Resting-state electroencephalographic characteristics related to mild cognitive impairments

Frontiers in Psychiatry

Psychiatry

Chanwoo Shin¹  Seong-Eun Kim¹  Kyoungwon Seo¹  Junyeop Yim²  Byoung-Kyong Min³  Hojin Choi⁴  Jinseok Park⁴  Hokyoung Ryu⁵ 

[1] Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea;Department of Applied Mathematics, Kongju National University, Gongju-si, Republic of Korea;Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea;Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea;Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea;

关键词: mild cognitive impairment;    EEG;    spectral power;    complexity;    functional connectivity;    graph analysis;   

DOI  :  10.3389/fpsyt.2023.1231861

 received in 2023-05-31, accepted in 2023-08-28,  发布年份 2023

来源: Frontiers

【 摘 要 】

Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI.

【 授权许可】

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Copyright © 2023 Kim, Shin, Yim, Seo, Ryu, Choi, Park and Min.