地方政府
认知功能衰退
符号(数学)
疾病
认知
心理学
神经科学
阿尔茨海默病
痴呆
医学
认知心理学
脑电图
内科学
数学
数学分析
作者
Christian Sandøe Musaeus,Knut Engedal,Peter Høgh,Vesna Jelić,Arjun R. Khanna,Troels W. Kjær,Morten Mørup,Mala Naik,Anne‐Rita Oeksengaard,Emiliano Santarnecchi,Jón Snædal,Lars‐Olof Wahlund,Gunhild Waldemar,Birgitte Andersen
摘要
Introduction: Large-scale brain networks are disrupted in the early stages of Alzheimer's disease (AD). Electroencephalography microstate analysis, a promising method for studying brain networks, parses EEG signals into topographies representing discrete, sequential network activations. Prior studies indicate that patients with AD show a pattern of global microstate disorganization. We investigated whether any specific microstate changes could be found in patients with AD and mild cognitive impairment (MCI) compared to healthy controls (HC). Materials and methods: Standard EEGs were obtained from 135 HC, 117 patients with MCI, and 117 patients with AD from six Nordic memory clinics. We parsed the data into four archetypal microstates. Results: There was significantly increased duration, occurrence, and coverage of microstate A in patients with AD and MCI compared to HC. When looking at microstates in specific frequency bands, we found that microstate A was affected in delta (1–4 Hz), theta (4–8 Hz), and beta (13–30 Hz), while microstate D was affected only in the delta and theta bands. Microstate features were able to separate HC from AD with an accuracy of 69.8% and HC from MCI with an accuracy of 58.7%. Conclusions: Further studies are needed to evaluate whether microstates represent a valuable disease classifier. Overall, patients with AD and MCI, as compared to HC, show specific microstate alterations, which are limited to specific frequency bands. These alterations suggest disruption of large-scale cortical networks in AD and MCI, which may be limited to specific frequency bands.
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