Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA.
Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
J Alzheimers Dis. 2023;91(4):1557-1572. doi: 10.3233/JAD-221152.
Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales.
To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD.
We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function.
SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power.
SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.
阿尔茨海默病（AD）与睡眠-觉醒周期中的脑电图变化有关。由于大脑是一个非线性系统，因此在行为状态下的非线性脑电图特征可能为 AD 提供有信息的生理生物标志物。多尺度波动分散熵（MFDE）为大脑在一系列生物相关时间尺度上的脑电图信息内容提供了敏感的非线性度量。
评估清醒和睡眠脑电图中的 MFDE 是否可作为 AD 的潜在生物标志物。
我们分析了 35 名认知正常的健康对照者、23 名轻度认知障碍（MCI）参与者和 19 名轻度痴呆症（AD）参与者的整夜头皮脑电图。我们检查了清醒和睡眠状态下的熵测度，包括熵的慢到快活动比（SFAR-entropy）。我们将 SFAR-entropy 与线性 EEG 测度（包括功率谱密度的慢到快活动比 SFAR-PSD 和相对阿尔法功率）以及认知功能进行了比较。
SFAR-entropy 可区分痴呆症与 MCI 和对照组。在 REM 睡眠中，这种差异最大，REM 睡眠与高胆碱能活动有关。在整个脑电中都存在差异，在颞区和枕区最为明显。5 分钟的 REM 睡眠足以区分痴呆症与 MCI 和对照组。REM 睡眠时 SFAR-entropy 较高与蒙特利尔认知评估表现较差有关。基于 REM 睡眠 SFAR-entropy 的分类器可准确地区分痴呆症与 MCI 和对照组，并且优于基于 SFAR-PSD 和相对阿尔法功率的分类器。
在 REM 睡眠中测量的 SFAR-entropy 可有力地区分 AD 中的痴呆症与 MCI 和健康对照组。
