Resting-state electroencephalographic characteristics related to mild cognitive impairments

Seong-Eun Kim; Chanwoo Shin; Junyeop Yim; Kyoungwon Seo; Hokyoung Ryu; Hojin Choi; Jinseok Park; Byoung-Kyong Min

doi:10.3389/fpsyt.2023.1231861

Resting-state electroencephalographic characteristics related to mild cognitive impairments

Front Psychiatry. 2023 Sep 13:14:1231861. doi: 10.3389/fpsyt.2023.1231861. eCollection 2023.

Authors

Seong-Eun Kim^#¹, Chanwoo Shin^#¹, Junyeop Yim², Kyoungwon Seo¹, Hokyoung Ryu³, Hojin Choi⁴, Jinseok Park⁴, Byoung-Kyong Min⁵

Affiliations

¹ Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea.
² Department of Applied Mathematics, Kongju National University, Gongju-si, Republic of Korea.
³ Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea.
⁴ Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea.
⁵ Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.

^# Contributed equally.

Abstract

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.

Keywords: EEG; complexity; functional connectivity; graph analysis; mild cognitive impairment; spectral power.

Grants and funding

This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean Government (MSIT) (RS-2023-00208492, 2020M3C1B8081320, 2020M3C1B8081319, and 2021R1A4A5033480).