Evidence map›Paper›PMID 42876674›Full record

ArticleFrontiers in aging neuroscience2026

Resting-state cortical alpha frequency in Parkinson's disease and multiple system atrophy: associations with diagnosis, age, and symptom burden.

Daichao Chen, Huada Tang, Yanfei Pan, Hui Sang, Saiya Gu, Xinpeng Wang, Kai Li, Fan Li, Yunchuang Sun, Jing Chen and 4 more

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Article in Frontiers in aging neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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14 authors.

Daichao ChenDepartment of Neurology, Peking University First Hospital, Beijing, China.
Huada TangDepartment of Neurology, Peking University First Hospital, Beijing, China.
Yanfei PanDepartment of Neurology, Peking University First Hospital, Beijing, China.
Hui SangDepartment of Neurology, Taiyuan City Central Hospital, Taiyuan, Shanxi, China.
Saiya GuThe First School of Clinical Medicine, Peking University, Beijing, China.
Xinpeng WangInstitute of Medical Technology, Peking University, Beijing, China.
Kai LiDepartment of Neurology, Peking University First Hospital, Beijing, China.
Fan LiDepartment of Neurology, Peking University First Hospital, Beijing, China.
Yunchuang SunDepartment of Neurology, Peking University First Hospital, Beijing, China.
Jing ChenDepartment of Neurology, Peking University First Hospital, Beijing, China.
Wei SunDepartment of Neurology, Peking University First Hospital, Beijing, China.
Jue ZhangAcademy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Zhaoxia WangDepartment of Neurology, Peking University First Hospital, Beijing, China.
Luhua WeiDepartment of Neurology, Peking University First Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Parkinson's disease (PD) and multiple system atrophy (MSA) are clinically overlapping neurodegenerative disorders that present with a complex, heterogeneous burden of motor and non-motor symptoms. Resting-state EEG reveals that cortical alpha slowing is a hallmark of pathological aging, yet its specific presence and clinical significance in these distinct synucleinopathies remain incompletely understood. Therefore, precisely quantifying the individual alpha frequency serves as a critical, non-invasive approach to characterize cortical network dysfunction across synucleinopathies and evaluate how aging moderates its association with clinical symptom burden. Methods: We analyzed source-reconstructed resting-state EEG data from 104 participants, comprising patients with PD, patients with the parkinsonian (MSA-P) and cerebellar subtypes (MSA-C) of MSA, and healthy controls. To accurately capture oscillatory dynamics, a data-driven masking empirical mode decomposition approach was applied to extract the individual alpha frequency across canonical cortical networks. General linear models tested diagnostic group differences, age-related effects, and age-dependent associations between individual alpha frequency and clinical symptom scores. Results: Relative to healthy controls, both PD and MSA-C exhibited significant lower cortical IAF, whereas MSA-P showed no significant global IAF difference. At the network level, significant reduction of IAF was detected in the visual and dorsal attention networks in both PD and MSA-C. Advancing age independently contributed to alpha slowing across all participants without accelerating the disease-related trajectories. Notably, chronological age significantly moderated the relationship between alpha slowing and non-motor symptoms. In older patients, reduced individual alpha frequency was strongly associated with a higher burden of overall non-motor, autonomic, and psychiatric/cognitive symptoms, a relationship not observed in younger patients or with motor symptom severity. Conclusion: Cortical alpha slowing was detected in PD and MSA-C, but not in MSA-P. Age independently contributes to alpha slowing and strengthens its association with non-motor symptom burden, supporting age-aware interpretation of resting-state electroencephalography biomarkers in synucleinopathies.

Indexed as

agingalpha slowingcortical networksindividual alpha frequencymultiple system atrophynon-motor symptomsParkinson’s diseaseresting-state EEG

Identifiers

PMID42876674
PMCPMC13661991

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