMT-R的标准化研究。25例患有轻度认知障碍（PD-MCI），而35例具有正常认知（PD-NC）。为了提供标准值，使用了多元回归分析，并且为了比较临床样本和对照样本，使用了贝叶斯层次线性模型。

结果：基于回归的规范的最佳模型显示以年龄+ ²岁+教育程度+性别为预测因子。从所有学习指标中，最好区分对照组或PD-NC和PD-MCI的L6（试验1-3的总和），然后是L4（试验1-3的总和乘以最高和最低分数之间的差）。 。

结论：我们提供了基于回归的BVMT-R规范值，可用于临床环境和荟萃分析工作。此外，我们揭示了PD-MCI的视觉空间学习和记忆缺陷。我们还确定了适用于BVMT-R的最具区别性的学习指数。