Suppr超能文献

神经心理学测试与机器学习可区分阿尔茨海默病与其他认知障碍病因。

Neuropsychological Testing and Machine Learning Distinguish Alzheimer's Disease from Other Causes for Cognitive Impairment.

作者信息

Gurevich Pavel, Stuke Hannes, Kastrup Andreas, Stuke Heiner, Hildebrandt Helmut

机构信息

Department of Mathematics, Free University of BerlinBerlin, Germany.

Faculty of Science, Peoples' Friendship University of RussiaMoscow, Russia.

出版信息

Front Aging Neurosci. 2017 Apr 25;9:114. doi: 10.3389/fnagi.2017.00114. eCollection 2017.

Abstract

With promising results in recent treatment trials for Alzheimer's disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aβ(1-42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer's Disease-Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease.

摘要

鉴于近期阿尔茨海默病(AD)治疗试验取得了有前景的结果,在早期将AD与其他导致认知障碍的原因区分开来变得越发重要。然而,现有的诊断方法要么具有侵入性(腰椎穿刺、PET),要么不准确(磁共振成像,MRI)。本研究调查了神经心理学测试(NPT)在158例因各种原因患有轻度认知障碍(MCI)或痴呆症的患者样本中特异性识别可能患有AD的患者的潜力。根据简易精神状态检查表(MMSE)评分将患者分为早期组和晚期组,并根据脑脊液(CSF)中总tau蛋白与β淀粉样蛋白比率(总tau/Aβ(1-42)比率,TB比率)的死后验证阈值将其标记为AD或非AD患者。所有患者均完成了既定的阿尔茨海默病神经心理学评估联合登记处电池测试(CERAD-NAB)以及另外两项新开发的旨在找出特定阿尔茨海默病典型缺陷的神经心理学测试(回忆和言语理解)。基于这些测试结果,使用机器学习算法预测潜在的AD(病理上TB比率升高)。为此,在每种情况下,算法都在除要预测的患者之外的所有患者身上进行训练(留一法验证)。在整个组中,82%的患者能够被正确识别为AD或非AD。在一般认知障碍较小的早期组中,分类准确率提高到了89%。因此,NPT似乎能够高精度地区分AD患者与因其他神经退行性或血管性原因导致认知障碍的患者,并且可用于临床常规筛查和药物研究,尤其是在该疾病的早期阶段。

相似文献

5

引用本文的文献

本文引用的文献

4
Why significant variables aren't automatically good predictors.为什么显著变量并非自动成为良好的预测指标。
Proc Natl Acad Sci U S A. 2015 Nov 10;112(45):13892-7. doi: 10.1073/pnas.1518285112. Epub 2015 Oct 26.
9
The CERAD Neuropsychological Battery in Patients with Frontotemporal Lobar Degeneration.额颞叶变性患者的CERAD神经心理成套测验
Dement Geriatr Cogn Dis Extra. 2015 Apr 14;5(1):147-54. doi: 10.1159/000380815. eCollection 2015 Jan-Apr.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验