ArticleScientific reports2019
Permutation Statistics for Connectivity Analysis between Regions of Interest in EEG and MEG Data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Safety, pharmacokinetics and quantitative EEG modulation of TAK-071, a novel muscarinic M1 receptor positive allosteric modulator, in healthy subjects.British journal of clinical pharmacology · 2022Trial
- Synchronization patterns reveal neuronal coding of working memory content.Cell reports · 2021Trial
- Manifold lateralisation and variability in the language connectome at 7T.Research square · 2026Article
- BROAD-NESS Uncovers Dual-Stream Mechanisms Underlying Predictive Coding in Auditory Memory Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Characterizing Directional Dynamics of Semantic Prediction Based on Inter-regional Temporal Generalization.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Exploring the Impact of Declarative Learning on the Consolidation of Acquired Motor Skills Under Valence Feedback.Human brain mapping · 2025Article
- Differences in spatiotemporal dynamics for processing specific semantic categories: An EEG study.Scientific reports · 2024Article
- Exploring brain network oscillations during seizures in drug-naïve patients with juvenile absence epilepsy.Frontiers in neurology · 2024Article
- Spectrotemporal content of human auditory working memory represented in functional connectivity patterns.Communications biology · 2023Article
- Children with autism spectrum disorder show altered functional connectivity and abnormal maturation trajectories in response to inverted faces.Autism research : official journal of the International Society for Autism Research · 2021Article
- Distinct Regional Oscillatory Connectivity Patterns During Auditory Target and Novelty Processing.Brain topography · 2020Article
- Altered neural oscillations and connectivity in the beta band underlie detail-oriented visual processing in autism.NeuroImage. Clinical · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Connectivity estimates based on electroencephalography (EEG) and magnetoencephalography (MEG) are unique in their ability to provide neurophysiologically meaningful spectral and temporal information non-invasively. This multi-dimensional aspect of the MEG/EEG based connectivity increases the challenges of the analysis and interpretation of the data. Many MEG/EEG studies address this complexity by using a hypothesis-driven approach, which focuses on particular regions of interest (ROI). However, if an effect is distributed unevenly over a large ROI and variable across subjects, it may not be detectable using conventional methods. Here, we propose a novel approach, which enhances the statistical power for weak and spatially discontinuous effects. This results in the ability to identify statistically significant connectivity patterns with spectral, temporal, and spatial specificity while correcting for multiple comparisons using nonparametric permutation methods. We call this new approach the Permutation Statistics for Connectivity Analysis between ROI (PeSCAR). We demonstrate the processing steps with simulated and real human data. The open-source Matlab code implementing PeSCAR are provided online.
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.