脑电图
神经科学
萎缩
神经生理学
帕金森病
心理学
神经影像学
生物标志物
同步脑电与功能磁共振
功能连接
大脑定位
医学
疾病
视觉处理
相关性
队列
扁桃形结构
连接体
神经学
默认模式网络
模式识别(心理学)
癫痫
作者
王春毅,Xue Zhu,Yi Zhang,Liche Zhou,Sijia Huang,Qianyi Yin,Yuchao Yang,Ningdi Luo,Tifei Yuan,Yuyan Tan,Wei Wu,J S Liu
标识
DOI:10.1038/s41531-026-01482-w
摘要
Multiple system atrophy (MSA) is an aggressive α-synucleinopathy characterized by motor and autonomic dysfunction. Elucidating neurophysiological patterns can provide crucial insights into the underlying neural mechanisms. Resting-state electroencephalography (EEG) and fMRI data were collected from 69 healthy controls and 123 MSA patients, comprising a discovery cohort ( n = 97) and an independent validation cohort ( n = 26). MSA patients showed reduced delta power in the cerebellar, frontoparietal, visual I, and limbic networks, with widespread increases in theta and high gamma power. Amplitude-based connectivity decreased across the alpha, beta, and gamma bands in subcortical, cerebellar, default mode, motor, and visual I networks, with increased theta-band synchrony involving motor, visual II, and frontoparietal networks. These EEG abnormalities were corroborated by fMRI connectivity, highlighting cross-modal consistency. Canonical correlation analysis revealed associations between EEG features and motor/non-motor symptom severity. Interpretable stacking models accurately classified MSA-C and MSA-P subtypes and predicted UMSARS progression in longitudinal follow-up. These findings identified aberrant neural activity and network dysfunction in MSA, highlighting EEG as a promising biomarker for disease monitoring.
科研通智能强力驱动
Strongly Powered by AbleSci AI