A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses.
A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses.
复制标题
DOI：
10.3389/fnins.2015.00350
复制
发表时间：
2015
影响因子：
4.3
通讯作者：
Tsolaki MN
中科院分区：
文献类型：
--
作者：
Dimitriadis SI;Laskaris NA;Bitzidou MP;Tarnanas I;Tsolaki MN
轻度认知障碍（mild cognitive impairment， MCI）是衰老引起的正常认知变化与AD引起的认知能力下降之间的过渡阶段，其检测具有至关重要的临床意义，因为MCI患者进展为AD的风险增加。脑电图（EEG）在静息状态下脑电波频谱内容和连通性的改变与早期AD有关。近年来，认知事件相关电位（cognitive event- relevant potential, ERPs）作为一种易于操作的筛选测试进入人们的视野。基于最近关于交叉频率耦合（CFC）在认知中的作用的发现，我们介绍了一种基于标准听觉怪异范式的认知反应来检测MCI的相关方法。通过使用Pz传感器记录的单次试验信号并比较目标和非目标刺激的反应，我们首次证明了CFC的增加与认知任务有关。然后，考虑到CFC的动态特性，我们确定了特定脑电波频率对之间的耦合携带足够信息的实例，用于区分正常受试者和轻度认知障碍患者。通过这种方式，我们形成了认知受损的多参数特征。新的复合生物标志物使用来自25名失忆性轻度认知障碍患者和15名年龄匹配的对照组的队列数据进行了测试。采用标准的机器学习算法来实现二元分类任务。基于留一交叉验证，发现测量的分类率达到非常高的水平（95%）。我们的方法与使用平均ERP反应的形态学进行诊断和使用单试验反应的光谱-时间分析特征的传统替代方法相比具有优势。这进一步表明，与任务相关的CFC测量可以为AD的诊断和预后提供宝贵的分析。
The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.