ArticlePLoS computational biology2019
Large-scale cortical travelling waves predict localized future cortical signals.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Article
- A practical preprocessing pipeline for concurrent TMS-iEEG: Critical steps and methodological considerations.NeuroImage · 2026Article
- Article
- Perceptual Bias in Motion Discrimination is Related to Asymmetric Interhemispheric Alpha Traveling Waves.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations.bioRxiv : the preprint server for biology · 2025Article
- Experienced meditators show greater forward traveling cortical alpha wave strengths.Annals of the New York Academy of Sciences · 2025Article
- Convergent effects of different anesthetics on changes in phase alignment of cortical oscillations.Cell reports · 2025Article
- Cortical travelling waves relate to variation in personality traits.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Alpha Traveling Waves during Working Memory: Disentangling Bottom-Up Gating and Top-Down Gain Control.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- CARLA: Adjusted common average referencing for cortico-cortical evoked potential data.Journal of neuroscience methods · 2024Article
- Article
- Article
- Advances in human intracranial electroencephalography research, guidelines and good practices.NeuroImage · 2022Review
- Spatially distributed computation in cortical circuits.Science advances · 2022Article
- Article
- DMT alters cortical travelling waves.eLife · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting future brain signal is highly sought-after, yet difficult to achieve. To predict the future phase of cortical activity at localized ECoG and MEG recording sites, we exploit its predominant, large-scale, spatiotemporal dynamics. The dynamics are extracted from the brain signal through Fourier analysis and principal components analysis (PCA) only, and cast in a data model that predicts future signal at each site and frequency of interest. The dominant eigenvectors of the PCA that map the large-scale patterns of past cortical phase to future ones take the form of smoothly propagating waves over the entire measurement array. In ECoG data from 3 subjects and MEG data from 20 subjects collected during a self-initiated motor task, mean phase prediction errors were as low as 0.5 radians at local sites, surpassing state-of-the-art methods of within-time-series or event-related models. Prediction accuracy was highest in delta to beta bands, depending on the subject, was more accurate during episodes of high global power, but was not strongly dependent on the time-course of the task. Prediction results did not require past data from the to-be-predicted site. Rather, best accuracy depended on the availability in the model of long wavelength information. The utility of large-scale, low spatial frequency traveling waves in predicting future phase activity at local sites allows estimation of the error introduced by failing to account for irreducible trajectories in the activity dynamics.
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.