Evidence map›Paper›PMID 42724388›Full record

ReviewBrain communications2026

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

Hamzeh Norouzi, Magdalena Ietswaart, Jason Adair, Gemma Learmonth

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Brain communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Electroencephalography-Based Clustering Reveals Robust Neurophysiological Subtypes in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hamzeh NorouziSchool of Psychology, University of Stirling, Stirling FK9 4LA, Scotland, UK.ORCID https://orcid.org/0009-0001-1312-3398
Magdalena IetswaartSchool of Psychology, University of Stirling, Stirling FK9 4LA, Scotland, UK.ORCID https://orcid.org/0000-0003-4576-9393
Jason AdairSchool of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA, Scotland, UK.ORCID https://orcid.org/0000-0003-0198-9095
Gemma LearmonthSchool of Psychology, University of Stirling, Stirling FK9 4LA, Scotland, UK.ORCID https://orcid.org/0000-0003-4061-4464

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease is characterized 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 forming the basis of therapeutic interventions. However, these studies do not present consistent results, and a formal meta-analysis is warranted to identify reliable EEG or magnetoencephalography (M/EEG) characteristics across datasets. In this meta-analysis of open-access M/EEG datasets (

Indexed as

alpha oscillationsbeta oscillationsEEGMEGspecparam

Identifiers

PMID42724388
PMCPMC13559478

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.