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脑电图区域β指数可能有助于区分阿尔茨海默病与抑郁症。

Regional Beta Index of Electroencephalography May Differentiate Alzheimer's Disease from Depression.

作者信息

Lee Kanghee, Han Ji Won, Kim Ki Woong

机构信息

Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.

出版信息

Psychiatry Investig. 2017 Sep;14(5):708-711. doi: 10.4306/pi.2017.14.5.708. Epub 2017 Sep 11.

Abstract

Differentiating early Alzheimer's disease (AD) from depression with cognitive impairment is challenging in the elderly. To develop a model for differentiating these two conditions using electroencephalography (EEG), we enrolled 11 patients with early probable AD and 11 age- and cognitive function-matched patients with major depressive disorder (MDD) and compared the EEG relative powers of 9 scalp regions. Compared to the MDD group, the AD group had a higher global theta relative power (p=0.021). In the MDD group, beta relative power was higher in the mid-central region than in the left or right central regions (p<0.01). The prediction model that included global theta relative power and regional beta index was able to discriminate AD from MDD (AUC=0.893, p=0.002). A combination of global theta relative power and intra-individual regional differences in beta may differentiate early AD from MDD with cognitive impairment.

摘要

在老年人中,将早期阿尔茨海默病(AD)与伴有认知障碍的抑郁症区分开来具有挑战性。为了开发一种利用脑电图(EEG)区分这两种情况的模型,我们招募了11例早期可能患有AD的患者以及11例年龄和认知功能相匹配的重度抑郁症(MDD)患者,并比较了9个头皮区域的EEG相对功率。与MDD组相比,AD组的全局θ相对功率更高(p = 0.021)。在MDD组中,中央中部区域的β相对功率高于左侧或右侧中央区域(p < 0.01)。包含全局θ相对功率和区域β指数的预测模型能够区分AD和MDD(AUC = 0.893,p = 0.002)。全局θ相对功率与个体内部β区域差异的组合可能有助于将早期AD与伴有认知障碍的MDD区分开来。

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