口语流利性测试
斯特罗普效应
执行职能
痴呆
流利
认知障碍
认知
接收机工作特性
心理学
听力学
执行功能障碍
内科学
医学
临床心理学
神经心理学
精神科
疾病
数学教育
作者
Sara Fernández Guinea,Almudena Junquera Fernández,Javier Olazarán,Javier González Marqués,Mario A. Parra
摘要
Abstract Background Recent research has pointed to impaired executive functions (EF) as a potential early predictor of progression from Mild Cognitive Impairment (MCI) to dementia in the Alzheimer’s clinical syndrome (ACS) (Junquera et al, 2020). In this study, we investigated the EF tests that hold the best sensitivity and specificity to discriminate, using baseline data, between cognitively unimpaired older adults and MCI patients who converted to ACS or remained stable after two years (aim 1). We then explored which EF components can best predict conversion from MCI to ACS in a two‐year follow‐up (aim 2). Method We assessed 210 participants, 71 cognitively unimpaired older adults (CU) and 130 MCI patients. Eight tests assessing EF (Similarities, Arithmetic, Reverse Digits, and Letters and Numbers, Trail‐Making Test, Stroop Test, Verbal Fluency – phonemic and categories, and Zoo Map) were administered at baseline and at 2‐year follow‐up, together with cognitive screening tools and IADL measures. ROC analysis was used to address aim 1. A binary regression model was then used to examine the EF components and tests identified via aim 1 that reliably predict progression from MCI to ACS (aim 2). Result Baseline scores from the TMT and semantic categories' fluency were significantly lower in MCI patients who later progressed to ACS (p<.001) than those who remained stable. ROC analysis showed that the TMT and semantic category fluency test were the best tests to differentiate at baseline between MCI converter and non‐converter (AUC = 0.768 and 0.77, respectively), CU and MCI converter (AUC = 0.864 and 0.876, respectively), and between CU and MCI no converter (AUC = 0.602 and 0.673, respectively). Binary logistic regression results revealed that indeed, the TMT and semantic categories fluency test significantly predicted the conversion from MCI to ACS (p<.05). Conclusion Switching abilities and verbal fluency (categories) were the executive function that best predicts MCI to ACS conversion in two years. TMT and semantic verbal fluency clearly identify people with high risk to develop ACS in two years. It is recommended to include these tests in the prodromal Alzheimer’s disease characterization.
科研通智能强力驱动
Strongly Powered by AbleSci AI