当前位置: X-MOL 学术Exp. Brain Res. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Harnessing forgetfulness: can episodic-memory tests predict early Alzheimer’s disease?
Experimental Brain Research ( IF 2 ) Pub Date : 2021-07-27 , DOI: 10.1007/s00221-021-06182-w
Samuel L Warren 1 , Ahmed A Moustafa 1, 2 , Hany Alashwal 3 ,
Affiliation  

A rapid increase in the number of patients with Alzheimer’s disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD. For our analysis, we studied 307 participants with MCI across four years using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Using a binary logistic regression, we compared episodic-memory tests to each other and to prominent neuroimaging methods in MCI converter (MCI participants who developed AD) and MCI non-converter groups (MCI participants who did not develop AD). We also combined variables to test the accuracy of mixed-predictor models. Our results indicated that the best predictors of MCI to AD conversion were the following: a combined episodic-memory and neuroimaging model in year one (59.8%), the Rey Auditory Verbal Learning Test in year two (71.7%), a mixed episodic-memory predictor model in year three (77.7%) and the Logical Memory Test in year four (77.2%) of ADNI. Overall, we found that individual episodic-memory measure and mixed models performed similarly when predicting MCI to AD conversion. Comparatively, individual neuroimaging measures predicted MCI conversion worse than chance. Accordingly, our results indicate that episodic-memory tests could be instrumental in detecting early-stage AD and enabling effective treatment.



中文翻译:

利用健忘:情景记忆测试可以预测早期阿尔茨海默病吗?

预计在接下来的几十年中,阿尔茨海默病 (AD) 患者的数量将迅速增加。因此,迫切需要能够实现有效治疗策略的早期 AD 检测方法。在这项研究中,我们考虑了情景记忆测量预测轻度认知障碍 (MCI) 到 AD 转换的能力,从而检测早期 AD。在我们的分析中,我们使用来自阿尔茨海默病神经影像学计划的数据在四年内研究了 307 名 MCI 参与者(ADNI)。我们使用二元逻辑回归,将情景记忆测试相互比较,并与 MCI 转换者(发生 AD 的 MCI 参与者)和 MCI 非转换组(未发生 AD 的 MCI 参与者)中的突出神经影像学方法进行比较。我们还结合变量来测试混合预测模型的准确性。我们的结果表明,MCI 到 AD 转换的最佳预测指标如下:第一年的情景记忆和神经影像学模型(59.8%),第二年的 Rey 听觉语言学习测试(71.7%),混合情景 - ADNI 第三年的记忆预测模型 (77.7%) 和第四年的逻辑记忆测试 (77.2%)。总的来说,我们发现在预测 MCI 到 AD 转换时,单个情景记忆测量和混合模型的表现相似。比较,个别神经影像测量预测 MCI 转换比机会更糟糕。因此,我们的结果表明,情景记忆测试可能有助于检测早期 AD 并实现有效治疗。

更新日期:2021-07-27
down
wechat
bug