본 연구실과 서울과학기술대학교 인공지능응용학과, 한양대학교 신경과와 공동 연구로 경도 인지 장애 환자의 휴지기 뇌파의 특성에 관한 연구로 Frontiers in Psychiatry 국제 학술지에 논문을 게재했습니다.
Title: Resting-State Electroencephalographic Characteristics Related to Mild Cognitive Impairments
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 eye-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, 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.
