Electroencephalography functional network for screening amyloid positivity in mild cognitive impairment: a cross-sectional study.

IF 3.6 3区医学 Q1 CLINICAL NEUROLOGY

Clinical Neurophysiology Pub Date : 2025-10-04 DOI:10.1016/j.clinph.2025.2111374

Jooheon Kong, Mingyeong So, Hyunsung Park, Young-Tak Kim, Hayom Kim, Kisoo Pahk, Sung Hoon Kang, Synho Do, Dong-Kwon Lim, Chan-Nyoung Lee, Jung Bin Kim

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引用次数: 0

Abstract

Objective: Monoclonal antibodies targeting amyloid-β (Aβ) show disease-modifying potential in Alzheimer's disease (AD), making early identification of Aβ-positive individuals at the mild cognitive impairment (MCI) stage essential. Functional network metrics derived from electroencephalography (EEG) may reflect Aβ-related network disruption and serve as viable screening tools.

Methods: This study included patients with cognitive decline who underwent ¹⁸F-flutemetamol PET/CT, EEG, and neuropsychological testing at Korea University Anam Hospital (2020-2024). Participants were categorized into subjective cognitive decline (SCD), MCI, or dementia. Resting-state EEG was analyzed using the weighted phase lag index to compute functional connectivity, followed by graph theoretical analysis to assess global network properties. Machine learning models were used to classify Aβ status in the MCI group based on EEG-derived features.

Results: Among 100 participants (19 SCD, 55 MCI, 26 dementia), 53 were Aβ-positive. In MCI, Aβ-positive individuals (n = 28) showed significantly reduced delta-band network strength, global/local efficiency, clustering coefficient, and transitivity (all p < 0.05). Classification models reached an AUC of up to 0.850.

Conclusions: Resting-state EEG network analysis provides a non-invasive, cost-effective approach for screening Aβ positivity in MCI.

Significance: EEG-based global network measures may aid in early AD diagnosis and patient selection for anti-Aβ therapies.

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在轻度认知障碍中筛选淀粉样蛋白阳性的脑电图功能网络：一项横断面研究。

目的：针对淀粉样蛋白-β (Aβ)的单克隆抗体在阿尔茨海默病（AD）中显示出改善疾病的潜力，这使得在轻度认知障碍（MCI）阶段早期识别Aβ阳性个体至关重要。从脑电图（EEG）得出的功能网络指标可以反映a β相关的网络中断，并作为可行的筛查工具。方法：本研究纳入了在高丽大学安岩医院（2020-2024）接受18f -氟替他莫PET/CT、EEG和神经心理测试的认知衰退患者。参与者被分为主观认知衰退（SCD）、轻度认知障碍（MCI）或痴呆。静息状态脑电分析采用加权相位滞后指数计算功能连通性，图论分析评估全局网络特性。使用机器学习模型根据脑电图衍生的特征对MCI组的Aβ状态进行分类。结果：在100名参与者中（SCD 19人，MCI 55人，痴呆26人），53人a β阳性。在MCI中，a β阳性个体（n = 28）表现出显著降低的δ波段网络强度、整体/局部效率、聚类系数和传递性（均为p）。结论：静息状态脑电图网络分析为筛查MCI中a β阳性提供了一种无创、经济有效的方法。意义：基于脑电图的全球网络测量可能有助于早期AD诊断和患者选择抗a β治疗。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Neurophysiology 医学-临床神经学

CiteScore

8.70

自引率

6.40%

发文量

932

审稿时长

59 days

期刊介绍： As of January 1999, The journal Electroencephalography and Clinical Neurophysiology, and its two sections Electromyography and Motor Control and Evoked Potentials have amalgamated to become this journal - Clinical Neurophysiology. Clinical Neurophysiology is the official journal of the International Federation of Clinical Neurophysiology, the Brazilian Society of Clinical Neurophysiology, the Czech Society of Clinical Neurophysiology, the Italian Clinical Neurophysiology Society and the International Society of Intraoperative Neurophysiology.The journal is dedicated to fostering research and disseminating information on all aspects of both normal and abnormal functioning of the nervous system. The key aim of the publication is to disseminate scholarly reports on the pathophysiology underlying diseases of the central and peripheral nervous system of human patients. Clinical trials that use neurophysiological measures to document change are encouraged, as are manuscripts reporting data on integrated neuroimaging of central nervous function including, but not limited to, functional MRI, MEG, EEG, PET and other neuroimaging modalities.