SynthesisBrain and behavior2025
Using Magnetoencephalography to Advance the Science of Parkinson Disease: A Systematic Review.
Synthesis in Brain and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using Magnetoencephalography to Advance the Science of Parkinson Disease: A Systematic Review.Brain and behavior · 2025Pooled it
- Phantom Quantification of Magnetoencephalography Source Imaging Distortion Caused by Deep Brain Stimulation.Brain sciences · 2026Article
- Individual cases of Parkinson's disease can be robustly classified using magnetoencephalography.NPJ Parkinson's disease · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
purposeMagnetoencephalography (MEG) offers high temporal resolution and whole-brain spatial coverage, making it a powerful tool to study brain networks. This systematic review examines the use of MEG to study Parkinson's disease (PD) brain network dysfunction, providing an update on scientific advances and identifying knowledge gaps that can be addressed using MEG as a research platform.
methodFive databases (Cochrane, Embase, PubMed, Scopus, and Web of Science) were systematically searched for MEG-based PD publications from October 2018 (the date of the latest search in the previous systematic review) to June 2025. We used the Joanna Briggs Institute (JBI) checklist to individually assess each paper for quality and relevancy. FINDING: Fifty-four publications were grouped into motor or cognitive domains. Motor resting-state studies were further categorized into four groups: (1) Cortical Pathophysiology and its Modulation by Therapies, (2) Basal Ganglia-Cortical (BGC) Network Pathophysiology and its Modulation by Therapies, (3) Temporal Dynamics, Aperiodicity, and Nonlinearity in PD, and (4) Clinical Utility of MEG. We highlight increases in cortical beta power in response to L-DOPA and its correlation with motor symptom improvement, the broad cortical distribution of beta-gamma phase-amplitude coupling (PAC) in PD and its link to motor symptoms, corticocortical connectivity modulation in response to different treatments, and the integration of MEG with local field potentials to study cortico-subcortical abnormalities. Temporal dynamics, such as cortical beta bursts and reduced functional repertoire, are explored, as well as aperiodic components and nonlinearities in the power spectrum in PD. Clinically, MEG shows promise in optimizing DBS, predicting L-DOPA response, and uncovering movement-related and cognitive pathophysiology in PD.
conclusionKey PD knowledge gaps that can be addressed using MEG are identified, including the need to characterize network temporal dynamics and signal interrelation in PD, the causal pathological mechanisms of the disease, and the response to different treatment modalities.
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Registered trials
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.