ArticleNeuroImage2022
Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Naturalistic stimuli in neuroimaging studies of autism spectrum disorder: a systematic review.Frontiers in human neuroscience · 2026Pooled it
- Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.Nature communications · 2026Article
- The dual interpretation of edge time series: Time-varying connectivity versus statistical interaction.iScience · 2026Article
- Oscillatory edge connectivity in pain-related regions supports machine learning identification of migraine.The journal of headache and pain · 2026Article
- Modulation of depression through the neurobiological underpinnings of the glymphatic-rumination relationship.Psychological medicine · 2026Article
- Temporally-resolved deep learning reveals autism symptom-specific neural signatures during naturalistic social experiences.Research square · 2026Article
- Sex differences in human brain dynamics assessed with edge functional connectivity.Brain imaging and behavior · 2026Article
- Regional activity and interregional functional connectivity uniquely contribute to social cognitive judgments during movie-watching.Network neuroscience (Cambridge, Mass.) · 2026Article
- Higher-order statistics for constructing centered edge functional connectivity.Network neuroscience (Cambridge, Mass.) · 2026Article
- Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder.Scientific reports · 2025Article
- Altered brain network dynamics and functional connectivity in subjective cognitive decline: an edge-centric network study.Frontiers in aging neuroscience · 2025Article
- The changes in brain network functional gradients and dynamic functional connectivity in SeLECTS patients revealing disruptive and compensatory mechanisms in brain networks.Frontiers in psychiatry · 2025Article
- Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety.Nature communications · 2024Article
- Reliability of variability and complexity measures for task and task-free BOLD fMRI.Human brain mapping · 2024Article
- Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants.Human brain mapping · 2024Article
- Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment.Nature communications · 2024Article
- Edge time series components of functional connectivity and cognitive function in Alzheimer's disease.Brain imaging and behavior · 2024Article
- Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains.Communications biology · 2024Article
- Temporal dysregulation of the somatomotor network in agitated depression.Brain communications · 2024Article
- Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The interaction between brain regions changes over time, which can be characterized using time-varying functional connectivity (tvFC). The common approach to estimate tvFC uses sliding windows and offers limited temporal resolution. An alternative method is to use the recently proposed edge-centric approach, which enables the tracking of moment-to-moment changes in co-fluctuation patterns between pairs of brain regions. Here, we first examined the dynamic features of edge time series and compared them to those in the sliding window tvFC (sw-tvFC). Then, we used edge time series to compare subjects with autism spectrum disorder (ASD) and healthy controls (CN). Our results indicate that relative to sw-tvFC, edge time series captured rapid and bursty network-level fluctuations that synchronize across subjects during movie-watching. The results from the second part of the study suggested that the magnitude of peak amplitude in the collective co-fluctuations of brain regions (estimated as root sum square (RSS) of edge time series) is similar in CN and ASD. However, the trough-to-trough duration in RSS signal is greater in ASD, compared to CN. Furthermore, an edge-wise comparison of high-amplitude co-fluctuations showed that the within-network edges exhibited greater magnitude fluctuations in CN. Our findings suggest that high-amplitude co-fluctuations captured by edge time series provide details about the disruption of functional brain dynamics that could potentially be used in developing new biomarkers of mental disorders.
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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 Socratic graph.