Poza Jesús, Gómez Carlos, García María, Tola-Arribas Miguel A, Carreres Alicia, Cano Mónica, Hornero Roberto
Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Campus Miguel Delibes, Paseo de Belén 15, 47011, Valladolid. Spain.
Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid. Spain.
Curr Alzheimer Res. 2017;14(9):924-936. doi: 10.2174/1567205014666170309115656.
An accurate characterization of neural dynamics in mild cognitive impairment (MCI) is of paramount importance to gain further insights into the underlying neural mechanisms in Alzheimer's disease (AD). Nevertheless, there has been relatively little research on brain dynamics in prodromal AD. As a consequence, its neural substrates remain unclear.
In the present research, electroencephalographic (EEG) recordings from patients with dementia due to AD, subjects with MCI due to AD and healthy controls (HC) were analyzed using relative power (RP) in conventional EEG frequency bands and a novel parameter useful to explore the spatio-temporal fluctuations of neural dynamics: the spectral flux (SF).
Our results suggest that dementia due to AD is associated with a significant slowing of EEG activity and several significant alterations in spectral fluctuations at low (i.e. theta) and high (i.e. beta and gamma) frequency bands compared to HC (p < 0.05). Furthermore, subjects with MCI due to AD exhibited a specific frequency-dependent pattern of spatio-temporal abnormalities, which can help identify neural mechanisms involved in cognitive impairment preceding AD. Classification analyses using linear discriminant analysis with a leave-one-out cross-validation procedure showed that the combination of RP and within-electrode SF at the beta band was useful to obtain a 77.3 % of accuracy to discriminate between HC and AD patients. In the case of comparison between HC and MCI subjects, the classification accuracy reached a value of 79.2 %, combining within-electrode SF at beta and gamma bands. SF has proven to be a useful measure to obtain an original description of brain dynamics at different stages of AD.
Consequently, SF may contribute to gain a more comprehensive understanding into neural substrates underlying MCI, as well as to develop potential early AD biomarkers.
准确描述轻度认知障碍(MCI)中的神经动力学对于深入了解阿尔茨海默病(AD)的潜在神经机制至关重要。然而,对前驱期AD的脑动力学研究相对较少。因此,其神经基质仍不清楚。
在本研究中,使用传统脑电图频段的相对功率(RP)和一个有助于探索神经动力学时空波动的新参数:频谱通量(SF),对AD所致痴呆患者、AD所致MCI受试者和健康对照(HC)的脑电图记录进行了分析。
我们的结果表明,与HC相比,AD所致痴呆与脑电图活动显著减慢以及低频(即θ波)和高频(即β波和γ波)频段的频谱波动有若干显著变化相关(p < 0.05)。此外,AD所致MCI受试者表现出特定的频率依赖性时空异常模式,这有助于识别AD之前认知障碍所涉及的神经机制。使用线性判别分析和留一法交叉验证程序进行的分类分析表明,β频段的RP和电极内SF的组合有助于获得77.3%的准确率来区分HC和AD患者。在HC和MCI受试者的比较中,结合β和γ频段的电极内SF,分类准确率达到79.2%。SF已被证明是一种有用的测量方法,可用于获得AD不同阶段脑动力学的原始描述。
因此,SF可能有助于更全面地了解MCI的神经基质,并开发潜在的早期AD生物标志物。