ArticleAnnals of neurology2026
Frequency- and Network-Specific Changes in Functional Connectivity Reflect Pathophysiological Mechanisms across Parkinson's Disease Stages.
Article in Annals of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- EEG functional connectivity for Parkinson's disease diagnosis and non-motor symptom prediction: a machine learning approach.Frontiers in human neuroscience · 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
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveParkinson's disease (PD) is increasingly conceptualized as a disorder of large-scale brain networks, yet whether and how frequency-specific functional connectivity reorganizes across stages remains poorly understood. In this study, we used high-density electroencephalography (EEG) to characterize cortico-cortical functional connectivity across the clinical spectrum of PD.
methodsWe performed high-density EEG in a cross-sectional cohort of 140 PD patients spanning early, intermediate, and advanced stages and 57 healthy controls. Cortico-cortical functional connectivity was reconstructed in source space across multiple frequency bands and analyzed using network-based statistics combined with machine-learning models to identify stage-dependent network alterations and evaluate their diagnostic and prognostic relevance.
resultsWe detected 3 distinct large-scale networks showing divergent trajectories across disease stages. An α-band network involving prefrontal and parieto-temporal regions exhibited progressive hypoconnectivity and was associated with cognitive and axial impairment. A β-band sensorimotor network showed progressive hyperconnectivity, paralleling bradykinesia severity. A high-γ network demonstrated increased connectivity in early PD, followed by a progressive connectivity breakdown, and was inversely associated with motor complications. Multiband integration achieved near-perfect discrimination between early PD and healthy controls and robust stratification across disease stages. Band-specific networks also predicted clinical milestones of disease progression, with preserved α connectivity identifying patients at lower risk for cognitive and axial impairment, and stronger high-γ connectivity indicating reduced vulnerability to motor complications.
interpretationTogether, these results identify frequency-specific cortical networks as markers of disease stage and clinical vulnerability and support high-density EEG connectivity as a scalable systems-level biomarker for diagnosis, staging, and risk stratification in PD. ANN NEUROL 2026;100:319-333.
Indexed as
Identifiers
What OpenQuestion holds
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.