ArticleHuman brain mapping2019
An average sliding window correlation method for dynamic functional connectivity.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- The association between spatiotemporal coupling of default mode network and behaviors is specifically modulated by peripheral inflammation in major depressive disorder.BMC psychiatry · 2026Article
- Altered motor network dynamics in myoclonus-dystonia.Brain communications · 2026Article
- Static and dynamic functional connectivity of the bilateral insula in patients with migraine without aura: a resting-state fMRI study.Frontiers in neurology · 2026Article
- State-specific disruptions of dynamic functional connectivity in young migraine without aura: a hidden Markov model approach.Frontiers in neuroscience · 2026Article
- st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.Human brain mapping · 2025Article
- Dynamic Functional Connectivity Between Amygdala and Cognitive Control Network Predicts Delay Discounting in Older Adolescents.Human brain mapping · 2025Article
- Event-Marked Windowed Communication: Inferring Activity Propagation from Neural Time Series.Human brain mapping · 2025Article
- Targeted Time-Varying Functional Connectivity.Human brain mapping · 2025Article
- Time-varying functional connectivity as Wishart processes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Dynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory study.PloS one · 2023Article
- Altered static and dynamic spontaneous neural activity in patients with ischemic pontine stroke.Frontiers in neuroscience · 2023Article
- Article
- Dynamical Complexity Fingerprints of Occupation-Dependent Brain Functional Networks in Professional Seafarers.Frontiers in neuroscience · 2022Article
- Analysis of instantaneous brain interactions contribution to a motor imagery classification task.Frontiers in computational neuroscience · 2022Article
- Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking.NeuroImage · 2021Article
- Tools of the trade: estimating time-varying connectivity patterns from fMRI data.Social cognitive and affective neuroscience · 2021Review
- Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions.Human brain mapping · 2020Article
- Filtered correlation and allowed frequency spectra in dynamic functional connectivity.Journal of neuroscience methods · 2020Article
- Abnormal static and dynamic functional connectivity of resting-state fMRI in multiple system atrophy.Aging · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
Sliding window correlation (SWC) is utilized in many studies to analyze the temporal characteristics of brain connectivity. However, spurious artifacts have been reported in simulated data using this technique. Several suggestions have been made through the development of the SWC technique. Recently, it has been proposed to utilize a SWC window length of 100 s given that the lowest nominal fMRI frequency is 0.01 Hz. The main pitfall is the loss of temporal resolution due to a large window length. In this work, we propose an average sliding window correlation (ASWC) approach that presents several advantages over the SWC. One advantage is the requirement for a smaller window length. This is important because shorter lengths allow for a more accurate estimation of transient dynamicity of functional connectivity. Another advantage is the behavior of ASWC as a tunable high pass filter. We demonstrate the advantages of ASWC over SWC using simulated signals with configurable functional connectivity dynamics. We present analytical models explaining the behavior of ASWC and SWC for several dynamic connectivity cases. We also include a real data example to demonstrate the application of the new method. In summary, ASWC shows lower artifacts and resolves faster transient connectivity fluctuations, resulting in a lower mean square error than in SWC.
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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.