A meta-analysis of periodic and aperiodic electrophysiological features in Parkinson’s disease

Citation

Norouzi H, Ietswaart M, Adair J & Learmonth G (2026) A meta-analysis of periodic and aperiodic electrophysiological features in Parkinson’s disease. Brain Communications, 8 (5). https://doi.org/10.1093/braincomms/fcag329

Abstract
Parkinson’s disease is characterised by a range of motor and non-motor changes that can negatively impact quality of life. Many studies have identified potential clinical electrophysiological correlates of Parkinson’s disease with an aim of developing new methods of identifying at-risk patients, and to form the basis of therapeutic interventions. However, these studies do not present consistent results, and a formal meta-analysis is warranted to identify reliable Electroencephalography or Magnetoencephalography (M/EEG) characteristics across datasets. In this meta-analysis of open-access M/EEG datasets (n = 6; 4 EEG and 2 MEG), we compared periodic and aperiodic characteristics of resting-state recordings in 368 patients with Parkinson’s disease and 570 age-matched healthy controls. Specifically, we compared the power and peak frequency of unadjusted and aperiodic-adjusted alpha- and beta-band oscillations, and the exponent and offset, across the two groups. Parkinson’s patients had a consistently elevated aperiodic exponent and offset. Patients also had higher unadjusted beta-band power, but this was no longer present when the aperiodic features were removed, suggesting that previous findings of elevated beta activity in Parkinson’s disease could be confounded by aperiodic activity rather than reflecting true oscillatory differences. Patients also had higher adjusted and unadjusted alpha-band power and a slower alpha peak frequency compared to controls, whereas no group difference in beta peak frequency was identified. In conclusion, this large cohort meta-analysis points to a broadly consistent pattern of both periodic and aperiodic changes in Parkinson's patients in M/EEG signal that may be used to develop diagnostics and targeted interventions in the future.

Keywords
alpha oscillations; beta oscillations; EEG; MEG; specparam

Journal
Brain Communications: Volume 8, Issue 5

• Published Version